42 CFR 435.234 - Individuals receiving only optional State supplements in States using more restrictive...

Code of Federal Regulations, 2011 CFR

2011-10-01

... under § 435.121 or SSI criteria, or to one or more of the following classifications of individuals who... that meets the conditions specified in this section. (8) Individuals in additional classifications... receiving optional State supplements); and (3) Available to all individuals in each classification in...

42 CFR 435.234 - Individuals receiving only optional State supplements in States using more restrictive...

Code of Federal Regulations, 2010 CFR

2010-10-01

... under § 435.121 or SSI criteria, or to one or more of the following classifications of individuals who... that meets the conditions specified in this section. (8) Individuals in additional classifications... receiving optional State supplements); and (3) Available to all individuals in each classification in...

Evaluation of gene expression classification studies: factors associated with classification performance.

PubMed

Novianti, Putri W; Roes, Kit C B; Eijkemans, Marinus J C

2014-01-01

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Towards a robust framework for catchment classification

NASA Astrophysics Data System (ADS)

Deshmukh, A.; Samal, A.; Singh, R.

2017-12-01

Classification of catchments based on various measures of similarity has emerged as an important technique to understand regional scale hydrologic behavior. Classification of catchment characteristics and/or streamflow response has been used reveal which characteristics are more likely to explain the observed variability of hydrologic response. However, numerous algorithms for supervised or unsupervised classification are available, making it hard to identify the algorithm most suitable for the dataset at hand. Consequently, existing catchment classification studies vary significantly in the classification algorithms employed with no previous attempt at understanding the degree of uncertainty in classification due to this algorithmic choice. This hinders the generalizability of interpretations related to hydrologic behavior. Our goal is to develop a protocol that can be followed while classifying hydrologic datasets. We focus on a classification framework for unsupervised classification and provide a step-by-step classification procedure. The steps include testing the clusterabiltiy of original dataset prior to classification, feature selection, validation of clustered data, and quantification of similarity of two clusterings. We test several commonly available methods within this framework to understand the level of similarity of classification results across algorithms. We apply the proposed framework on recently developed datasets for India to analyze to what extent catchment properties can explain observed catchment response. Our testing dataset includes watershed characteristics for over 200 watersheds which comprise of both natural (physio-climatic) characteristics and socio-economic characteristics. This framework allows us to understand the controls on observed hydrologic variability across India.

42 CFR 435.232 - Individuals receiving only optional State supplements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Medicaid, in one or more of the following classifications, to individuals who receive only an optional... additional classifications specified by the Secretary for federally administered supplementary payments under... State supplements); and (3) Available to all individuals in each classification in paragraph (a) of this...

42 CFR 435.232 - Individuals receiving only optional State supplements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Medicaid, in one or more of the following classifications, to individuals who receive only an optional... additional classifications specified by the Secretary for federally administered supplementary payments under... State supplements); and (3) Available to all individuals in each classification in paragraph (a) of this...

Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition

PubMed Central

Koutsouleris, Nikolaos; Meisenzahl, Eva M.; Davatzikos, Christos; Bottlender, Ronald; Frodl, Thomas; Scheuerecker, Johanna; Schmitt, Gisela; Zetzsche, Thomas; Decker, Petra; Reiser, Maximilian; Möller, Hans-Jürgen; Gaser, Christian

2014-01-01

Context Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. Objective To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Design Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Setting Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Participants The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Main Outcome Measures Specificity, sensitivity, and accuracy of classification. Results The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Conclusions Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis. PMID:19581561

Classification-based reasoning

NASA Technical Reports Server (NTRS)

Gomez, Fernando; Segami, Carlos

1991-01-01

A representation formalism for N-ary relations, quantification, and definition of concepts is described. Three types of conditions are associated with the concepts: (1) necessary and sufficient properties, (2) contingent properties, and (3) necessary properties. Also explained is how complex chains of inferences can be accomplished by representing existentially quantified sentences, and concepts denoted by restrictive relative clauses as classification hierarchies. The representation structures that make possible the inferences are explained first, followed by the reasoning algorithms that draw the inferences from the knowledge structures. All the ideas explained have been implemented and are part of the information retrieval component of a program called Snowy. An appendix contains a brief session with the program.

Influence of socioeconomic factors on pregnancy outcome in women with structural heart disease.

PubMed

van Hagen, Iris M; Baart, Sara; Fong Soe Khioe, Rebekah; Sliwa-Hahnle, Karen; Taha, Nasser; Lelonek, Malgorzata; Tavazzi, Luigi; Maggioni, Aldo Pietro; Johnson, Mark R; Maniadakis, Nikolaos; Fordham, Richard; Hall, Roger; Roos-Hesselink, Jolien W

2018-05-01

Cardiac disease is the leading cause of indirect maternal mortality. The aim of this study was to analyse to what extent socioeconomic factors influence the outcome of pregnancy in women with heart disease. The Registry of Pregnancy and Cardiac disease is a global prospective registry. For this analysis, countries that enrolled ≥10 patients were included. A combined cardiac endpoint included maternal cardiac death, arrhythmia requiring treatment, heart failure, thromboembolic event, aortic dissection, endocarditis, acute coronary syndrome, hospitalisation for cardiac reason or intervention. Associations between patient characteristics, country characteristics (income inequality expressed as Gini coefficient, health expenditure, schooling, gross domestic product, birth rate and hospital beds) and cardiac endpoints were checked in a three-level model (patient-centre-country). A total of 30 countries enrolled 2924 patients from 89 centres. At least one endpoint occurred in 645 women (22.1%). Maternal age, New York Heart Association classification and modified WHO risk classification were associated with the combined endpoint and explained 37% of variance in outcome. Gini coefficient and country-specific birth rate explained an additional 4%. There were large differences between the individual countries, but the need for multilevel modelling to account for these differences disappeared after adjustment for patient characteristics, Gini and country-specific birth rate. While there are definite interregional differences in pregnancy outcome in women with cardiac disease, these differences seem to be mainly driven by individual patient characteristics. Adjustment for country characteristics refined the results to a limited extent, but maternal condition seems to be the main determinant of outcome. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A bayesian approach to classification criteria for spectacled eiders

USGS Publications Warehouse

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Lauren classification and individualized chemotherapy in gastric cancer.

PubMed

Ma, Junli; Shen, Hong; Kapesa, Linda; Zeng, Shan

2016-05-01

Gastric cancer is one of the most common malignancies worldwide. During the last 50 years, the histological classification of gastric carcinoma has been largely based on Lauren's criteria, in which gastric cancer is classified into two major histological subtypes, namely intestinal type and diffuse type adenocarcinoma. This classification was introduced in 1965, and remains currently widely accepted and employed, since it constitutes a simple and robust classification approach. The two histological subtypes of gastric cancer proposed by the Lauren classification exhibit a number of distinct clinical and molecular characteristics, including histogenesis, cell differentiation, epidemiology, etiology, carcinogenesis, biological behaviors and prognosis. Gastric cancer exhibits varied sensitivity to chemotherapy drugs and significant heterogeneity; therefore, the disease may be a target for individualized therapy. The Lauren classification may provide the basis for individualized treatment for advanced gastric cancer, which is increasingly gaining attention in the scientific field. However, few studies have investigated individualized treatment that is guided by pathological classification. The aim of the current review is to analyze the two major histological subtypes of gastric cancer, as proposed by the Lauren classification, and to discuss the implications of this for personalized chemotherapy.

Life history factors, personality and the social clustering of sexual experience in adolescents.

PubMed

van Leeuwen, Abram J; Mace, Ruth

2016-10-01

Adolescent sexual behaviour may show clustering in neighbourhoods, schools and friendship networks. This study aims to assess how experience with sexual intercourse clusters across the social world of adolescents and whether predictors implicated by life history theory or personality traits can account for its between-individual variation and social patterning. Using data on 2877 adolescents from the Avon Longitudinal Study of Parents and Children, we ran logistic multiple classification models to assess the clustering of sexual experience by approximately 17.5 years in schools, neighbourhoods and friendship networks. We examined how much clustering at particular levels could be accounted for by life history predictors and Big Five personality factors. Sexual experience exhibited substantial clustering in friendship networks, while clustering at the level of schools and neighbourhoods was minimal, suggesting a limited role for socio-ecological influences at those levels. While life history predictors did account for some variation in sexual experience, they did not explain clustering in friendship networks. Personality, especially extraversion, explained about a quarter of friends' similarity. After accounting for life history factors and personality, substantial unexplained similarity among friends remained, which may reflect a tendency to associate with similar individuals or the social transmission of behavioural norms.

Life history factors, personality and the social clustering of sexual experience in adolescents

PubMed Central

2016-01-01

Adolescent sexual behaviour may show clustering in neighbourhoods, schools and friendship networks. This study aims to assess how experience with sexual intercourse clusters across the social world of adolescents and whether predictors implicated by life history theory or personality traits can account for its between-individual variation and social patterning. Using data on 2877 adolescents from the Avon Longitudinal Study of Parents and Children, we ran logistic multiple classification models to assess the clustering of sexual experience by approximately 17.5 years in schools, neighbourhoods and friendship networks. We examined how much clustering at particular levels could be accounted for by life history predictors and Big Five personality factors. Sexual experience exhibited substantial clustering in friendship networks, while clustering at the level of schools and neighbourhoods was minimal, suggesting a limited role for socio-ecological influences at those levels. While life history predictors did account for some variation in sexual experience, they did not explain clustering in friendship networks. Personality, especially extraversion, explained about a quarter of friends' similarity. After accounting for life history factors and personality, substantial unexplained similarity among friends remained, which may reflect a tendency to associate with similar individuals or the social transmission of behavioural norms. PMID:27853543

Classification of Nortes in the Gulf of Mexico derived from wave energy maps

NASA Astrophysics Data System (ADS)

Appendini, C. M.; Hernández-Lasheras, J.

2016-02-01

Extreme wave climate in the Gulf of Mexico is determined by tropical cyclones and winds from the Central American Cold Surges, locally referred to as Nortes. While hurricanes can have catastrophic effects, extreme waves and storm surge from Nortes occur several times a year, and thus have greater impacts on human activities along the Mexican coast of the Gulf of Mexico. Despite the constant impacts from Nortes, there is no available classification that relates their characteristics (e.g. pressure gradients, wind speed), to the associated coastal impacts. This work presents a first approximation to characterize and classify Nortes, which is based on the assumption that the derived wave energy synthetizes information (i.e. wind intensity, direction and duration) of individual Norte events as they pass through the Gulf of Mexico. First, we developed an index to identify Nortes based on surface pressure differences of two locations. To validate the methodology we compared the events identified with other studies and available Nortes logs. Afterwards, we detected Nortes from the 1986/1987, 2008/2009 and 2009/2010 seasons and used their corresponding wind fields to derive the wave energy maps using a numerical wave model. We used the energy maps to classify the events into groups using manual (visual) and automatic classifications (principal component analysis and k-means). The manual classification identified 3 types of Nortes and the automatic classification identified 5, although 3 of them had a high degree of similarity. The principal component analysis indicated that all events have similar characteristics, as few components are necessary to explain almost all of the variance. The classification from the k-means indicated that 81% of analyzed Nortes affect the southeastern Gulf of Mexico, while a smaller percentage affects the northern Gulf of Mexico and even less affect the western Caribbean.

MERRF Classification: Implications for Diagnosis and Clinical Trials.

PubMed

Finsterer, Josef; Zarrouk-Mahjoub, Sinda; Shoffner, John M

2018-03-01

Given the etiologic heterogeneity of disease classification using clinical phenomenology, we employed contemporary criteria to classify variants associated with myoclonic epilepsy with ragged-red fibers (MERRF) syndrome and to assess the strength of evidence of gene-disease associations. Standardized approaches are used to clarify the definition of MERRF, which is essential for patient diagnosis, patient classification, and clinical trial design. Systematic literature and database search with application of standardized assessment of gene-disease relationships using modified Smith criteria and of variants reported to be associated with MERRF using modified Yarham criteria. Review of available evidence supports a gene-disease association for two MT-tRNAs and for POLG. Using modified Smith criteria, definitive evidence of a MERRF gene-disease association is identified for MT-TK. Strong gene-disease evidence is present for MT-TL1 and POLG. Functional assays that directly associate variants with oxidative phosphorylation impairment were critical to mtDNA variant classification. In silico analysis was of limited utility to the assessment of individual MT-tRNA variants. With the use of contemporary classification criteria, several mtDNA variants previously reported as pathogenic or possibly pathogenic are reclassified as neutral variants. MERRF is primarily an MT-TK disease, with pathogenic variants in this gene accounting for ~90% of MERRF patients. Although MERRF is phenotypically and genotypically heterogeneous, myoclonic epilepsy is the clinical feature that distinguishes MERRF from other categories of mitochondrial disorders. Given its low frequency in mitochondrial disorders, myoclonic epilepsy is not explained simply by an impairment of cellular energetics. Although MERRF phenocopies can occur in other genes, additional data are needed to establish a MERRF disease-gene association. This approach to MERRF emphasizes standardized classification rather than clinical phenomenology, thus improving patient diagnosis and clinical trial design. Copyright © 2017 Elsevier Inc. All rights reserved.

Ecoregions and ecodistricts: Ecological regionalizations for the Netherlands' environmental policy

NASA Astrophysics Data System (ADS)

Klijn, Frans; de Waal, Rein W.; Oude Voshaar, Jan H.

1995-11-01

For communicating data on the state of the environment to policy makers, various integrative frameworks are used, including regional integration. For this kind of integration we have developed two related ecological regionalizations, ecoregions and ecodistricts, which are two levels in a series of classifications for hierarchically nested ecosystems at different spatial scale levels. We explain the compilation of the maps from existing geographical data, demonstrating the relatively holistic, a priori integrated approach. The resulting maps are submitted to discriminant analysis to test the consistancy of the use of mapping characteristics, using data on individual abiotic ecosystem components from a national database on a 1-km2 grid. This reveals that the spatial patterns of soil, groundwater, and geomorphology correspond with the ecoregion and ecodistrict maps. Differences between the original maps and maps formed by automatically reclassifying 1-km2 cells with these discriminant components are found to be few. These differences are discussed against the background of the principal dilemma between deductive, a priori integrated, and inductive, a posteriori, classification.

A practical classification of untoward drug effects.

PubMed Central

Gysling, E.; Heisler, S.

1975-01-01

All drug effects can be explained as results of complex interactions between the drug, the patient and his condition, and additional extrinsic factors. On the basis of these three "determinants", a practical classification of untoward drug effects (UDE) is suggested. UDE lists using this classification would fulfill the physician's informational needs better than the material with which he is presently provided. PMID:1148971

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…

Classifying High-noise EEG in Complex Environments for Brain-computer Interaction Technologies

DTIC Science & Technology

2012-02-01

differentiation in the brain signal that our classification approach seeks to identify despite the noise in the recorded EEG signal and the complexity of...performed two offline classifications , one using BCILab (1), the other using LibSVM (2). Distinct classifiers were trained for each individual in...order to improve individual classifier performance (3). The highest classification performance results were obtained using individual frequency bands

Response to Comment on “Mode of Action (MOA) Assignment Classifications for Ecotoxicology: An Evaluation of Approaches”

EPA Science Inventory

This letter to the editor that does not have an abstract. It explains mode of action in aquatic toxicology, and rebuts comments about our mode of action classification caused by confusion of the commenter.

26 CFR 1.871-1 - Classification and manner of taxing alien individuals.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 26 Internal Revenue 9 2013-04-01 2013-04-01 false Classification and manner of taxing alien... (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES (CONTINUED) Nonresident Aliens and Foreign Corporations § 1.871-1 Classification and manner of taxing alien individuals. (a) Classes of aliens. For purposes of...

26 CFR 1.871-1 - Classification and manner of taxing alien individuals.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 26 Internal Revenue 9 2012-04-01 2012-04-01 false Classification and manner of taxing alien... (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES (CONTINUED) Nonresident Aliens and Foreign Corporations § 1.871-1 Classification and manner of taxing alien individuals. (a) Classes of aliens. For purposes of...

26 CFR 1.871-1 - Classification and manner of taxing alien individuals.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 26 Internal Revenue 9 2014-04-01 2014-04-01 false Classification and manner of taxing alien... (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES (CONTINUED) Nonresident Aliens and Foreign Corporations § 1.871-1 Classification and manner of taxing alien individuals. (a) Classes of aliens. For purposes of...

26 CFR 1.871-1 - Classification and manner of taxing alien individuals.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 26 Internal Revenue 9 2011-04-01 2011-04-01 false Classification and manner of taxing alien... (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES (CONTINUED) Nonresident Aliens and Foreign Corporations § 1.871-1 Classification and manner of taxing alien individuals. (a) Classes of aliens. For purposes of...

Associations among hydrologic classifications and fish traits to support environmental flow standards

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

McManamay, Ryan A; Bevelhimer, Mark S; Frimpong, Dr. Emmanuel A,

2014-01-01

Classification systems are valuable to ecological management in that they organize information into consolidated units thereby providing efficient means to achieve conservation objectives. Of the many ways classifications benefit management, hypothesis generation has been discussed as the most important. However, in order to provide templates for developing and testing ecologically relevant hypotheses, classifications created using environmental variables must be linked to ecological patterns. Herein, we develop associations between a recent US hydrologic classification and fish traits in order to form a template for generating flow ecology hypotheses and supporting environmental flow standard development. Tradeoffs in adaptive strategies for fish weremore » observed across a spectrum of stable, perennial flow to unstable intermittent flow. In accordance with theory, periodic strategists were associated with stable, predictable flow, whereas opportunistic strategists were more affiliated with intermittent, variable flows. We developed linkages between the uniqueness of hydrologic character and ecological distinction among classes, which may translate into predictions between losses in hydrologic uniqueness and ecological community response. Comparisons of classification strength between hydrologic classifications and other frameworks suggested that spatially contiguous classifications with higher regionalization will tend to explain more variation in ecological patterns. Despite explaining less ecological variation than other frameworks, we contend that hydrologic classifications are still useful because they provide a conceptual linkage between hydrologic variation and ecological communities to support flow ecology relationships. Mechanistic associations among fish traits and hydrologic classes support the presumption that environmental flow standards should be developed uniquely for stream classes and ecological communities, therein.« less

The Network Structure of Symptoms of the Diagnostic and Statistical Manual of Mental Disorders.

PubMed

Boschloo, Lynn; van Borkulo, Claudia D; Rhemtulla, Mijke; Keyes, Katherine M; Borsboom, Denny; Schoevers, Robert A

2015-01-01

Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from the causal interplay between psychiatric symptoms and focuses specifically on these symptoms and their complex associations. By using a sophisticated network analysis technique, this study constructed an empirically based network structure of 120 psychiatric symptoms of twelve major DSM-IV diagnoses using cross-sectional data of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, second wave; N = 34,653). The resulting network demonstrated that symptoms within the same diagnosis showed differential associations and indicated that the strategy of summing symptoms, as in current classification systems, leads to loss of information. In addition, some symptoms showed strong connections with symptoms of other diagnoses, and these specific symptom pairs, which both concerned overlapping and non-overlapping symptoms, may help to explain the comorbidity across diagnoses. Taken together, our findings indicated that psychopathology is very complex and can be more adequately captured by sophisticated network models than current classification systems. The network approach is, therefore, promising in improving our understanding of psychopathology and moving our field forward.

26 CFR 1.871-1 - Classification and manner of taxing alien individuals.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 9 2010-04-01 2010-04-01 false Classification and manner of taxing alien... (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES Nonresident Aliens and Foreign Corporations § 1.871-1 Classification and manner of taxing alien individuals. (a) Classes of aliens. For purposes of the income tax...

A Lifespan Study of Classification Preference.

ERIC Educational Resources Information Center

Pearce, Kathy A.; Denney, Nancy Wadsworth

Previous research in classification preference has focused on only a few selected age groups. To investigate the classification preferences of individuals from early childhood through old age in the same study, 144 individuals between the ages of 4 and 70 completed a revised version of the Conceptual Styles Test. Analysis of results showed that…

Developing a New Zealand casemix classification for mental health services.

PubMed

Eagar, Kathy; Gaines, Phillipa; Burgess, Philip; Green, Janette; Bower, Alison; Buckingham, Bill; Mellsop, Graham

2004-10-01

This study aimed to develop a casemix classification of characteristics of New Zealand mental health services users. Over a six month period, patient information, staff time and service costs were collected from 8 district health boards. This information was analysed seeking the classification of service user characteristics which best predicted the cost drivers of the services provided. A classification emerged which explained more than two thirds of the variance in service user costs. It can be used to inform service management and funding, but it is premature to have it determine funding.

Medicolegal aspects of doping in football

PubMed Central

Graf‐Baumann, T

2006-01-01

This article describes the historical background of the medicolegal aspects of doping in sports and especially in football. The definitions of legal terms are explained and the procedure of individual case management as part of FIFA's approach to doping is presented. Finally, three medicolegal problems awaiting urgent solution are outlined: firstly, the difficulties in decision making arising from the decrease of the T/E ratio from 6 to 4; secondly, the therapeutic application of α‐reductase inhibitors for male pattern baldness in the face of the classification of finasteride as a forbidden masking agent; and lastly, the increasing use of recreational drugs and its social and legal implications in positive cases. PMID:16799105

Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

PubMed

Bouktif, Salah; Hanna, Eileen Marie; Zaki, Nazar; Abu Khousa, Eman

2014-01-01

Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models' interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions. The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

Experimental Investigation into the Fatigue Life of Hybrid Joints Under Fully Reversed Flexure Loading

DTIC Science & Technology

2008-06-01

joint classification 3 b. Hot spot-stress approach c. Notch-stress approach * d. Mesh-insensitive approach 2. Fracture mechanics (used for crack... classification approach, which is an adaptation of the nominal stress approach just discussed, with the welded joint fatigue curves as given in Table...used. More detail is provided on the joint classifications , and -- 19 I graphic representations are also included. It is explained that the stress

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

USGS Publications Warehouse

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

2015-01-01

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

Imaging of juvenile idiopathic arthritis. Part I: Clinical classifications and radiographs

PubMed Central

Matuszewska, Genowefa; Gietka, Piotr; Płaza, Mateusz; Walentowska-Janowicz, Marta

2016-01-01

Juvenile idiopathic arthritis is the most common autoimmune systemic disease of the connective tissue affecting individuals at the developmental age. Radiography is the primary modality employed in the diagnostic imaging in order to identify changes typical of this disease entity and rule out other bone-related pathologies, such as neoplasms, posttraumatic changes, developmental defects and other forms of arthritis. The standard procedure involves the performance of comparative joint radiographs in two planes. Radiographic changes in juvenile idiopathic arthritis are detected in later stages of the disease. Bone structures are assessed in the first place. Radiographs can also indirectly indicate the presence of soft tissue inflammation (i.e. in joint cavities, sheaths and bursae) based on swelling and increased density of the soft tissue as well as dislocation of fat folds. Signs of articular cartilage defects are also seen in radiographs indirectly – based on joint space width changes. The first part of the publication presents the classification of juvenile idiopathic arthritis and discusses its radiographic images. The authors list the affected joints as well as explain the spectrum and specificity of radiographic signs resulting from inflammatory changes overlapping with those caused by the maturation of the skeletal system. Moreover, certain dilemmas associated with the monitoring of the disease are reviewed. The second part of the publication will explain issues associated with ultrasonography and magnetic resonance imaging, which are more and more commonly applied in juvenile idiopathic arthritis for early detection of pathological features as well as the disease complications. PMID:27679726

Developing a New Zealand casemix classification for mental health services

PubMed Central

Eagar, Kathy; Gaines, Phillipa; Burgess, Philip; Green, Janette; Bower, Alison; Buckingham, Bill; Mellsop, Graham

2004-01-01

This study aimed to develop a casemix classification of characteristics of New Zealand mental health services users. Over a six month period, patient information, staff time and service costs were collected from 8 district health boards. This information was analysed seeking the classification of service user characteristics which best predicted the cost drivers of the services provided. A classification emerged which explained more than two thirds of the variance in service user costs. It can be used to inform service management and funding, but it is premature to have it determine funding. PMID:16633490

Mental Retardation: Definition, Classification, and Systems of Supports. 10th Edition.

ERIC Educational Resources Information Center

Luckasson, Ruth; Borthwick-Duffy, Sharon; Buntinx, Wil H. E.; Coulter, David L.; Craig, Ellis M.; Reeve, Alya; Schalock, Robert L.; Snell, Martha E.; Spitalnik, Deborah M.; Spreat, Scott; Tasse, Marc J.

This manual, the 10th edition of a regularly published definition and classification work on mental retardation, presents five key assumptions upon which the definition of mental retardation is based and a theoretical model of five essential dimensions that explain mental retardation and how to use the companion system. These dimensions include…

Prediction of performance on the RCMP physical ability requirement evaluation.

PubMed

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

1999-08-01

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

Comparison between two race/skin color classifications in relation to health-related outcomes in Brazil.

PubMed

Travassos, Claudia; Laguardia, Josué; Marques, Priscilla M; Mota, Jurema C; Szwarcwald, Celia L

2011-08-25

This paper aims to compare the classification of race/skin color based on the discrete categories used by the Demographic Census of the Brazilian Institute of Geography and Statistics (IBGE) and a skin color scale with values ranging from 1 (lighter skin) to 10 (darker skin), examining whether choosing one alternative or the other can influence measures of self-evaluation of health status, health care service utilization and discrimination in the health services. This is a cross-sectional study based on data from the World Health Survey carried out in Brazil in 2003 with a sample of 5000 individuals older than 18 years. Similarities between the two classifications were evaluated by means of correspondence analysis. The effect of the two classifications on health outcomes was tested through logistic regression models for each sex, using age, educational level and ownership of consumer goods as covariables. Both measures of race/skin color represent the same race/skin color construct. The results show a tendency among Brazilians to classify their skin color in shades closer to the center of the color gradient. Women tend to classify their race/skin color as a little lighter than men in the skin color scale, an effect not observed when IBGE categories are used. With regard to health and health care utilization, race/skin color was not relevant in explaining any of them, regardless of the race/skin color classification. Lack of money and social class were the most prevalent reasons for discrimination in healthcare reported in the survey, suggesting that in Brazil the discussion about discrimination in the health care must not be restricted to racial discrimination and should also consider class-based discrimination. The study shows that the differences of the two classifications of race/skin color are small. However, the interval scale measure appeared to increase the freedom of choice of the respondent.

Comparison between two race/skin color classifications in relation to health-related outcomes in Brazil

PubMed Central

2011-01-01

Background This paper aims to compare the classification of race/skin color based on the discrete categories used by the Demographic Census of the Brazilian Institute of Geography and Statistics (IBGE) and a skin color scale with values ranging from 1 (lighter skin) to 10 (darker skin), examining whether choosing one alternative or the other can influence measures of self-evaluation of health status, health care service utilization and discrimination in the health services. Methods This is a cross-sectional study based on data from the World Health Survey carried out in Brazil in 2003 with a sample of 5000 individuals older than 18 years. Similarities between the two classifications were evaluated by means of correspondence analysis. The effect of the two classifications on health outcomes was tested through logistic regression models for each sex, using age, educational level and ownership of consumer goods as covariables. Results Both measures of race/skin color represent the same race/skin color construct. The results show a tendency among Brazilians to classify their skin color in shades closer to the center of the color gradient. Women tend to classify their race/skin color as a little lighter than men in the skin color scale, an effect not observed when IBGE categories are used. With regard to health and health care utilization, race/skin color was not relevant in explaining any of them, regardless of the race/skin color classification. Lack of money and social class were the most prevalent reasons for discrimination in healthcare reported in the survey, suggesting that in Brazil the discussion about discrimination in the health care must not be restricted to racial discrimination and should also consider class-based discrimination. The study shows that the differences of the two classifications of race/skin color are small. However, the interval scale measure appeared to increase the freedom of choice of the respondent. PMID:21867522

Stomach emptiness in fishes: Sources of variation and study design implications

USGS Publications Warehouse

Vinson, M.R.; Angradi, T.R.

2011-01-01

This study summarizes fish stomach content data from 369,000 fish from 402 species in 1,096 collections and reports on the percentage of individuals with empty stomachs. The mean percentage of individuals with empty stomachs among all species, locations, habitats, seasons, regions, and collection methods was 26.4%. Mean percentage of individuals with empty stomachs varied significantly among fish collection gear types, taxonomic orders, trophic groups, feeding behaviors, and habitats, and with species length at maturity. Most of the variation in percentage of individuals with empty stomachs was explained by species length at maturity, fish collection gear type, and two autecological factors: trophic group (piscivore percentage of individuals with empty stomachs > non-piscivore percentage of individuals with empty stomachs) and feeding habitat (water column feeder percentage of individuals with empty stomachs > benthic feeder percentage of individuals with empty stomachs). After accounting for variation with fish length, the percentage of individuals with empty stomachs did not vary with the stomach removal collection method (dissection vs. gastric lavage), feeding time (diurnal or nocturnal), or time of collection (day or night). The percentage of individuals with empty stomachs was similar between fresh and saltwater fish, but differed within finer habitat classifications and appeared to follow a general prey availability or productivity gradient: percentage of individuals with empty stomachs of open ocean collections > estuary collections, lentic > lotic, and pelagic > littoral. Gear type (active or passive) was the most influential factor affecting the occurrence of empty stomachs that can be readily controlled by researchers.

Inferring heuristic classification hierarchies from natural language input

NASA Technical Reports Server (NTRS)

Hull, Richard; Gomez, Fernando

1993-01-01

A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies.

Pluricentric Views towards English and Implications for ELT in China

ERIC Educational Resources Information Center

Jianli, Liang

2015-01-01

Descriptions of the classifications or models of English language have been proposed by a number of scholars who attempt to explain the differences in the ways English is used in different localities. This paper reviews three models of classification of English language, with an aim of drawing implications on how English Language Teaching (ELT) in…

Emotion models for textual emotion classification

NASA Astrophysics Data System (ADS)

Bruna, O.; Avetisyan, H.; Holub, J.

2016-11-01

This paper deals with textual emotion classification which gained attention in recent years. Emotion classification is used in user experience, product evaluation, national security, and tutoring applications. It attempts to detect the emotional content in the input text and based on different approaches establish what kind of emotional content is present, if any. Textual emotion classification is the most difficult to handle, since it relies mainly on linguistic resources and it introduces many challenges to assignment of text to emotion represented by a proper model. A crucial part of each emotion detector is emotion model. Focus of this paper is to introduce emotion models used for classification. Categorical and dimensional models of emotion are explained and some more advanced approaches are mentioned.

Expert identification of visual primitives used by CNNs during mammogram classification

NASA Astrophysics Data System (ADS)

Wu, Jimmy; Peck, Diondra; Hsieh, Scott; Dialani, Vandana; Lehman, Constance D.; Zhou, Bolei; Syrgkanis, Vasilis; Mackey, Lester; Patterson, Genevieve

2018-02-01

This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms. We propose an expert-in-the-loop inter- pretation method to label the behavior of internal units in convolutional neural networks (CNNs). Expert radiologists identify that the visual patterns detected by the units are correlated with meaningful medical phenomena such as mass tissue and calcificated vessels. We demonstrate that several trained CNN models are able to produce explanatory descriptions to support the final classification decisions. We view this as an important first step toward interpreting the internal representations of medical classification CNNs and explaining their predictions.

Classification as clustering: a Pareto cooperative-competitive GP approach.

PubMed

McIntyre, Andrew R; Heywood, Malcolm I

2011-01-01

Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.

Damaging de novo mutations diminish motor skills in children on the autism spectrum

PubMed Central

Buja, Andreas; Volfovsky, Natalia; Krieger, Abba M.; Lord, Catherine; Lash, Alex E.; Wigler, Michael; Iossifov, Ivan

2018-01-01

In individuals with autism spectrum disorder (ASD), de novo mutations have previously been shown to be significantly correlated with lower IQ but not with the core characteristics of ASD: deficits in social communication and interaction and restricted interests and repetitive patterns of behavior. We extend these findings by demonstrating in the Simons Simplex Collection that damaging de novo mutations in ASD individuals are also significantly and convincingly correlated with measures of impaired motor skills. This correlation is not explained by a correlation between IQ and motor skills. We find that IQ and motor skills are distinctly associated with damaging mutations and, in particular, that motor skills are a more sensitive indicator of mutational severity than is IQ, as judged by mutational type and target gene. We use this finding to propose a combined classification of phenotypic severity: mild (little impairment of either), moderate (impairment mainly to motor skills), and severe (impairment of both IQ and motor skills). PMID:29434036

Damaging de novo mutations diminish motor skills in children on the autism spectrum.

PubMed

Buja, Andreas; Volfovsky, Natalia; Krieger, Abba M; Lord, Catherine; Lash, Alex E; Wigler, Michael; Iossifov, Ivan

2018-02-20

In individuals with autism spectrum disorder (ASD), de novo mutations have previously been shown to be significantly correlated with lower IQ but not with the core characteristics of ASD: deficits in social communication and interaction and restricted interests and repetitive patterns of behavior. We extend these findings by demonstrating in the Simons Simplex Collection that damaging de novo mutations in ASD individuals are also significantly and convincingly correlated with measures of impaired motor skills. This correlation is not explained by a correlation between IQ and motor skills. We find that IQ and motor skills are distinctly associated with damaging mutations and, in particular, that motor skills are a more sensitive indicator of mutational severity than is IQ, as judged by mutational type and target gene. We use this finding to propose a combined classification of phenotypic severity: mild (little impairment of either), moderate (impairment mainly to motor skills), and severe (impairment of both IQ and motor skills). Copyright © 2018 the Author(s). Published by PNAS.

Classification of Ancient Mammal Individuals Using Dental Pulp MALDI-TOF MS Peptide Profiling

PubMed Central

Tran, Thi-Nguyen-Ny; Aboudharam, Gérard; Gardeisen, Armelle; Davoust, Bernard; Bocquet-Appel, Jean-Pierre; Flaudrops, Christophe; Belghazi, Maya; Raoult, Didier; Drancourt, Michel

2011-01-01

Background The classification of ancient animal corpses at the species level remains a challenging task for forensic scientists and anthropologists. Severe damage and mixed, tiny pieces originating from several skeletons may render morphological classification virtually impossible. Standard approaches are based on sequencing mitochondrial and nuclear targets. Methodology/Principal Findings We present a method that can accurately classify mammalian species using dental pulp and mass spectrometry peptide profiling. Our work was organized into three successive steps. First, after extracting proteins from the dental pulp collected from 37 modern individuals representing 13 mammalian species, trypsin-digested peptides were used for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis. The resulting peptide profiles accurately classified every individual at the species level in agreement with parallel cytochrome b gene sequencing gold standard. Second, using a 279–modern spectrum database, we blindly classified 33 of 37 teeth collected in 37 modern individuals (89.1%). Third, we classified 10 of 18 teeth (56%) collected in 15 ancient individuals representing five mammal species including human, from five burial sites dating back 8,500 years. Further comparison with an upgraded database comprising ancient specimen profiles yielded 100% classification in ancient teeth. Peptide sequencing yield 4 and 16 different non-keratin proteins including collagen (alpha-1 type I and alpha-2 type I) in human ancient and modern dental pulp, respectively. Conclusions/Significance Mass spectrometry peptide profiling of the dental pulp is a new approach that can be added to the arsenal of species classification tools for forensics and anthropology as a complementary method to DNA sequencing. The dental pulp is a new source for collagen and other proteins for the species classification of modern and ancient mammal individuals. PMID:21364886

Predictive Utility and Classification Accuracy of Oral Reading Fluency and the Measures of Academic Progress for the Wisconsin Knowledge and Concepts Exam

ERIC Educational Resources Information Center

Ball, Carrie R.; O'Connor, Edward

2016-01-01

This study examined the predictive validity and classification accuracy of two commonly used universal screening measures relative to a statewide achievement test. Results indicated that second-grade performance on oral reading fluency and the Measures of Academic Progress (MAP), together with special education status, explained 68% of the…

Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?

PubMed

Kohler, Friedbert; Renton, Roger; Dickson, Hugh G; Estell, John; Connolly, Carol E

2011-02-01

We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes.

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

PubMed

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

2015-04-01

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

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

PubMed Central

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

2016-01-01

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

A Comparison of Two-Group Classification Methods

ERIC Educational Resources Information Center

Holden, Jocelyn E.; Finch, W. Holmes; Kelley, Ken

2011-01-01

The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis,…

8 CFR 103.22 - Records exempt in whole or in part.

Code of Federal Regulations, 2011 CFR

2011-01-01

... classification under the criteria of the Executive Order. Information which no longer warrants classification... continues to warrant classification, the individual shall be advised that the information sought is classified; that it has been reviewed and continues to warrant classification; and that it has been exempted...

A detailed procedure for the use of small-scale photography in land use classification

NASA Technical Reports Server (NTRS)

Vegas, P. L.

1974-01-01

A procedure developed to produce accurate land use maps from available high-altitude, small-scale photography in a cost-effective manner is presented. An alternative procedure, for use when the capability for updating the resultant land use map is not required, is also presented. The technical approach is discussed in detail, and personnel and equipment needs are analyzed. Accuracy percentages are listed, and costs are cited. The experiment land use classification categories are explained, and a proposed national land use classification system is recommended.

Differential coding of conspecific vocalizations in the ventral auditory cortical stream.

PubMed

Fukushima, Makoto; Saunders, Richard C; Leopold, David A; Mishkin, Mortimer; Averbeck, Bruno B

2014-03-26

The mammalian auditory cortex integrates spectral and temporal acoustic features to support the perception of complex sounds, including conspecific vocalizations. Here we investigate coding of vocal stimuli in different subfields in macaque auditory cortex. We simultaneously measured auditory evoked potentials over a large swath of primary and higher order auditory cortex along the supratemporal plane in three animals chronically using high-density microelectrocorticographic arrays. To evaluate the capacity of neural activity to discriminate individual stimuli in these high-dimensional datasets, we applied a regularized multivariate classifier to evoked potentials to conspecific vocalizations. We found a gradual decrease in the level of overall classification performance along the caudal to rostral axis. Furthermore, the performance in the caudal sectors was similar across individual stimuli, whereas the performance in the rostral sectors significantly differed for different stimuli. Moreover, the information about vocalizations in the caudal sectors was similar to the information about synthetic stimuli that contained only the spectral or temporal features of the original vocalizations. In the rostral sectors, however, the classification for vocalizations was significantly better than that for the synthetic stimuli, suggesting that conjoined spectral and temporal features were necessary to explain differential coding of vocalizations in the rostral areas. We also found that this coding in the rostral sector was carried primarily in the theta frequency band of the response. These findings illustrate a progression in neural coding of conspecific vocalizations along the ventral auditory pathway.

Differential Coding of Conspecific Vocalizations in the Ventral Auditory Cortical Stream

PubMed Central

Saunders, Richard C.; Leopold, David A.; Mishkin, Mortimer; Averbeck, Bruno B.

2014-01-01

The mammalian auditory cortex integrates spectral and temporal acoustic features to support the perception of complex sounds, including conspecific vocalizations. Here we investigate coding of vocal stimuli in different subfields in macaque auditory cortex. We simultaneously measured auditory evoked potentials over a large swath of primary and higher order auditory cortex along the supratemporal plane in three animals chronically using high-density microelectrocorticographic arrays. To evaluate the capacity of neural activity to discriminate individual stimuli in these high-dimensional datasets, we applied a regularized multivariate classifier to evoked potentials to conspecific vocalizations. We found a gradual decrease in the level of overall classification performance along the caudal to rostral axis. Furthermore, the performance in the caudal sectors was similar across individual stimuli, whereas the performance in the rostral sectors significantly differed for different stimuli. Moreover, the information about vocalizations in the caudal sectors was similar to the information about synthetic stimuli that contained only the spectral or temporal features of the original vocalizations. In the rostral sectors, however, the classification for vocalizations was significantly better than that for the synthetic stimuli, suggesting that conjoined spectral and temporal features were necessary to explain differential coding of vocalizations in the rostral areas. We also found that this coding in the rostral sector was carried primarily in the theta frequency band of the response. These findings illustrate a progression in neural coding of conspecific vocalizations along the ventral auditory pathway. PMID:24672012

A New Data Mining Scheme Using Artificial Neural Networks

PubMed Central

Kamruzzaman, S. M.; Jehad Sarkar, A. M.

2011-01-01

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems. PMID:22163866

HLA-DRB1 rheumatoid arthritis risk in African Americans at multiple levels: Hierarchical classification systems, amino acid positions and residues

PubMed Central

Reynolds, Richard J.; Ahmed, Altan F.; Danila, Maria I.; Hughes, Laura B.; Gregersen, Peter K.; Raychaudhuri, Soumya; Plenge, Robert M.; Bridges, S. Louis

2014-01-01

Objective To evaluate African American rheumatoid arthritis HLA-DRB1 genetic risk by three validated allele classification systems, and by amino acid position and residue. To compare the genetic risk between African American and European ancestries. Methods Four-digit HLA-DRB1 genotyping was performed on 561 autoantibody-positive African American cases and 776 African American controls. Association analysis was performed on Tezenas du Montcel (TdM); de Vries (DV); and Mattey classification system alleles and separately by amino acid position and individual residues. Results TdM S2 and S3P alleles were associated with RA (odds ratios (95% CI) 2.8 (2.0, 3.9) and 2.1 (1.7, 2.7), respectively). The DV (P-value=3.2 x 10−12) and Mattey (P-value=6.5 x 10−13) system alleles were both protective in African Americans. Amino acid position 11 (permutation P-value < 0.00001) accounted for nearly all variability explained by HLA-DRB1, although conditional analysis demonstrated that position 57 was also significant (0.01<= permutation P-val <=0.05). The valine and aspartic acid residues at position 11 conferred the highest risk for RA in African Americans. Conclusion With some exceptions, the genetic risk conferred by HLA-DRB1 in African Americans is similar to European ancestry at multiple levels: classification system (e.g., TdM), amino acid position (e.g. 11) and residue (Val 11). Unlike that reported from European ancestry, amino acid position 57 was associated with RA in African Americans, but positions 71 and 74 were not. Asp11 (OR = 1 in European ancestry) corresponds to the four digit classical allele, *09:01, also a risk allele for RA in Koreans. PMID:25524867

Duane retraction syndrome: causes, effects and management strategies

PubMed Central

Kekunnaya, Ramesh; Negalur, Mithila

2017-01-01

Duane retraction syndrome (DRS) is a congenital eye movement anomaly characterized by variable horizontal duction deficits, with narrowing of the palpebral fissure and globe retraction on attempted adduction, occasionally accompanied by upshoot or down-shoot. The etiopathogenesis of this condition can be explained by a spectrum of mechanical, innervational, neurologic and genetic abnormalities occurring independently or which influence each other giving rise to patterns of clinical presentations along with a complex set of ocular and systemic anomalies. Huber type I DRS is the most common form of DRS with an earlier presentation, while Huber type II is the least common presentation. Usually, patients with unilateral type I Duane syndrome have esotropia more frequently than exotropia, those with type II have exotropia and those with type III have esotropia and exotropia occurring equally common. Cases of bilateral DRS may have variable presentation depending upon the type of presentation in each eye. As regards its management, DRS classification based on primary position deviation as esotropic, exotropic or orthotropic is more relevant than Huber’s classification before planning surgery. Surgical approach to these patients is challenging and must be individualized based on the amount of ocular deviation, abnormal head position, associated globe retraction and overshoots. PMID:29133973

Information processing systems, reasoning modules, and reasoning system design methods

DOEpatents

Hohimer, Ryan E.; Greitzer, Frank L.; Hampton, Shawn D.

2016-08-23

Information processing systems, reasoning modules, and reasoning system design methods are described. According to one aspect, an information processing system includes working memory comprising a semantic graph which comprises a plurality of abstractions, wherein the abstractions individually include an individual which is defined according to an ontology and a reasoning system comprising a plurality of reasoning modules which are configured to process different abstractions of the semantic graph, wherein a first of the reasoning modules is configured to process a plurality of abstractions which include individuals of a first classification type of the ontology and a second of the reasoning modules is configured to process a plurality of abstractions which include individuals of a second classification type of the ontology, wherein the first and second classification types are different.

Information processing systems, reasoning modules, and reasoning system design methods

DOEpatents

Hohimer, Ryan E.; Greitzer, Frank L.; Hampton, Shawn D.

2015-08-18

Information processing systems, reasoning modules, and reasoning system design methods are described. According to one aspect, an information processing system includes working memory comprising a semantic graph which comprises a plurality of abstractions, wherein the abstractions individually include an individual which is defined according to an ontology and a reasoning system comprising a plurality of reasoning modules which are configured to process different abstractions of the semantic graph, wherein a first of the reasoning modules is configured to process a plurality of abstractions which include individuals of a first classification type of the ontology and a second of the reasoning modules is configured to process a plurality of abstractions which include individuals of a second classification type of the ontology, wherein the first and second classification types are different.

Information processing systems, reasoning modules, and reasoning system design methods

DOEpatents

Hohimer, Ryan E; Greitzer, Frank L; Hampton, Shawn D

2014-03-04

Information processing systems, reasoning modules, and reasoning system design methods are described. According to one aspect, an information processing system includes working memory comprising a semantic graph which comprises a plurality of abstractions, wherein the abstractions individually include an individual which is defined according to an ontology and a reasoning system comprising a plurality of reasoning modules which are configured to process different abstractions of the semantic graph, wherein a first of the reasoning modules is configured to process a plurality of abstractions which include individuals of a first classification type of the ontology and a second of the reasoning modules is configured to process a plurality of abstractions which include individuals of a second classification type of the ontology, wherein the first and second classification types are different.

A Taxonomy of Introductory Physics Concepts.

NASA Astrophysics Data System (ADS)

Mokaya, Fridah; Savkar, Amit; Valente, Diego

We have designed and implemented a hierarchical taxonomic classification of physics concepts for our introductory physics for engineers course sequence taught at the University of Connecticut. This classification can be used to provide a mechanism to measure student progress in learning at the level of individual concepts or clusters of concepts, and also as part of a tool to measure effectiveness of teaching pedagogy. We examine our pre- and post-test FCI results broken down by topics using Hestenes et al.'s taxonomy classification for the FCI, and compare these results with those found using our own taxonomy classification. In addition, we expand this taxonomic classification to measure performance in our other course exams, investigating possible correlations in results achieved across different assessments at the individual topic level. UCONN CLAS(College of Liberal Arts and Science).

Intra- and Interobserver Reliability of Three Classification Systems for Hallux Rigidus.

PubMed

Dillard, Sarita; Schilero, Christina; Chiang, Sharon; Pham, Peter

2018-04-18

There are over ten classification systems currently used in the staging of hallux rigidus. This results in confusion and inconsistency with radiographic interpretation and treatment. The reliability of hallux rigidus classification systems has not yet been tested. The purpose of this study was to evaluate intra- and interobserver reliability using three commonly used classifications for hallux rigidus. Twenty-one plain radiograph sets were presented to ten ACFAS board-certified foot and ankle surgeons. Each physician classified each radiograph based on clinical experience and knowledge according to the Regnauld, Roukis, and Hattrup and Johnson classification systems. The two-way mixed single-measure consistency intraclass correlation was used to calculate intra- and interrater reliability. The intrarater reliability of individual sets for the Roukis and Hattrup and Johnson classification systems was "fair to good" (Roukis, 0.62±0.19; Hattrup and Johnson, 0.62±0.28), whereas the intrarater reliability of individual sets for the Regnauld system bordered between "fair to good" and "poor" (0.43±0.24). The interrater reliability of the mean classification was "excellent" for all three classification systems. Conclusions Reliable and reproducible classification systems are essential for treatment and prognostic implications in hallux rigidus. In our study, Roukis classification system had the best intrarater reliability. Although there are various classification systems for hallux rigidus, our results indicate that all three of these classification systems show reliability and reproducibility.

Assassination: A Military View.

DTIC Science & Technology

1987-03-23

Assassination: A Military View Individual Essay S. PERFORMING ORG. REPORT NUMBER 7. AUTI4OR(e) S. CONTRACT OR GRANT NUMGER(e) COL Charles K. Eden S...CLASSIFICATION OF THIS PAGE(hen Data Entered) USAWC MILITARY STUDIES PROGRAM PAPER ASSASSINATION: A MILITARY VILW An Individual Essay hceess ’. :’r by...Military View FORMAT: Individual Essay DATE: 23 March 1987 PAGES: 17 CLASSIFICATION: Unclassified -Assassination is a topic with which most Americans

Explaining Match Outcome During The Men’s Basketball Tournament at The Olympic Games

PubMed Central

Leicht, Anthony S.; Gómez, Miguel A.; Woods, Carl T.

2017-01-01

In preparation for the Olympics, there is a limited opportunity for coaches and athletes to interact regularly with team performance indicators providing important guidance to coaches for enhanced match success at the elite level. This study examined the relationship between match outcome and team performance indicators during men’s basketball tournaments at the Olympic Games. Twelve team performance indicators were collated from all men’s teams and matches during the basketball tournament of the 2004-2016 Olympic Games (n = 156). Linear and non-linear analyses examined the relationship between match outcome and team performance indicator characteristics; namely, binary logistic regression and a conditional interference (CI) classification tree. The most parsimonious logistic regression model retained ‘assists’, ‘defensive rebounds’, ‘field-goal percentage’, ‘fouls’, ‘fouls against’, ‘steals’ and ‘turnovers’ (delta AIC <0.01; Akaike weight = 0.28) with a classification accuracy of 85.5%. Conversely, four performance indicators were retained with the CI classification tree with an average classification accuracy of 81.4%. However, it was the combination of ‘field-goal percentage’ and ‘defensive rebounds’ that provided the greatest probability of winning (93.2%). Match outcome during the men’s basketball tournaments at the Olympic Games was identified by a unique combination of performance indicators. Despite the average model accuracy being marginally higher for the logistic regression analysis, the CI classification tree offered a greater practical utility for coaches through its resolution of non-linear phenomena to guide team success. Key points A unique combination of team performance indicators explained 93.2% of winning observations in men’s basketball at the Olympics. Monitoring of these team performance indicators may provide coaches with the capability to devise multiple game plans or strategies to enhance their likelihood of winning. Incorporation of machine learning techniques with team performance indicators may provide a valuable and strategic approach to explain patterns within multivariate datasets in sport science. PMID:29238245

Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?

PubMed

Rossini, Paolo M; Buscema, Massimo; Capriotti, Massimiliano; Grossi, Enzo; Rodriguez, Guido; Del Percio, Claudio; Babiloni, Claudio

2008-07-01

It has been shown that a new procedure (implicit function as squashing time, IFAST) based on artificial neural networks (ANNs) is able to compress eyes-closed resting electroencephalographic (EEG) data into spatial invariants of the instant voltage distributions for an automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects with classification accuracy of individual subjects higher than 92%. Here we tested the hypothesis that this is the case also for the classification of individual normal elderly (Nold) vs. MCI subjects, an important issue for the screening of large populations at high risk of AD. Eyes-closed resting EEG data (10-20 electrode montage) were recorded in 171 Nold and in 115 amnesic MCI subjects. The data inputs for the classification by IFAST were the weights of the connections within a nonlinear auto-associative ANN trained to generate the instant voltage distributions of 60-s artifact-free EEG data. The most relevant features were selected and coincidently the dataset was split into two halves for the final binary classification (training and testing) performed by a supervised ANN. The classification of the individual Nold and MCI subjects reached 95.87% of sensitivity and 91.06% of specificity (93.46% of accuracy). These results indicate that IFAST can reliably distinguish eyes-closed resting EEG in individual Nold and MCI subjects. IFAST may be used for large-scale periodic screening of large populations at risk of AD and personalized care.

Categorization in the wild.

PubMed

Glushko, Robert J; Maglio, Paul P; Matlock, Teenie; Barsalou, Lawrence W

2008-04-01

In studying categorization, cognitive science has focused primarily on cultural categorization, ignoring individual and institutional categorization. Because recent technological developments have made individual and institutional classification systems much more available and powerful, our understanding of the cognitive and social mechanisms that produce these systems is increasingly important. Furthermore, key aspects of categorization that have received little previous attention emerge from considering diverse types of categorization together, such as the social factors that create stability in classification systems, and the interoperability that shared conceptual systems establish between agents. Finally, the profound impact of recent technological developments on classification systems indicates that basic categorization mechanisms are highly adaptive, producing new classification systems as the situations in which they operate change.

A Classification System to Guide Physical Therapy Management in Huntington Disease: A Case Series.

PubMed

Fritz, Nora E; Busse, Monica; Jones, Karen; Khalil, Hanan; Quinn, Lori

2017-07-01

Individuals with Huntington disease (HD), a rare neurological disease, experience impairments in mobility and cognition throughout their disease course. The Medical Research Council framework provides a schema that can be applied to the development and evaluation of complex interventions, such as those provided by physical therapists. Treatment-based classifications, based on expert consensus and available literature, are helpful in guiding physical therapy management across the stages of HD. Such classifications also contribute to the development and further evaluation of well-defined complex interventions in this highly variable and complex neurodegenerative disease. The purpose of this case series was to illustrate the use of these classifications in the management of 2 individuals with late-stage HD. Two females, 40 and 55 years of age, with late-stage HD participated in this case series. Both experienced progressive declines in ambulatory function and balance as well as falls or fear of falling. Both individuals received daily care in the home for activities of daily living. Physical therapy Treatment-Based Classifications for HD guided the interventions and outcomes. Eight weeks of in-home balance training, strength training, task-specific practice of functional activities including transfers and walking tasks, and family/carer education were provided. Both individuals demonstrated improvements that met or exceeded the established minimal detectible change values for gait speed and Timed Up and Go performance. Both also demonstrated improvements on Berg Balance Scale and Physical Performance Test performance, with 1 of the 2 individuals exceeding the established minimal detectible changes for both tests. Reductions in fall risk were evident in both cases. These cases provide proof-of-principle to support use of treatment-based classifications for physical therapy management in individuals with HD. Traditional classification of early-, mid-, and late-stage disease progression may not reflect patients' true capabilities; those with late-stage HD may be as responsive to interventions as those at an earlier disease stage.Video Abstract available for additional insights from the authors (see Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A172).

Classifying Language-Minority Students: A Closer Look at Individual Student Data

ERIC Educational Resources Information Center

Okhremtchouk, Irina S.

2014-01-01

This article presents an exploratory case study of site-level classification practices for language-minority students. The study examines individual classification data of 439 language-minority student cases from one California urban school district and two of its schools. The review process involved a thorough examination of data integrity,…

Remembering Left–Right Orientation of Pictures

PubMed Central

Bartlett, James C.; Gernsbacher, Morton Ann; Till, Robert E.

2015-01-01

In a study of recognition memory for pictures, we observed an asymmetry in classifying test items as “same” versus “different” in left–right orientation: Identical copies of previously viewed items were classified more accurately than left–right reversals of those items. Response bias could not explain this asymmetry, and, moreover, correct “same” and “different” classifications were independently manipulable: Whereas repetition of input pictures (one vs. two presentations) affected primarily correct “same” classifications, retention interval (3 hr vs. 1 week) affected primarily correct “different” classifications. In addition, repetition but not retention interval affected judgments that previously seen pictures (both identical and reversed) were “old”. These and additional findings supported a dual-process hypothesis that links “same” classifications to high familiarity, and “different” classifications to conscious sampling of images of previously viewed pictures. PMID:2949051

Real-time and simultaneous control of artificial limbs based on pattern recognition algorithms.

PubMed

Ortiz-Catalan, Max; Håkansson, Bo; Brånemark, Rickard

2014-07-01

The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigate different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. On the other hand, classifiers inherently capable of simultaneous predictions, such as the multi-layer perceptron (MLP), were found to be more cost effective, as they can be successfully employed in their simplest form. In the prediction of individual movements, the one-vs-one (OVO) topology was found to improve classification accuracy across different classifiers and it was therefore used to benchmark the benefits of simultaneous control. As opposed to previous work reporting only offline accuracy, the classification performance and the resulting controllability are evaluated in real time using the motion test and target achievement control (TAC) test, respectively. We propose a simultaneous classification strategy based on MLP that outperformed a top classifier for individual movements (LDA-OVO), thus improving the state-of-the-art classification approach. Furthermore, all the presented classification strategies and data collected in this study are freely available in BioPatRec, an open source platform for the development of advanced prosthetic control strategies.

Lidar-based individual tree species classification using convolutional neural network

NASA Astrophysics Data System (ADS)

Mizoguchi, Tomohiro; Ishii, Akira; Nakamura, Hiroyuki; Inoue, Tsuyoshi; Takamatsu, Hisashi

2017-06-01

Terrestrial lidar is commonly used for detailed documentation in the field of forest inventory investigation. Recent improvements of point cloud processing techniques enabled efficient and precise computation of an individual tree shape parameters, such as breast-height diameter, height, and volume. However, tree species are manually specified by skilled workers to date. Previous works for automatic tree species classification mainly focused on aerial or satellite images, and few works have been reported for classification techniques using ground-based sensor data. Several candidate sensors can be considered for classification, such as RGB or multi/hyper spectral cameras. Above all candidates, we use terrestrial lidar because it can obtain high resolution point cloud in the dark forest. We selected bark texture for the classification criteria, since they clearly represent unique characteristics of each tree and do not change their appearance under seasonable variation and aged deterioration. In this paper, we propose a new method for automatic individual tree species classification based on terrestrial lidar using Convolutional Neural Network (CNN). The key component is the creation step of a depth image which well describe the characteristics of each species from a point cloud. We focus on Japanese cedar and cypress which cover the large part of domestic forest. Our experimental results demonstrate the effectiveness of our proposed method.

Contextual Classification of Point Cloud Data by Exploiting Individual 3d Neigbourhoods

NASA Astrophysics Data System (ADS)

Weinmann, M.; Schmidt, A.; Mallet, C.; Hinz, S.; Rottensteiner, F.; Jutzi, B.

2015-03-01

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification.

A subject-independent pattern-based Brain-Computer Interface

PubMed Central

Ray, Andreas M.; Sitaram, Ranganatha; Rana, Mohit; Pasqualotto, Emanuele; Buyukturkoglu, Korhan; Guan, Cuntai; Ang, Kai-Keng; Tejos, Cristián; Zamorano, Francisco; Aboitiz, Francisco; Birbaumer, Niels; Ruiz, Sergio

2015-01-01

While earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG) signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs) from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e., happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to “match” their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders. PMID:26539089

Objective classification of atmospheric circulation over southern Scandinavia

NASA Astrophysics Data System (ADS)

Linderson, Maj-Lena

2001-02-01

A method for calculating circulation indices and weather types following the Lamb classification is applied to southern Scandinavia. The main objective is to test the ability of the method to describe the atmospheric circulation over the area, and to evaluate the extent to which the pressure patterns determine local precipitation and temperature in Scania, southernmost Sweden. The weather type classification method works well and produces distinct groups. However, the variability within the group is large with regard to the location of the low pressure centres, which may have implications for the precipitation over the area. The anticyclonic weather type dominates, together with the cyclonic and westerly types. This deviates partly from the general picture for Sweden and may be explained by the southerly location of the study area. The cyclonic type is most frequent in spring, although cloudiness and amount of rain are lowest during this season. This could be explained by the occurrence of weaker cyclones or low air humidity during this time of year. Local temperature and precipitation were modelled by stepwise regression for each season, designating weather types as independent variables. Only the winter season-modelled temperature and precipitation show a high and robust correspondence to the observed temperature and precipitation, even though <60% of the precipitation variance is explained. In the other seasons, the connection between atmospheric circulation and the local temperature and precipitation is low. Other meteorological parameters may need to be taken into account. The time and space resolution of the mean sea level pressure (MSLP) grid may affect the results, as many important features might not be covered by the classification. Local physiography may also influence the local climate in a way that cannot be described by the atmospheric circulation pattern alone, stressing the importance of using more than one observation series.

Lenke and King classification systems for adolescent idiopathic scoliosis: interobserver agreement and postoperative results.

PubMed

Hosseinpour-Feizi, Hojjat; Soleimanpour, Jafar; Sales, Jafar Ganjpour; Arzroumchilar, Ali

2011-01-01

The aim of this study was to investigate the interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis, and to compare the results of surgery performed based on classification of the scoliosis according to each of these classification systems. The study was conducted in Shohada Hospital in Tabriz, Iran, between 2009 and 2010. First, a reliability assessment was undertaken to assess interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis. Second, postoperative efficacy and safety of surgery performed based on the Lenke and King classifications were compared. Kappa coefficients of agreement were calculated to assess the agreement. Outcomes were compared using bivariate tests and repeated measures analysis of variance. A low to moderate interobserver agreement was observed for the King classification; the Lenke classification yielded mostly high agreement coefficients. The outcome of surgery was not found to be substantially different between the two systems. Based on the results, the Lenke classification method seems advantageous. This takes into consideration the Lenke classification's priority in providing details of curvatures in different anatomical surfaces to explain precise intensity of scoliosis, that it has higher interobserver agreement scores, and also that it leads to noninferior postoperative results compared with the King classification method.

ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

USGS Publications Warehouse

Rosenfield, George H.; Fitzpatrick-Lins, Katherine

1984-01-01

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

5 CFR 9901.223 - Appeal to DoD for review of classification decisions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... RESOURCES MANAGEMENT AND LABOR RELATIONS SYSTEMS (DEPARTMENT OF DEFENSE-OFFICE OF PERSONNEL MANAGEMENT) DEPARTMENT OF DEFENSE NATIONAL SECURITY PERSONNEL SYSTEM (NSPS) Classification Classification Process § 9901... an appeal. A management official may disallow an employee's representative when— (1) An individual's...

Ethnopedology and soil quality of bamboo (Bambusa sp.) based agroforestry system.

PubMed

Arun Jyoti, Nath; Lal, Rattan; Das, Ashesh Kumar

2015-07-15

It is widely recognized that farmers' hold important knowledge of folk soil classification for agricultural land for its uses, yet little has been studied for traditional agroforestry systems. This article explores the ethnopedology of bamboo (Bambusa sp.) based agroforestry system in North East India, and establishes the relationship of soil quality index (SQI) with bamboo productivity. The study revealed four basic folk soil (mati) types: kalo (black soil), lal (red soil), pathal (stony soil) and balu (sandy soil). Of these, lal mati soil was the most predominant soil type (~ 40%) in bamboo-based agroforestry system. Soil physio-chemical parameters were studied to validate the farmers' soil hierarchal classification and also to correlate with productivity of the bamboo stand. Farmers' hierarchal folk soil classification was consistent with the laboratory scientific analysis. Culm production (i.e. measure of productivity of bamboo) was the highest (27culmsclump(-1)) in kalo mati (black soil) and the lowest (19culmsclump(-1)) in balu mati (sandy soil). Linear correlation of individual soil quality parameter with bamboo productivity explained 16 to 49% of the variability. A multiple correlation of the best fitted linear soil quality parameter (soil organic carbon or SOC, water holding capacity or WHC, total nitrogen) with productivity improved explanatory power to 53%. Development of SQI from ten relevant soil quality parameters and its correlation with bamboo productivity explained the 64% of the variation and therefore, suggest SQI as the best determinant of bamboo yield. Data presented indicate that the kalo mati (black soil) is sustainable or sustainable with high input. However, the other three folk soil types (red, stony and sandy soil) are also sustainable but for other land uses. Therefore, ethnopedological studies may move beyond routine laboratory analysis and incorporate SQI for assessing the sustainability of land uses managed by the farmers'. Additional research is required to incorporate principal component analysis for improving the SQI and site potential assessment. It is also important to evaluate the minimum data set (MDS) required for SQI and productivity assessment in agroforestry systems. Copyright © 2015 Elsevier B.V. All rights reserved.

Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support.

PubMed

Grossberg, Stephen

2017-03-01

The hard problem of consciousness is the problem of explaining how we experience qualia or phenomenal experiences, such as seeing, hearing, and feeling, and knowing what they are. To solve this problem, a theory of consciousness needs to link brain to mind by modeling how emergent properties of several brain mechanisms interacting together embody detailed properties of individual conscious psychological experiences. This article summarizes evidence that Adaptive Resonance Theory, or ART, accomplishes this goal. ART is a cognitive and neural theory of how advanced brains autonomously learn to attend, recognize, and predict objects and events in a changing world. ART has predicted that "all conscious states are resonant states" as part of its specification of mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony. It hereby provides functional and mechanistic explanations of data ranging from individual spikes and their synchronization to the dynamics of conscious perceptual, cognitive, and cognitive-emotional experiences. ART has reached sufficient maturity to begin classifying the brain resonances that support conscious experiences of seeing, hearing, feeling, and knowing. Psychological and neurobiological data in both normal individuals and clinical patients are clarified by this classification. This analysis also explains why not all resonances become conscious, and why not all brain dynamics are resonant. The global organization of the brain into computationally complementary cortical processing streams (complementary computing), and the organization of the cerebral cortex into characteristic layers of cells (laminar computing), figure prominently in these explanations of conscious and unconscious processes. Alternative models of consciousness are also discussed. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.

Enhanced gauge symmetry in type II and F-theory compactifications: Dynkin diagrams from polyhedra

NASA Astrophysics Data System (ADS)

Perevalov, Eugene; Skarke, Harald

1997-02-01

We explain the observation by Candelas and Font that the Dynkin diagrams of non-abelian gauge groups occurring in type IIA and F-theory can be read off from the polyhedron Δ ∗ that provides the toric description of the Calabi-Yau manifold used for compactification. We show how the intersection pattern of toric divisors corresponding to the degeneration of elliptic fibers follows the ADE classification of singularities and the Kodaira classification of degenerations. We treat in detail the cases of elliptic K3 surfaces and K3 fibered threefolds where the fiber is again elliptic. We also explain how even the occurrence of monodromy and non-simply laced groups in the latter case is visible in the toric picture. These methods also work in the fourfold case.

Classifying coastal resources by integrating optical and radar imagery and color infrared photography

USGS Publications Warehouse

Ramsey, Elijah W.; Nelson, Gene A.; Sapkota, Sijan

1998-01-01

A progressive classification of a marsh and forest system using Landsat Thematic Mapper (TM), color infrared (CIR) photograph, and ERS-1 synthetic aperture radar (SAR) data improved classification accuracy when compared to classification using solely TM reflective band data. The classification resulted in a detailed identification of differences within a nearly monotypic black needlerush marsh. Accuracy percentages of these classes were surprisingly high given the complexities of classification. The detailed classification resulted in a more accurate portrayal of the marsh transgressive sequence than was obtainable with TM data alone. Individual sensor contribution to the improved classification was compared to that using only the six reflective TM bands. Individually, the green reflective CIR and SAR data identified broad categories of water, marsh, and forest. In combination with TM, SAR and the green CIR band each improved overall accuracy by about 3% and 15% respectively. The SAR data improved the TM classification accuracy mostly in the marsh classes. The green CIR data also improved the marsh classification accuracy and accuracies in some water classes. The final combination of all sensor data improved almost all class accuracies from 2% to 70% with an overall improvement of about 20% over TM data alone. Not only was the identification of vegetation types improved, but the spatial detail of the classification approached 10 m in some areas.

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

NASA Astrophysics Data System (ADS)

Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

2010-12-01

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

Nearest Neighbor Classification of Stationary Time Series: An Application to Anesthesia Level Classification by EEG Analysis.

DTIC Science & Technology

1980-12-05

classification procedures that are common in speech processing. The anesthesia level classification by EEG time series population screening problem example is in...formance. The use of the KL number type metric in NN rule classification, in a delete-one subj ect ’s EE-at-a-time KL-NN and KL- kNN classification of the...17 individual labeled EEG sample population using KL-NN and KL- kNN rules. The results obtained are shown in Table 1. The entries in the table indicate

The International Classification of Functioning as an explanatory model of health after distal radius fracture: A cohort study

PubMed Central

Harris, Jocelyn E; MacDermid, Joy C; Roth, James

2005-01-01

Background Distal radius fractures are common injuries that have an increasing impact on health across the lifespan. The purpose of this study was to identify health impacts in body structure/function, activity, and participation at baseline and follow-up, to determine whether they support the ICF model of health. Methods This is a prospective cohort study of 790 individuals who were assessed at 1 week, 3 months, and 1 year post injury. The Patient Rated Wrist Evaluation (PRWE), The Wrist Outcome Measure (WOM), and the Medical Outcome Survey Short-Form (SF-36) were used to measure impairment, activity, participation, and health. Multiple regression was used to develop explanatory models of health outcome. Results Regression analysis showed that the PRWE explained between 13% (one week) and 33% (three months) of the SF-36 Physical Component Summary Scores with pain, activities and participation subscales showing dominant effects at different stages of recovery. PRWE scores were less related to Mental Component Summary Scores, 10% (three months) and 8% (one year). Wrist impairment scores were less powerful predictors of health status than the PRWE. Conclusion The ICF is an informative model for examining distal radius fracture. Difficulty in the domains of activity and participation were able to explain a significant portion of physical health. Post-fracture rehabilitation and outcome assessments should extend beyond physical impairment to insure comprehensive treatment to individuals with distal radius fracture. PMID:16288664

Lenke and King classification systems for adolescent idiopathic scoliosis: interobserver agreement and postoperative results

PubMed Central

Hosseinpour-Feizi, Hojjat; Soleimanpour, Jafar; Sales, Jafar Ganjpour; Arzroumchilar, Ali

2011-01-01

Purpose The aim of this study was to investigate the interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis, and to compare the results of surgery performed based on classification of the scoliosis according to each of these classification systems. Methods The study was conducted in Shohada Hospital in Tabriz, Iran, between 2009 and 2010. First, a reliability assessment was undertaken to assess interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis. Second, postoperative efficacy and safety of surgery performed based on the Lenke and King classifications were compared. Kappa coefficients of agreement were calculated to assess the agreement. Outcomes were compared using bivariate tests and repeated measures analysis of variance. Results A low to moderate interobserver agreement was observed for the King classification; the Lenke classification yielded mostly high agreement coefficients. The outcome of surgery was not found to be substantially different between the two systems. Conclusion Based on the results, the Lenke classification method seems advantageous. This takes into consideration the Lenke classification’s priority in providing details of curvatures in different anatomical surfaces to explain precise intensity of scoliosis, that it has higher interobserver agreement scores, and also that it leads to noninferior postoperative results compared with the King classification method. PMID:22267934

Project development process.

DOT National Transportation Integrated Search

2007-08-01

The following chapter explains the purpose of this document, outlines the essential elements involved in : the Project Development Process, describes the differences in the three main project classifications, and : provides the necessary background i...

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

PubMed Central

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

2010-01-01

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

An Australian casemix classification for palliative care: technical development and results.

PubMed

Eagar, Kathy; Green, Janette; Gordon, Robert

2004-04-01

To develop a palliative care casemix classification for use in all settings including hospital, hospice and home-based care. 3866 palliative care patients who, in a three-month period, had 4596 episodes of care provided by 58 palliative care services in Australia and New Zealand. A detailed clinical and service utilization profile was collected on each patient with staff time and other resources measured on a daily basis. Each day of care was costed using actual cost data from each study site. Regression tree analysis was used to group episodes of care with similar costs and clinical characteristics. In the resulting classification, the Australian National Sub-acute and Non-acute Patient (AN-SNAP) Classification Version 1, the branch for classifying inpatient palliative care episodes (including hospice care) has 11 classes and explains 20.98% of the variance in inpatient palliative care phase costs using trimmed data. There are 22 classes in the ambulatory palliative care branch that explains 17.14% variation in ambulatory phase cost using trimmed data. The term 'subacute' is used in Australia to describe health care in which the goal--a change in functional status or improvement in quality of life--is a better predictor of the need for, and the cost of, care than the patient's underlying diagnosis. The results suggest that phase of care (stage of illness) is the best predictor of the cost of Australian palliative care. Other predictors of cost are functional status and age. In the ambulatory setting, symptom severity and the model of palliative care are also predictive of cost. These variables are used in the AN-SNAP Version 1 classification to create 33 palliative care classes. The classification has clinical meaning but the overall statistical performance is only moderate. The structure of the classification allows for it to be improved over time as models of palliative care service delivery develop.

Secondary instabilities modulate cortical complexity in the mammalian brain

NASA Astrophysics Data System (ADS)

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2015-10-01

Disclosing the origin of convolutions in the mammalian brain remains a scientific challenge. Primary folds form before we are born: they are static, well defined and highly preserved across individuals. Secondary folds occur and disappear throughout our entire lifetime: they are dynamic, irregular and highly variable among individuals. While extensive research has improved our understanding of primary folding in the mammalian brain, secondary folding remains understudied and poorly understood. Here, we show that secondary instabilities can explain the increasing complexity of our brain surface as we age. Using the nonlinear field theories of mechanics supplemented by the theory of finite growth, we explore the critical conditions for secondary instabilities. We show that with continuing growth, our brain surface continues to bifurcate into increasingly complex morphologies. Our results suggest that even small geometric variations can have a significant impact on surface morphogenesis. Secondary bifurcations, and with them morphological changes during childhood and adolescence, are closely associated with the formation and loss of neuronal connections. Understanding the correlation between neuronal connectivity, cortical thickness, surface morphology and ultimately behaviour, could have important implications on the diagnostics, classification and treatment of neurological disorders.

Developing and Validating the Communication Function Classification System for Individuals with Cerebral Palsy

ERIC Educational Resources Information Center

Hidecker, Mary Jo Cooley; Paneth, Nigel; Rosenbaum, Peter L.; Kent, Raymond D.; Lillie, Janet; Eulenberg, John B.; Chester, Ken, Jr.; Johnson, Brenda; Michalsen, Lauren; Evatt, Morgan; Taylor, Kara

2011-01-01

Aim: The purpose of this study was to create and validate the Communication Function Classification System (CFCS) for children with cerebral palsy (CP), for use by a wide variety of individuals who are interested in CP. This paper reports the content validity, interrater reliability, and test-retest reliability of the CFCS for children with CP.…

Occupations, work characteristics and common mental disorder.

PubMed

Stansfeld, S A; Pike, C; McManus, S; Harris, J; Bebbington, P; Brugha, T; Hassiotis, A; Jenkins, R; Meltzer, H; Moran, P; Clark, C

2013-05-01

The present study aimed to assess the prevalence of common mental disorders (CMDs) by occupation in a representative sample of the English adult population. Another aim was to examine whether the increased risk of CMD in some occupations could be explained by adverse work characteristics. Method We derived a sample of 3425 working-age respondents from the Adult Psychiatric Morbidity Survey 2007. Occupations were classified by Standard Occupational Classification group, and CMD measured by the Revised Clinical Interview Schedule. Job characteristics were measured by questionnaire, and tested as explanatory factors in associations of occupation and CMD. After adjusting for age, gender, housing tenure and marital status, caring personal service occupations had the greatest risk of CMD compared with all occupations (odds ratio 1.73, 95% confidence interval 1.16-2.58). The prevalence of adverse psychosocial work characteristics did not follow the pattern of CMD by occupation. Work characteristics did not explain the increased risk of CMDs associated with working in personal service occupations. Contrary to our hypotheses, adding work characteristics individually to the association of occupation and CMD tended to increase rather than decrease the odds for CMD. As has been found by others, psychosocial work characteristics were associated with CMD. However, we found that in our English national dataset they could not explain the high rates of CMD in particular occupations. We suggest that selection into occupations may partly explain high CMD rates in certain occupations. Also, we did not measure emotional demands, and these may be important mediators of the relationship between occupation type and CMDs.

EL68D Wasteway Watershed Land-Cover Generation

USGS Publications Warehouse

Ruhl, Sheila; Usery, E. Lynn; Finn, Michael P.

2007-01-01

Classification of land cover from Landsat Enhanced Thematic Mapper Plus (ETM+) for the EL68D Wasteway Watershed in the State of Washington is documented. The procedures for classification include use of two ETM+ scenes in a simultaneous unsupervised classification process supported by extensive field data collection using Global Positioning System receivers and digital photos. The procedure resulted in a detailed classification at the individual crop species level.

Evaluation of management measures of software development. Volume 1: Analysis summary

NASA Technical Reports Server (NTRS)

Page, J.; Card, D.; Mcgarry, F.

1982-01-01

The conceptual model, the data classification scheme, and the analytic procedures are explained. The analytic results are summarized and specific software measures for collection and monitoring are recommended.

IRIS COLOUR CLASSIFICATION SCALES--THEN AND NOW.

PubMed

Grigore, Mariana; Avram, Alina

2015-01-01

Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual's eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale.

The computer treatment of remotely sensed data: An introduction to techniques which have geologic applications. [image enhancement and thematic classification in Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Paradella, W. R.; Vitorello, I.

1982-01-01

Several aspects of computer-assisted analysis techniques for image enhancement and thematic classification by which LANDSAT MSS imagery may be treated quantitatively are explained. On geological applications, computer processing of digital data allows, possibly, the fullest use of LANDSAT data, by displaying enhanced and corrected data for visual analysis and by evaluating and assigning each spectral pixel information to a given class.

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

PubMed

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

2009-06-01

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

Secular change of sexually dimorphic cranial variables in Euro-Americans and Germans.

PubMed

Manthey, Laura; Jantz, Richard L; Bohnert, Michael; Jellinghaus, Katharina

2017-07-01

Crania are a reliable source for sex estimation in Euro-Americans, Europeans, and most other populations. Besides morphological assessments, the application of Fordisc® has become a useful tool within the last two decades, creating discriminant functions from morphometric data. Unfortunately, until now, white populations are mostly represented by measurements of American individuals. Therefore, classification rates are lower for European skulls than for Euro-Americans. The aim of this study was to show differences in sexual dimorphism between German and Euro-American crania. Furthermore, their secular change from the nineteenth to the twentieth century has been investigated. Analyses have been performed on glabella subtense (GLS), mastoid height (MDH), and bizygomatic breadth (ZYB). Fordisc® 3.1 was used to study sexual dimorphism and secular change, whereas SAS® was used to perform a two-level ANOVA to test for variation in sex dimorphism. Euro-Americans show greater dimorphism than Germans in all three measurements tested. This larger difference is even increasing from the late nineteenth through the late twentieth century in terms of GLS and MDH, while it stays almost the same in the present Europeans. These results explain the unsatisfying classification rates of German and other European crania on Fordisc®. Data collection for European Fordisc® samples is in progress and should improve the current situation.

A personality classification system for eating disorders: a longitudinal study.

PubMed

Thompson-Brenner, Heather; Eddy, Kamryn T; Franko, Debra L; Dorer, David J; Vashchenko, Maryna; Kass, Andrea E; Herzog, David B

2008-01-01

Studies of eating disorders (EDs) suggest that empirically derived personality subtypes may explain heterogeneity in ED samples that is not captured by the current diagnostic system. Longitudinal outcomes for personality subtypes have not been examined. In this study, personality pathology was assessed by clinical interview in 213 individuals with anorexia nervosa and bulimia nervosa at baseline. Interview data on EDs, comorbid diagnoses, global functioning, and treatment utilization were collected at baseline and at 6-month follow-up intervals over a median of 9 years. Q-factor analysis of the participants based on personality items produced a 5-prototype system, including high-functioning, behaviorally dysregulated, emotionally dysregulated, avoidant-insecure, and obsessional-sensitive types. Dimensional prototype scores were associated with baseline functioning and longitudinal outcome. Avoidant-Insecure scores showed consistent associations with poor functioning and outcome, including failure to show ED improvement, poor global functioning after 5 years, and high treatment utilization after 5 years. Behavioral dysregulation was associated with poor baseline functioning but did not show strong associations with ED or global outcome when histories of major depression and substance use disorder were covaried. Emotional dysregulation and obsessional-sensitivity were not associated with negative outcomes. High-functioning prototype scores were consistently associated with positive outcomes. Longitudinal results support the importance of personality subtypes to ED classification.

Classification Behavior in Children Thirty-Six Months of Age.

ERIC Educational Resources Information Center

Shimada, Shoko; Sano, Ryogoro

The purposes of this study were to examine the development of classification ability in 36 month olds and to clarify the positive relationship between classification ability and general cognitive development. Subjects, 16 Japanese children (8 males, 8 females), were individually tested by the use of 12 colored pictures of animals and vehicles.…

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

ERIC Educational Resources Information Center

Zwick, Rebecca; Lenaburg, Lubella

2009-01-01

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

10 CFR 9.61 - Procedures for processing requests for records exempt in whole or in part.

Code of Federal Regulations, 2011 CFR

2011-01-01

... whether it continues to warrant classification under criteria established by an Executive Order to be kept... classification under these criteria shall be declassified and made available to the individual. If the requested... classifying agency to review the information to ascertain if classification is still warranted. If the...

10 CFR 9.61 - Procedures for processing requests for records exempt in whole or in part.

Code of Federal Regulations, 2014 CFR

2014-01-01

... whether it continues to warrant classification under criteria established by an Executive Order to be kept... classification under these criteria shall be declassified and made available to the individual. If the requested... classifying agency to review the information to ascertain if classification is still warranted. If the...

10 CFR 9.61 - Procedures for processing requests for records exempt in whole or in part.

Code of Federal Regulations, 2013 CFR

2013-01-01

... whether it continues to warrant classification under criteria established by an Executive Order to be kept... classification under these criteria shall be declassified and made available to the individual. If the requested... classifying agency to review the information to ascertain if classification is still warranted. If the...

10 CFR 9.61 - Procedures for processing requests for records exempt in whole or in part.

Code of Federal Regulations, 2010 CFR

2010-01-01

... whether it continues to warrant classification under criteria established by an Executive Order to be kept... classification under these criteria shall be declassified and made available to the individual. If the requested... classifying agency to review the information to ascertain if classification is still warranted. If the...

10 CFR 9.61 - Procedures for processing requests for records exempt in whole or in part.

Code of Federal Regulations, 2012 CFR

2012-01-01

... whether it continues to warrant classification under criteria established by an Executive Order to be kept... classification under these criteria shall be declassified and made available to the individual. If the requested... classifying agency to review the information to ascertain if classification is still warranted. If the...

Study of the validity of a job-exposure matrix for psychosocial work factors: results from the national French SUMER survey.

PubMed

Niedhammer, Isabelle; Chastang, Jean-François; Levy, David; David, Simone; Degioanni, Stéphanie; Theorell, Töres

2008-10-01

To construct and evaluate the validity of a job-exposure matrix (JEM) for psychosocial work factors defined by Karasek's model using national representative data of the French working population. National sample of 24,486 men and women who filled in the Job Content Questionnaire (JCQ) by Karasek measuring the scores of psychological demands, decision latitude, and social support (individual scores) in 2003 (response rate 96.5%). Median values of the three scores in the total sample of men and women were used to define high demands, low latitude, and low support (individual binary exposures). Job title was defined by both occupation and economic activity that were coded using detailed national classifications (PCS and NAF/NACE). Two JEM measures were calculated from the individual scores of demands, latitude and support for each job title: JEM scores (mean of the individual score) and JEM binary exposures (JEM score dichotomized at the median). The analysis of the variance of the individual scores of demands, latitude, and support explained by occupations and economic activities, of the correlation and agreement between individual measures and JEM measures, and of the sensitivity and specificity of JEM exposures, as well as the study of the associations with self-reported health showed a low validity of JEM measures for psychological demands and social support, and a relatively higher validity for decision latitude compared with individual measures. Job-exposure matrix measure for decision latitude might be used as a complementary exposure assessment. Further research is needed to evaluate the validity of JEM for psychosocial work factors.

The ability of video image analysis to predict lean meat yield and EUROP score of lamb carcasses.

PubMed

Einarsson, E; Eythórsdóttir, E; Smith, C R; Jónmundsson, J V

2014-07-01

A total of 862 lamb carcasses that were evaluated by both the VIAscan® and the current EUROP classification system were deboned and the actual yield was measured. Models were derived for predicting lean meat yield of the legs (Leg%), loin (Loin%) and shoulder (Shldr%) using the best VIAscan® variables selected by stepwise regression analysis of a calibration data set (n=603). The equations were tested on validation data set (n=259). The results showed that the VIAscan® predicted lean meat yield in the leg, loin and shoulder with an R 2 of 0.60, 0.31 and 0.47, respectively, whereas the current EUROP system predicted lean yield with an R 2 of 0.57, 0.32 and 0.37, respectively, for the three carcass parts. The VIAscan® also predicted the EUROP score of the trial carcasses, using a model derived from an earlier trial. The EUROP classification from VIAscan® and the current system were compared for their ability to explain the variation in lean yield of the whole carcass (LMY%) and trimmed fat (FAT%). The predicted EUROP scores from the VIAscan® explained 36% of the variation in LMY% and 60% of the variation in FAT%, compared with the current EUROP system that explained 49% and 72%, respectively. The EUROP classification obtained by the VIAscan® was tested against a panel of three expert classifiers (n=696). The VIAscan® classification agreed with 82% of conformation and 73% of the fat classes assigned by a panel of expert classifiers. It was concluded that VIAscan® provides a technology that can directly predict LMY% of lamb carcasses with more accuracy than the current EUROP classification system. The VIAscan® is also capable of classifying lamb carcasses into EUROP classes with an accuracy that fulfils minimum demands for the Icelandic sheep industry. Although the VIAscan® prediction of the Loin% is low, it is comparable to the current EUROP system, and should not hinder the adoption of the technology to estimate the yield of Icelandic lambs as it delivered a more accurate prediction for the Leg%, Shldr% and overall LMY% with negligible prediction bias.

Superiority of artificial neural networks for a genetic classification procedure.

PubMed

Sant'Anna, I C; Tomaz, R S; Silva, G N; Nascimento, M; Bhering, L L; Cruz, C D

2015-08-19

The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

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

PubMed Central

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

2016-01-01

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

Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

PubMed

Zhang, Jianhua; Li, Sunan; Wang, Rubin

2017-01-01

In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

Training echo state networks for rotation-invariant bone marrow cell classification.

PubMed

Kainz, Philipp; Burgsteiner, Harald; Asslaber, Martin; Ahammer, Helmut

2017-01-01

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna

PubMed Central

Swanson, Alexandra; Kosmala, Margaret; Lintott, Chris; Simpson, Robert; Smith, Arfon; Packer, Craig

2015-01-01

Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final ‘consensus’ dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research. PMID:26097743

Types of Seizures Affecting Individuals with TSC

MedlinePlus

... Cannabis you can review. *New Terms for Seizure Classifications The International League Against Epilepsy has approved a ... seizures. This new system will make diagnosis and classification of seizures easier and more accurate. Learn more ...

Diagnostic Classification Models and Multidimensional Adaptive Testing: A Commentary on Rupp and Templin

ERIC Educational Resources Information Center

Frey, Andreas; Carstensen, Claus H.

2009-01-01

On a general level, the objective of diagnostic classifications models (DCMs) lies in a classification of individuals regarding multiple latent skills. In this article, the authors show that this objective can be achieved by multidimensional adaptive testing (MAT) as well. The authors discuss whether or not the restricted applicability of DCMs can…

Effect of Adding McKenzie Syndrome, Centralization, Directional Preference, and Psychosocial Classification Variables to a Risk-Adjusted Model Predicting Functional Status Outcomes for Patients With Lumbar Impairments.

PubMed

Werneke, Mark W; Edmond, Susan; Deutscher, Daniel; Ward, Jason; Grigsby, David; Young, Michelle; McGill, Troy; McClenahan, Brian; Weinberg, Jon; Davidow, Amy L

2016-09-01

Study Design Retrospective cohort. Background Patient-classification subgroupings may be important prognostic factors explaining outcomes. Objectives To determine effects of adding classification variables (McKenzie syndrome and pain patterns, including centralization and directional preference; Symptom Checklist Back Pain Prediction Model [SCL BPPM]; and the Fear-Avoidance Beliefs Questionnaire subscales of work and physical activity) to a baseline risk-adjusted model predicting functional status (FS) outcomes. Methods Consecutive patients completed a battery of questionnaires that gathered information on 11 risk-adjustment variables. Physical therapists trained in Mechanical Diagnosis and Therapy methods classified each patient by McKenzie syndromes and pain pattern. Functional status was assessed at discharge by patient-reported outcomes. Only patients with complete data were included. Risk of selection bias was assessed. Prediction of discharge FS was assessed using linear stepwise regression models, allowing 13 variables to enter the model. Significant variables were retained in subsequent models. Model power (R(2)) and beta coefficients for model variables were estimated. Results Two thousand sixty-six patients with lumbar impairments were evaluated. Of those, 994 (48%), 10 (<1%), and 601 (29%) were excluded due to incomplete psychosocial data, McKenzie classification data, and missing FS at discharge, respectively. The final sample for analyses was 723 (35%). Overall R(2) for the baseline prediction FS model was 0.40. Adding classification variables to the baseline model did not result in significant increases in R(2). McKenzie syndrome or pain pattern explained 2.8% and 3.0% of the variance, respectively. When pain pattern and SCL BPPM were added simultaneously, overall model R(2) increased to 0.44. Although none of these increases in R(2) were significant, some classification variables were stronger predictors compared with some other variables included in the baseline model. Conclusion The small added prognostic capabilities identified when combining McKenzie or pain-pattern classifications with the SCL BPPM classification did not significantly improve prediction of FS outcomes in this study. Additional research is warranted to investigate the importance of classification variables compared with those used in the baseline model to maximize predictive power. Level of Evidence Prognosis, level 4. J Orthop Sports Phys Ther 2016;46(9):726-741. Epub 31 Jul 2016. doi:10.2519/jospt.2016.6266.

Round Cell Tumors: Classification and Immunohistochemistry.

PubMed

Sharma, Shweta; Kamala, R; Nair, Divya; Ragavendra, T Raju; Mhatre, Swapnil; Sabharwal, Robin; Choudhury, Basanta Kumar; Rana, Vivek

2017-01-01

Round cell tumors as the name suggest are comprised round cells with increased nuclear-cytoplasmic ratio. This group of tumor includes entities such as peripheral neuroectodermal tumor, rhabdomyosarcoma, synovial sarcoma, non-Hodgkin's lymphoma, neuroblastoma, hepatoblastoma, Wilms' tumor, and desmoplastic small round cell tumor. These round cells tumors are characterized by typical histological pattern, immunohistochemical, and electron microscopic features that can help in differential diagnosis. The present article describes the classification and explains the histopathology and immunohistochemistry of some important round cell tumors.

Application of remotely sensed multispectral data to automated analysis of marshland vegetation. Inference to the location of breeding habitats of the salt marsh mosquito (Aedes Sollicitans)

NASA Technical Reports Server (NTRS)

Cibula, W. G.

1976-01-01

The techniques used for the automated classification of marshland vegetation and for the color-coded display of remotely acquired data to facilitate the control of mosquito breeding are presented. A multispectral scanner system and its mode of operation are described, and the computer processing techniques are discussed. The procedures for the selection of calibration sites are explained. Three methods for displaying color-coded classification data are presented.

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Ecosystem classifications based on summer and winter conditions.

PubMed

Andrew, Margaret E; Nelson, Trisalyn A; Wulder, Michael A; Hobart, George W; Coops, Nicholas C; Farmer, Carson J Q

2013-04-01

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g., snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks.

[To represent needs of nursing care using nursing diagnoses: potentials and restrictions of the NANDA classification and ICNP].

PubMed

Schilder, Michael

2005-03-01

Nursing diagnoses represent individual reactions to existing or potential changes in one's state of health. They are result of a diagnostic process, which is part of the dynamic nursing care process in its whole. Thus, as a basis of nursing interventions diagnoses have to be proved continuously. The classification of the North American Nursing Diagnosis Association (NANDA) as well as the International Classification for Nursing Practice (ICNP) can be account to the international well-known classifications of nursing diagnoses. Comparing their structures, some fundamental differences between both classifications become obvious. While the NANDA classification represents a systematic structured body of nursing knowledge with regard to human health reactions patterns, the ICNP reflects a more comprehensive part of the nursing reality, since it also contains nursing interventions and outcomes. Until the latest changes by establishing the taxonomy II, NANDA diagnoses have primarily focused deficits. But in contrast to the diagnoses of the ICNP they also comprise etiological factors. To prove the applicability of both classifications to nursing practice, they have been applied to a case study of a female resident living in a nursing home. The results of analysis show that because of their different structures the NANDA classification and ICNP have their own possibilities and limitations in covering the resident's individual needs of nursing care. These characteristic potentials and restrictions have to be taken into account when one of the classification systems is going to be implemented into nursing practice.

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback

PubMed Central

Cisler, Josh M.; Bush, Keith; James, G. Andrew; Smitherman, Sonet; Kilts, Clinton D.

2015-01-01

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD. PMID:26241958

Decoding the Traumatic Memory among Women with PTSD: Implications for Neurocircuitry Models of PTSD and Real-Time fMRI Neurofeedback.

PubMed

Cisler, Josh M; Bush, Keith; James, G Andrew; Smitherman, Sonet; Kilts, Clinton D

2015-01-01

Posttraumatic Stress Disorder (PTSD) is characterized by intrusive recall of the traumatic memory. While numerous studies have investigated the neural processing mechanisms engaged during trauma memory recall in PTSD, these analyses have only focused on group-level contrasts that reveal little about the predictive validity of the identified brain regions. By contrast, a multivariate pattern analysis (MVPA) approach towards identifying the neural mechanisms engaged during trauma memory recall would entail testing whether a multivariate set of brain regions is reliably predictive of (i.e., discriminates) whether an individual is engaging in trauma or non-trauma memory recall. Here, we use a MVPA approach to test 1) whether trauma memory vs neutral memory recall can be predicted reliably using a multivariate set of brain regions among women with PTSD related to assaultive violence exposure (N=16), 2) the methodological parameters (e.g., spatial smoothing, number of memory recall repetitions, etc.) that optimize classification accuracy and reproducibility of the feature weight spatial maps, and 3) the correspondence between brain regions that discriminate trauma memory recall and the brain regions predicted by neurocircuitry models of PTSD. Cross-validation classification accuracy was significantly above chance for all methodological permutations tested; mean accuracy across participants was 76% for the methodological parameters selected as optimal for both efficiency and accuracy. Classification accuracy was significantly better for a voxel-wise approach relative to voxels within restricted regions-of-interest (ROIs); classification accuracy did not differ when using PTSD-related ROIs compared to randomly generated ROIs. ROI-based analyses suggested the reliable involvement of the left hippocampus in discriminating memory recall across participants and that the contribution of the left amygdala to the decision function was dependent upon PTSD symptom severity. These results have methodological implications for real-time fMRI neurofeedback of the trauma memory in PTSD and conceptual implications for neurocircuitry models of PTSD that attempt to explain core neural processing mechanisms mediating PTSD.

[Tobacco quality analysis of industrial classification of different producing area using near-infrared (NIR) spectrum].

PubMed

Wang, Yi; Ma, Xiang; Wen, Ya-Dong; Yu, Chun-Xia; Wang, Luo-Ping; Zhao, Long-Lian; Li, Jun-Hui

2012-10-01

In this study, tobacco quality analysis of industrial classification of different producing area was carried out applying spectrum projection and correlation methods. The group of industrial classification data was near-infrared (NIR) spectrum in 2010 year from different tobacco plant parts and colors of Hongta Tobacco (Group) Co., Ltd. 6 064 tobacco leaf samples of 17 classes from Yuxi, Chuxiong and Zhaotong, in Yunnan province and 6 industrial classifications were collected using near infrared spectroscopy, which from different parts and colors and all belong to tobacco varieties of K326. The conclusion showed that, the probability of the grading belonging by the first dimension was 84%, the probability of the producing area belonging by the second dimension was 71%. The study can explain the difference of tobacco quality of industrial classification and producing area by a projection method to get the quantitative similarity values. The quantitative similarity values were instructive in combination of tobacco leaf blending.

Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images.

PubMed

Quirós, Elia; Felicísimo, Angel M; Cuartero, Aurora

2009-01-01

This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data

PubMed Central

Puttonen, Eetu; Jaakkola, Anttoni; Litkey, Paula; Hyyppä, Juha

2011-01-01

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin. PMID:22163894

Tree classification with fused mobile laser scanning and hyperspectral data.

PubMed

Puttonen, Eetu; Jaakkola, Anttoni; Litkey, Paula; Hyyppä, Juha

2011-01-01

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin.

Improved classification of evidence for EMF health risks.

PubMed

Leitgeb, Norbert

2012-08-01

Classifying evidence of causality between a risk factor and its potential health effect is challenging, in particular in an already emotional situation. Even the assessment of health risks by designated bodies may still depend on their composition of individuals with their background, bias, and, in worst case, their interests. This may explain opposing conclusions from the same pool of data which, consequently, may undermine credibility if not communicated properly. To overcome existing weakness in classifying and communicating evidence of health risks such as from electromagnetic fields, a new rule-based approach is presented. Developed by the German Commission on Radiological Protection (SSK), it discloses step-by-step the criteria for weighing scientific data and pools partial evidences of different scientific approaches to conclude on the overall evidence of causality between risk factor and effects. The validity of the approach is demonstrated by analyzing evidence of carcinogenicity of ionizing radiation, mobile phone use, and nocturnal exposure to visible light.

Towards in vivo focal cortical dysplasia phenotyping using quantitative MRI.

PubMed

Adler, Sophie; Lorio, Sara; Jacques, Thomas S; Benova, Barbora; Gunny, Roxana; Cross, J Helen; Baldeweg, Torsten; Carmichael, David W

2017-01-01

Focal cortical dysplasias (FCDs) are a range of malformations of cortical development each with specific histopathological features. Conventional radiological assessment of standard structural MRI is useful for the localization of lesions but is unable to accurately predict the histopathological features. Quantitative MRI offers the possibility to probe tissue biophysical properties in vivo and may bridge the gap between radiological assessment and ex-vivo histology. This review will cover histological, genetic and radiological features of FCD following the ILAE classification and will explain how quantitative voxel- and surface-based techniques can characterise these features. We will provide an overview of the quantitative MRI measures available, their link with biophysical properties and finally the potential application of quantitative MRI to the problem of FCD subtyping. Future research linking quantitative MRI to FCD histological properties should improve clinical protocols, allow better characterisation of lesions in vivo and tailored surgical planning to the individual.

Framework for evaluating disease severity measures in older adults with comorbidity.

PubMed

Boyd, Cynthia M; Weiss, Carlos O; Halter, Jeff; Han, K Carol; Ershler, William B; Fried, Linda P

2007-03-01

Accounting for the influence of concurrent conditions on health and functional status for both research and clinical decision-making purposes is especially important in older adults. Although approaches to classifying severity of individual diseases and conditions have been developed, the utility of these classification systems has not been evaluated in the presence of multiple conditions. We present a framework for evaluating severity classification systems for common chronic diseases. The framework evaluates the: (a) goal or purpose of the classification system; (b) physiological and/or functional criteria for severity graduation; and (c) potential reliability and validity of the system balanced against burden and costs associated with classification. Approaches to severity classification of individual diseases were not originally conceived for the study of comorbidity. Therefore, they vary greatly in terms of objectives, physiological systems covered, level of severity characterization, reliability and validity, and costs and burdens. Using different severity classification systems to account for differing levels of disease severity in a patient with multiple diseases, or, assessing global disease burden may be challenging. Most approaches to severity classification are not adequate to address comorbidity. Nevertheless, thoughtful use of some existing approaches and refinement of others may advance the study of comorbidity and diagnostic and therapeutic approaches to patients with multimorbidity.

Developing and validating the Communication Function Classification System for individuals with cerebral palsy

PubMed Central

HIDECKER, MARY JO COOLEY; PANETH, NIGEL; ROSENBAUM, PETER L; KENT, RAYMOND D; LILLIE, JANET; EULENBERG, JOHN B; CHESTER, KEN; JOHNSON, BRENDA; MICHALSEN, LAUREN; EVATT, MORGAN; TAYLOR, KARA

2011-01-01

Aim The purpose of this study was to create and validate a Communication Function Classification System (CFCS) for children with cerebral palsy (CP) that can be used by a wide variety of individuals who are interested in CP. This paper reports the content validity, interrater reliability, and test–retest reliability of the CFCS for children with CP. Method An 11-member development team created comprehensive descriptions of the CFCS levels, and four nominal groups comprising 27 participants critiqued these levels. Within a Delphi survey, 112 participants commented on the clarity and usefulness of the CFCS. Interrater reliability was completed by 61 professionals and 68 parents/relatives who classified 69 children with CP aged 2 to 18 years. Test–retest reliability was completed by 48 professionals who allowed at least 2 weeks between classifications. The participants who assessed the CFCS were all relevant stakeholders: adults with CP, parents of children with CP, educators, occupational therapists, physical therapists, physicians, and speech–language pathologists. Results The interrater reliability of the CFCS was 0.66 between two professionals and 0.49 between a parent and a professional. Professional interrater reliability improved to 0.77 for classification of children older than 4 years. The test–retest reliability was 0.82. Interpretation The CFCS demonstrates content validity and shows very good test–retest reliability, good professional interrater reliability, and moderate parent–professional interrater reliability. Combining the CFCS with the Gross Motor Function Classification System and the Manual Ability Classification System contributes to a functional performance view of daily life for individuals with CP, in accordance with the World Health Organization’s International Classification of Functioning, Disability and Health. PMID:21707596

Interannual rainfall variability and SOM-based circulation classification

NASA Astrophysics Data System (ADS)

Wolski, Piotr; Jack, Christopher; Tadross, Mark; van Aardenne, Lisa; Lennard, Christopher

2018-01-01

Self-Organizing Maps (SOM) based classifications of synoptic circulation patterns are increasingly being used to interpret large-scale drivers of local climate variability, and as part of statistical downscaling methodologies. These applications rely on a basic premise of synoptic climatology, i.e. that local weather is conditioned by the large-scale circulation. While it is clear that this relationship holds in principle, the implications of its implementation through SOM-based classification, particularly at interannual and longer time scales, are not well recognized. Here we use a SOM to understand the interannual synoptic drivers of climate variability at two locations in the winter and summer rainfall regimes of South Africa. We quantify the portion of variance in seasonal rainfall totals that is explained by year to year differences in the synoptic circulation, as schematized by a SOM. We furthermore test how different spatial domain sizes and synoptic variables affect the ability of the SOM to capture the dominant synoptic drivers of interannual rainfall variability. Additionally, we identify systematic synoptic forcing that is not captured by the SOM classification. The results indicate that the frequency of synoptic states, as schematized by a relatively disaggregated SOM (7 × 9) of prognostic atmospheric variables, including specific humidity, air temperature and geostrophic winds, captures only 20-45% of interannual local rainfall variability, and that the residual variance contains a strong systematic component. Utilising a multivariate linear regression framework demonstrates that this residual variance can largely be explained using synoptic variables over a particular location; even though they are used in the development of the SOM their influence, however, diminishes with the size of the SOM spatial domain. The influence of the SOM domain size, the choice of SOM atmospheric variables and grid-point explanatory variables on the levels of explained variance, is consistent with the general understanding of the dominant processes and atmospheric variables that affect rainfall variability at a particular location.

Exploring differences in adiposity in two U.S. Hispanic populations of Mexican origin using social, behavioral, physiologic and genetic markers: the IRAS Family Study.

PubMed

Young, Kendra A; Fingerlin, Tasha E; Langefeld, Carl D; Lorenzo, Carlos; Haffner, Steven M; Wagenknecht, Lynne E; Norris, Jill M

2012-01-01

The census classification of Hispanic origin is used in epidemiological studies to group individuals, even though there is geographical, cultural, and genetic diversity within Hispanic Americans of purportedly similar backgrounds. We observed differences in our measures of adiposity between our two Mexican American populations, and examined whether these differences were attributed to social, behavioral, physiologic or genetic differences between the two populations. In the IRAS Family Study, we examined 478 Hispanics from San Antonio, Texas and 447 Hispanics from the San Luis Valley, Colorado. Associations with body mass index (BMI), visceral adipose tissue area (VAT), and subcutaneous adipose tissue area (SAT) using social, behavioral, physiologic and genetic variables were examined. Hispanics of Mexican origin in our clinic population in San Antonio had significantly higher mean BMI (31.09 vs. 28.35 kg/m2), VAT (126.3 vs. 105.5 cm2), and SAT (391.6 vs. 336.9 cm2), than Hispanics of Mexican origin in the San Luis Valley. The amount of variation in adiposity explained by clinic population was 4.5% for BMI, 2.8% for VAT, and 2.7% for SAT. After adjustment, clinic population was no longer associated with VAT and SAT, but remained associated with BMI, although the amount of variation explained by population was substantially less (1.0% for BMI). Adiposity differences within this population of Mexican origin can be largely explained by social, behavioral, physiologic and genetic differences.

Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

PubMed

Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas

2014-07-01

Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using data mining techniques to characterize participation in observational studies.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care-based medical home pilot programme and compare the performance of commonly used classification approaches - logistic regression, support vector machines, random forests and classification tree analysis (CTA) - in correctly classifying participants and non-participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention. © 2016 John Wiley & Sons, Ltd.

Implicit and Explicit Number-Space Associations Differentially Relate to Interference Control in Young Adults With ADHD

PubMed Central

Georges, Carrie; Hoffmann, Danielle; Schiltz, Christine

2018-01-01

Behavioral evidence for the link between numerical and spatial representations comes from the spatial-numerical association of response codes (SNARC) effect, consisting in faster reaction times to small/large numbers with the left/right hand respectively. The SNARC effect is, however, characterized by considerable intra- and inter-individual variability. It depends not only on the explicit or implicit nature of the numerical task, but also relates to interference control. To determine whether the prevalence of the latter relation in the elderly could be ascribed to younger individuals’ ceiling performances on executive control tasks, we determined whether the SNARC effect related to Stroop and/or Flanker effects in 26 young adults with ADHD. We observed a divergent pattern of correlation depending on the type of numerical task used to assess the SNARC effect and the type of interference control measure involved in number-space associations. Namely, stronger number-space associations during parity judgments involving implicit magnitude processing related to weaker interference control in the Stroop but not Flanker task. Conversely, stronger number-space associations during explicit magnitude classifications tended to be associated with better interference control in the Flanker but not Stroop paradigm. The association of stronger parity and magnitude SNARC effects with weaker and better interference control respectively indicates that different mechanisms underlie these relations. Activation of the magnitude-associated spatial code is irrelevant and potentially interferes with parity judgments, but in contrast assists explicit magnitude classifications. Altogether, the present study confirms the contribution of interference control to number-space associations also in young adults. It suggests that magnitude-associated spatial codes in implicit and explicit tasks are monitored by different interference control mechanisms, thereby explaining task-related intra-individual differences in number-space associations. PMID:29881363

Case-control study of male germ cell tumors nested in a cohort of car-manufacturing workers: Findings from the occupational history.

PubMed

Langner, Ingo; Schmeisser, Nils; Mester, Birte; Behrens, Thomas; Gottlieb, Andrea; Ahrens, Wolfgang

2010-10-01

To examine whether the previously observed excess risk of male germ cell cancer in a cohort of car-manufacturing workers can be attributed to occupational activities inside and/or outside the car industry. A nested case-control study among workers in six plants included 205 cases of germ cell cancer and 1,105 controls, individually matched by year of birth (±2 years). Job periods of the individual occupational histories were coded based on the International Standard Classification of Occupations (ISCO) and the industrial classification of economic activities (NACE). Odds ratios (ORs) and corresponding 95%-confidence intervals (CI) for ever-never and cumulative employment were calculated by conditional multivariate logistic regression adjusted for cryptorchidism. Significantly increased risks were observed for machinery fitters and assemblers (A) (OR = 1.8, 95% CI 1.25-2.53) and "workers not elsewhere classified" (OR = 2.10, 95% CI 1.27-3.54), but no trend was observed for employment duration in either occupational group. Stratification of job group A by metal-cutting and non-cutting jobs yielded ORs of 1.87 (95% CI 1.31-2.67) and of 1.24 (95% CI 0.68-2.28), respectively. Among "plumbers, welders, sheet & structural metal workers" (adjusted OR 1.4, 95% CI 0.99-1.95) only "structural metal preparers and erectors" showed a substantially increased risk (OR = 2.30; 95% CI 1.27-4.27). Our results do not fully explain the increased incidence of germ cell cancer in the cohort, but support previous findings showing increased risks among metal workers. These risks were most strongly pronounced in metal-cutting activities. © 2010 Wiley-Liss, Inc.

Bats in the Classroom: A Conceptual Guide for Biology Teachers.

ERIC Educational Resources Information Center

Rankin, W. T.; Lewis, Norma G.

2002-01-01

Explains how to use bats to introduce different biological concepts such as classification and phylogeny, altruistic behavior, flight, coevolution, or physiological adaptations. Discusses common myths regarding bats and provides information on additional classroom materials. (YDS)

How Do Evergreens Stay Ever-Green? Hands on Science.

ERIC Educational Resources Information Center

Kepler, Lynne

1993-01-01

Provides instructional techniques, using samples from evergreen trees, to explain to school children the concept of adaptation. The techniques help children develop skills in observation, classification, communication, inferring, and predicting. A teacher's reproducible is included. (GLR)

Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques

PubMed Central

Cheng, Wei; Ji, Xiaoxi; Zhang, Jie; Feng, Jianfeng

2012-01-01

Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15% across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27% and the specificity being 85.11%. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders. PMID:22888314

Beluga whale (Delphinapterus leucas) vocalizations and call classification from the eastern Beaufort Sea population.

PubMed

Garland, Ellen C; Castellote, Manuel; Berchok, Catherine L

2015-06-01

Beluga whales, Delphinapterus leucas, have a graded call system; call types exist on a continuum making classification challenging. A description of vocalizations from the eastern Beaufort Sea beluga population during its spring migration are presented here, using both a non-parametric classification tree analysis (CART), and a Random Forest analysis. Twelve frequency and duration measurements were made on 1019 calls recorded over 14 days off Icy Cape, Alaska, resulting in 34 identifiable call types with 83% agreement in classification for both CART and Random Forest analyses. This high level of agreement in classification, with an initial subjective classification of calls into 36 categories, demonstrates that the methods applied here provide a quantitative analysis of a graded call dataset. Further, as calls cannot be attributed to individuals using single sensor passive acoustic monitoring efforts, these methods provide a comprehensive analysis of data where the influence of pseudo-replication of calls from individuals is unknown. This study is the first to describe the vocal repertoire of a beluga population using a robust and repeatable methodology. A baseline eastern Beaufort Sea beluga population repertoire is presented here, against which the call repertoire of other seasonally sympatric Alaskan beluga populations can be compared.

Operation Ghost Dancer: The Use of Active Duty Army Forces in Marijuana Eradication.

DTIC Science & Technology

1991-03-11

NO. NO. ACCESSION NO. 11. TITLE (Include Security Classification) OPERATION GHOST DANCER: The Use of Active Duty Army Forces in Marijuana Eradication...The Use of Active Duty Army Forces in Marijuana Eradication An Individual Study Project by Lieutenant Colonel Henry J. Richter United States Army...Dancer: The Use of Active Duty Army Forces in Marijuana Eradication Format: Individual Study Project Date: 11 March 1991 Pages: 70 Classification

Global Stress Classification System for Materials Used in Solar Energy Applications

NASA Astrophysics Data System (ADS)

Slamova, Karolina; Schill, Christian; Herrmann, Jan; Datta, Pawan; Chih Wang, Chien

2016-08-01

Depending on the geographical location, the individual or combined impact of environmental stress factors and corresponding performance losses for solar applications varies significantly. Therefore, as a strategy to reduce investment risks and operating and maintenance costs, it is necessary to adapt the materials and components of solar energy systems specifically to regional environmental conditions. The project «GloBe Solar» supports this strategy by focusing on the development of a global stress classification system for materials in solar energy applications. The aim of this classification system is to assist in the identification of the individual stress conditions for every location on the earth's surface. The stress classification system could serve as a decision support tool for the industry (manufacturers, investors, lenders and project developers) and help to improve knowledge and services that can provide higher confidence to solar power systems.

Identifying functional groups for response to disturbance in an abandoned pasture

NASA Astrophysics Data System (ADS)

Lavorel, Sandra; Touzard, Blaise; Lebreton, Jean-Dominique; Clément, Bernard

1998-06-01

In an abandoned pasture in Brittany, we compared artificial small-scale disturbances to natural disturbances by wild boar and undisturbed vegetation. We developed a multivariate statistical approach which analyses how species biological attributes explain the response of community composition to disturbances. This technique, which reconciles the inductive and deductive approaches for functional classifications, identifies groups of species with similar responses to disturbance and characterizes their biological profiles. After 5 months of recolonization, artificial disturbances had a greater species richness than undisturbed vegetation as a result of recruitment of new species without the exclusion of pre-existing matrix species. Species morphology, described by canopy structure, canopy height and lateral spread, explained a large part (16 %) of community response to disturbance. Regeneration strategies, described by life history, seed mass, dispersal agent, dormancy and the existence of vegetative multiplication, explained a smaller part of community response to disturbance (8 %). Artificial disturbances were characterized by therophyte and compact rosettes with moderately dormant seeds, including a number of Asteraceae and other early successional species. Natural disturbances were colonized by leafy guerrilla species without seed dormancy. Few species were tightly related to undisturbed vegetation and were essentially grasses with a phalanx rosette morphology. The functional classification obtained is consistent with the classification of the community into fugitives, regenerators and persistors. These groups are structured according to Grubb's model for temperate grasslands, with regenerators and persistors in the matrix and fugitives taking advantage of gaps open by small-scale disturbances. The conjunction of functional diversity and species diversity within functional groups is the key to resilience to disturbance, an important ecosystem function.

Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species.

PubMed

Campbell, Hamish A; Gao, Lianli; Bidder, Owen R; Hunter, Jane; Franklin, Craig E

2013-12-15

Distinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time-consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here, we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted and used to build the classification module through the application of support vector machines (SVMs). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo and wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (>90%) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the similarity of the individual's spinal length-to-height above the ground ratio (SL:SH) to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.

SCN5A (NaV1.5) Variant Functional Perturbation and Clinical Presentation: Variants of a Certain Significance.

PubMed

Kroncke, Brett M; Glazer, Andrew M; Smith, Derek K; Blume, Jeffrey D; Roden, Dan M

2018-05-01

Accurately predicting the impact of rare nonsynonymous variants on disease risk is an important goal in precision medicine. Variants in the cardiac sodium channel SCN5A (protein Na V 1.5; voltage-dependent cardiac Na+ channel) are associated with multiple arrhythmia disorders, including Brugada syndrome and long QT syndrome. Rare SCN5A variants also occur in ≈1% of unaffected individuals. We hypothesized that in vitro electrophysiological functional parameters explain a statistically significant portion of the variability in disease penetrance. From a comprehensive literature review, we quantified the number of carriers presenting with and without disease for 1712 reported SCN5A variants. For 356 variants, data were also available for 5 Na V 1.5 electrophysiological parameters: peak current, late/persistent current, steady-state V1/2 of activation and inactivation, and recovery from inactivation. We found that peak and late current significantly associate with Brugada syndrome ( P <0.001; ρ=-0.44; Spearman rank test) and long QT syndrome disease penetrance ( P <0.001; ρ=0.37). Steady-state V1/2 activation and recovery from inactivation associate significantly with Brugada syndrome and long QT syndrome penetrance, respectively. Continuous estimates of disease penetrance align with the current American College of Medical Genetics classification paradigm. Na V 1.5 in vitro electrophysiological parameters are correlated with Brugada syndrome and long QT syndrome disease risk. Our data emphasize the value of in vitro electrophysiological characterization and incorporating counts of affected and unaffected carriers to aid variant classification. This quantitative analysis of the electrophysiological literature should aid the interpretation of Na V 1.5 variant electrophysiological abnormalities and help improve Na V 1.5 variant classification. © 2018 American Heart Association, Inc.

A Proposal for a Dimensional Classification System Based on the Shared Features of the DSM-IV Anxiety and Mood Disorders: Implications for Assessment and Treatment

PubMed Central

Brown, Timothy A.; Barlow, David H.

2010-01-01

A wealth of evidence attests to the extensive current and lifetime diagnostic comorbidity of the DSM-IV anxiety and mood disorders. Research has shown that the considerable cross-sectional covariation of DSM-IV emotional disorders is accounted for by common higher-order dimensions such as neuroticism/behavioral inhibition (N/BI) and low positive affect/behavioral activation. Longitudinal studies have indicated that the temporal covariation of these disorders can be explained by changes in N/BI and in some cases, initial levels of N/BI are predictive of the temporal course of emotional disorders. Moreover, the marked phenotypal overlap of the DSM-IV anxiety and mood disorder constructs is a frequent source of diagnostic unreliability (e.g., temporal overlap in the shared features of generalized anxiety disorder and mood disorders, situation specificity of panic attacks in panic disorder and specific phobia). Although dimensional approaches have been considered as a method to address the drawbacks associated with the extant prototypical nosology (e.g., inadequate assessment of individual differences in disorder severity), these proposals do not reconcile key problems in current classification such as modest reliability and high comorbidity. The current paper considers an alternative approach that emphasizes empirically supported common dimensions of emotional disorders over disorder-specific criteria sets. The selection and assessment of these dimensions are discussed along with how these methods could be implemented to promote more reliable and valid diagnosis, prognosis, and treatment planning. For instance, the advantages of this classification system are discussed in context of current transdiagnostic treatment protocols that are efficaciously applied to a variety of disorders by targeting their shared features. PMID:19719339

Do diagnosis-related groups appropriately explain variations in costs and length of stay of hip replacement? A comparative assessment of DRG systems across 10 European countries.

PubMed

Geissler, Alexander; Scheller-Kreinsen, David; Quentin, Wilm

2012-08-01

This paper assesses the variations in costs and length of stay for hip replacement cases in Austria, England, Estonia, Finland, France, Germany, Ireland, Poland, Spain and Sweden and examines the ability of national diagnosis-related group (DRG) systems to explain the variation in resource use against a set of patient characteristic and treatment specific variables. In total, 195,810 cases clustered in 712 hospitals were analyzed using OLS fixed effects models for cost data (n=125,698) and negative binominal models for length-of-stay data (n=70,112). The number of DRGs differs widely across the 10 European countries (range: 2-14). Underlying this wide range is a different use of classification variables, especially secondary diagnoses and treatment options are considered to a different extent. In six countries, a standard set of patient characteristics and treatment variables explain the variation in costs or length of stay better than the DRG variables. This raises questions about the adequacy of the countries' DRG system or the lack of specific criteria, which could be used as classification variables. Copyright © 2012 John Wiley & Sons, Ltd.

The Effects of Individual Disengagement on Insurgency Campaigns

DTIC Science & Technology

2010-12-01

PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY CLASSIFICATION OF THIS PAGE Unclassified 19. SECURITY CLASSIFICATION...and A. Kieser, 1981, “Development of Organizations over Time,” In Handbook of Organizational Design, edited by P. C. Nystrom and W. H. Starbuck , New...Hills: Sage Publications, 1981. William H. Starbuck , Arent Greve, and Bo Hedberg, Responding to Crises, Stockholm: Arbetslivscentrum, 1979. 36 D

Classification of diffuse lung diseases: why and how.

PubMed

Hansell, David M

2013-09-01

The understanding of complex lung diseases, notably the idiopathic interstitial pneumonias and small airways diseases, owes as much to repeated attempts over the years to classify them as to any single conceptual breakthrough. One of the many benefits of a successful classification scheme is that it allows workers, within and between disciplines, to be clear that they are discussing the same disease. This may be of particular importance in the recruitment of individuals for a clinical trial that requires a standardized and homogeneous study population. Different specialties require fundamentally different things from a classification: for epidemiologic studies, a classification that requires categorization of individuals according to histopathologic pattern is not usually practicable. Conversely, a scheme that simply divides diffuse parenchymal disease into inflammatory and noninflammatory categories is unlikely to further the understanding about the pathogenesis of disease. Thus, for some disease groupings, for example, pulmonary vasculopathies, there may be several appropriate classifications, each with its merits and demerits. There has been an interesting shift in the past few years, from the accepted primacy of histopathology as the sole basis on which the classification of parenchymal lung disease has rested, to new ways of considering how these entities relate to each other. Some inventive thinking has resulted in new classifications that undoubtedly benefit patients and clinicians in their endeavor to improve management and outcome. The challenge of understanding the logic behind current classifications and their shortcomings are explored in various examples of lung diseases.

Individually adapted imagery improves brain-computer interface performance in end-users with disability.

PubMed

Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V C; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R

2015-01-01

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.

Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability

PubMed Central

Scherer, Reinhold; Faller, Josef; Friedrich, Elisabeth V. C.; Opisso, Eloy; Costa, Ursula; Kübler, Andrea; Müller-Putz, Gernot R.

2015-01-01

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage. PMID:25992718

Usual Populations, Unusual Individuals: Insights into the Behavior and Management of Asian Elephants in Fragmented Landscapes

PubMed Central

Srinivasaiah, Nishant M.; Vaidyanathan, Srinivas; Sinha, Anindya

2012-01-01

Background A dearth in understanding the behavior of Asian elephants (Elephas maximus) at the scale of populations and individuals has left important management issues, particularly related to human-elephant conflict (HEC), unresolved. Evaluation of differences in behavior and decision-making among individual elephants across groups in response to changing local ecological settings is essential to fill this gap in knowledge and to improve our approaches towards the management and conservation of elephants. Methodology/Principal Findings We hypothesized certain behavioral decisions that would be made by Asian elephants as reflected in their residence time and movement rates, time-activity budgets, social interactions and group dynamics in response to resource availability and human disturbance in their habitat. This study is based on 200 h of behavioral observations on 60 individually identified elephants and a 184-km2 grid-based survey of their natural and anthropogenic habitats within and outside the Bannerghatta National Park, southern India during the dry season. At a general population level, the behavioral decisions appeared to be guided by the gender, age and group-type of the elephants. At the individual level, the observed variation could be explained only by the idiosyncratic behaviors of individuals and that of their associating conspecific individuals. Recursive partitioning classification trees for residence time of individual elephants indicated that the primary decisions were taken by individuals, independently of their above-mentioned biological and ecological attributes. Conclusions/Significance Decision-making by Asian elephants thus appears to be determined at two levels, that of the population and, more importantly, the individual. Models based on decision-making by individual elephants have the potential to predict conflict in fragmented landscapes that, in turn, could aid in mitigating HEC. Thus, we must target individuals, in addition to populations, in our efforts to manage and conserve this threatened species, particularly in human-dominated landscapes. PMID:22916135

Usual populations, unusual individuals: insights into the behavior and management of Asian elephants in fragmented landscapes.

PubMed

Srinivasaiah, Nishant M; Anand, Vijay D; Vaidyanathan, Srinivas; Sinha, Anindya

2012-01-01

A dearth in understanding the behavior of Asian elephants (Elephas maximus) at the scale of populations and individuals has left important management issues, particularly related to human-elephant conflict (HEC), unresolved. Evaluation of differences in behavior and decision-making among individual elephants across groups in response to changing local ecological settings is essential to fill this gap in knowledge and to improve our approaches towards the management and conservation of elephants. We hypothesized certain behavioral decisions that would be made by Asian elephants as reflected in their residence time and movement rates, time-activity budgets, social interactions and group dynamics in response to resource availability and human disturbance in their habitat. This study is based on 200 h of behavioral observations on 60 individually identified elephants and a 184-km(2) grid-based survey of their natural and anthropogenic habitats within and outside the Bannerghatta National Park, southern India during the dry season. At a general population level, the behavioral decisions appeared to be guided by the gender, age and group-type of the elephants. At the individual level, the observed variation could be explained only by the idiosyncratic behaviors of individuals and that of their associating conspecific individuals. Recursive partitioning classification trees for residence time of individual elephants indicated that the primary decisions were taken by individuals, independently of their above-mentioned biological and ecological attributes. Decision-making by Asian elephants thus appears to be determined at two levels, that of the population and, more importantly, the individual. Models based on decision-making by individual elephants have the potential to predict conflict in fragmented landscapes that, in turn, could aid in mitigating HEC. Thus, we must target individuals, in addition to populations, in our efforts to manage and conserve this threatened species, particularly in human-dominated landscapes.

Meta-Analysis of Human Factors Engineering Studies Comparing Individual Differences, Practice Effects and Equipment Design Variations.

DTIC Science & Technology

1985-02-21

Approvoid foT public 90Ieleol, 2* . tJni7nited " - . - o . - ’--. * . -... . 1 UNCLASSIFIED S, E CURITY CLASSIFICATION OF THIS PAGE-" REPORT DOCUMENTATION...ACCESSION NO. 11. TITLE (Include Security Classification) . Veta -Analysis of Human Factors Engineering Studies Comparing Individual Differences, Practice...Background C Opportunity D Significance E History III. PHASE I FINAL REPORT A Literature Review B Formal Analysis C Results D Implications for Phase II IV

Interobserver agreement in analysis of cardiotocograms recorded during trial of labor after cesarean.

PubMed

Caning, M M; Thisted, D L A; Amer-Wählin, I; Laier, G H; Krebs, L

2018-05-17

To examine interobserver agreement in intrapartum cardiotocography (CTG) classification in women undergoing trial of labor after a cesarean section (TOLAC) at term with or without complete uterine rupture. Nineteen blinded and independent Danish obstetricians assessed CTG tracings from 47 women (174 individual pages) with a complete uterine rupture during TOLAC and 37 women (133 individual pages) with no uterine rupture during TOLAC. Individual pages with CTG tracings lasting at least 20 min were evaluated by three different assessors and counted as an individual case. The tracings were analyzed according to the modified version of the Federation of Gynaecology and Obstetrics (FIGO) guidelines elaborated for the use of STAN (ST-analysis). Occurrence of defined abnormalities was recorded and the tracings were classified as normal, suspicious, pathological, or preterminal. The interobserver agreement was evaluated using Fleiss' kappa. Agreement on classification of a preterminal CTG was almost perfect. The interobserver agreement on normal, suspicious or pathological CTG was moderate to substantial. Regarding the presence of severe variable decelerations, the agreement was moderate. No statistical difference was found in the interobserver agreement between classification of tracings from women undergoing TOLAC with and without complete uterine rupture. The interobserver agreement on classification of CTG tracings from high-risk deliveries during TOLAC is best for assessment of a preterminal CTG and the poorest for the identification of severe variable decelerations.

Quantification of urban structure on building block level utilizing multisensoral remote sensing data

NASA Astrophysics Data System (ADS)

Wurm, Michael; Taubenböck, Hannes; Dech, Stefan

2010-10-01

Dynamics of urban environments are a challenge to a sustainable development. Urban areas promise wealth, realization of individual dreams and power. Hence, many cities are characterized by a population growth as well as physical development. Traditional, visual mapping and updating of urban structure information of cities is a very laborious and cost-intensive task, especially for large urban areas. For this purpose, we developed a workflow for the extraction of the relevant information by means of object-based image classification. In this manner, multisensoral remote sensing data has been analyzed in terms of very high resolution optical satellite imagery together with height information by a digital surface model to retrieve a detailed 3D city model with the relevant land-use / land-cover information. This information has been aggregated on the level of the building block to describe the urban structure by physical indicators. A comparison between the indicators derived by the classification and a reference classification has been accomplished to show the correlation between the individual indicators and a reference classification of urban structure types. The indicators have been used to apply a cluster analysis to group the individual blocks into similar clusters.

TRANSITIVITY OF ODOR PREFERENCES: CONSTANT AND PARTICULARITIES IN HEDONIC PERCEPTION

PubMed Central

Brand, Gérard; Haaz, Virginie; Jacquot, Laurence

2012-01-01

Transitivity of preferences has been investigated for a long time in decision-making. In the field of perception, the pleasantness of odors raises several questions related to individual versus cultural or universal preferences and the existence of a classification in a delimited hedonic space. The aim of this study was to test transitivity in olfactory hedonicity using a first panel of 10 mixed odors and a second panel of 10 odors from a delimited floral category. Data were collected by paired comparisons in a two-alternative forced choice. Results in both panels showed a strong transitivity for each participant leading to a linear range of 10 odors classified by preference. However, ranges varied from one participant to another and the mean preferences of the group did not allow one to infer individual's hedonic classification of odors. Moreover, the individual classification appeared stable over time and undisturbed by odorant distractors. These findings suggest that humans have considerable ability to classify odors hedonically as a model of individual preferences in a sensory space usually considered to be more involved in affective/emotional states than in cognitive performances. PMID:23008522

Vocal classification of vocalizations of a pair of Asian small-clawed otters to determine stress.

PubMed

Scheifele, Peter M; Johnson, Michael T; Fry, Michelle; Hamel, Benjamin; Laclede, Kathryn

2015-07-01

Asian Small-Clawed Otters (Aonyx cinerea) are a small, protected but threatened species living in freshwater. They are gregarious and live in monogamous pairs for their lifetimes, communicating via scent and acoustic vocalizations. This study utilized a hidden Markov model (HMM) to classify stress versus non-stress calls from a sibling pair under professional care. Vocalizations were expertly annotated by keepers into seven contextual categories. Four of these-aggression, separation anxiety, pain, and prefeeding-were identified as stressful contexts, and three of them-feeding, training, and play-were identified as non-stressful contexts. The vocalizations were segmented, manually categorized into broad vocal type call types, and analyzed to determine signal to noise ratios. From this information, vocalizations from the most common contextual categories were used to implement HMM-based automatic classification experiments, which included individual identification, stress vs non-stress, and individual context classification. Results indicate that both individual identity and stress vs non-stress were distinguishable, with accuracies above 90%, but that individual contexts within the stress category were not easily separable.

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

PubMed Central

Guan, Li; Hao, Bibo; Cheng, Qijin; Yip, Paul SF

2015-01-01

Background Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. Objective The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. Methods There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric “Screening Efficiency” that were adopted to evaluate model effectiveness. Results Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Conclusions Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media. PMID:26543921

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model.

PubMed

Guan, Li; Hao, Bibo; Cheng, Qijin; Yip, Paul Sf; Zhu, Tingshao

2015-01-01

Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric "Screening Efficiency" that were adopted to evaluate model effectiveness. Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media.

The Bolivian "Altiplano" and "Valle" sheep are two different peripatric breeds.

PubMed

Parés-Casanova, Pere M; Pérezgrovas Garza, Raúl

2014-06-01

Forty-nine sheep belonged to the Andean Altiplano region ("Altiplano") and 30 in the lowland regions of Bolivia ("Valle"), aged 1 to 4 years, were wool sampled to determine the extent of difference between these local breeds. Fibre length and the percentage of each type of fibre (long-thick, short-thin and kemp), yield and fibre diameter were measured. There was a highly significant difference between the two sheep populations that were not clearly separated in the first two principal component of a principal components analysis (PC); the first PC explained 67.1 % and the second PC explained 26.6 % of the total variation. The variables that contributed most to the separation of the sheep populations were the percentage of long-thick and short-thin fibres in the first PC and yield in the second PC. A discriminant analysis, which was used to classify individuals with respect to their breeding, achieved an accurate classification rate of 84.2 %. Thus, the Altiplano and Valle sheep must be viewed as two closely peripatric breeds rather than different "ecotypes", as more than 80 % could be correctly assigned to one of the breeds; however, the differences are based on composition of long-thick and short-thin fibres and yield after alcohol scouring.

What are they thinking? Automated analysis of student writing about acid-base chemistry in introductory biology.

PubMed

Haudek, Kevin C; Prevost, Luanna B; Moscarella, Rosa A; Merrill, John; Urban-Lurain, Mark

2012-01-01

Students' writing can provide better insight into their thinking than can multiple-choice questions. However, resource constraints often prevent faculty from using writing assessments in large undergraduate science courses. We investigated the use of computer software to analyze student writing and to uncover student ideas about chemistry in an introductory biology course. Students were asked to predict acid-base behavior of biological functional groups and to explain their answers. Student explanations were rated by two independent raters. Responses were also analyzed using SPSS Text Analysis for Surveys and a custom library of science-related terms and lexical categories relevant to the assessment item. These analyses revealed conceptual connections made by students, student difficulties explaining these topics, and the heterogeneity of student ideas. We validated the lexical analysis by correlating student interviews with the lexical analysis. We used discriminant analysis to create classification functions that identified seven key lexical categories that predict expert scoring (interrater reliability with experts = 0.899). This study suggests that computerized lexical analysis may be useful for automatically categorizing large numbers of student open-ended responses. Lexical analysis provides instructors unique insights into student thinking and a whole-class perspective that are difficult to obtain from multiple-choice questions or reading individual responses.

What Are They Thinking? Automated Analysis of Student Writing about Acid–Base Chemistry in Introductory Biology

PubMed Central

Haudek, Kevin C.; Prevost, Luanna B.; Moscarella, Rosa A.; Merrill, John; Urban-Lurain, Mark

2012-01-01

Students’ writing can provide better insight into their thinking than can multiple-choice questions. However, resource constraints often prevent faculty from using writing assessments in large undergraduate science courses. We investigated the use of computer software to analyze student writing and to uncover student ideas about chemistry in an introductory biology course. Students were asked to predict acid–base behavior of biological functional groups and to explain their answers. Student explanations were rated by two independent raters. Responses were also analyzed using SPSS Text Analysis for Surveys and a custom library of science-related terms and lexical categories relevant to the assessment item. These analyses revealed conceptual connections made by students, student difficulties explaining these topics, and the heterogeneity of student ideas. We validated the lexical analysis by correlating student interviews with the lexical analysis. We used discriminant analysis to create classification functions that identified seven key lexical categories that predict expert scoring (interrater reliability with experts = 0.899). This study suggests that computerized lexical analysis may be useful for automatically categorizing large numbers of student open-ended responses. Lexical analysis provides instructors unique insights into student thinking and a whole-class perspective that are difficult to obtain from multiple-choice questions or reading individual responses. PMID:22949425

Function and position determine relative proportions of different fiber types in limb muscles of the lizard Tropidurus psammonastes.

PubMed

Pereira, Anieli G; Abdala, Virginia; Kohlsdorf, Tiana

2015-02-01

Skeletal muscles can be classified as flexors or extensors according to their function, and as dorsal or ventral according to their position. The latter classification evokes their embryological origin from muscle masses initially divided during limb development, and muscles sharing a given position do not necessarily perform the same function. Here, we compare the relative proportions of different fiber types among six limb muscles in the lizard Tropidurus psammonastes. Individual fibers were classified as slow oxidative (SO), fast glycolytic (FG) or fast oxidative-glycolytic (FOG) based on mitochondrial content; muscles were classified according to position and function. Mixed linear models considering one or both effects were compared using likelihood ratio tests. Variation in the proportion of FG and FOG fibers is mainly explained by function (flexor muscles have on average lower proportions of FG and higher proportions of FOG fibers), while variation in SO fibers is better explained by position (they are less abundant in ventral muscles than in those developed from a dorsal muscle mass). Our results clarify the roles of position and function in determining the relative proportions of the various muscle fibers and provide evidence that these factors may differentially affect distinct fiber types. Copyright © 2014. Published by Elsevier GmbH.

Johnny Depp, Reconsidered: How Category-Relative Processing Fluency Determines the Appeal of Gender Ambiguity.

PubMed

Owen, Helen E; Halberstadt, Jamin; Carr, Evan W; Winkielman, Piotr

2016-01-01

Individuals that combine features of both genders-gender blends-are sometimes appealing and sometimes not. Heretofore, this difference was explained entirely in terms of sexual selection. In contrast, we propose that part of individuals' preference for gender blends is due to the cognitive effort required to classify them, and that such effort depends on the context in which a blend is judged. In two studies, participants judged the attractiveness of male-female morphs. Participants did so after classifying each face in terms of its gender, which was selectively more effortful for gender blends, or classifying faces on a gender-irrelevant dimension, which was equally effortful for gender blends. In both studies, gender blends were disliked when, and only when, the faces were first classified by gender, despite an overall preference for feminine features in all conditions. Critically, the preferences were mediated by the effort of stimulus classification. The results suggest that the variation in attractiveness of gender-ambiguous faces may derive from context-dependent requirements to determine gender membership. More generally, the results show that the difficulty of resolving social category membership-not just attitudes toward a social category-feed into perceivers' overall evaluations toward category members.

Does a Measure of Support Needs Predict Funding Need Better Than a Measure of Adaptive and Maladaptive Behavior?

PubMed

Arnold, Samuel R C; Riches, Vivienne C; Stancliffe, Roger J

2015-09-01

Internationally, various approaches are used for the allocation of individualized funding. When using a databased approach, a key question is the predictive validity of adaptive behavior versus support needs assessment. This article reports on a subset of data from a larger project that allowed for a comparison of support needs and adaptive behavior assessments when predicting person-centered funding allocation. The first phase of the project involved a trial of the Inventory for Client and Agency Planning (ICAP) adaptive behavior and Instrument for the Classification and Assessment of Support Needs (I-CAN)-Brief Research version support needs assessments. Participants were in receipt of an individual support package allocated using a person-centered planning process, and were stable in their support arrangements. Regression analysis showed that the most useful items in predicting funding allocation came from the I-CAN-Brief Research. No additional variance could be explained by adding the ICAP, or using the ICAP alone. A further unique approach of including only items from the I-CAN-Brief Research marked as funded supports showed high predictive validity. It appears support need is more effective at determining resource need than adaptive behavior.

Developing a case mix classification for child and adolescent mental health services: the influence of presenting problems, complexity factors and service providers on number of appointments.

PubMed

Martin, Peter; Davies, Roger; Macdougall, Amy; Ritchie, Benjamin; Vostanis, Panos; Whale, Andy; Wolpert, Miranda

2017-09-01

Case-mix classification is a focus of international attention in considering how best to manage and fund services, by providing a basis for fairer comparison of resource utilization. Yet there is little evidence of the best ways to establish case mix for child and adolescent mental health services (CAMHS). To develop a case mix classification for CAMHS that is clinically meaningful and predictive of number of appointments attended and to investigate the influence of presenting problems, context and complexity factors and provider variation. We analysed 4573 completed episodes of outpatient care from 11 English CAMHS. Cluster analysis, regression trees and a conceptual classification based on clinical best practice guidelines were compared regarding their ability to predict number of appointments, using mixed effects negative binomial regression. The conceptual classification is clinically meaningful and did as well as data-driven classifications in accounting for number of appointments. There was little evidence for effects of complexity or context factors, with the possible exception of school attendance problems. Substantial variation in resource provision between providers was not explained well by case mix. The conceptually-derived classification merits further testing and development in the context of collaborative decision making.

Sorting Potatoes for Miss Bonner.

ERIC Educational Resources Information Center

Herreid, Clyde Freeman

1998-01-01

Discusses the basis of a classification scheme for types of case studies. Four major classification headings are identified: (1) individual assignment; (2) lecture; (3) discussion; and (4) small group activities. Describes each heading from the point of view of several teaching methods. (DDR)

Integration of Chinese medicine with Western medicine could lead to future medicine: molecular module medicine.

PubMed

Zhang, Chi; Zhang, Ge; Chen, Ke-ji; Lu, Ai-ping

2016-04-01

The development of an effective classification method for human health conditions is essential for precise diagnosis and delivery of tailored therapy to individuals. Contemporary classification of disease systems has properties that limit its information content and usability. Chinese medicine pattern classification has been incorporated with disease classification, and this integrated classification method became more precise because of the increased understanding of the molecular mechanisms. However, we are still facing the complexity of diseases and patterns in the classification of health conditions. With continuing advances in omics methodologies and instrumentation, we are proposing a new classification approach: molecular module classification, which is applying molecular modules to classifying human health status. The initiative would be precisely defining the health status, providing accurate diagnoses, optimizing the therapeutics and improving new drug discovery strategy. Therefore, there would be no current disease diagnosis, no disease pattern classification, and in the future, a new medicine based on this classification, molecular module medicine, could redefine health statuses and reshape the clinical practice.

Comparison of simple screening criteria with the International Caries Detection and Assessment System classification in determining restorative treatment need.

PubMed

Kämppi, Antti; Tanner, Tarja; Päkkilä, Jari; Patinen, Pertti; Tjäderhane, Leo; Anttonen, Vuokko

2016-04-01

The Finnish Defence Forces' unique oral health-screening protocol (FDFsp) has been in use for decades. In FDFsp, restorative treatment need is determined based on the World Health Organization (WHO) criteria. The aim of this study was to compare the outcome of screening restorative treatment need with the outcome of using the International Caries Detection and Assessment System (ICDAS) classification at both individual and tooth levels. Our hypothesis was that the outcome of screening with FDFsp agrees with the outcome of using the ICDAS classification. In this study, a trained, calibrated examiner estimated, in a visual-tactile manner the restorative treatment need of 337 young healthy adults using the FDFsp. During the screening, 74 conscripts were selected for a thorough inspection. The inclusion criteria for those selected were: having no, having one to five, or having six or more caries lesions needing restorative treatment. In the thorough inspection, the participants were inspected in a visual-tactile manner using the ICDAS classification. The association of the outcomes achieved using the two different methods was analysed at individual and tooth levels. Sensitivity, specificity, and kappa values were calculated. Wisdom teeth were excluded. At the individual level, the agreement between the outcomes of using FDFsp and ICDAS ≥4 was excellent: sensitivity, 94.1%; specificity, 97.5%; and kappa = 0.92. When ICDAS ≥3 was used, the values were 72.7%, 96.7%, and 0.66%, respectively. Screening performed by a trained examiner using specific criteria is a reliable method for detecting individuals with restorative treatment need. The outcome of screening agrees strongly with results using the ICDAS classification. © 2015 FDI World Dental Federation.

Mild Depressive Symptoms Among Americans in Relation to Physical Activity, Current Overweight/Obesity, and Self-Reported History of Overweight/Obesity.

PubMed

Dankel, Scott J; Loenneke, Jeremy P; Loprinzi, Paul D

2016-10-01

Overweight/obese individuals are at an increased risk for depression with some evidence of a bidirectional association. The preventative effects of physical activity among overweight/obese individuals have been well documented; however, less is known on how the duration of overweight/obesity alters the association with negative health outcomes. Therefore, the purpose of this investigation was to determine how the classification, and more specifically duration, of overweight/obesity alters the association between physical activity and depressive symptoms. The 2005-2006 National Health and Nutrition Examination Survey (NHANES) data were used (n = 764), and individuals were divided into six mutually exclusive groups based on physical activity status, weight classification (measured BMI), and duration of weight classification (assessed via recall). Multivariable linear and logistic regression analyses were computed to examine odds of depressive symptoms (patient health questionnaire (PHQ)-9) among groups. After adjusting for covariates, only individuals who were inactive and overweight/obese at the examination and 10 years prior were at an increased odds of depressive symptoms in comparison to those who were active and normal weight (odds ratio (OR) = 2.40; 95 % confidence interval (CI) 1.03, 5.61; p = 0.04). Physical activity appeared to ameliorate the association with depressive symptoms independent of overweight/obesity classification or duration. The cyclic nature of overweight/obesity and depression (i.e., bidirectional association) appears to increase the odds of depression as the length of overweight/obesity is increased. These results provide support for clinicians to assess not only their clients' current BMI but also the duration in which they have been at a certain weight classification and to further promote physical activity as a preventative measure against depressive symptoms.

One-year persistence of individual gait patterns identified in a follow-up study - A call for individualised diagnose and therapy.

PubMed

Horst, F; Mildner, M; Schöllhorn, W I

2017-10-01

Although a hunch about the individuality of human movements generally exists, differences in gait patterns between individuals are often neglected. To date, only a few studies distinguished individual gait patterns in terms of uniqueness and emphasised the relevance of individualised diagnoses and therapy. However, small sample sizes have been a limitation on identifying subjects based on gait patterns, and little is known about the permanence of subject-specific characteristics over time. The purpose of this study was (1) to prove the uniqueness of individual gait patterns within a larger sample and (2) to prove the long-term permanence of individual gait patterns. A sample of 128 healthy participants each walked a distance of 10m barefoot 10 times. Two force plates recorded the ground reaction forces during a double step at a self-selected walking speed. A subsample of 46 participants repeated this procedure after a period of 7-16 months. The application of support vector machines resulted in classification rates of 99.8% (1278 out of 1280) and 99.4% (914 out of 920) for the initial subject-classification and the subsample follow-up-classification, respectively. The results showed that gait patterns based on time-continuous ground reaction forces were unique to an individual and could be differentiated from those of other individuals. Support vector machines classified gait patterns to the corresponding individual almost error-free. Hence, human gait is not only different between individuals but also exhibits unique individual characteristics that are persistent over years. Our findings provide evidence for the individual nature of human walking and emphasise the need to evaluate individualised clinical approaches for diagnoses and therapy. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

PubMed

Salvatore, C; Cerasa, A; Castiglioni, I; Gallivanone, F; Augimeri, A; Lopez, M; Arabia, G; Morelli, M; Gilardi, M C; Quattrone, A

2014-01-30

Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP). Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method. The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice. Copyright © 2013 Elsevier B.V. All rights reserved.

A consensus view of fold space: Combining SCOP, CATH, and the Dali Domain Dictionary

PubMed Central

Day, Ryan; Beck, David A.C.; Armen, Roger S.; Daggett, Valerie

2003-01-01

We have determined consensus protein-fold classifications on the basis of three classification methods, SCOP, CATH, and Dali. These classifications make use of different methods of defining and categorizing protein folds that lead to different views of protein-fold space. Pairwise comparisons of domains on the basis of their fold classifications show that much of the disagreement between the classification systems is due to differing domain definitions rather than assigning the same domain to different folds. However, there are significant differences in the fold assignments between the three systems. These remaining differences can be explained primarily in terms of the breadth of the fold classifications. Many structures may be defined as having one fold in one system, whereas far fewer are defined as having the analogous fold in another system. By comparing these folds for a nonredundant set of proteins, the consensus method breaks up broad fold classifications and combines restrictive fold classifications into metafolds, creating, in effect, an averaged view of fold space. This averaged view requires that the structural similarities between proteins having the same metafold be recognized by multiple classification systems. Thus, the consensus map is useful for researchers looking for fold similarities that are relatively independent of the method used to compare proteins. The 30 most populated metafolds, representing the folds of about half of a nonredundant subset of the PDB, are presented here. The full list of metafolds is presented on the Web. PMID:14500873

A consensus view of fold space: combining SCOP, CATH, and the Dali Domain Dictionary.

PubMed

Day, Ryan; Beck, David A C; Armen, Roger S; Daggett, Valerie

2003-10-01

We have determined consensus protein-fold classifications on the basis of three classification methods, SCOP, CATH, and Dali. These classifications make use of different methods of defining and categorizing protein folds that lead to different views of protein-fold space. Pairwise comparisons of domains on the basis of their fold classifications show that much of the disagreement between the classification systems is due to differing domain definitions rather than assigning the same domain to different folds. However, there are significant differences in the fold assignments between the three systems. These remaining differences can be explained primarily in terms of the breadth of the fold classifications. Many structures may be defined as having one fold in one system, whereas far fewer are defined as having the analogous fold in another system. By comparing these folds for a nonredundant set of proteins, the consensus method breaks up broad fold classifications and combines restrictive fold classifications into metafolds, creating, in effect, an averaged view of fold space. This averaged view requires that the structural similarities between proteins having the same metafold be recognized by multiple classification systems. Thus, the consensus map is useful for researchers looking for fold similarities that are relatively independent of the method used to compare proteins. The 30 most populated metafolds, representing the folds of about half of a nonredundant subset of the PDB, are presented here. The full list of metafolds is presented on the Web.

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.

Classification of earth terrain using polarimetric synthetic aperture radar images

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

Endoscopic ultrasound as an adjunctive evaluation in patients with esophageal motor disorders subtyped by high-resolution manometry.

PubMed

Krishnan, K; Lin, C-Y; Keswani, R; Pandolfino, J E; Kahrilas, P J; Komanduri, S

2014-08-01

Esophageal motor disorders are a heterogeneous group of conditions identified by esophageal manometry that lead to esophageal dysfunction. The aim of this study was to assess the clinical utility of endoscopic ultrasound (EUS) in the further evaluation of patients with esophageal motor disorders categorized using the updated Chicago Classification. We performed a retrospective, single center study of 62 patients with esophageal motor disorders categorized according to the Chicago Classification. All patients underwent standard radial endosonography to assess for extra-esophageal findings or alternative explanations for esophageal outflow obstruction. Secondary outcomes included esophageal wall thickness among the different patient subsets within the Chicago Classification. EUS identified 9/62 (15%) clinically relevant findings that altered patient management and explained the etiology of esophageal outflow obstruction. We further identified substantial variability in esophageal wall thickness in a proportion of patients including some with a significantly thickened non-muscular layer. EUS findings are clinically relevant in a significant number of patients with motor disorders and can alter clinical management. Variability in esophageal wall thickness of the muscularis propria and non-muscular layers identified by EUS may also explain the observed variability in response to standard therapies for achalasia. © 2014 John Wiley & Sons Ltd.

Endoscopic ultrasound as an adjunctive evaluation in patients with esophageal motor disorders subtyped by high-resolution manometry

PubMed Central

Krishnan, Kumar; Lin, Chen-Yuan; Keswani, Rajesh; Pandolfino, John E; Kahrilas, Peter J; Komanduri, Srinadh

2015-01-01

Background and aims Esophageal motor disorders are a heterogenous group of conditions identified by esophageal manometry that lead to esophageal dysfunction. The aim of this study was to assess the clinical utility of endoscopic ultrasound in the further evaluation of patients with esophageal motor disorders categorized using the updated Chicago Classification. Methods We performed a retrospective, single center study of 62 patients with esophageal motor disorders categorized according to the Chicago Classification. All patients underwent standard radial endosonography to assess for extra esophageal findings or alternative explanations for esophageal outflow obstruction. Secondary outcomes included esophageal wall thickness among the different patient subsets within the Chicago Classification Key Results EUS identified 9/62 (15%) clinically relevant findings that altered patient management and explained the etiology of esophageal outflow obstruction. We further identified substantial variability in esophageal wall thickness in a proportion of patients including some with a significantly thickened non-muscular layer. Conclusions EUS findings are clinically relevant in a significant number of patients with motor disorders and can alter clinical management. Variability in esophageal wall thickness of the muscularis propria and non-muscular layers identified by EUS may also explain the observed variability in response to standard therapies for achalasia. PMID:25041229

Word pair classification during imagined speech using direct brain recordings

NASA Astrophysics Data System (ADS)

Martin, Stephanie; Brunner, Peter; Iturrate, Iñaki; Millán, José Del R.; Schalk, Gerwin; Knight, Robert T.; Pasley, Brian N.

2016-05-01

People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58% p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.

Word pair classification during imagined speech using direct brain recordings

PubMed Central

Martin, Stephanie; Brunner, Peter; Iturrate, Iñaki; Millán, José del R.; Schalk, Gerwin; Knight, Robert T.; Pasley, Brian N.

2016-01-01

People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70–150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications. PMID:27165452

Explaining resource consumption among non-normal neonates

PubMed Central

Schwartz, Rachel M.; Michelman, Thomas; Pezzullo, John; Phibbs, Ciaran S.

1991-01-01

The adoption by Medicare in 1983 of prospective payment using diagnosis-related groups (DRGs) has stimulated research to develop case-mix grouping schemes that more accurately predict resource consumption by patients. In this article, the authors explore a new method designed to improve case-mix classification for newborns through the use of birth weight in combination with DRGs to adjust the unexplained case-mix severity. Although the findings are developmental in nature, they reveal that the model significantly improves our ability to explain resource use. PMID:10122360

5 CFR 511.608 - Employee representatives.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Employee representatives. 511.608 Section... CLASSIFICATION UNDER THE GENERAL SCHEDULE Classification Appeals § 511.608 Employee representatives. An employee... appeal. An agency may disallow an employee's representative when the individual's activities as a...

12 CFR 403.1 - General policies and definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 403.1 Banks and Banking EXPORT-IMPORT BANK OF THE UNITED STATES CLASSIFICATION, DECLASSIFICATION, AND... the Export-Import Bank (the Bank) implements executive orders which govern the classification... the United States. (5) Confidential source means any individual or organization that has provided, or...

12 CFR 403.1 - General policies and definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 403.1 Banks and Banking EXPORT-IMPORT BANK OF THE UNITED STATES CLASSIFICATION, DECLASSIFICATION, AND... the Export-Import Bank (the Bank) implements executive orders which govern the classification... the United States. (5) Confidential source means any individual or organization that has provided, or...

12 CFR 403.1 - General policies and definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 403.1 Banks and Banking EXPORT-IMPORT BANK OF THE UNITED STATES CLASSIFICATION, DECLASSIFICATION, AND... the Export-Import Bank (the Bank) implements executive orders which govern the classification... the United States. (5) Confidential source means any individual or organization that has provided, or...

12 CFR 403.1 - General policies and definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 403.1 Banks and Banking EXPORT-IMPORT BANK OF THE UNITED STATES CLASSIFICATION, DECLASSIFICATION, AND... the Export-Import Bank (the Bank) implements executive orders which govern the classification... the United States. (5) Confidential source means any individual or organization that has provided, or...

28 CFR 524.10 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Purpose. 524.10 Section 524.10 Judicial Administration BUREAU OF PRISONS, DEPARTMENT OF JUSTICE INMATE ADMISSION, CLASSIFICATION, AND TRANSFER... subpart is to explain the Bureau of Prisons (Bureau) process for classifying newly committed inmates and...

Discrimination and supervised classification of volcanic flows of the Puna-Altiplano, Central Andes Mountains using Landsat TM data

NASA Technical Reports Server (NTRS)

Mcbride, J. H.; Fielding, E. J.; Isacks, B. L.

1987-01-01

Landsat Thematic Mapper (TM) images of portions of the Central Andean Puna-Altiplano volcanic belt have been tested for the feasibility of discriminating individual volcanic flows using supervised classifications. This technique distinguishes volcanic rock classes as well as individual phases (i.e., relative age groups) within each class. The spectral signature of a volcanic rock class appears to depend on original texture and composition and on the degree of erosion, weathering, and chemical alteration. Basalts and basaltic andesite stand out as a clearly distinguishable class. The age dependent degree of weathering of these generally dark volcanic rocks can be correlated with reflectance: older rocks have a higher reflectance. On the basis of this relationship, basaltaic lava flows can be separated into several subclasses. These individual subclasses would correspond to mappable geologic units on the ground at a reconnaissance scale. The supervised classification maps are therefore useful for establishing a general stratigraphic framework for later detailed surface mapping of volcanic sequences.

Single-trial EEG RSVP classification using convolutional neural networks

NASA Astrophysics Data System (ADS)

Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

2016-05-01

Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

Advances in metaheuristics for gene selection and classification of microarray data.

PubMed

Duval, Béatrice; Hao, Jin-Kao

2010-01-01

Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy for classification. Gene selection can be considered as a combinatorial search problem and thus be conveniently handled with optimization methods. In this article, we summarize some recent developments of using metaheuristic-based methods within an embedded approach for gene selection. In particular, we put forward the importance and usefulness of integrating problem-specific knowledge into the search operators of such a method. To illustrate the point, we explain how ranking coefficients of a linear classifier such as support vector machine (SVM) can be profitably used to reinforce the search efficiency of Local Search and Evolutionary Search metaheuristic algorithms for gene selection and classification.

Diagnostic Criteria, Classification and Treatment Goals in Multiple Sclerosis: The Chronicles of Time and Space.

PubMed

Ntranos, Achilles; Lublin, Fred

2016-10-01

Multiple sclerosis (MS) is one of the most diverse human diseases. Since its first description by Charcot in the nineteenth century, the diagnostic criteria, clinical course classification, and treatment goals for MS have been constantly revised and updated to improve diagnostic accuracy, physician communication, and clinical trial design. These changes have improved the clinical outcomes and quality of life for patients with the disease. Recent technological and research breakthroughs will almost certainly further change how we diagnose, classify, and treat MS in the future. In this review, we summarize the key events in the history of MS, explain the reasoning behind the current criteria for MS diagnosis, classification, and treatment, and provide suggestions for further improvements that will keep enhancing the clinical practice of MS.

Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach

NASA Astrophysics Data System (ADS)

Tarai, Madhumita; Kumar, Keshav; Divya, O.; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar

2017-09-01

The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix.

Logical-rule models of classification response times: a synthesis of mental-architecture, random-walk, and decision-bound approaches.

PubMed

Fific, Mario; Little, Daniel R; Nosofsky, Robert M

2010-04-01

We formalize and provide tests of a set of logical-rule models for predicting perceptual classification response times (RTs) and choice probabilities. The models are developed by synthesizing mental-architecture, random-walk, and decision-bound approaches. According to the models, people make independent decisions about the locations of stimuli along a set of component dimensions. Those independent decisions are then combined via logical rules to determine the overall categorization response. The time course of the independent decisions is modeled via random-walk processes operating along individual dimensions. Alternative mental architectures are used as mechanisms for combining the independent decisions to implement the logical rules. We derive fundamental qualitative contrasts for distinguishing among the predictions of the rule models and major alternative models of classification RT. We also use the models to predict detailed RT-distribution data associated with individual stimuli in tasks of speeded perceptual classification. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Simultaneous fecal microbial and metabolite profiling enables accurate classification of pediatric irritable bowel syndrome.

PubMed

Shankar, Vijay; Reo, Nicholas V; Paliy, Oleg

2015-12-09

We previously showed that stool samples of pre-adolescent and adolescent US children diagnosed with diarrhea-predominant IBS (IBS-D) had different compositions of microbiota and metabolites compared to healthy age-matched controls. Here we explored whether observed fecal microbiota and metabolite differences between these two adolescent populations can be used to discriminate between IBS and health. We constructed individual microbiota- and metabolite-based sample classification models based on the partial least squares multivariate analysis and then applied a Bayesian approach to integrate individual models into a single classifier. The resulting combined classification achieved 84 % accuracy of correct sample group assignment and 86 % prediction for IBS-D in cross-validation tests. The performance of the cumulative classification model was further validated by the de novo analysis of stool samples from a small independent IBS-D cohort. High-throughput microbial and metabolite profiling of subject stool samples can be used to facilitate IBS diagnosis.

Performance analysis of distributed applications using automatic classification of communication inefficiencies

DOEpatents

Vetter, Jeffrey S.

2005-02-01

The method and system described herein presents a technique for performance analysis that helps users understand the communication behavior of their message passing applications. The method and system described herein may automatically classifies individual communication operations and reveal the cause of communication inefficiencies in the application. This classification allows the developer to quickly focus on the culprits of truly inefficient behavior, rather than manually foraging through massive amounts of performance data. Specifically, the method and system described herein trace the message operations of Message Passing Interface (MPI) applications and then classify each individual communication event using a supervised learning technique: decision tree classification. The decision tree may be trained using microbenchmarks that demonstrate both efficient and inefficient communication. Since the method and system described herein adapt to the target system's configuration through these microbenchmarks, they simultaneously automate the performance analysis process and improve classification accuracy. The method and system described herein may improve the accuracy of performance analysis and dramatically reduce the amount of data that users must encounter.

Predicting Individual Tree and Shrub Species Distributions with Empirically Derived Microclimate Surfaces in a Complex Mountain Ecosystem in Northern Idaho, USA

NASA Astrophysics Data System (ADS)

Holden, Z.; Cushman, S.; Evans, J.; Littell, J. S.

2009-12-01

The resolution of current climate interpolation models limits our ability to adequately account for temperature variability in complex mountainous terrain. We empirically derive 30 meter resolution models of June-October day and nighttime temperature and April nighttime Vapor Pressure Deficit (VPD) using hourly data from 53 Hobo dataloggers stratified by topographic setting in mixed conifer forests near Bonners Ferry, ID. 66%, of the variability in average June-October daytime temperature is explained by 3 variables (elevation, relative slope position and topographic roughness) derived from 30 meter digital elevation models. 69% of the variability in nighttime temperatures among stations is explained by elevation, relative slope position and topographic dissection (450 meter window). 54% of variability in April nighttime VPD is explained by elevation, soil wetness and the NDVIc derived from Landsat. We extract temperature and VPD predictions at 411 intensified Forest Inventory and Analysis plots (FIA). We use these variables with soil wetness and solar radiation indices derived from a 30 meter DEM to predict the presence and absence of 10 common forest tree species and 25 shrub species. Classification accuracies range from 87% for Pinus ponderosa , to > 97% for most other tree species. Shrub model accuracies are also high with greater than 90% accuracy for the majority of species. Species distribution models based on the physical variables that drive species occurrence, rather than their topographic surrogates, will eventually allow us to predict potential future distributions of these species with warming climate at fine spatial scales.

Exploring Differences in Adiposity in Two US Hispanic Populations of Mexican Origin Using Social, Behavioral, Physiologic and Genetic Markers: The IRAS Family Study

PubMed Central

Young, Kendra A.; Fingerlin, Tasha E.; Langefeld, Carl D.; Lorenzo, Carlos; Haffner, Steven M.; Wagenknecht, Lynne E.; Norris, Jill M.

2014-01-01

Objective The census classification of Hispanic origin is used in epidemiological studies to group individuals, even though there is geographical, cultural, and genetic diversity within Hispanic Americans of purportedly similar backgrounds. We observed differences in our measures of adiposity between our two Mexican American populations, and examined whether these differences were attributed to social, behavioral, physiologic or genetic differences between the two populations. Research Design and Methods In the IRAS Family Study, we examined 478 Hispanics from San Antonio, Texas and 447 Hispanics from the San Luis Valley, Colorado. Associations with body mass index (BMI), visceral adipose tissue area (VAT), and subcutaneous adipose tissue area (SAT) using social, behavioral, physiologic and genetic variables were examined. Results Hispanics of Mexican origin in our clinic population in San Antonio had significantly higher mean BMI (31.09 vs 28.35 kg/m2), VAT (126.3 vs 105.5 cm2), and SAT (391.6 vs 336.9 cm2), than Hispanics of Mexican origin in the San Luis Valley. The amount of variation in adiposity explained by clinic population was 4.5% for BMI, 2.8% for VAT, and 2.7% for SAT. After adjustment, clinic population was no longer associated with VAT and SAT, but remained associated with BMI, although the amount of variation explained by population was substantially less (1.0% for BMI). Conclusion Adiposity differences within this population of Mexican origin can be largely explained by social, behavioral, physiologic and genetic differences. (Ethn Dis. 2012;22(1):65–71) PMID:22774311

Predicting assemblages and species richness of endemic fish in the upper Yangtze River.

PubMed

He, Yongfeng; Wang, Jianwei; Lek-Ang, Sithan; Lek, Sovan

2010-09-01

The present work describes the ability of two modeling methods, Classification and Regression Tree (CART) and Random Forest (RF), to predict endemic fish assemblages and species richness in the upper Yangtze River, and then to identify the determinant environmental factors contributing to the models. The models included 24 predictor variables and 2 response variables (fish assemblage and species richness) for a total of 46 site units. The predictive quality of the modeling approaches was judged with a leave-one-out validation procedure. There was an average success of 60.9% and 71.7% to assign each site unit to the correct assemblage of fish, and 73% and 84% to explain the variance in species richness, by using CART and RF models, respectively. RF proved to be better than CART in terms of accuracy and efficiency in ecological applications. In any case, the mixed models including both land cover and river characteristic variables were more powerful than either individual one in explaining the endemic fish distribution pattern in the upper Yangtze River. For instance, altitude, slope, length, discharge, runoff, farmland and alpine and sub-alpine meadow played important roles in driving the observed endemic fish assemblage structure, while farmland, slope grassland, discharge, runoff, altitude and drainage area in explaining the observed patterns of endemic species richness. Therefore, the various effects of human activity on natural aquatic ecosystems, in particular, the flow modification of the river and the land use changes may have a considerable effect on the endemic fish distribution patterns on a regional scale. Copyright 2010 Elsevier B.V. All rights reserved.

DSM-5 Tobacco Use Disorder and Sleep Disturbance: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III).

PubMed

Hayley, Amie C; Stough, Con; Downey, Luke A

2017-12-06

The DSM-5 Tobacco use disorder diagnosis incorporates tobacco misuse, addictive behaviors and withdrawal symptomology. Tobacco use is bidirectionally associated with sleep pathology; however, no epidemiological studies have yet evaluated the associations between DSM-5 Tobacco use disorder and self-reported sleep disturbance. The current study aimed to evaluate health, medical and sleep-related factors among individuals within this diagnostic stratum. A total of N = 36,177 adults who participated in the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III) were included for analyses. The adjusted odd ratios (AOR) for individual classifications of DSM-5 Tobacco use disorder among those with subjective sleep disturbances were used as the primary outcome measure and relevant demographic, clinical and medical factors were considered in all univariate and multivariable analyses. Current and lifetime DSM-5 tobacco use disorder diagnoses were associated with poorer health and medical outcomes and higher rates of subjective sleep disturbances (all p < 0.001). Associations between current and lifetime DSM-5 tobacco use disorder and subjective sleep disturbances were maintained in multivariable analyses following adjustment for a range of health, lifestyle, and psychiatric factors (adjusted OR 1.11, 95%CI 1.00-1.23 and adjusted OR = 1.24, 95%CI 1.15-1.34, respectively); however, these relationships were fully explained by diagnoses of DSM-5 alcohol use disorder. Data from this large, representative survey indicate that the association between DSM-5 Tobacco use disorder and sleep disturbance is explained by underlying diagnoses of DSM-5 alcohol use disorder. Multifaceted substance abuse treatment protocols may improve treatment outcomes for affected patient groups.

Back pain was less explained than leg pain: a cross-sectional study using magnetic resonance imaging in low back pain patients with and without radiculopathy.

PubMed

Jensen, Ole Kudsk; Nielsen, Claus Vinther; Sørensen, Joan Solgaard; Stengaard-Pedersen, Kristian

2015-12-03

Cross-sectional studies have shown associations between lumbar degenerative manifestations on magnetic resonance imaging (MRI) and low back pain (LBP). Disc herniations and other degenerative manifestations, however, frequently occur in asymptomatic individuals. The purpose of this cross-sectional study was to analyze for associations between pain intensity and degenerative manifestations and other pain variables in patients for whom prognostic factors have been published previously. Included were 141 consecutive patients with and without radiculopathy, all sick-listed 1-4 months due to low back pain and subsequently examined by MRI of the lumbar spine. Using different methods of grouping the degenerative manifestations, linear regression analyses were performed with the intensity of back + leg pain, back pain and leg pain as dependent variables covering actual pain and pain the preceding 2 weeks. The clinical classification into +/- radiculopathy was established before and independently of the standardised description of MRI findings. Radiculopathy was present in 43 % of the patients. Pain was best explained using rank-ordered degenerative manifestations on MRI. Back pain and leg pain were differently associated, and back pain was less explained than leg pain in the multivariate analyses (15 % vs. 31 % of the variation). Back pain intensity was higher in patients with type 1 Modic changes and in some patients with nerve root touch, but was not associated with disc herniations. Leg pain intensity was well explained by disc herniations causing MRI nerve root compromise and radiculopathy. In patients with radiculopathy, nerve root touch caused as much leg pain as nerve root displacement or compression. High intensity zones and osteophytes were not associated with back pain, but only associated with leg pain in patients with radiculopathy. Tender points explained some of the back pain, and widespread pain explained leg pain in some of the patients without radiculopathy. Back pain was associated with type 1 Modic changes, nerve root touch and tender points, whereas leg pain was associated with osteophytes, HIZ, disc herniation, all sorts of MRI nerve root compromise, radiculopathy and widespread pain.

Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning.

PubMed

Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

2014-07-03

Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

20 CFR 422.105 - Presumption of authority of nonimmigrant alien to engage in employment.

Code of Federal Regulations, 2013 CFR

2013-04-01

... immigration classification F-1. If you are an F-1 student and do not have a separate DHS employment... work, you must give us proof (as explained in § 422.107(e)(2)) that: (1) You have authorization from...

20 CFR 422.105 - Presumption of authority of nonimmigrant alien to engage in employment.

Code of Federal Regulations, 2012 CFR

2012-04-01

... immigration classification F-1. If you are an F-1 student and do not have a separate DHS employment... work, you must give us proof (as explained in § 422.107(e)(2)) that: (1) You have authorization from...

The Modern View of Nature's Building Blocks.

ERIC Educational Resources Information Center

Akyeampong, D. A.

1985-01-01

Explains current knowledge about the makeup of matter at the microscopic (and even more infinitesimal) levels, accentuating the role of accelerators in the process and considering whether more fundamental particles may exist. Classification of subatomic particles, hadrons, quarks, and gluons are among the areas examined. (JN)

13 CFR 124.3 - What definitions are important in the 8(a) BD program?

Code of Federal Regulations, 2013 CFR

2013-01-01

... the marketing, production, sales, and administrative functions of the firm. Immediate family member... Native Hawaiians. Negative control is defined in part 121 of this title. Non-disadvantaged individual.... Primary industry classification means the six digit North American Industry Classification System (NAICS...

13 CFR 124.3 - What definitions are important in the 8(a) BD program?

Code of Federal Regulations, 2012 CFR

2012-01-01

... the marketing, production, sales, and administrative functions of the firm. Immediate family member... Native Hawaiians. Negative control is defined in part 121 of this title. Non-disadvantaged individual.... Primary industry classification means the six digit North American Industry Classification System (NAICS...

A renewed perspective on agroforestry concepts and classification.

PubMed

Torquebiau, E F

2000-11-01

Agroforestry, the association of trees with farming practices, is progressively becoming a recognized land-use discipline. However, it is still perceived by some scientists, technicians and farmers as a sort of environmental fashion which does not deserve credit. The peculiar history of agroforestry and the complex relationships between agriculture and forestry explain some misunderstandings about the concepts and classification of agroforestry and reveal that, contrarily to common perception, agroforestry is closer to agriculture than to forestry. Based on field experience from several countries, a structural classification of agroforestry into six simple categories is proposed: crops under tree cover, agroforests, agroforestry in a linear arrangement, animal agroforestry, sequential agroforestry and minor agroforestry techniques. It is argued that this pragmatic classification encompasses all major agroforestry associations and allows simultaneous agroforestry to be clearly differentiated from sequential agroforestry, two categories showing contrasting ecological tree-crop interactions. It can also contribute to a betterment of the image of agroforestry and lead to a simplification of its definition.

The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges.

PubMed

Phillips, Charles D

2015-01-01

Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges.

The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges

PubMed Central

Phillips, Charles D.

2015-01-01

Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges. PMID:26740744

EEG-based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

PubMed Central

Wade, Joshua W.; Key, Alexandra P.; Warren, Zachary E.; Sarkar, Nilanjan

2017-01-01

objective To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention. PMID:28422647

Classification Management. Journal of the National Classification Management Society, Volume 19, 1983,

DTIC Science & Technology

1984-01-01

let me say how much I appreciate and Frank Hucker from Boston. Later, I’ll explain having the chance to come here and get together why we don’t have...takes like 4 months or longer, if it’s TOP SECRET business at the same old stand but they admin- It is well to consider how long it takes to get the...254 to communicate that infor- Q: I mean, how does that get communicated mation to the contractor. The second one is that to the contracting officer on

The Upper and Lower Bounds of the Prediction Accuracies of Ensemble Methods for Binary Classification

PubMed Central

Wang, Xueyi; Davidson, Nicholas J.

2011-01-01

Ensemble methods have been widely used to improve prediction accuracy over individual classifiers. In this paper, we achieve a few results about the prediction accuracies of ensemble methods for binary classification that are missed or misinterpreted in previous literature. First we show the upper and lower bounds of the prediction accuracies (i.e. the best and worst possible prediction accuracies) of ensemble methods. Next we show that an ensemble method can achieve > 0.5 prediction accuracy, while individual classifiers have < 0.5 prediction accuracies. Furthermore, for individual classifiers with different prediction accuracies, the average of the individual accuracies determines the upper and lower bounds. We perform two experiments to verify the results and show that it is hard to achieve the upper and lower bounds accuracies by random individual classifiers and better algorithms need to be developed. PMID:21853162

Resting State EEG-based biometrics for individual identification using convolutional neural networks.

PubMed

Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

2015-08-01

Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

A novel method to guide classification of para swimmers with limb deficiency.

PubMed

Hogarth, Luke; Payton, Carl; Van de Vliet, Peter; Connick, Mark; Burkett, Brendan

2018-05-30

The International Paralympic Committee has directed International Federations that govern Para sports to develop evidence-based classification systems. This study defined the impact of limb deficiency impairment on 100 m freestyle performance to guide an evidence-based classification system in Para Swimming, which will be implemented following the 2020 Tokyo Paralympic games. Impairment data and competitive race performances of 90 international swimmers with limb deficiency were collected. Ensemble partial least squares regression established the relationship between relative limb length measures and competitive 100 m freestyle performance. The model explained 80% of the variance in 100 m freestyle performance, and found hand length and forearm length to be the most important predictors of performance. Based on the results of this model, Para swimmers were clustered into four-, five-, six- and seven-class structures using nonparametric kernel density estimations. The validity of these classification structures, and effectiveness against the current classification system, were examined by establishing within-class variations in 100 m freestyle performance and differences between adjacent classes. The derived classification structures were found to be more effective than current classification based on these criteria. This study provides a novel method that can be used to improve the objectivity and transparency of decision-making in Para sport classification. Expert consensus from experienced coaches, Para swimmers, classifiers and sport science and medicine personnel will benefit the translation of these findings into a revised classification system that is accepted by the Para swimming community. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Polarization in Raman spectroscopy helps explain bone brittleness in genetic mouse models

NASA Astrophysics Data System (ADS)

Makowski, Alexander J.; Pence, Isaac J.; Uppuganti, Sasidhar; Zein-Sabatto, Ahbid; Huszagh, Meredith C.; Mahadevan-Jansen, Anita; Nyman, Jeffry S.

2014-11-01

Raman spectroscopy (RS) has been extensively used to characterize bone composition. However, the link between bone biomechanics and RS measures is not well established. Here, we leveraged the sensitivity of RS polarization to organization, thereby assessing whether RS can explain differences in bone toughness in genetic mouse models for which traditional RS peak ratios are not informative. In the selected mutant mice-activating transcription factor 4 (ATF4) or matrix metalloproteinase 9 (MMP9) knock-outs-toughness is reduced but differences in bone strength do not exist between knock-out and corresponding wild-type controls. To incorporate differences in the RS of bone occurring at peak shoulders, a multivariate approach was used. Full spectrum principal components analysis of two paired, orthogonal bone orientations (relative to laser polarization) improved genotype classification and correlation to bone toughness when compared to traditional peak ratios. When applied to femurs from wild-type mice at 8 and 20 weeks of age, the principal components of orthogonal bone orientations improved age classification but not the explanation of the maturation-related increase in strength. Overall, increasing polarization information by collecting spectra from two bone orientations improves the ability of multivariate RS to explain variance in bone toughness, likely due to polarization sensitivity to organizational changes in both mineral and collagen.

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

PubMed

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

PubMed Central

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method. PMID:24820966

Minimum Requirements for the CUS (Common User Subsystem) Workstation

DTIC Science & Technology

1987-04-20

PAGE -2- / ’ " I& REPORT SECURITY CLASSIFICATION lb RESTRICTIVE MARKINGS Unclassified 2a SECURITY CLASSIFICATION AUTHORITY 3 DISTRMBUTION...CLASSIFICATION UNCLASSIID/UNLIMITED r" SAME AS RPT. [ 3 DTIC USERS Unclassified tNM F RESPONSIBLE INDIVIDUAL 22b TELEPHONE (Include area codi) 22c OFFICE...Summary 1 1. Introduction 3 1.1 Purpose 3 1.2 Scope 3 1.3 Reference 4 2. Background 5 3 . Minimal WIS Workstation Requirements 8 3.1 Overview 8 4. Overview

Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

NASA Astrophysics Data System (ADS)

Liu, Haijian; Wu, Changshan

2018-06-01

Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

A model-based test for treatment effects with probabilistic classifications.

PubMed

Cavagnaro, Daniel R; Davis-Stober, Clintin P

2018-05-21

Within modern psychology, computational and statistical models play an important role in describing a wide variety of human behavior. Model selection analyses are typically used to classify individuals according to the model(s) that best describe their behavior. These classifications are inherently probabilistic, which presents challenges for performing group-level analyses, such as quantifying the effect of an experimental manipulation. We answer this challenge by presenting a method for quantifying treatment effects in terms of distributional changes in model-based (i.e., probabilistic) classifications across treatment conditions. The method uses hierarchical Bayesian mixture modeling to incorporate classification uncertainty at the individual level into the test for a treatment effect at the group level. We illustrate the method with several worked examples, including a reanalysis of the data from Kellen, Mata, and Davis-Stober (2017), and analyze its performance more generally through simulation studies. Our simulations show that the method is both more powerful and less prone to type-1 errors than Fisher's exact test when classifications are uncertain. In the special case where classifications are deterministic, we find a near-perfect power-law relationship between the Bayes factor, derived from our method, and the p value obtained from Fisher's exact test. We provide code in an online supplement that allows researchers to apply the method to their own data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Enterprise Standard Industrial Classification Manual. 1974.

ERIC Educational Resources Information Center

Executive Office of the President, Washington, DC. Statistical Policy Div.

This classification is presented to provide a standard for use with statistics about enterprises (i.e., companies, rather than their individual establishments) by kind of economic activity. The enterprise unit consists of all establishments under common direct or indirect ownership. It is defined to include all entities, including subsidiaries,…

Nutrition Problem Classification for Children and Youth.

ERIC Educational Resources Information Center

Health Services Administration (DHEW/PHS), Rockville, MD. Bureau of Community Health Services.

This nutrition problem classification system is an attempt to classify the nutritional needs and problems of children and youth. Its two most important uses are problem identification and monitoring for individual patients and creation of an information base for developing program plans for intervention in a service population. The classification…

A Classification Framework for Exploring Technology-Enabled Practice--Frame TEP

ERIC Educational Resources Information Center

Prestridge, Sarah; de Aldama, Carlos

2016-01-01

This article theorizes the construction of a classification framework to explore teachers' beliefs and pedagogical practices for the use of digital technologies in the classroom. There are currently many individual schemas and models that represent both developmental and divergent concepts associated with technology-enabled practice. This article…

13 CFR 124.3 - What definitions are important in the 8(a) BD program?

Code of Federal Regulations, 2011 CFR

2011-01-01

... functions of the firm. Immediate family member means father, mother, husband, wife, son, daughter, brother... Native Hawaiians. Negative control is defined in part 121 of this title. Non-disadvantaged individual.... Primary industry classification means the four digit Standard Industrial Classification (SIC) code...

Connecting Middle School, Oceanography, and the Real World.

ERIC Educational Resources Information Center

Brown, Susan W.; Hansen, Terri M.

2000-01-01

Introduces an activity that features 16 oceanography work stations and integrates other disciplines. Assigns students different oceanic life forms and requires students to work in stations. Explains seven of 16 stations which cover oil spills, the periodic table, ocean floor, currents, and classification of oceanic organisms. (YDS)

California Community Colleges Student Attendance Accounting Manual.

ERIC Educational Resources Information Center

Cook, Gary L.; Nussbaum, Thomas J.

Presenting guidelines for student attendance accounting developed by the Chancellor's Office of the California Community Colleges, this manual provides an update to the original guide published in 1983. Chapter 1 explains general items such as the academic calendar, admissions policies, student classification by enrollment status, and conditions…

Does boldness explain vulnerability to angling in Eurasian perch Perca fluviatilis?

PubMed

Vainikka, Anssi; Tammela, Ilkka; Hyvärinen, Pekka

2016-04-01

Consistent individual differences (CIDs) in behavior are of interest to both basic and applied research, because any selection acting on them could induce evolution of animal behavior. It has been suggested that CIDs in the behavior of fish might explain individual differences in vulnerability to fishing. If so, fishing could impose selection on fish behavior. In this study, we assessed boldness-indicating behaviors of Eurasian perch Perca fluviatilis using individually conducted experiments measuring the time taken to explore a novel arena containing predator (burbot, Lota lota ) cues. We studied if individual differences in boldness would explain vulnerability of individually tagged perch to experimental angling in outdoor ponds, or if fishing would impose selection on boldness-indicating behavior. Perch expressed repeatable individual differences in boldness-indicating behavior but the individual boldness-score (the first principal component) obtained using principal component analysis combining all the measured behavioral responses did not explain vulnerability to experimental angling. Instead, large body size appeared as the only statistically significant predictor of capture probability. Our results suggest that angling is selective for large size, but not always selective for high boldness.

Does boldness explain vulnerability to angling in Eurasian perch Perca fluviatilis?

PubMed Central

Vainikka, Anssi; Tammela, Ilkka; Hyvärinen, Pekka

2016-01-01

Abstract Consistent individual differences (CIDs) in behavior are of interest to both basic and applied research, because any selection acting on them could induce evolution of animal behavior. It has been suggested that CIDs in the behavior of fish might explain individual differences in vulnerability to fishing. If so, fishing could impose selection on fish behavior. In this study, we assessed boldness-indicating behaviors of Eurasian perch Perca fluviatilis using individually conducted experiments measuring the time taken to explore a novel arena containing predator (burbot, Lota lota) cues. We studied if individual differences in boldness would explain vulnerability of individually tagged perch to experimental angling in outdoor ponds, or if fishing would impose selection on boldness-indicating behavior. Perch expressed repeatable individual differences in boldness-indicating behavior but the individual boldness-score (the first principal component) obtained using principal component analysis combining all the measured behavioral responses did not explain vulnerability to experimental angling. Instead, large body size appeared as the only statistically significant predictor of capture probability. Our results suggest that angling is selective for large size, but not always selective for high boldness. PMID:29491897

The Communication Function Classification System: cultural adaptation, validity, and reliability of the Farsi version for patients with cerebral palsy.

PubMed

Soleymani, Zahra; Joveini, Ghodsiye; Baghestani, Ahmad Reza

2015-03-01

This study developed a Farsi language Communication Function Classification System and then tested its reliability and validity. Communication Function Classification System is designed to classify the communication functions of individuals with cerebral palsy. Up until now, there has been no instrument for assessment of this communication function in Iran. The English Communication Function Classification System was translated into Farsi and cross-culturally modified by a panel of experts. Professionals and parents then assessed the content validity of the modified version. A backtranslation of the Farsi version was confirmed by the developer of the English Communication Function Classification System. Face validity was assessed by therapists and parents of 10 patients. The Farsi Communication Function Classification System was administered to 152 individuals with cerebral palsy (age, 2 to 18 years; median age, 10 years; mean age, 9.9 years; standard deviation, 4.3 years). Inter-rater reliability was analyzed between parents, occupational therapists, and speech and language pathologists. The test-retest reliability was assessed for 75 patients with a 14 day interval between tests. The inter-rater reliability of the Communication Function Classification System was 0.81 between speech and language pathologists and occupational therapists, 0.74 between parents and occupational therapists, and 0.88 between parents and speech and language pathologists. The test-retest reliability was 0.96 for occupational therapists, 0.98 for speech and language pathologists, and 0.94 for parents. The findings suggest that the Farsi version of Communication Function Classification System is a reliable and valid measure that can be used in clinical settings to assess communication function in patients with cerebral palsy. Copyright © 2015 Elsevier Inc. All rights reserved.

Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

PubMed

Horst, Fabian; Eekhoff, Alexander; Newell, Karl M; Schöllhorn, Wolfgang I

2017-01-01

Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of these findings, especially towards more individualized and situational diagnoses and therapy.

Magnetic hysteresis classification of the lunar surface and the interpretation of permanent remanence in lunar surface samples

NASA Technical Reports Server (NTRS)

Wasilewski, P.

1972-01-01

A magnetic hysteresis classification of the lunar surface is presented. It was found that there is a distinct correlation between natural remanence (NRM), saturation magnetization, and the hysteresis ratios for the rock samples. The hysteresis classification is able to explain some aspects of time dependent magnetization in the lunar samples and relates the initial susceptibility to NRM, viscous remanence, and to other aspects of magnetization in lunar samples. It is also considered that since up to 60% of the iron in the lunar soil may be super paramagnetic at 400 K, and only 10% at 100 K, the 50% which becomes ferromagnetic over the cycle has the characteristics of thermoremanence and may provide for an enhancement in measurable field on the dark side during a subsatellite magnetometer circuit.

Universal etiology, multifactorial diseases and the constitutive model of disease classification.

PubMed

Fuller, Jonathan

2018-02-01

Infectious diseases are often said to have a universal etiology, while chronic and noncommunicable diseases are said to be multifactorial in their etiology. It has been argued that the universal etiology of an infectious disease results from its classification using a monocausal disease model. In this article, I will reconstruct the monocausal model and argue that modern 'multifactorial diseases' are not monocausal by definition. 'Multifactorial diseases' are instead defined according to a constitutive disease model. On closer analysis, infectious diseases are also defined using the constitutive model rather than the monocausal model. As a result, our classification models alone cannot explain why infectious diseases have a universal etiology while chronic and noncommunicable diseases lack one. The explanation is instead provided by the Nineteenth Century germ theorists. Copyright © 2017 Elsevier Ltd. All rights reserved.

Eigenvalue-eigenvector decomposition (EED) analysis of dissimilarity and covariance matrix obtained from total synchronous fluorescence spectral (TSFS) data sets of herbal preparations: Optimizing the classification approach.

PubMed

Tarai, Madhumita; Kumar, Keshav; Divya, O; Bairi, Partha; Mishra, Kishor Kumar; Mishra, Ashok Kumar

2017-09-05

The present work compares the dissimilarity and covariance based unsupervised chemometric classification approaches by taking the total synchronous fluorescence spectroscopy data sets acquired for the cumin and non-cumin based herbal preparations. The conventional decomposition method involves eigenvalue-eigenvector analysis of the covariance of the data set and finds the factors that can explain the overall major sources of variation present in the data set. The conventional approach does this irrespective of the fact that the samples belong to intrinsically different groups and hence leads to poor class separation. The present work shows that classification of such samples can be optimized by performing the eigenvalue-eigenvector decomposition on the pair-wise dissimilarity matrix. Copyright © 2017 Elsevier B.V. All rights reserved.

Interannual drought index variations in Central Europe related to the large-scale atmospheric circulation—application and evaluation of statistical downscaling approaches based on circulation type classifications

NASA Astrophysics Data System (ADS)

Beck, Christoph; Philipp, Andreas; Jacobeit, Jucundus

2015-08-01

This contribution investigates the relationship between the large-scale atmospheric circulation and interannual variations of the standardized precipitation index (SPI) in Central Europe. To this end, circulation types (CT) have been derived from a variety of circulation type classifications (CTC) applied to daily sea level pressure (SLP) data and mean circulation indices of vorticity ( V), zonality ( Z) and meridionality ( M) have been calculated. Occurrence frequencies of CTs and circulation indices have been utilized as predictors within multiple regression models (MRM) for the estimation of gridded 3-month SPI values over Central Europe, for the period 1950 to 2010. CTC-based MRMs used in the analyses comprise variants concerning the basic method for CT classification, the number of CTs, the size and location of the spatial domain used for CTCs and the exclusive use of CT frequencies or the combined use of CT frequencies and mean circulation indices as predictors. Adequate MRM predictor combinations have been identified by applying stepwise multiple regression analyses within a resampling framework. The performance (robustness) of the resulting MRMs has been quantified based on a leave-one-out cross-validation procedure applying several skill scores. Furthermore, the relative importance of individual predictors has been estimated for each MRM. From these analyses, it can be stated that model skill is improved by (i) the consideration of vorticity characteristics within CTCs, (ii) a relatively small size of the spatial domain to which CTCs are applied and (iii) the inclusion of mean circulation indices. However, model skill exhibits distinct variations between seasons and regions. Whereas promising skill can be stated for the western and northwestern parts of the Central European domain, only unsatisfactory skill is reached in the more continental regions and particularly during summer. Thus, it can be concluded that the presented approaches feature the potential for the downscaling of Central European drought index variations from the large-scale circulation, at least for some regions. Further improvements of CTC-based approaches may be expected from the optimization of CTCs for explaining the SPI, e.g. via the inclusion of additional variables in the classification procedure.

[Insurance medical consultation in the private health insurance--which questions are asked for to the medical consultant?].

PubMed

Hakimi, R

2013-03-01

There have been medical consultants in the insurance business since the 19th century. Still the tasks and competences of the medical consultants in private health insurance in Germany are more or less unknown to the public and among doctors. There are only few publications about this topic. All questions submitted to the medical consultant from 1 January 2011 to 31 December 2011 have been examined and classified. Most of the questions to the medical consultant concern the medical necessity of medications, with life style medication playing an especially important role. Alternative medicine, the classification of cure and rehabilitation measures of hospital medical treatments, out-patient operations, new treatments, the length of hospital treatments and out-patient psychotherapy, medical consultation on artificial insemination and cosmetic surgery intervention give more and more reason for a consultation of the medical examiner. The discussion considers the details of individual insurance medicine blocks of consultation in insurance medicine and explains their significance.

Acoustic, genetic and morphological variations within the katydid Gampsocleis sedakovii (Orthoptera, Tettigonioidea)

PubMed Central

Zhang, Xue; Wen, Ming; Li, Junjian; Zhu, Hui; Wang, Yinliang; Ren, Bingzhong

2015-01-01

Abstract In an attempt to explain the variation within this species and clarify the subspecies classification, an analysis of the genetic, calling songs, and morphological variations within the species Gampsocleis sedakovii is presented from Inner Mongolia, China. Recordings were compared of the male calling songs and analysis performed of selected acoustic variables. This analysis is combined with sequencing of mtDNA - COI and examination of morphological traits to perform cluster analyses. The trees constructed from different datasets were structurally similar, bisecting the six geographical populations studied. Based on two large branches in the analysis, the species Gampsocleis sedakovii was partitioned into two subspecies, Gampsocleis sedakovii sedakovii (Fischer von Waldheim, 1846) and Gampsocleis sedakovii obscura (Walker, 1869). Comparing all the traits, the individual of Elunchun (ELC) was the intermediate type in this species according to the acoustic, genetic, and morphological characteristics. This study provides evidence for insect acoustic signal divergence and the process of subspeciation. PMID:26692795

Object-based methods for individual tree identification and tree species classification from high-spatial resolution imagery

NASA Astrophysics Data System (ADS)

Wang, Le

2003-10-01

Modern forest management poses an increasing need for detailed knowledge of forest information at different spatial scales. At the forest level, the information for tree species assemblage is desired whereas at or below the stand level, individual tree related information is preferred. Remote Sensing provides an effective tool to extract the above information at multiple spatial scales in the continuous time domain. To date, the increasing volume and readily availability of high-spatial-resolution data have lead to a much wider application of remotely sensed products. Nevertheless, to make effective use of the improving spatial resolution, conventional pixel-based classification methods are far from satisfactory. Correspondingly, developing object-based methods becomes a central challenge for researchers in the field of Remote Sensing. This thesis focuses on the development of methods for accurate individual tree identification and tree species classification. We develop a method in which individual tree crown boundaries and treetop locations are derived under a unified framework. We apply a two-stage approach with edge detection followed by marker-controlled watershed segmentation. Treetops are modeled from radiometry and geometry aspects. Specifically, treetops are assumed to be represented by local radiation maxima and to be located near the center of the tree-crown. As a result, a marker image was created from the derived treetop to guide a watershed segmentation to further differentiate overlapping trees and to produce a segmented image comprised of individual tree crowns. The image segmentation method developed achieves a promising result for a 256 x 256 CASI image. Then further effort is made to extend our methods to the multiscales which are constructed from a wavelet decomposition. A scale consistency and geometric consistency are designed to examine the gradients along the scale-space for the purpose of separating true crown boundary from unwanted textures occurring due to branches and twigs. As a result from the inverse wavelet transform, the tree crown boundary is enhanced while the unwanted textures are suppressed. Based on the enhanced image, an improvement is achieved when applying the two-stage methods to a high resolution aerial photograph. To improve tree species classification, we develop a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. The optimal scale parameter is then fed in the process of a region-growing-based segmentation as a break-off value. Our object classification achieves a better accuracy in separating tree species when compared to the conventional Maximum Likelihood Classification (MLC). In summary, we develop two object-based methods for identifying individual trees and classifying tree species from high-spatial resolution imagery. Both methods achieve promising results and will promote integration of Remote Sensing and GIS in forest applications.

Quality assurance: The 10-Group Classification System (Robson classification), induction of labor, and cesarean delivery.

PubMed

Robson, Michael; Murphy, Martina; Byrne, Fionnuala

2015-10-01

Quality assurance in labor and delivery is needed. The method must be simple and consistent, and be of universal value. It needs to be clinically relevant, robust, and prospective, and must incorporate epidemiological variables. The 10-Group Classification System (TGCS) is a simple method providing a common starting point for further detailed analysis within which all perinatal events and outcomes can be measured and compared. The system is demonstrated in the present paper using data for 2013 from the National Maternity Hospital in Dublin, Ireland. Interpretation of the classification can be easily taught. The standard table can provide much insight into the philosophy of care in the population of women studied and also provide information on data quality. With standardization of audit of events and outcomes, any differences in either sizes of groups, events or outcomes can be explained only by poor data collection, significant epidemiological variables, or differences in practice. In April 2015, WHO proposed that the TGCS (also known as the Robson classification) is used as a global standard for assessing, monitoring, and comparing cesarean delivery rates within and between healthcare facilities. Copyright © 2015. Published by Elsevier Ireland Ltd.

Alzheimer's Disease Diagnosis in Individual Subjects using Structural MR Images: Validation Studies

PubMed Central

Vemuri, Prashanthi; Gunter, Jeffrey L.; Senjem, Matthew L.; Whitwell, Jennifer L.; Kantarci, Kejal; Knopman, David S.; Boeve, Bradley F.; Petersen, Ronald C.; Jack, Clifford R.

2008-01-01

OBJECTIVE To develop and validate a tool for Alzheimer's disease (AD) diagnosis in individual subjects using support vector machine (SVM) based classification of structural MR (sMR) images. BACKGROUND Libraries of sMR scans of clinically well characterized subjects can be harnessed for the purpose of diagnosing new incoming subjects. METHODS 190 patients with probable AD were age- and gender-matched with 190 cognitively normal (CN) subjects. Three different classification models were implemented: Model I uses tissue densities obtained from sMR scans to give STructural Abnormality iNDex (STAND)-score; and Models II and III use tissue densities as well as covariates (demographics and Apolipoprotein E genotype) to give adjusted-STAND (aSTAND)-score. Data from 140 AD and 140 CN were used for training. The SVM parameter optimization and training was done by four-fold cross validation. The remaining independent sample of 50 AD and 50 CN were used to obtain a minimally biased estimate of the generalization error of the algorithm. RESULTS The CV accuracy of Model II and Model III aSTAND-scores was 88.5% and 89.3% respectively and the developed models generalized well on the independent test datasets. Anatomic patterns best differentiating the groups were consistent with the known distribution of neurofibrillary AD pathology. CONCLUSIONS This paper presents preliminary evidence that application of SVM-based classification of an individual sMR scan relative to a library of scans can provide useful information in individual subjects for diagnosis of AD. Including demographic and genetic information in the classification algorithm slightly improves diagnostic accuracy. PMID:18054253

Faculty Member's Guide to U.S. Immigration Law. Revised.

ERIC Educational Resources Information Center

Smith, Eugene H.; Baron, Marvin J.

Immigration laws and regulations pertaining to foreign students and scholars are summarized as an aid to faculty members. Basic immigration documents and terminology are explained, including the passport, visa, immigration status or classification, Form 1-20 ID, the "green card", and Departure Record. Classes of nonimmigrants are described,…

Classification of Student Affective Responses to Teaching Films.

ERIC Educational Resources Information Center

Haller, Charles R.

To help teachers assess students' affective responses to teaching films, a scale as established and displayed graphically under which reactions may be rated as positive/active, positive/passive, negative/active, and negative/passive. Procedure in using the scale is explained and a "film reaction sheet" provided. Suggested ways of utilizing the…

The Value of a European Security and Defense Policy

DTIC Science & Technology

2007-05-08

Union, Theory in International Affairs, US Foreign and Security Policy, Crisis Management, Crisis Response, 16. SECURITY CLASSIFICATION OF: 17...1 2. The importance of theory in...will examine which paradigm of international relations is most suited to explain current developments in the EU and which theory in international

A Multicentre Italian Validation Study in Aging Adults with Down Syndrome and Other Forms of Intellectual Disabilities: Dementia Screening Questionnaire for Individuals with Intellectual Disabilities.

PubMed

Gomiero, Tiziano; Bertelli, Marco; Deb, Shoumitro; Weger, Elisabeth; Marangoni, Annachiara; De Bastiani, Elisa; Mantesso, Ulrico; De Vreese, Luc Pieter

2017-01-01

The USA National Task Group (NTG) guidelines advocate the use of an adapted version of Dementia Screening Questionnaire for Individuals with Intellectual Disabilities (DSQIID) for dementia screening of individuals with Down syndrome (DS) and with other forms of ID (non-DS). In order to meet these guidelines, this study verifies the psychometric properties of an Italian version of the original DSQIID in a population composed of adults aged 40 years and over with DS and non-DS ID. Internal consistency, inter-rater and intra-rater reliabilities, structural validity, convergent validity and known group differences of DSQIID-I were assessed with 200 individuals with ID (mean of 55.2 years; range: 40-80 years) recruited from 15 different centers in Italy. Diagnosis of dementia was done according to IASSID diagnostic criteria and its degree of clinical certainty was defined according to Silverman et al.'s classification (2004). Cronbach's alpha for the DSQIID-I was 0.94. The ICCs for inter-rater and test-retest reliability were both 0.89. A Principal Component analysis revealed three domains, namely memory and confusion- related items, motor and functional disabilities, depression and apathy, which explained almost 40% of the overall variance. The total DSQIID-I score correlated significantly with DMR and differed significantly among those individuals (n = 34) with cognitive decline from those without (n = 166). Age, gender and severity of ID were unrelated to the DSQIID-I. The present study confirms the cross-cultural value of DSQIID which was proved to be a psychometrically valid and user-friendly observer-rated scale for dementia screening in adults with both DS and non-DS ID. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Towards automated sleep classification in infants using symbolic and subsymbolic approaches.

PubMed

Kubat, M; Flotzinger, D; Pfurtscheller, G

1993-04-01

The paper addresses the problem of automatic sleep classification. A special effort is made to find a method of extracting reasonable descriptions of the individual sleep stages from sample measurements of EGG, EMG, EOG, etc., and from a classification of these measurements provided by an expert. The method should satisfy three requirements: classification accuracy, interpretability of the results, and the ability to select the relevant and discard the irrelevant variables. The solution suggested in this paper consists of a combination of the subsymbolic algorithm LVQ with the symbolic decision tree generator ID3. Results demonstrating the feasibility and utility of our approach are also presented.

Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution.

PubMed

Messier, Kyle P; Jackson, Laura E; White, Jennifer L; Hilborn, Elizabeth D

2015-01-01

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution. Published by Elsevier Ltd.

A Quality Classification System for Young Hardwood Trees - The First Step in Predicting Future Products

Treesearch

David L. Sonderman; Robert L. Brisbin

1978-01-01

Forest managers have no objective way to determine the relative value of culturally treated forest stands in terms of product potential. This paper describes the first step in the development of a quality classification system based on the measurement of individual tree characteristics for young hardwood stands.

A hierarchical classification of freshwater mussel diversity in North America

Treesearch

Wendell R. Haag

2010-01-01

Aim North America harbours the most diverse freshwater mussel fauna on Earth. This fauna has high endemism at the continental scale and within individual river systems. Previous faunal classifications for North America were based on intuitive, subjective assessments of species distributions, primarily the occurrence of endemic species, and do not portray continent-wide...

Multiple-Primitives Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

NASA Astrophysics Data System (ADS)

Ni, H.; Lin, X. G.; Zhang, J. X.

2017-09-01

A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point. In the first stage, the input ALS data is divided into smooth surfaces and rough surfaces by employing a step-wise point cloud segmentation method. In the second stage, classification based on smooth surfaces and rough surfaces is performed. Points in the smooth surfaces are first classified into ground and buildings based on semantic rules. Next, features of rough surfaces are extracted. Then, points in rough surfaces are classified into vegetation and vehicles based on the derived features and Random Forests (RF). In the third stage, point-based features are extracted for the ground points, and then, an individual point classification procedure is performed to classify the ground points into bare land, artificial ground and greenbelt. Moreover, the shortages of the existing studies are analyzed, and experiments show that the proposed method overcomes these shortages and handles more types of objects.

The impact of self-reported ethnicity versus genetic ancestry on phenotypic characteristics of polycystic ovary syndrome (PCOS).

PubMed

Louwers, Y V; Lao, O; Fauser, B C J M; Kayser, M; Laven, J S E

2014-10-01

It is well established that ethnicity is associated with the phenotype of polycystic ovary syndrome (PCOS). Self-reported ethnicity was shown to be an inaccurate proxy for ethnic origin in other disease traits, and it remains unclear how in PCOS patients self-reported ethnicity compares with a biological proxy such as genetic ancestry. We compared the impact of self-reported ethnicity versus genetic ancestry on PCOS and tested which of these 2 classifications better predicts the variability in phenotypic characteristics of PCOS. A total of 1499 PCOS patients from The Netherlands, comprising 11 self-reported ethnic groups of European, African, American, and Asian descent were genotyped with the Illumina 610K Quad BeadChip and merged with the data genotyped with the Illumina HumanHap650K available for the reference panel collected by the Human Genome Diversity Project (HGDP), in a collaboration with the Centre Etude Polymorphism Humain (CEPH), including 53 populations for ancestry reference. Algorithms for inferring genetic relationships among individuals, including multidimensional scaling and ADMIXTURE, were applied to recover genetic ancestry for each individual. Regression analysis was used to determine the best predictor for the variability in PCOS characteristics. The association between self-reported ethnicity and genetic ancestry was moderate. For amenorrhea, total follicle count, body mass index, SHBG, dehydroepiandrosterone sulfate, and insulin, mainly genetic ancestry clusters ended up in the final models (P values < .004), indicating that they explain a larger proportion of variability of these PCOS characteristics compared with self-reported ethnicity. Especially variability of insulin levels seems predominantly explained by genetic ancestry. Self-reported ancestry is not a perfect proxy for genetic ancestry in patients with PCOS, emphasizing that by using genetic ancestry data instead of self-reported ethnicity, PCOS-relevant misclassification can be avoided. Moreover, because genetic ancestry explained a larger proportion of phenotypic variability associated with PCOS than self-reported ethnicity, future studies should focus on genetic ancestry verification of PCOS patients for research questions and treatment as well as preventive strategies in these women.

Aggregation of Sentinel-2 time series classifications as a solution for multitemporal analysis

NASA Astrophysics Data System (ADS)

Lewiński, Stanislaw; Nowakowski, Artur; Malinowski, Radek; Rybicki, Marcin; Kukawska, Ewa; Krupiński, Michał

2017-10-01

The general aim of this work was to elaborate efficient and reliable aggregation method that could be used for creating a land cover map at a global scale from multitemporal satellite imagery. The study described in this paper presents methods for combining results of land cover/land use classifications performed on single-date Sentinel-2 images acquired at different time periods. For that purpose different aggregation methods were proposed and tested on study sites spread on different continents. The initial classifications were performed with Random Forest classifier on individual Sentinel-2 images from a time series. In the following step the resulting land cover maps were aggregated pixel by pixel using three different combinations of information on the number of occurrences of a certain land cover class within a time series and the posterior probability of particular classes resulting from the Random Forest classification. From the proposed methods two are shown superior and in most cases were able to reach or outperform the accuracy of the best individual classifications of single-date images. Moreover, the aggregations results are very stable when used on data with varying cloudiness. They also enable to reduce considerably the number of cloudy pixels in the resulting land cover map what is significant advantage for mapping areas with frequent cloud coverage.

Genomic ancestry and ethnoracial self-classification based on 5,871 community-dwelling Brazilians (The Epigen Initiative)

PubMed Central

Lima-Costa, M. Fernanda; Rodrigues, Laura C.; Barreto, Maurício L.; Gouveia, Mateus; Horta, Bernardo L.; Mambrini, Juliana; Kehdy, Fernanda S. G.; Pereira, Alexandre; Rodrigues-Soares, Fernanda; Victora, Cesar G.; Tarazona-Santos, Eduardo; Cesar, Cibele C.; Conceição, Jackson S.; Costa, Gustavo N.O.; Esteban, Nubia; Fiaccone, Rosemeire L.; Figueiredo, Camila A.; Firmo, Josélia O.A.; Horimoto, Andrea R.V.R.; Leal, Thiago P.; Machado, Moara; Magalhães, Wagner C.S.; de Oliveira, Isabel Oliveira; Peixoto, Sérgio V.; Rodrigues, Maíra R.; Santos, Hadassa C.; Silva, Thiago M.

2015-01-01

Brazil never had segregation laws defining membership of an ethnoracial group. Thus, the composition of the Brazilian population is mixed, and its ethnoracial classification is complex. Previous studies showed conflicting results on the correlation between genome ancestry and ethnoracial classification in Brazilians. We used 370,539 Single Nucleotide Polymorphisms to quantify this correlation in 5,851 community-dwelling individuals in the South (Pelotas), Southeast (Bambui) and Northeast (Salvador) Brazil. European ancestry was predominant in Pelotas and Bambui (median = 85.3% and 83.8%, respectively). African ancestry was highest in Salvador (median = 50.5%). The strength of the association between the phenotype and median proportion of African ancestry varied largely across populations, with pseudo R2 values of 0.50 in Pelotas, 0.22 in Bambui and 0.13 in Salvador. The continuous proportion of African genomic ancestry showed a significant S-shape positive association with self-reported Blacks in the three sites, and the reverse trend was found for self reported Whites, with most consistent classifications in the extremes of the high and low proportion of African ancestry. In self-classified Mixed individuals, the predicted probability of having African ancestry was bell-shaped. Our results support the view that ethnoracial self-classification is affected by both genome ancestry and non-biological factors. PMID:25913126

Comparison on three classification techniques for sex estimation from the bone length of Asian children below 19 years old: an analysis using different group of ages.

PubMed

Darmawan, M F; Yusuf, Suhaila M; Kadir, M R Abdul; Haron, H

2015-02-01

Sex estimation is used in forensic anthropology to assist the identification of individual remains. However, the estimation techniques tend to be unique and applicable only to a certain population. This paper analyzed sex estimation on living individual child below 19 years old using the length of 19 bones of left hand applied for three classification techniques, which were Discriminant Function Analysis (DFA), Support Vector Machine (SVM) and Artificial Neural Network (ANN) multilayer perceptron. These techniques were carried out on X-ray images of the left hand taken from an Asian population data set. All the 19 bones of the left hand were measured using Free Image software, and all the techniques were performed using MATLAB. The group of age "16-19" years old and "7-9" years old were the groups that could be used for sex estimation with as their average of accuracy percentage was above 80%. ANN model was the best classification technique with the highest average of accuracy percentage in the two groups of age compared to other classification techniques. The results show that each classification technique has the best accuracy percentage on each different group of age. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Genomic ancestry and ethnoracial self-classification based on 5,871 community-dwelling Brazilians (The Epigen Initiative).

PubMed

Lima-Costa, M Fernanda; Rodrigues, Laura C; Barreto, Maurício L; Gouveia, Mateus; Horta, Bernardo L; Mambrini, Juliana; Kehdy, Fernanda S G; Pereira, Alexandre; Rodrigues-Soares, Fernanda; Victora, Cesar G; Tarazona-Santos, Eduardo

2015-04-27

Brazil never had segregation laws defining membership of an ethnoracial group. Thus, the composition of the Brazilian population is mixed, and its ethnoracial classification is complex. Previous studies showed conflicting results on the correlation between genome ancestry and ethnoracial classification in Brazilians. We used 370,539 Single Nucleotide Polymorphisms to quantify this correlation in 5,851 community-dwelling individuals in the South (Pelotas), Southeast (Bambui) and Northeast (Salvador) Brazil. European ancestry was predominant in Pelotas and Bambui (median = 85.3% and 83.8%, respectively). African ancestry was highest in Salvador (median = 50.5%). The strength of the association between the phenotype and median proportion of African ancestry varied largely across populations, with pseudo R(2) values of 0.50 in Pelotas, 0.22 in Bambui and 0.13 in Salvador. The continuous proportion of African genomic ancestry showed a significant S-shape positive association with self-reported Blacks in the three sites, and the reverse trend was found for self reported Whites, with most consistent classifications in the extremes of the high and low proportion of African ancestry. In self-classified Mixed individuals, the predicted probability of having African ancestry was bell-shaped. Our results support the view that ethnoracial self-classification is affected by both genome ancestry and non-biological factors.

Effects of stress typicality during speeded grammatical classification.

PubMed

Arciuli, Joanne; Cupples, Linda

2003-01-01

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

Patterns of brain structural connectivity differentiate normal weight from overweight subjects

PubMed Central

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

2015-01-01

Background Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69% accuracy in discriminating overweight from normal weight. In both brain signatures regions of the reward, salience, executive control and emotional arousal networks were associated with lower morphological values in overweight individuals compared to normal weight individuals, while the opposite pattern was seen for regions of the somatosensory network. Conclusions 1. An increased BMI (i.e., overweight subjects) is associated with distinct changes in gray-matter and fiber density of the brain. 2. Classification algorithms based on white-matter connectivity involving regions of the reward and associated networks can identify specific targets for mechanistic studies and future drug development aimed at abnormal ingestive behavior and in overweight/obesity. PMID:25737959

Retinex Preprocessing for Improved Multi-Spectral Image Classification

NASA Technical Reports Server (NTRS)

Thompson, B.; Rahman, Z.; Park, S.

2000-01-01

The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed before classification, so as to reduce the adverse effects of image formation. In this paper, we discuss the overall impact on multi-spectral image classification when the retinex image enhancement algorithm is used to preprocess multi-spectral images. The retinex is a multi-purpose image enhancement algorithm that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The retinex has been successfully applied to the enhancement of many different types of grayscale and color images. We show in this paper that retinex preprocessing improves the spatial structure of multi-spectral images and thus provides better within-class variations than would otherwise be obtained without the preprocessing. For a series of multi-spectral images obtained with diffuse and direct lighting, we show that without retinex preprocessing the class spectral signatures vary substantially with the lighting conditions. Whereas multi-dimensional clustering without preprocessing produced one-class homogeneous regions, the classification on the preprocessed images produced multi-class non-homogeneous regions. This lack of homogeneity is explained by the interaction between different agronomic treatments applied to the regions: the preprocessed images are closer to ground truth. The principle advantage that the retinex offers is that for different lighting conditions classifications derived from the retinex preprocessed images look remarkably "similar", and thus more consistent, whereas classifications derived from the original images, without preprocessing, are much less similar.

Dysregulation of Innate and Adaptive Serum Mediators Precedes Systemic Lupus Erythematosus Classification and Improves Prognostic Accuracy of Autoantibodies

PubMed Central

Guthridge, Joel M.; Bean, Krista M.; Fife, Dustin A.; Chen, Hua; Slight-Webb, Samantha R.; Keith, Michael P.; Harley, John B.; James, Judith A.

2016-01-01

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a poorly understood preclinical stage of immune dysregulation and symptom accrual. Accumulation of antinuclear autoantibody (ANA) specificities is a hallmark of impending clinical disease. Yet, many ANA-positive individuals remain healthy, suggesting that additional immune dysregulation underlies SLE pathogenesis. Indeed, we have recently demonstrated that interferon (IFN) pathways are dysregulated in preclinical SLE. To determine if other forms of immune dysregulation contribute to preclinical SLE pathogenesis, we measured SLE-associated autoantibodies and soluble mediators in samples from 84 individuals collected prior to SLE classification (average timespan = 5.98 years), compared to unaffected, healthy control samples matched by race, gender, age (± 5 years), and time of sample procurement. We found that multiple soluble mediators, including interleukin (IL)-5, IL-6, and IFN-γ, were significantly elevated in cases compared to controls more than 3.5 years pre-classification, prior to or concurrent with autoantibody positivity. Additional mediators, including innate cytokines, IFN-associated chemokines, and soluble tumor necrosis factor (TNF) superfamily mediators increased longitudinally in cases approaching SLE classification, but not in controls. In particular, levels of B lymphocyte stimulator (BLyS) and a proliferation-inducing ligand (APRIL) were comparable in cases and controls until less than 10 months pre-classification. Over the entire pre-classification period, random forest models incorporating ANA and anti-Ro/SSA positivity with levels of IL-5, IL-6, and the IFN-γ-induced chemokine, MIG, distinguished future SLE patients with 92% (± 1.8%) accuracy, compared to 78% accuracy utilizing ANA positivity alone. These data suggest that immune dysregulation involving multiple pathways contributes to SLE pathogenesis. Importantly, distinct immunological profiles are predictive for individuals who will develop clinical SLE and may be useful for delineating early pathogenesis, discovering therapeutic targets, and designing prevention trials. PMID:27338520

In Search of Effective Scales for Stream Management: Does Agroecoregion, Watershed, or Their Intersection Best Explain the Variance in Stream Macroinvertebrate Communities?

NASA Astrophysics Data System (ADS)

Dovciak, A. L.; Perry, J. A.

2002-09-01

Our lack of understanding of relationships between stream biotic communities and surrounding landscape conditions makes it difficult to determine the spatial scale at which management practices are best assessed. We investigated these relationships in the Minnesota River Basin, which is divided into major watersheds and agroecoregions which are based on soil type, geologic parent material, landscape slope steepness, and climatic factors affecting crop productivity. We collected macroinvertebrate and stream habitat data from 68 tributaries among three major watersheds and two agroecoregions. We tested the effectiveness of the two landscape classification systems (i.e., watershed, agroecoregion) in explaining variance in habitat and macroinvertebrate metrics, and analyzed the relative influence on macroinvertebrates of local habitat versus regional characteristics. Macroinvertebrate community composition was most strongly influenced by local habitat; the variance in habitat conditions was best explained at the scale of intersection of major watershed and agroecoregion (i.e., stream habitat conditions were most homogeneous within the physical regions of intersection of these two landscape classification systems). Our results are consistent with findings of other authors that most variation in macroinvertebrate community data from large agricultural catchments is attributable to local physical conditions. Our results are the first to test the hypothesis and demonstrate that the scale of intersection best explains these variances. The results suggest that management practices adjusted for both watershed and ecoregion characteristics, with the goal of improving physical habitat characteristics of local streams, may lead to better basin-wide water quality conditions and stream biological integrity.

Problems in the Context Evaluation of Individualized Courses

ERIC Educational Resources Information Center

Plomp, Tjeerd; Van der Meer, Adri

1977-01-01

The development of the Individualized Study System (ISS) in The Netherlands from 1970 to 1975 is reviewed and a case study for first-year engineering is described. A classification of ISS courses illustrates the complexity of the system, with advice offered on the management of individualized study systems. (Author/LBH)

29 CFR 14.20 - Dissemination to individuals and firms outside the executive branch.

Code of Federal Regulations, 2013 CFR

2013-07-01

... consideration on an individual basis. (b) Secret and Confidential information. Subject to the restrictions below, Secret or Confidential information may be furnished to an individual or firm outside the executive branch... classification for another Federal department, or outside agency whose security clearances are acceptable to the...

29 CFR 14.20 - Dissemination to individuals and firms outside the executive branch.

Code of Federal Regulations, 2012 CFR

2012-07-01

... consideration on an individual basis. (b) Secret and Confidential information. Subject to the restrictions below, Secret or Confidential information may be furnished to an individual or firm outside the executive branch... classification for another Federal department, or outside agency whose security clearances are acceptable to the...

Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

PubMed

Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

2016-08-31

Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease

PubMed Central

Gray, Katherine R.; Wolz, Robin; Heckemann, Rolf A.; Aljabar, Paul; Hammers, Alexander; Rueckert, Daniel

2012-01-01

Imaging biomarkers for Alzheimer’s disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer’s Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer’s disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer’s disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer’s disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials. PMID:22236449

Update on diabetes classification.

PubMed

Thomas, Celeste C; Philipson, Louis H

2015-01-01

This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal. Copyright © 2015 Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

Systematic review and meta-analysis of prevalence studies in transsexualism.

PubMed

Arcelus, J; Bouman, W P; Van Den Noortgate, W; Claes, L; Witcomb, G; Fernandez-Aranda, F

2015-09-01

Over the last 50 years, several studies have provided estimates of the prevalence of transsexualism. The variation in reported prevalence is considerable and may be explained by factors such as the methodology and diagnostic classification used and the year and country in which the studies took place. Taking these into consideration, this study aimed to critically and systematically review the available literature measuring the prevalence of transsexualism as well as performing a meta-analysis using the available data. Databases were systematically searched and 1473 possible studies were identified. After initial scrutiny of the article titles and removal of those not relevant, 250 studies were selected for further appraisal. Of these, 211 were excluded after reading the abstracts and a further 18 after reading the full article. This resulted in 21 studies on which to perform a systematic review, with only 12 having sufficient data for meta-analysis. The primary data of the epidemiological studies were extracted as raw numbers. An aggregate effect size, weighted by sample size, was computed to provide an overall effect size across the studies. Risk ratios and 95% confidence intervals (CIs) were calculated. The relative weighted contribution of each study was also assessed. The overall meta-analytical prevalence for transsexualism was 4.6 in 100,000 individuals; 6.8 for trans women and 2.6 for trans men. Time analysis found an increase in reported prevalence over the last 50 years. The overall prevalence of transsexualism reported in the literature is increasing. However, it is still very low and is mainly based on individuals attending clinical services and so does not provide an overall picture of prevalence in the general population. However, this study should be considered as a starting point and the field would benefit from more rigorous epidemiological studies acknowledging current changes in the classification system and including different locations worldwide. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Problematic gaming exists and is an example of disordered gaming.

PubMed

Griffiths, Mark D; Kuss, Daria J; Lopez-Fernandez, Olatz; Pontes, Halley M

2017-09-01

Background The recent paper by Aarseth et al. (2016) questioned whether problematic gaming should be considered a new disorder particularly because "Gaming Disorder" (GD) has been identified as a disorder to be included in the next (11th) revision of the World Health Organization's International Classification of Diseases (ICD-11). Methods This study uses contemporary literature to argue why GD should be included in the ICD-11. Results Aarseth and colleagues acknowledge that there is much literature (including papers by some of the authors themselves) that some individuals experience serious problems with video gaming. How can such an activity be seriously problematic yet not disordered? Similar to other addictions, gaming addiction is relatively rare and is in essence a syndrome (i.e., a condition or disorder characterized by a set of associated symptoms that tend to occur under specific circumstances). Consequently, not everyone will exhibit exactly the same set of symptoms and consequences, and this partly explains why those working in the problematic gaming field often disagree on symptomatology. Conclusions Research into gaming is not about pathologizing healthy entertainment, but about pathologizing excessive and problematic behaviors that cause significant psychological distress and impairment in an individual's life. These are two related, but (ultimately) very distinct phenomena. While being aware that gaming is a pastime activity which is enjoyed non-problematically by many millions of individuals worldwide, it is concluded that problematic gaming exists and that it is an example of disordered gaming.

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

NASA Astrophysics Data System (ADS)

Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

2012-10-01

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

Classifying individuals based on a densely captured sequence of vital signs: An example using repeated blood pressure measurements during hemodialysis treatment.

PubMed

Goldstein, Benjamin A; Chang, Tara I; Winkelmayer, Wolfgang C

2015-10-01

Electronic Health Records (EHRs) present the opportunity to observe serial measurements on patients. While potentially informative, analyzing these data can be challenging. In this work we present a means to classify individuals based on a series of measurements collected by an EHR. Using patients undergoing hemodialysis, we categorized people based on their intradialytic blood pressure. Our primary criteria were that the classifications were time dependent and independent of other subjects. We fit a curve of intradialytic blood pressure using regression splines and then calculated first and second derivatives to come up with four mutually exclusive classifications at different time points. We show that these classifications relate to near term risk of cardiac events and are moderately stable over a succeeding two-week period. This work has general application for analyzing dense EHR data. Copyright © 2015 Elsevier Inc. All rights reserved.

Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson's Disease

NASA Astrophysics Data System (ADS)

Figueiredo, Thiago C.; Vivas, Jamile; Peña, Norberto; Miranda, José G. V.

2016-11-01

Parkinson's Disease is one of the most prevalent neurodegenerative diseases in the world and affects millions of individuals worldwide. The clinical criteria for classification of motor subtypes in Parkinson's Disease are subjective and may be misleading when symptoms are not clearly identifiable. A video recording protocol was used to measure hand tremor of 14 individuals with Parkinson's Disease and 7 healthy subjects. A method for motor subtype classification was proposed based on the spectral distribution of the movement and compared with the existing clinical criteria. Box-counting dimension and Hurst Exponent calculated from the trajectories were used as the relevant measures for the statistical tests. The classification based on the power-spectrum is shown to be well suited to separate patients with and without tremor from healthy subjects and could provide clinicians with a tool to aid in the diagnosis of patients in an early stage of the disease.

Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study.

PubMed

Li, Peng; Jing, Ri-Xing; Zhao, Rong-Jiang; Ding, Zeng-Bo; Shi, Le; Sun, Hong-Qiang; Lin, Xiao; Fan, Teng-Teng; Dong, Wen-Tian; Fan, Yong; Lu, Lin

2017-05-11

Previous studies suggested that electroconvulsive therapy can influence regional metabolism and dopamine signaling, thereby alleviating symptoms of schizophrenia. It remains unclear what patients may benefit more from the treatment. The present study sought to identify biomarkers that predict the electroconvulsive therapy response in individual patients. Thirty-four schizophrenia patients and 34 controls were included in this study. Patients were scanned prior to treatment and after 6 weeks of treatment with antipsychotics only (n = 16) or a combination of antipsychotics and electroconvulsive therapy (n = 13). Subject-specific intrinsic connectivity networks were computed for each subject using a group information-guided independent component analysis technique. Classifiers were built to distinguish patients from controls and quantify brain states based on intrinsic connectivity networks. A general linear model was built on the classification scores of first scan (referred to as baseline classification scores) to predict treatment response. Classifiers built on the default mode network, the temporal lobe network, the language network, the corticostriatal network, the frontal-parietal network, and the cerebellum achieved a cross-validated classification accuracy of 83.82%, with specificity of 91.18% and sensitivity of 76.47%. After the electroconvulsive therapy, psychosis symptoms of the patients were relieved and classification scores of the patients were decreased. Moreover, the baseline classification scores were predictive for the treatment outcome. Schizophrenia patients exhibited functional deviations in multiple intrinsic connectivity networks which were able to distinguish patients from healthy controls at an individual level. Patients with lower classification scores prior to treatment had better treatment outcome, indicating that the baseline classification scores before treatment is a good predictor for treatment outcome. CONNECTIVITY NETWORKS REVEAL GOOD CANDIDATES FOR BRAIN STIMULATION: Connectivity patterns in the brain may help identify patients with schizophrenia most likely to benefit from electroconvulsive therapy. A team led by Lin Lu from Peking University, China, and Yong Fan from the University of Pennsylvania, USA, took functional magnetic resonance imaging (MRI) scans of 34 people with schizophrenia and 34 control individuals without mental illness. Those with schizophrenia were scanned before and after treatment; some received antipsychotics alone, others received medication plus electroconvulsive therapy. The researchers created organizational brain maps known as "intrinsic connectivity networks" for each individual, and showed that the neuroimaging pattern could discriminate between people with and without schizophrenia. For the schizophrenia patients, the connectivity networks taken prior to treatment also helped predict who would benefit from the brain-stimulation procedure. Such a biomarker could prove a useful diagnostic tool for clinicians.

Classification of "multipole" superconductivity in multiorbital systems and its implications

NASA Astrophysics Data System (ADS)

Nomoto, T.; Hattori, K.; Ikeda, H.

2016-11-01

Motivated by a growing interest in multiorbital superconductors with spin-orbit interactions, we perform the group-theoretical classification of various unconventional superconductivity emerging in symmorphic O , D4, and D6 space groups. The generalized Cooper pairs, which we here call "multipole" superconductivity, possess spin-orbital coupled (multipole) degrees of freedom, instead of the conventional spin singlet/triplet in single-orbital systems. From the classification, we obtain the following key consequences, which have never been focused in the long history of research in this field: (1) A superconducting gap function with Γ9⊗Γ9 in D6 possesses nontrivial momentum dependence different from the usual spin-1/2 classification. (2) Unconventional gap structure can be realized in the BCS approximation of purely local (onsite) interactions irrespective of attraction/repulsion. It implies the emergence of an electron-phonon (e-ph) driven unconventional superconductivity. (3) Reflecting symmetry of orbital basis functions there appear not symmetry protected but inevitable line nodes/gap minima, and thus, anisotropic s -wave superconductivity can be naturally explained even in the absence of competing fluctuations.

Algorithmic framework for group analysis of differential equations and its application to generalized Zakharov-Kuznetsov equations

NASA Astrophysics Data System (ADS)

Huang, Ding-jiang; Ivanova, Nataliya M.

2016-02-01

In this paper, we explain in more details the modern treatment of the problem of group classification of (systems of) partial differential equations (PDEs) from the algorithmic point of view. More precisely, we revise the classical Lie algorithm of construction of symmetries of differential equations, describe the group classification algorithm and discuss the process of reduction of (systems of) PDEs to (systems of) equations with smaller number of independent variables in order to construct invariant solutions. The group classification algorithm and reduction process are illustrated by the example of the generalized Zakharov-Kuznetsov (GZK) equations of form ut +(F (u)) xxx +(G (u)) xyy +(H (u)) x = 0. As a result, a complete group classification of the GZK equations is performed and a number of new interesting nonlinear invariant models which have non-trivial invariance algebras are obtained. Lie symmetry reductions and exact solutions for two important invariant models, i.e., the classical and modified Zakharov-Kuznetsov equations, are constructed. The algorithmic framework for group analysis of differential equations presented in this paper can also be applied to other nonlinear PDEs.

77 FR 71720 - Fisheries of the Northeastern United States; Atlantic Mackerel, Squid, and Butterfish Fisheries...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-04

... individual credentialed as an Accredited Marine Surveyor with a fishing specialty by the Society of... completed by employees or agents of a classification society approved by the Coast Guard pursuant to 46 U.S... members of a classification society such as NAMS or SAMS. They noted that independent surveyors have...

From ancient Greece to the cognitive revolution: A comprehensive view of physical rehabilitation sciences.

PubMed

Martínez-Pernía, David; González-Castán, Óscar; Huepe, David

2017-02-01

The development of rehabilitation has traditionally focused on measurements of motor disorders and measurements of the improvements produced during the therapeutic process; however, physical rehabilitation sciences have not focused on understanding the philosophical and scientific principles in clinical intervention and how they are interrelated. The main aim of this paper is to explain the foundation stones of the disciplines of physical therapy, occupational therapy, and speech/language therapy in recovery from motor disorder. To reach our goals, the mechanistic view and how it is integrated into physical rehabilitation will first be explained. Next, a classification into mechanistic therapy based on an old version (automaton model) and a technological version (cyborg model) will be shown. Then, it will be shown how physical rehabilitation sciences found a new perspective in motor recovery, which is based on functionalism, during the cognitive revolution in the 1960s. Through this cognitive theory, physical rehabilitation incorporated into motor recovery of those therapeutic strategies that solicit the activation of the brain and/or symbolic processing; aspects that were not taken into account in mechanistic therapy. In addition, a classification into functionalist rehabilitation based on a computational therapy and a brain therapy will be shown. At the end of the article, the methodological principles in physical rehabilitation sciences will be explained. It will allow us to go deeper into the differences and similarities between therapeutic mechanism and therapeutic functionalism.

Using Concept Maps to Reveal Conceptual Typologies

ERIC Educational Resources Information Center

Hay, David B.; Kinchin, Ian M.

2006-01-01

Purpose: The purpose of this paper is to explain and develop a classification of cognitive structures (or typologies of thought), previously designated as spoke, chain and network thinking by Kinchin "et al." Design/methodology/approach: The paper shows how concept mapping can be used to reveal these conceptual typologies and endeavours to place…

Another Strategy for Teaching Histology to A&P Students: Classification versus Memorization.

ERIC Educational Resources Information Center

Bavis, Ryan W.; Seveyka, Jerred; Shigeoka, Cassie A.

2000-01-01

Defines dichotomous keys as common learning tools based on identification rather than memorization. Provides an example of a dichotomous key developed for introducing histology in human anatomy and physiology (A&P) courses and explains how students can use the dichotomous key. Discusses the goals of the exercises and the process of…

Associations among Attachment Classifications of Mothers, Fathers, and Their Infants.

ERIC Educational Resources Information Center

Steele, Howard; And Others

1996-01-01

Tested 90 infants in the Strange Situation Procedure (SSP) with both parents. Found that mothers' Adult Attachment Interview (AAI) scores predicted infant-mother SSPs and fathers' AAIs predicted infant-father SSPs. Counter to expectation, infant-father SSPs were associated with infant-mother SSPs, which might be explained by the influence of…

Arabic Supervised Learning Method Using N-Gram

ERIC Educational Resources Information Center

Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun

2008-01-01

Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…

77 FR 75299 - Standards To Prevent, Detect, and Respond to Sexual Abuse and Assault in Confinement Facilities

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-19

... Proficiency NAICS North American Industry Classification System NPREC National Prison Rape Elimination... National Detention Standards PLRA Prison Litigation Reform Act PREA Prison Rape Elimination Act of 2003 PSA... the National Prison Rape Elimination Commission explained in its 2009 report: \\1\\ As discussed in...

Science 101: Why Are Hippos Unguligrades?

ERIC Educational Resources Information Center

Robertson, Bill

2014-01-01

In this article, Bill Robinson describes the three main classifications of gait for animals: (1) Plantigrade--animals that walk on their entire foot; (2) Digitigrade--animals that walk on their "toes"; and (3) Unguligrade--animals that walk on the tips of their toes or hooves. Robinson explains each gait in great detail, but leaves his…

Making Failure Matter: Enacting No Child Left Behind's Standards, Accountabilities, and Classifications

ERIC Educational Resources Information Center

Koyama, Jill P.

2012-01-01

This article ethnographically examines the paradoxical situation in which one high-achieving New York City public school is "constructed" as failing when No Child Left Behind (NCLB) assessments are miscalculated. Drawing upon actor-network theory (ANT)--a perspective that aims to explain how people, their ideas, and the material objects…

The Woman Athlete and Alternative Approaches to Explaining Sport Participation.

ERIC Educational Resources Information Center

DeSensi, Joy T.

This study explored the model of feminist frameworks offered by Jagger and Struhl (1978) in the women's sport setting. The framework offers a classification of models of feminism (Liberalism, Marxism, Radicalism, and Socialism) describing the forms of women's oppression and offers a solution for eliminating such oppression. The Jaggen and Struhl's…

Bureaucratic versus Craft Administration: The Relationship of Market Structure to the Construction Firm.

ERIC Educational Resources Information Center

Eccles, Robert G.

1981-01-01

Examines the factors that influence subcontracting and type of management in the construction industry. Argues that subcontracting is explained by a construction firm's size, complexity, and market extent, and that management type results from a firm's size and role in the production process and from census classification problems. (Author/RW)

Implementing a Knowledge-Based Library Information System with Typed Horn Logic.

ERIC Educational Resources Information Center

Ait-Kaci, Hassan; And Others

1990-01-01

Describes a prototype library expert system called BABEL which uses a new programing language, LOGIN, that combines the idea of attribute inheritance with logic programing. Use of hierarchical classification of library objects to build a knowledge base for a library information system is explained, and further research is suggested. (11…

Students' Ideas about Animals: Results from a National Study.

ERIC Educational Resources Information Center

Barman, Charles R.; Barman, Natalie S.; Cox, Mary Lou; Newhouse, Kay Berglund; Goldston, M. Jenice

2000-01-01

Explains a study that assesses students' ideas about animals. Evaluates textbooks and trade books according to the identifications and words they use. Discusses student responses from different grade levels on the classification of animals and identifying what is an animal. Summarizes the results of the study and makes recommendations on the…

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

PubMed

Yates, Katherine L; Mellin, Camille; Caley, M Julian; Radford, Ben T; Meeuwig, Jessica J

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

PubMed Central

Yates, Katherine L.; Mellin, Camille; Caley, M. Julian; Radford, Ben T.; Meeuwig, Jessica J.

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability. PMID:27333202

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.

Systems effects on family planning innovativeness.

PubMed

Lee, S B

1983-12-01

Data from Korea were used to explore the importance of community level variables in explaining family planning adoption at the individual level. An open system concept was applied, assuming that individual family planning behavior is influenced by both environmental and individual factors. The environmental factors were measured at the village level and designated as community characteristics. The dimension of communication network variables was introduced. Each individual was characterized in terms of the degree of her involvement in family planning communication with others in her village. It was assumed that the nature of the communication network linking individuals with each other effects family planning adoption at the individual level. Specific objectives were to determine 1) the relative importance of the specific independent variables in explaining family planning adoption and 2) the relative importance of the community level variables in comparison with the individual level variables in explaining family planning adoption at the individual level. The data were originally gathered in a 1973 research project on Korea's mothers' clubs. 1047 respondents were interviewed, comprising all married women in 25 sample villages having mothers' clubs. The dependent variable was family planning adoption behavior, defined as current use of any of the modern methods of family planning. The independent variables were defined at 3 levels: individual, community, and at a level intermediate between them involving communication links between individuals. More of the individual level independent variables were significantly correlated with the dependent variables than the community level variables. Among those variables with statistically significant correlations, the correlation coefficients were consistently higher for the individual level than for the community level variables. More of the variance in the dependent variable was explained by individual level than by community level variables. Community level variables accounted for only about 2.5% of the total variance in the dependent variable, in marked contrast to the result showing individual level variables accounting for as much as 19% of the total variance. When both individual and community level variables were entered into a multiple correlation analysis, a multiple correlation coefficient of .4714 was obtained together they explained about 20% of the total variance. The 2 communication network variables--connectedness and integrativeness--were correlated with the dependent variable at much higher levels than most of the individual or community level variables. Connectedness accounted for the greatest amount of the total variance. The communication network variables as a group explained as much of the total variance in the dependent variable as the individual level variables and greatly more that the community level variables.

The Hispanic mortality advantage and ethnic misclassification on US death certificates.

PubMed

Arias, Elizabeth; Eschbach, Karl; Schauman, William S; Backlund, Eric L; Sorlie, Paul D

2010-04-01

We tested the data artifact hypothesis regarding the Hispanic mortality advantage by investigating whether and to what degree this advantage is explained by Hispanic origin misclassification on US death certificates. We used the National Longitudinal Mortality Study, which links Current Population Survey records to death certificates for 1979 through 1998, to estimate the sensitivity, specificity, and net ascertainment of Hispanic ethnicity on death certificates compared with survey classifications. Using national vital statistics mortality data, we estimated Hispanic age-specific and age-adjusted death rates, which were uncorrected and corrected for death certificate misclassification, and produced death rate ratios comparing the Hispanic with the non-Hispanic White population. Hispanic origin reporting on death certificates in the United States is reasonably good. The net ascertainment of Hispanic origin is just 5% higher on survey records than on death certificates. Corrected age-adjusted death rates for Hispanics are lower than those for the non-Hispanic White population by close to 20%. The Hispanic mortality paradox is not explained by an incongruence between ethnic classification in vital registration and population data systems.

Classification of co-occurring depression and substance abuse symptoms predicts suicide attempts in adolescents.

PubMed

Effinger, Jenell M; Stewart, David G

2012-08-01

Although both depression and substance use have been found to contribute to suicide attempts, the synergistic impact of these disorders has not been fully explored. Additionally, the impact of subthreshold presentations of these disorders has not been researched. We utilized the Quadrant Model of Classification (a matrix of severity of two disorders) to assess for suicide attempt risk among adolescents. Logistic regression was used to examine the impact of co-occurring disorder classification on suicide risk attempts. Results indicate that quadrant classification had a dramatic impact on suicide attempt risk, with individuals with high severity co-occurring disorders at greatest risk. © 2012 The American Association of Suicidology.

Differentiation of subspecies and sexes of Beringian Dunlins using morphometric measures

USGS Publications Warehouse

Gates, H. River; Yezerinac, Stephen; Powell, Abby N.; Tomkovich, Pavel S.; Valchuk, Olga P.; Lanctot, Richard B.

2013-01-01

Five subspecies of Dunlins (Calidris alpina) that breed in Beringia are potentially sympatric during the non-breeding season. Studying their ecology during this period requires techniques to distinguish individuals by subspecies. Our objectives were to determine (1) if five morphometric measures (body mass, culmen, head, tarsus, and wing chord) differed between sexes and among subspecies (C. a. actites, arcticola, kistchinski, pacifica, and sakhalina), and (2) if these differences were sufficient to allow for correct classification of individuals using equations derived from discriminant function analyses. We conducted analyses using morphometric data from 10 Dunlin populations breeding in northern Russia and Alaska, USA. Univariate tests revealed significant differences between sexes in most morphometric traits of all subspecies, and discriminant function equations predicted the sex of individuals with an accuracy of 83–100% for each subspecies. We provide equations to determine sex and subspecies of individuals in mixed subspecies groups, including the (1) Western Alaska group of arcticola and pacifica (known to stage together in western Alaska) and (2) East Asia group of arcticola, actites, kistchinski, and sakhalina (known to winter together in East Asia). Equations that predict the sex of individuals in mixed groups had classification accuracies between 75% and 87%, yielding reliable classification equations. We also provide equations that predict the subspecies of individuals with an accuracy of 22–96% for different mixed subspecies groups. When the sex of individuals can be predetermined, the accuracy of these equations is increased substantially. Investigators are cautioned to consider limitations due to age and feather wear when using these equations during the non-breeding season. These equations will allow determination of sexual and subspecies segregation in non-breeding areas, allowing implementation of taxonomic-specific conservation actions.

Hepatic Arterial Configuration in Relation to the Segmental Anatomy of the Liver; Observations on MDCT and DSA Relevant to Radioembolization Treatment

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

Hoven, Andor F. van den, E-mail: a.f.vandenhoven@umcutrecht.nl; Leeuwen, Maarten S. van, E-mail: m.s.vanleeuwen@umcutrecht.nl; Lam, Marnix G. E. H., E-mail: m.lam@umcutrecht.nl

PurposeCurrent anatomical classifications do not include all variants relevant for radioembolization (RE). The purpose of this study was to assess the individual hepatic arterial configuration and segmental vascularization pattern and to develop an individualized RE treatment strategy based on an extended classification.MethodsThe hepatic vascular anatomy was assessed on MDCT and DSA in patients who received a workup for RE between February 2009 and November 2012. Reconstructed MDCT studies were assessed to determine the hepatic arterial configuration (origin of every hepatic arterial branch, branching pattern and anatomical course) and the hepatic segmental vascularization territory of all branches. Aberrant hepatic arteries weremore » defined as hepatic arterial branches that did not originate from the celiac axis/CHA/PHA. Early branching patterns were defined as hepatic arterial branches originating from the celiac axis/CHA.ResultsThe hepatic arterial configuration and segmental vascularization pattern could be assessed in 110 of 133 patients. In 59 patients (54 %), no aberrant hepatic arteries or early branching was observed. Fourteen patients without aberrant hepatic arteries (13 %) had an early branching pattern. In the 37 patients (34 %) with aberrant hepatic arteries, five also had an early branching pattern. Sixteen different hepatic arterial segmental vascularization patterns were identified and described, differing by the presence of aberrant hepatic arteries, their respective vascular territory, and origin of the artery vascularizing segment four.ConclusionsThe hepatic arterial configuration and segmental vascularization pattern show marked individual variability beyond well-known classifications of anatomical variants. We developed an individualized RE treatment strategy based on an extended anatomical classification.« less

Development of a brain MRI-based hidden Markov model for dementia recognition.

PubMed

Chen, Ying; Pham, Tuan D

2013-01-01

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

Skin problems in individuals with lower-limb loss: literature review and proposed classification system.

PubMed

Bui, Kelly M; Raugi, Gregory J; Nguyen, Viet Q; Reiber, Gayle E

2009-01-01

Problems with skin integrity can disrupt daily prosthesis use and lead to decreased mobility and function in individuals with lower-limb loss. This study reviewed the literature to examine how skin problems are defined and diagnosed and to identify the prevalence and types of skin problems in individuals with lower-limb loss. We searched the literature for terms related to amputation and skin problems. We identified 777 articles. Of the articles, 90 met criteria for review of research methodology. Four clinical studies met our selection criteria. The prevalence rate of skin problems was 15% to 41%. The most commonly reported skin problems were wounds, abscesses, and blisters. Given the lack of standardized definitions of skin problems on residual limbs, we conclude this article with a system for classification.

Classification: A Comparison of Middle and Old Age

ERIC Educational Resources Information Center

Denney, Nancy Wadsworth; Lennon, Madonna L.

1972-01-01

Whereas middle-aged individuals tended to group the entire stimulus array into piles of similar items, the elderly individuals grouped very much as young children do--arranging only a portion of the stimulus array into elaborate designs. (Authors)

29 CFR 14.20 - Dissemination to individuals and firms outside the executive branch.

Code of Federal Regulations, 2014 CFR

2014-07-01

... individual basis. (b) Secret and Confidential information. Subject to the restrictions below, Secret or Confidential information may be furnished to an individual or firm outside the executive branch if the action... the appropriate DOL clearance, or at least clearance for the same or higher classification for another...

Examining transgender health through the International Classification of Functioning, Disability, and Health's (ICF) Contextual Factors.

PubMed

Jacob, Melissa; Cox, Steven R

2017-12-01

For many transgender individuals, medical intervention is necessary to live as their desired gender. However, little is known about Contextual Factors (i.e., Environmental and Personal) that may act as facilitators and barriers in the health of transgender individuals. Therefore, this paper sought to examine Contextual Factors of the World Health Organization's International Classification of Functioning, Disability, and Health that may facilitate or negatively impact the physical, psychological, and social functioning of transgender individuals. A literature review was conducted to identify Environmental and Personal Factors that may influence transgender individuals' physical, psychological, and social functioning. Seven electronic databases were searched. In total, 154 records were reviewed, and 41 articles and other records met inclusion criteria. Three general themes emerged for Environmental Factors: family and social networks, education, and health care. Three general themes also emerged for Personal Factors: socioeconomic status, race, and age. Transgender individuals benefit from gender-affirming services, improved family and social support systems, and competent provider care. Educational training programs, including medical curricula or workshops, might provide the greatest benefit in improving transgender health by increasing the knowledge and cultural competency of health professionals working with this population. Given the diversity of gender expression, differences in lived experiences, and potential for enduring persistent "double discrimination" due to the intersectional relationships between socioeconomic status, race, and/or age, health professionals must approach transgender health using a holistic lens such as the World Health Organization's International Classification of Functioning, Disability, and Health.

An algorithm for identification and classification of individuals with type 1 and type 2 diabetes mellitus in a large primary care database

PubMed Central

Sharma, Manuj; Petersen, Irene; Nazareth, Irwin; Coton, Sonia J

2016-01-01

Background Research into diabetes mellitus (DM) often requires a reproducible method for identifying and distinguishing individuals with type 1 DM (T1DM) and type 2 DM (T2DM). Objectives To develop a method to identify individuals with T1DM and T2DM using UK primary care electronic health records. Methods Using data from The Health Improvement Network primary care database, we developed a two-step algorithm. The first algorithm step identified individuals with potential T1DM or T2DM based on diagnostic records, treatment, and clinical test results. We excluded individuals with records for rarer DM subtypes only. For individuals to be considered diabetic, they needed to have at least two records indicative of DM; one of which was required to be a diagnostic record. We then classified individuals with T1DM and T2DM using the second algorithm step. A combination of diagnostic codes, medication prescribed, age at diagnosis, and whether the case was incident or prevalent were used in this process. We internally validated this classification algorithm through comparison against an independent clinical examination of The Health Improvement Network electronic health records for a random sample of 500 DM individuals. Results Out of 9,161,866 individuals aged 0–99 years from 2000 to 2014, we classified 37,693 individuals with T1DM and 418,433 with T2DM, while 1,792 individuals remained unclassified. A small proportion were classified with some uncertainty (1,155 [3.1%] of all individuals with T1DM and 6,139 [1.5%] with T2DM) due to unclear health records. During validation, manual assignment of DM type based on clinical assessment of the entire electronic record and algorithmic assignment led to equivalent classification in all instances. Conclusion The majority of individuals with T1DM and T2DM can be readily identified from UK primary care electronic health records. Our approach can be adapted for use in other health care settings. PMID:27785102

An algorithm for identification and classification of individuals with type 1 and type 2 diabetes mellitus in a large primary care database.

PubMed

Sharma, Manuj; Petersen, Irene; Nazareth, Irwin; Coton, Sonia J

2016-01-01

Research into diabetes mellitus (DM) often requires a reproducible method for identifying and distinguishing individuals with type 1 DM (T1DM) and type 2 DM (T2DM). To develop a method to identify individuals with T1DM and T2DM using UK primary care electronic health records. Using data from The Health Improvement Network primary care database, we developed a two-step algorithm. The first algorithm step identified individuals with potential T1DM or T2DM based on diagnostic records, treatment, and clinical test results. We excluded individuals with records for rarer DM subtypes only. For individuals to be considered diabetic, they needed to have at least two records indicative of DM; one of which was required to be a diagnostic record. We then classified individuals with T1DM and T2DM using the second algorithm step. A combination of diagnostic codes, medication prescribed, age at diagnosis, and whether the case was incident or prevalent were used in this process. We internally validated this classification algorithm through comparison against an independent clinical examination of The Health Improvement Network electronic health records for a random sample of 500 DM individuals. Out of 9,161,866 individuals aged 0-99 years from 2000 to 2014, we classified 37,693 individuals with T1DM and 418,433 with T2DM, while 1,792 individuals remained unclassified. A small proportion were classified with some uncertainty (1,155 [3.1%] of all individuals with T1DM and 6,139 [1.5%] with T2DM) due to unclear health records. During validation, manual assignment of DM type based on clinical assessment of the entire electronic record and algorithmic assignment led to equivalent classification in all instances. The majority of individuals with T1DM and T2DM can be readily identified from UK primary care electronic health records. Our approach can be adapted for use in other health care settings.

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

Variability of lotic macroinvertebrate assemblages and stream habitat characteristics across hierarchical landscape classifications.

PubMed

Mykrä, Heikki; Heino, Jani; Muotka, Timo

2004-09-01

Streams are naturally hierarchical systems, and their biota are affected by factors effective at regional to local scales. However, there have been only a few attempts to quantify variation in ecological attributes across multiple spatial scales. We examined the variation in several macroinvertebrate metrics and environmental variables at three hierarchical scales (ecoregions, drainage systems, streams) in boreal headwater streams. In nested analyses of variance, significant spatial variability was observed for most of the macroinvertebrate metrics and environmental variables examined. For most metrics, ecoregions explained more variation than did drainage systems. There was, however, much variation attributable to residuals, suggesting high among-stream variation in macroinvertebrate assemblage characteristics. Nonmetric multidimensional scaling (NMDS) and multiresponse permutation procedure (MRPP) showed that assemblage composition differed significantly among both drainage systems and ecoregions. The associated R-statistics were, however, very low, indicating wide variation among sites within the defined landscape classifications. Regional delineations explained most of the variation in stream water chemistry, ecoregions being clearly more influential than drainage systems. For physical habitat characteristics, by contrast, the among-stream component was the major source of variation. Distinct differences attributable to stream size were observed for several metrics, especially total number of taxa and abundance of algae-scraping invertebrates. Although ecoregions clearly account for a considerable amount of variation in macroinvertebrate assemblage characteristics, we suggest that a three-tiered classification system (stratification through ecoregion and habitat type, followed by assemblage prediction within these ecologically meaningful units) will be needed for effective bioassessment of boreal running waters.

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

Classifying Measures of Biological Variation

PubMed Central

Gregorius, Hans-Rolf; Gillet, Elizabeth M.

2015-01-01

Biological variation is commonly measured at two basic levels: variation within individual communities, and the distribution of variation over communities or within a metacommunity. We develop a classification for the measurement of biological variation on both levels: Within communities into the categories of dispersion and diversity, and within metacommunities into the categories of compositional differentiation and partitioning of variation. There are essentially two approaches to characterizing the distribution of trait variation over communities in that individuals with the same trait state or type tend to occur in the same community (describes differentiation tendencies), and individuals with different types tend to occur in different communities (describes apportionment tendencies). Both approaches can be viewed from the dual perspectives of trait variation distributed over communities (CT perspective) and community membership distributed over trait states (TC perspective). This classification covers most of the relevant descriptors (qualified measures) of biological variation, as is demonstrated with the help of major families of descriptors. Moreover, the classification is shown to open ways to develop new descriptors that meet current needs. Yet the classification also reveals the misclassification of some prominent and widely applied descriptors: Dispersion is often misclassified as diversity, particularly in cases where dispersion descriptor allow for the computation of effective numbers; the descriptor GST of population genetics is commonly misclassified as compositional differentiation and confused with partitioning-oriented differentiation, whereas it actually measures partitioning-oriented apportionment; descriptors of β-diversity are ambiguous about the differentiation effects they are supposed to represent and therefore require conceptual reconsideration. PMID:25807558

Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification

PubMed Central

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2015-01-01

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice. PMID:25823003

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification.

PubMed

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2015-01-01

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Cities through the Prism of People’s Spending Behavior

PubMed Central

Hawelka, Bartosz; Murillo Arias, Juan; Ratti, Carlo

2016-01-01

Scientific studies of society increasingly rely on digital traces produced by various aspects of human activity. In this paper, we exploit a relatively unexplored source of data–anonymized records of bank card transactions collected in Spain by a big European bank, and propose a new classification scheme of cities based on the economic behavior of their residents. First, we study how individual spending behavior is qualitatively and quantitatively affected by various factors such as customer’s age, gender, and size of his/her home city. We show that, similar to other socioeconomic urban quantities, individual spending activity exhibits a statistically significant superlinear scaling with city size. With respect to the general trends, we quantify the distinctive signature of each city in terms of residents’ spending behavior, independently from the effects of scale and demographic heterogeneity. Based on the comparison of city signatures, we build a novel classification of cities across Spain in three categories. That classification exhibits a substantial stability over different city definitions and connects with a meaningful socioeconomic interpretation. Furthermore, it corresponds with the ability of cities to attract foreign visitors, which is a particularly remarkable finding given that the classification was based exclusively on the behavioral patterns of city residents. This highlights the far-reaching applicability of the presented classification approach and its ability to discover patterns that go beyond the quantities directly involved in it. PMID:26849218

Cities through the Prism of People's Spending Behavior.

PubMed

Sobolevsky, Stanislav; Sitko, Izabela; Tachet des Combes, Remi; Hawelka, Bartosz; Murillo Arias, Juan; Ratti, Carlo

2016-01-01

Scientific studies of society increasingly rely on digital traces produced by various aspects of human activity. In this paper, we exploit a relatively unexplored source of data-anonymized records of bank card transactions collected in Spain by a big European bank, and propose a new classification scheme of cities based on the economic behavior of their residents. First, we study how individual spending behavior is qualitatively and quantitatively affected by various factors such as customer's age, gender, and size of his/her home city. We show that, similar to other socioeconomic urban quantities, individual spending activity exhibits a statistically significant superlinear scaling with city size. With respect to the general trends, we quantify the distinctive signature of each city in terms of residents' spending behavior, independently from the effects of scale and demographic heterogeneity. Based on the comparison of city signatures, we build a novel classification of cities across Spain in three categories. That classification exhibits a substantial stability over different city definitions and connects with a meaningful socioeconomic interpretation. Furthermore, it corresponds with the ability of cities to attract foreign visitors, which is a particularly remarkable finding given that the classification was based exclusively on the behavioral patterns of city residents. This highlights the far-reaching applicability of the presented classification approach and its ability to discover patterns that go beyond the quantities directly involved in it.

A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

NASA Astrophysics Data System (ADS)

Liu, Tao; Im, Jungho; Quackenbush, Lindi J.

2015-12-01

This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features-two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)-were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest.

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

PubMed

Oi, Shizuo

2011-10-01

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

The Effects of Aggregation Method and Variations in the Performance of Individual Students on Degree Classifications in Modular Degree Courses.

ERIC Educational Resources Information Center

Simonite, Vanessa

2000-01-01

Considers implications of modularization of first degree courses in the United Kingdom, especially the effects of different systems for selecting and combining module marks on students' degree classifications. Discusses the effects of different systems of aggregation on student marks in different modules and ultimately on class placement and…

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...

Internet Use in Aphasia: A Case Study Viewed through the International Classification of Functioning, Disability, and Health

ERIC Educational Resources Information Center

Menger, Fion; Morris, Julie; Salis, Christos

2017-01-01

Purpose: This article uses an illustrative case example to discuss a means of producing a holistic profile of Internet use for individuals with aphasia. Methods: The authors used the International Classification of Functioning, Disability, and Health as a framework to select novel and existing assessments to explore the Internet use and skills of…

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

PubMed

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

2016-11-01

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

Review of Psychodynamic diagnostics manual (PDM).

PubMed

Moses, Ira

2008-03-01

Reviews the book, Psychodynamic diagnostics manual (PDM) by Alliance of Psychoanalytic Organizations (2006). This volume is divided into three major sections, Part 1--Classification of Adult Mental Heath Disorder, Part 2--Classification of Child and Adolescent Mental Health Disorder, and Part 3--Conceptual and Research Foundations for a Psychodynamically Based Classification System for Mental Health Disorders. Unlike the standard DSM which highlights the patient's presenting symptom (Axis I) with secondary consideration given to an underlying personality disorder (Axis II), the major thesis of classification scheme of this volume is that diagnostic evaluation should provide a more patient centered and a more clinically useful picture of the individual by understanding the symptom(s) through the essential dimensions of the patient's personality and mental functions (interpersonal and cognitive capacities). Part 3, which could stand on its own as a separate volume, is a thorough critique of psychotherapy outcome research in which the authors delineate how major design flaws have derived from "favoring what is measurable over what is meaningful." The authors cogently demonstrate that diagnostic assessment is a continuous effort toward providing individualized and clinically relevant evaluations. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

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

PubMed

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

2018-01-01

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

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

PubMed Central

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

2018-01-01

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

Bayesian classification theory

NASA Technical Reports Server (NTRS)

Hanson, Robin; Stutz, John; Cheeseman, Peter

1991-01-01

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass.

Multiscale corner detection and classification using local properties and semantic patterns

NASA Astrophysics Data System (ADS)

Gallo, Giovanni; Giuoco, Alessandro L.

2002-05-01

A new technique to detect, localize and classify corners in digital closed curves is proposed. The technique is based on correct estimation of support regions for each point. We compute multiscale curvature to detect and to localize corners. As a further step, with the aid of some local features, it's possible to classify corners into seven distinct types. Classification is performed using a set of rules, which describe corners according to preset semantic patterns. Compared with existing techniques, the proposed approach inscribes itself into the family of algorithms that try to explain the curve, instead of simple labeling. Moreover, our technique works in manner similar to what is believed are typical mechanisms of human perception.

Tried and True: The Romance of the Atoms--Animated Atomic Attractions

ERIC Educational Resources Information Center

Hibbitt, Catherine

2010-01-01

Since the formation of atomic bonds is active, the authors sought a way of learning through drama or kinetic activity. To achieve this goal, they developed an activity called Romance of the Atoms. The activity requires students to use computer-animation technology to develop short cartoons that explain atomic classification and bonds. This…

Public Choice and Private Interest: Explaining the Vote for Property Tax Classification in Massachusetts.

ERIC Educational Resources Information Center

Bloom, Howard S.

1979-01-01

Argues that citizens' perceptions of their monetary self-interest can markedly influence their votes and that such self-interested voting requires clear definition of the alternatives, widespread publicity about the issues, and a simple presentation of what each alternative implies. Available from NTA-TIA, 21 East State Street, Columbus, OH 43215.…

Neural Nets for Generalization and Classification: Comment on Staddon and Reid (1990).

ERIC Educational Resources Information Center

Shepard, Roger N.

1990-01-01

The neural net model of J. E. R. Staddon and A. K. Reid (1990) explains exponential and Gaussian generalization gradients in the same way as the diffusion model of R. N. Shepard (1958). The cognitive generalization theory of Shepard (1987), also implemented as a connectionist network, goes beyond both models in accounting for classification…

A Study of the Relationship between Student Final Exam Performance and Simulation Game Participation.

ERIC Educational Resources Information Center

Whiteley, T. R.; Faria, A. J.

1989-01-01

Describes study that investigated the relationship between participation in a business simulation game and performance on a final exam in a principles of marketing course. Past research on business games is reviewed; the use of midterm exam performance level as a pretest variable is explained; and question classification is described. (44…

The V-Flex, Triangle Orientation, and Catalan Numbers in Hexaflexagons

ERIC Educational Resources Information Center

Iacob, Ionut E.; McLean, T. Bruce; Wang, Hua

2012-01-01

Regular hexaflexagons mysteriously change faces as you pinch flex them. This paper describes a different flex, the V-flex, which allows the hexahexaflexagon (with only 9 faces under the pinch flex) to have 3420 faces. The article goes on to explain the classification of triangle orientations in a hexaflexagon and gives an example of the…

Procedural Error and Task Interruption

DTIC Science & Technology

2016-09-30

red for research on errors and individual differences . Results indicate predictive validity for fluid intelligence and specifi c forms of work...TERMS procedural error, task interruption, individual differences , fluid intelligence, sleep deprivation 16. SECURITY CLASSIFICATION OF: 17...and individual differences . It generates rich data on several kinds of errors, including procedural errors in which steps are skipped or repeated

A tree classification for the selection forests of the Sierra Nevada

Treesearch

Duncan Dunning

1928-01-01

Individuality in man is accepted without question. In domestic animals, also, good and bad individuals are generally recognized. Even in some cultivated plants —orange trees and rubber trees— the poor producers are searched out and eliminated. Indeed, individual variability is a normal condition in all groups of organisms. Yet forest trees are...

Modularising ontology and designing inference patterns to personalise health condition assessment: the case of obesity.

PubMed

Sojic, Aleksandra; Terkaj, Walter; Contini, Giorgia; Sacco, Marco

2016-05-04

The public health initiatives for obesity prevention are increasingly exploiting the advantages of smart technologies that can register various kinds of data related to physical, physiological, and behavioural conditions. Since individual features and habits vary among people, the design of appropriate intervention strategies for motivating changes in behavioural patterns towards a healthy lifestyle requires the interpretation and integration of collected information, while considering individual profiles in a personalised manner. The ontology-based modelling is recognised as a promising approach in facing the interoperability and integration of heterogeneous information related to characterisation of personal profiles. The presented ontology captures individual profiles across several obesity-related knowledge-domains structured into dedicated modules in order to support inference about health condition, physical features, behavioural habits associated with a person, and relevant changes over time. The modularisation strategy is designed to facilitate ontology development, maintenance, and reuse. The domain-specific modules formalised in the Web Ontology Language (OWL) integrate the domain-specific sets of rules formalised in the Semantic Web Rule Language (SWRL). The inference rules follow a modelling pattern designed to support personalised assessment of health condition as age- and gender-specific. The test cases exemplify a personalised assessment of the obesity-related health conditions for the population of teenagers. The paper addresses several issues concerning the modelling of normative concepts related to obesity and depicts how the public health concern impacts classification of teenagers according to their phenotypes. The modelling choices regarding the ontology-structure are explained in the context of the modelling goal to integrate multiple knowledge-domains and support reasoning about the individual changes over time. The presented modularisation pattern enhances reusability of the domain-specific modules across various health care domains.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

PubMed

Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

PubMed Central

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases. PMID:26571112

15 CFR 4a.8 - Access to classified information by individuals outside the Government.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Commerce CLASSIFICATION, DECLASSIFICATION, AND PUBLIC AVAILABILITY OF NATIONAL SECURITY INFORMATION § 4a.8 Access to classified information by individuals outside the Government. (a) Industrial, Educational, and Commercial Entities. Certain bidders, contractors, grantees, educational, scientific, or industrial...

Explaining the Origins and Expansion of Mass Education.

ERIC Educational Resources Information Center

Boli, John; And Others

1985-01-01

Theories of mass education that emphasize processes of differentiation or the reproduction of inequalities ignore the universal and institutional character of mass education. A theoretical framework emphasizing individualism and the rationalization of individual and collective authority better explains the relationship of mass education to…

Preprocessing and meta-classification for brain-computer interfaces.

PubMed

Hammon, Paul S; de Sa, Virginia R

2007-03-01

A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.

[Research progress in molecular classification of gastric cancer].

PubMed

Zhou, Menglong; Li, Guichao; Zhang, Zhen

2016-09-25

Gastric cancer(GC) is a highly heterogeneous malignancy. The present widely used histopathological classifications have gradually failed to meet the needs of individualized diagnosis and treatment. Development of technologies such as microarray and next-generation sequencing (NGS) has allowed GC to be studied at the molecular level. Mechanisms about tumorigenesis and progression of GC can be elucidated in the aspects of gene mutations, chromosomal alterations, transcriptional and epigenetic changes, on the basis of which GC can be divided into several subtypes. The classifications of Tan's, Lei's, TCGA and ACRG are relatively comprehensive. Especially the TCGA and ACRG classifications have large sample size and abundant molecular profiling data, thus, the genomic characteristics of GC can be depicted more accurately. However, significant differences between both classifications still exist so that they cannot be substituted for each other. So far there is no widely accepted molecular classification of GC. Compared with TCGA classification, ACRG system may have more clinical significance in Chinese GC patients since the samples are mostly from Asian population and show better association with prognosis. The molecular classification of GC may provide the theoretical and experimental basis for early diagnosis, therapeutic efficacy prediction and treatment stratification while their clinical application is still limited. Future work should involve the application of molecular classifications in the clinical settings for improving the medical management of GC.

Classification of Indonesian quote on Twitter using Naïve Bayes

NASA Astrophysics Data System (ADS)

Rachmadany, A.; Pranoto, Y. M.; Gunawan; Multazam, M. T.; Nandiyanto, A. B. D.; Abdullah, A. G.; Widiaty, I.

2018-01-01

Quote is sentences made in the hope that someone can become strong personalities, individuals who always improve themselves to move forward and achieve success. Social media is a place for people to express his heart to the world that sometimes the expression of the heart is quotes. Here, the purpose of this study was to classify Indonesian quote on Twitter using Naïve Bayes. This experiment uses text classification from Twitter data written by Twitter users which are quote then classification again grouped into 6 categories (Love, Life, Motivation, Education, Religion, Others). The language used is Indonesian. The method used is Naive Bayes. The results of this experiment are a web application collection of Indonesian quote that have been classified. This classification gives the user ease in finding quote based on class or keyword. For example, when a user wants to find a 'motivation' quote, this classification would be very useful.

Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals.

PubMed

Deng, Yi; Hung, Karen S Y; Lui, Simon S Y; Chui, William W H; Lee, Joe C W; Wang, Yi; Li, Zhi; Mak, Henry K F; Sham, Pak C; Chan, Raymond C K; Cheung, Eric F C

2018-06-20

Schizophrenia has been characterized as a neurodevelopmental disorder of brain disconnectivity. However, whether disrupted integrity of white matter tracts in schizophrenia can potentially serve as individual discriminative biomarkers remains unclear. A random forest algorithm was applied to tractography-based diffusion properties obtained from a cohort of 65 patients with first-episode schizophrenia (FES) and 60 healthy individuals to investigate the machine-learning discriminative power of white matter disconnectivity. Recursive feature elimination was used to select the ultimate white matter features in the classification. Relationships between algorithm-predicted probabilities and clinical characteristics were also examined in the FES group. The classifier was trained by 80% of the sample. Patients were distinguished from healthy individuals with an overall accuracy of 71.0% (95% confident interval: 61.1%, 79.6%), a sensitivity of 67.3%, a specificity of 75.0%, and the area under receiver operating characteristic curve (AUC) was 79.3% (χ2 p < 0.001). In validation using the held-up 20% of the sample, patients were distinguished from healthy individuals with an overall accuracy of 76.0% (95% confident interval: 54.9%, 90.6%), a sensitivity of 76.9%, a specificity of 75.0%, and an AUC of 73.1% (χ2 p = 0.012). Diffusion properties of inter-hemispheric fibres, the cerebello-thalamo-cortical circuits and the long association fibres were identified to be the most discriminative in the classification. Higher predicted probability scores were found in younger patients. Our findings suggest that the widespread connectivity disruption observed in FES patients, especially in younger patients, might be considered potential individual discriminating biomarkers. Copyright © 2018. Published by Elsevier Inc.

Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

NASA Astrophysics Data System (ADS)

Kukunda, Collins B.; Duque-Lazo, Joaquín; González-Ferreiro, Eduardo; Thaden, Hauke; Kleinn, Christoph

2018-03-01

Distinguishing tree species is relevant in many contexts of remote sensing assisted forest inventory. Accurate tree species maps support management and conservation planning, pest and disease control and biomass estimation. This study evaluated the performance of applying ensemble techniques with the goal of automatically distinguishing Pinus sylvestris L. and Pinus uncinata Mill. Ex Mirb within a 1.3 km2 mountainous area in Barcelonnette (France). Three modelling schemes were examined, based on: (1) high-density LiDAR data (160 returns m-2), (2) Worldview-2 multispectral imagery, and (3) Worldview-2 and LiDAR in combination. Variables related to the crown structure and height of individual trees were extracted from the normalized LiDAR point cloud at individual-tree level, after performing individual tree crown (ITC) delineation. Vegetation indices and the Haralick texture indices were derived from Worldview-2 images and served as independent spectral variables. Selection of the best predictor subset was done after a comparison of three variable selection procedures: (1) Random Forests with cross validation (AUCRFcv), (2) Akaike Information Criterion (AIC) and (3) Bayesian Information Criterion (BIC). To classify the species, 9 regression techniques were combined using ensemble models. Predictions were evaluated using cross validation and an independent dataset. Integration of datasets and models improved individual tree species classification (True Skills Statistic, TSS; from 0.67 to 0.81) over individual techniques and maintained strong predictive power (Relative Operating Characteristic, ROC = 0.91). Assemblage of regression models and integration of the datasets provided more reliable species distribution maps and associated tree-scale mapping uncertainties. Our study highlights the potential of model and data assemblage at improving species classifications needed in present-day forest planning and management.

Evaluation of air quality zone classification methods based on ambient air concentration exposure.

PubMed

Freeman, Brian; McBean, Ed; Gharabaghi, Bahram; Thé, Jesse

2017-05-01

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO 2 and 8-hr O 3 within a zone that meets the compliance requirements of each method. The first method, the "3 Strike" method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method. A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.

How the Use of the International Classification of Functioning, Disability and Health for Children and Youth Changed the Individualized Education Programs in Portugal

ERIC Educational Resources Information Center

Silveira-Maia, Mónica; Lopes-dos-Santos, Pedro; Sanches-Ferreira, Manuela

2017-01-01

Current Portuguese Public Law No. 3/2008 requires the use of the International Classification of Functioning, Health and Disability for Children and Youth (ICF-CY) as a reference framework to guide assessment and intervention procedures for students with additional support needs. This study explores whether the ICF-CY use fostered multidimensional…

Engineering Design Handbook. Development Guide for Reliability. Part 6. Mathematical Appendix and Glossary

DTIC Science & Technology

1976-01-08

Corps, nonmilitary Government agencies, contractors, private industry, individuals, universities , and others must purchase these Handbooks from...verified by an official Department of Army representative and processed from Defense Documentation Center ( DDC ), ATTN: DDC -TSR, Cameron Station...tell, by looking at a failed item, what classification of failure is involved. Some of the classifications are for mathematical conven- ience only

Development of the Enlisted Panel Research Data Base

DTIC Science & Technology

1990-01-01

Loss Files, Accession File, Army Classification Battery Composite Scores pertaining to accession, the Skills Qualifying Test (SQT) data from the SQT...inclusive. Specific accession data variables, including composite score data from the Army Classification Battery Test (ACB), are cap- tured for each...included. To broaden the scope of information for each individual, Skill Qualifying Test (SQT) scores were kept beginning in 1980 and, as of fiscal year

Crown-Level Tree Species Classification Using Integrated Airborne Hyperspectral and LIDAR Remote Sensing Data

NASA Astrophysics Data System (ADS)

Wang, Z.; Wu, J.; Wang, Y.; Kong, X.; Bao, H.; Ni, Y.; Ma, L.; Jin, J.

2018-05-01

Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79.86 %, Kc = 0.81) and spectral-metircs method (OA = 71.26, Kc = 0.69) in terms of classification accuracy, which indicated that the advanced method of data processing and sensitive feature selection are critical for improving the accuracy of crown-level tree species classification.

From distress to disease: a critique of the medicalisation of possession in DSM-5.

PubMed

Padmanabhan, Divya

2017-12-01

This paper critiques the category of possession-form dissociative identity disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) published in 2013 by the American Psychiatric Association (APA). The DSM as an index of psychiatry pathologises possession by categorising it as a form of dissociative identity disorder. Drawing upon ethnographic fieldwork, this paper argues that such a pathologisation medicalises possession, which is understood as a non-pathological condition in other contexts such as by those individuals who manifest possession at a temple in Kerala, South India. Through medicalising and further by creating distinctions between acceptable and pathological possession, the DSM converts a form of distress into a disease. This has both conceptual and pragmatic implications. The temple therefore becomes reduced to a culturally acceptable site for the manifestation of a mental illness in a form that is culturally available and possession is explained solely through a biomedical framework, denying alternative conceptualisations and theories which inform possession. By focussing on the DSM-5 classification of possession and the limitations of such a classification, this paper seeks to posit an alternative conceptualisation of possession by engaging with three primary areas which are significant in the DSM categorisation of possession: the DSM's conceptualisation of self in the singular, the distinction between pathological and non-pathological forms of possession, and the limitations of the DSM's equation of the condition of possession with the manifestation of possession. Finally, the paper briefly highlights alternative conceptualisations of possession, which emerged from the perspective of those seeking to heal possession at the Chottanikkara temple.

Genome-wide meta-analyses of stratified depression in Generation Scotland and UK Biobank.

PubMed

Hall, Lynsey S; Adams, Mark J; Arnau-Soler, Aleix; Clarke, Toni-Kim; Howard, David M; Zeng, Yanni; Davies, Gail; Hagenaars, Saskia P; Maria Fernandez-Pujals, Ana; Gibson, Jude; Wigmore, Eleanor M; Boutin, Thibaud S; Hayward, Caroline; Scotland, Generation; Porteous, David J; Deary, Ian J; Thomson, Pippa A; Haley, Chris S; McIntosh, Andrew M

2018-01-10

Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. Recent studies of MDD have identified common risk variants by using a broader phenotype definition in very large samples, or by reducing phenotypic and ancestral heterogeneity. We sought to ascertain whether it is more informative to maximize the sample size using data from all available cases and controls, or to use a sex or recurrent stratified subset of affected individuals. To test this, we compared heritability estimates, genetic correlation with other traits, variance explained by MDD polygenic score, and variants identified by genome-wide meta-analysis for broad and narrow MDD classifications in two large British cohorts - Generation Scotland and UK Biobank. Genome-wide meta-analysis of MDD in males yielded one genome-wide significant locus on 3p22.3, with three genes in this region (CRTAP, GLB1, and TMPPE) demonstrating a significant association in gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD yielded equivalent heritability estimates, showed no detectable difference in association with polygenic scores, and were each genetically correlated with six health-correlated traits (neuroticism, depressive symptoms, subjective well-being, MDD, a cross-disorder phenotype and Bipolar Disorder). Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, and the consistent pattern of results in other MDD classifications suggests that phenotypic stratification using recurrence or sex in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain.

How social and non-social information influence classification decisions: A computational modelling approach.

PubMed

Puskaric, Marin; von Helversen, Bettina; Rieskamp, Jörg

2017-08-01

Social information such as observing others can improve performance in decision making. In particular, social information has been shown to be useful when finding the best solution on one's own is difficult, costly, or dangerous. However, past research suggests that when making decisions people do not always consider other people's behaviour when it is at odds with their own experiences. Furthermore, the cognitive processes guiding the integration of social information with individual experiences are still under debate. Here, we conducted two experiments to test whether information about other persons' behaviour influenced people's decisions in a classification task. Furthermore, we examined how social information is integrated with individual learning experiences by testing different computational models. Our results show that social information had a small but reliable influence on people's classifications. The best computational model suggests that in categorization people first make up their own mind based on the non-social information, which is then updated by the social information.

Individual variation and the endocrine regulation of behaviour and physiology in birds: a cellular/molecular perspective.

PubMed

Ball, Gregory F; Balthazart, Jacques

2008-05-12

Investigations of the cellular and molecular mechanisms of physiology and behaviour have generally avoided attempts to explain individual differences. The goal has rather been to discover general processes. However, understanding the causes of individual variation in many phenomena of interest to avian eco-physiologists will require a consideration of such mechanisms. For example, in birds, changes in plasma concentrations of steroid hormones are important in the activation of social behaviours related to reproduction and aggression. Attempts to explain individual variation in these behaviours as a function of variation in plasma hormone concentrations have generally failed. Cellular variables related to the effectiveness of steroid hormone have been useful in some cases. Steroid hormone target sensitivity can be affected by variables such as metabolizing enzyme activity, hormone receptor expression as well as receptor cofactor expression. At present, no general theory has emerged that might provide a clear guidance when trying to explain individual variability in birds or in any other group of vertebrates. One strategy is to learn from studies of large units of intraspecific variation such as population or sex differences to provide ideas about variables that might be important in explaining individual variation. This approach along with the use of newly developed molecular genetic tools represents a promising avenue for avian eco-physiologists to pursue.

Introduction to multivariate discrimination

NASA Astrophysics Data System (ADS)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either relevant to or even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.

The classification and application of toxic Chinese materia medica.

PubMed

Liu, Xinmin; Wang, Qiong; Song, Guangqing; Zhang, Guangping; Ye, Zuguang; Williamson, Elizabeth M

2014-03-01

Many important drugs in the Chinese materia medica (CMM) are known to be toxic, and it has long been recognized in classical Chinese medical theory that toxicity can arise directly from the components of a single CMM or may be induced by an interaction between combined CMM. Traditional Chinese Medicine presents a unique set of pharmaceutical theories that include particular methods for processing, combining and decocting, and these techniques contribute to reducing toxicity as well as enhancing efficacy. The current classification of toxic CMM drugs, traditional methods for processing toxic CMM and the prohibited use of certain combinations, is based on traditional experience and ancient texts and monographs, but accumulating evidence increasingly supports their use to eliminate or reduce toxicity. Modern methods are now being used to evaluate the safety of CMM; however, a new system for describing the toxicity of Chinese herbal medicines may need to be established to take into account those herbs whose toxicity is delayed or otherwise hidden, and which have not been incorporated into the traditional classification. This review explains the existing classification and justifies it where appropriate, using experimental results often originally published in Chinese and previously not available outside China. Copyright © 2013 John Wiley & Sons, Ltd.

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

PubMed

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

2013-01-01

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

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.

Topological crystalline materials: General formulation, module structure, and wallpaper groups

NASA Astrophysics Data System (ADS)

Shiozaki, Ken; Sato, Masatoshi; Gomi, Kiyonori

2017-06-01

We formulate topological crystalline materials on the basis of the twisted equivariant K theory. Basic ideas of the twisted equivariant K theory are explained with application to topological phases protected by crystalline symmetries in mind, and systematic methods of topological classification for crystalline materials are presented. Our formulation is applicable to bulk gapful topological crystalline insulators/superconductors and their gapless boundary and defect states, as well as bulk gapless topological materials such as Weyl and Dirac semimetals, and nodal superconductors. As an application of our formulation, we present a complete classification of topological crystalline surface states, in the absence of time-reversal invariance. The classification works for gapless surface states of three-dimensional insulators, as well as full gapped two-dimensional insulators. Such surface states and two-dimensional insulators are classified in a unified way by 17 wallpaper groups, together with the presence or the absence of (sublattice) chiral symmetry. We identify the topological numbers and their representations under the wallpaper group operation. We also exemplify the usefulness of our formulation in the classification of bulk gapless phases. We present a class of Weyl semimetals and Weyl superconductors that are topologically protected by inversion symmetry.

What Makes a Defender? A Multilevel Study of Individual Correlates and Classroom Norms in Explaining Defending Behaviors

ERIC Educational Resources Information Center

Lucas-Molina, Beatriz; Giménez-Dasí, Marta; Fonseca-Pedrero, Eduardo; Pérez-Albéniz, Alicia

2018-01-01

This study examines the interplay between individual characteristics (social status, provictim attitudes, and family messages about conflict resolution) and classroom descriptive and injunctive norms (peer victimization behaviors and bullying-related beliefs, respectively) in explaining defending behavior. For this purpose, we used a…

Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach.

PubMed

Ecker, Christine; Marquand, Andre; Mourão-Miranda, Janaina; Johnston, Patrick; Daly, Eileen M; Brammer, Michael J; Maltezos, Stefanos; Murphy, Clodagh M; Robertson, Dene; Williams, Steven C; Murphy, Declan G M

2010-08-11

Autism spectrum disorder (ASD) is a neurodevelopmental condition with multiple causes, comorbid conditions, and a wide range in the type and severity of symptoms expressed by different individuals. This makes the neuroanatomy of autism inherently difficult to describe. Here, we demonstrate how a multiparameter classification approach can be used to characterize the complex and subtle structural pattern of gray matter anatomy implicated in adults with ASD, and to reveal spatially distributed patterns of discriminating regions for a variety of parameters describing brain anatomy. A set of five morphological parameters including volumetric and geometric features at each spatial location on the cortical surface was used to discriminate between people with ASD and controls using a support vector machine (SVM) analytic approach, and to find a spatially distributed pattern of regions with maximal classification weights. On the basis of these patterns, SVM was able to identify individuals with ASD at a sensitivity and specificity of up to 90% and 80%, respectively. However, the ability of individual cortical features to discriminate between groups was highly variable, and the discriminating patterns of regions varied across parameters. The classification was specific to ASD rather than neurodevelopmental conditions in general (e.g., attention deficit hyperactivity disorder). Our results confirm the hypothesis that the neuroanatomy of autism is truly multidimensional, and affects multiple and most likely independent cortical features. The spatial patterns detected using SVM may help further exploration of the specific genetic and neuropathological underpinnings of ASD, and provide new insights into the most likely multifactorial etiology of the condition.

Perceptual integration of acoustic cues to laryngeal contrasts in Korean fricatives.

PubMed

Lee, Sarah; Katz, Jonah

2016-02-01

This paper provides evidence that multiple acoustic cues involving the presence of low-frequency energy integrate in the perception of Korean coronal fricatives. This finding helps explain a surprising asymmetry between the production and perception of these fricatives found in previous studies: lower F0 onset in the following vowel leads to a response bias for plain [s] over fortis [s*], despite the fact that there is no evidence for a corresponding acoustic asymmetry in the production of [s] and [s*]. A fixed classification task using the Garner paradigm provides evidence that low F0 in a following vowel and the presence of voicing during frication perceptually integrate. This suggests that Korean listeners in previous experiments were responding to an "intermediate perceptual property" of stimuli, despite the fact that the individual acoustic components of that property are not all present in typical Korean fricative productions. The finding also broadens empirical support for the general idea of perceptual integration to a language, a different manner of consonant, and a situation where covariance of the acoustic cues under investigation is not generally present in a listener's linguistic input.

Non-invasive regime for language lateralization in right- and left-handers by means of functional MRI and dichotic listening.

PubMed

Hund-Georgiadis, Margret; Lex, Ulrike; Friederici, Angela D; von Cramon, D Yves

2002-07-01

Language lateralization was assessed by two independent functional techniques, fMRI and a dichotic listening test (DLT), in an attempt to establish a reliable and non-invasive protocol of dominance determination. This should particularly address the high intraindividual variability of language lateralization and allow decision-making in individual cases. Functional MRI of word classification tasks showed robust language lateralization in 17 right-handers and 17 left-handers in terms of activation in the inferior frontal gyrus. The DLT was introduced as a complementary tool to MR mapping for language dominance assessment, providing information on perceptual language processing located in superior temporal cortices. The overall agreement of lateralization assessment between the two techniques was 97.1%. Conflicting results were found in one subject, and diverging indices in ten further subjects. Increasing age, non-familial sinistrality, and a non-dominant writing hand were identified as the main factors explaining the observed mismatch between the two techniques. This finding stresses the concept of an intrahemispheric distribution of language function that is obviously associated with certain behavioral characteristics.

Remotely sensing the German Wadden Sea-a new approach to address national and international environmental legislation.

PubMed

Müller, Gabriele; Stelzer, Kerstin; Smollich, Susan; Gade, Martin; Adolph, Winny; Melchionna, Sabrina; Kemme, Linnea; Geißler, Jasmin; Millat, Gerald; Reimers, Hans-Christian; Kohlus, Jörn; Eskildsen, Kai

2016-10-01

The Wadden Sea along the North Sea coasts of Denmark, Germany, and the Netherlands is the largest unbroken system of intertidal sand and mud flats in the world. Its habitats are highly productive and harbour high standing stocks and densities of benthic species, well adapted to the demanding environmental conditions. Therefore, the Wadden Sea is one of the most important areas for migratory birds in the world and thus protected by national and international legislation, which amongst others requires extensive monitoring. Due to the inaccessibility of major areas of the Wadden Sea, a classification approach based on optical and radar remote sensing has been developed to support environmental monitoring programmes. In this study, the general classification framework as well as two specific monitoring cases, mussel beds and seagrass meadows, are presented. The classification of mussel beds profits highly from inclusion of radar data due to their rough surface and achieves agreements of up to 79 % with areal data from the regular monitoring programme. Classification of seagrass meadows reaches even higher agreements with monitoring data (up to 100 %) and furthermore captures seagrass densities as low as 10 %. The main classification results are information on area and location of individual habitats. These are needed to fulfil environmental legislation requirements. One of the major advantages of this approach is the large areal coverage with individual satellite images, allowing simultaneous assessment of both accessible and inaccessible areas and thus providing a more complete overall picture.

Semi-supervised SVM for individual tree crown species classification

NASA Astrophysics Data System (ADS)

Dalponte, Michele; Ene, Liviu Theodor; Marconcini, Mattia; Gobakken, Terje; Næsset, Erik

2015-12-01

In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns

PubMed Central

Dijksterhuis, Chris; de Waard, Dick; Brookhuis, Karel A.; Mulder, Ben L. J. M.; de Jong, Ritske

2013-01-01

A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75–80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications. PMID:23970851

On the impact of different approaches to classify age-related macular degeneration: Results from the German AugUR study.

PubMed

Brandl, Caroline; Zimmermann, Martina E; Günther, Felix; Barth, Teresa; Olden, Matthias; Schelter, Sabine C; Kronenberg, Florian; Loss, Julika; Küchenhoff, Helmut; Helbig, Horst; Weber, Bernhard H F; Stark, Klaus J; Heid, Iris M

2018-06-06

While age-related macular degeneration (AMD) poses an important personal and public health burden, comparing epidemiological studies on AMD is hampered by differing approaches to classify AMD. In our AugUR study survey, recruiting residents from in/around Regensburg, Germany, aged 70+, we analyzed the AMD status derived from color fundus images applying two different classification systems. Based on 1,040 participants with gradable fundus images for at least one eye, we show that including individuals with only one gradable eye (n = 155) underestimates AMD prevalence and we provide a correction procedure. Bias-corrected and standardized to the Bavarian population, late AMD prevalence is 7.3% (95% confidence interval = [5.4; 9.4]). We find substantially different prevalence estimates for "early/intermediate AMD" depending on the classification system: 45.3% (95%-CI = [41.8; 48.7]) applying the Clinical Classification (early/intermediate AMD) or 17.1% (95%-CI = [14.6; 19.7]) applying the Three Continent AMD Consortium Severity Scale (mild/moderate/severe early AMD). We thus provide a first effort to grade AMD in a complete study with different classification systems, a first approach for bias-correction from individuals with only one gradable eye, and the first AMD prevalence estimates from a German elderly population. Our results underscore substantial differences for early/intermediate AMD prevalence estimates between classification systems and an urgent need for harmonization.

Health care frames - from Virchow to Obama and beyond: the changing frames in health care and their implications for patient care.

PubMed

Sturmberg, Joachim P; O'Halloran, Di; Colagiuri, Ruth; Fernandez, Ana; Lukersmith, Sue; Torkfar, Ghazal; Salvador-Carulla, Luis

2014-12-01

Framing allows us to highlight some aspects of an issue, thereby bringing them to the forefront of our thinking, talking and acting. As a consequence, framing also distracts our attention away from other issues. Over time, health care has used various frames to explain its activities. This paper traces the emergence of various health care frames since the 1850s to better understand how we reached current ways of thinking and practicing. The succession of the most prominent frames can be summarized as: medicine as a social science; the germ theory of disease; health care as a battleground (or the war metaphor); managing health care resources (or the market metaphor); Health for All (the social justice model); evidence-based medicine; and Obama Care. The focus of these frames is causal, instrumental, political/economic or social in nature. All remain relevant; however, recycling individual past frames in response to current problems will not achieve the outcomes we seek. Placing the individual and his/her needs at the centre (the attractor for the health system) of our thinking, as emphasized by the World Health Organization's International Classification of Function framework and the European Society of Person Centered Health Care, may provide the frame to refocus health and health care as interdependent experiences across individual, community and societal domains. Shifting beyond the entrenched instrumental and economic thinking will be challenging but necessary for the sake of patients, health professionals, society and the economy. © 2014 John Wiley & Sons, Ltd.

Hierarchical analysis of cardiovascular risk factors in relation to the development of acute coronary syndromes, in different parts of Greece: the CARDIO2000 study.

PubMed

Panagiotakos, Demosthenes B; Pitsavos, Christos; Chrysohoou, Christine; Stefanadis, Christodoulos

2008-01-01

During 2000 to 2002, 700 men (59 +/- 10 years) and 148 women (65 +/- 9 years) patients with first event of an ACS were randomly selected from cardiology clinics of Greek regions. Afterwards, 1078 population-based, age-matched and sex-matched controls were randomly selected from the same hospitals. The frequency ratio between men and women in the case series of patients was about 4:1, in both south and north Greek areas. Hierarchical classification analysis showed that for north Greek areas family history of coronary heart disease, hypercholesterolemia, hypertension, diabetes (explained variability 35%), and less significantly, dietary habits, smoking, body mass index, and physical activity status (explained variability 4%) were associated with the development of ACS, whereas for south Greek areas hypercholesterolemia, family history of coronary heart disease, diabetes, smoking, hypertension, dietary habits, physical activity (explained variability 34%), and less significantly body mass index (explained variability <1%), were associated with the development of the disease.

Endometrial stromal tumors: the new WHO classification.

PubMed

Conklin, Christopher M J; Longacre, Teri A

2014-11-01

Endometrial stromal tumors are rare uterine mesenchymal neoplasms that have intrigued pathologists for years, not only because they commonly pose diagnostic dilemmas, but also because the classification and pathogenesis of these tumors has been widely debated. The current World Health Organization recognizes 4 categories of endometrial stromal tumor: endometrial stromal nodule (ESN), low-grade endometrial stromal sarcoma (LG-ESS), high-grade endometrial stromal sarcoma (HG-ESS), and undifferentiated uterine sarcoma (UUS). uterine sarcoma. These categories are defined by the presence of distinct translocations as well as tumor morphology and prognosis. Specifically, the JAZF1-SUZ12 (formerly JAZF1-JJAZ1) fusion identifies a large proportion of ESN and LG-ESSs, whereas the YWHAE-FAM22 translocation identifies HG-ESSs. The latter tumors appear to have a prognosis intermediate between LG-ESS and UUS, which exhibits no specific translocation pattern. This review (1) presents the clinicopathologic features of endometrial stromal tumors; (2) discusses their immunophenotype; and (3) highlights the recent advances in molecular genetics which explain their pathogenesis and lend support for a new classification system.

Esophageal cancer: anatomic particularities, staging, and imaging techniques.

PubMed

Encinas de la Iglesia, J; Corral de la Calle, M A; Fernández Pérez, G C; Ruano Pérez, R; Álvarez Delgado, A

2016-01-01

Cancer of the esophagus is a tumor with aggressive behavior that is usually diagnosed in advanced stages. The absence of serosa allows it to spread quickly to neighboring mediastinal structures, and an extensive lymphatic drainage network facilitates tumor spread even in early stages. The current TNM classification, harmonized with the classification for gastric cancer, provides new definitions for the anatomic classification, adds non-anatomic characteristics of the tumor, and includes tumors of the gastroesophageal junction. Combining endoscopic ultrasound, computed tomography, positron emission tomography, and magnetic resonance imaging provides greater accuracy in determining the initial clinical stage, and these imaging techniques play an essential role in the selection, planning, and evaluation of treatment. In this article, we review some particularities that explain the behavior of this tumor and we describe the current TNM staging system; furthermore, we discuss the different imaging tests available for its evaluation and include a diagnostic algorithm. Copyright © 2016 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Classification of non-Hodgkin lymphoma in Algeria according to the World Health Organization classification.

PubMed

Boudjerra, Nadia; Perry, Anamarija M; Audouin, Josée; Diebold, Jacques; Nathwani, Bharat N; MacLennan, Kenneth A; Müller-Hermelink, Hans K; Bast, Martin; Boilesen, Eugene; Armitage, James O; Weisenburger, Dennis D

2015-04-01

The relative distribution of non-Hodgkin lymphoma (NHL) subtypes differs markedly around the world. The aim of this study was to report this distribution in Algeria. A panel of four hematopathologists classified 197 consecutive cases according to the World Health Organization classification, including 87.3% B-cell and 12.7% T- or natural killer (NK)-cell NHLs. This series was compared with similar cohorts from Western Europe (WEU) and North America (NA). Algeria had a significantly higher frequency of diffuse large B-cell lymphoma (DLBCL: 52.8%) and a lower frequency of follicular lymphoma (FL: 13.2%) compared with WEU (DLBCL: 32.2%; FL: 20.0%) and NA (DLBCL: 29.3%; FL: 33.6%). The frequency of mantle cell lymphoma was lower in Algeria (2.5%) compared with WEU (8.3%). Smaller differences were also found among the NK/T-cell lymphomas. In conclusion, we found important differences between Algeria and Western countries, and further epidemiologic studies are needed to explain these differences.

Data-driven heterogeneity in mathematical learning disabilities based on the triple code model.

PubMed

Peake, Christian; Jiménez, Juan E; Rodríguez, Cristina

2017-12-01

Many classifications of heterogeneity in mathematical learning disabilities (MLD) have been proposed over the past four decades, however no empirical research has been conducted until recently, and none of the classifications are derived from Triple Code Model (TCM) postulates. The TCM proposes MLD as a heterogeneous disorder, with two distinguishable profiles: a representational subtype and a verbal subtype. A sample of elementary school 3rd to 6th graders was divided into two age cohorts (3rd - 4th grades, and 5th - 6th grades). Using data-driven strategies, based on the cognitive classification variables predicted by the TCM, our sample of children with MLD clustered as expected: a group with representational deficits and a group with number-fact retrieval deficits. In the younger group, a spatial subtype also emerged, while in both cohorts a non-specific cluster was produced whose profile could not be explained by this theoretical approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

Decision Making in the Management of Extracapsular Fractures of the Proximal Femur - is the Dynamic Hip Screw the Prevailing Gold Standard?

PubMed

Jacob, Joshua; Desai, Ankit; Trompeter, Alex

2017-01-01

Currently, approximately half of all hip fractures are extracapsular, with an incidence as high as 50 in 100,000 in some countries. The common classification systems fail to explain the logistics of fracture classification and whether they all behave in the same manner. The Muller AO classification system is a useful platform to delineate stable and unstable fractures. The Dynamic hip screw (DHS) however, has remained the 'gold standard' implant of choice for application in all extracapsular fractures. The DHS relies on the integrity and strength of the lateral femoral wall as well as the postero-medial fragment. An analysis of several studies indicates significant improvements in design and techniques to ensure a better outcome with intramedullary nails. This article reviews the historical trends that helped to evolve the DHS implant as well as discussing if the surgeon should remain content with this implant. We suggest that the gold standard surgical management of extracapsular fractures can, and should, evolve.

Petrogenesis of mare basalts - A record of lunar volcanism

NASA Astrophysics Data System (ADS)

Neal, Clive R.; Taylor, Lawrence A.

1992-06-01

The classification, sources, and overall petrogenesis of mare basalts are reviewed. All mare basalt analyses are used to define a sixfold classification scheme using TiO2 contents as the primary division. A secondary division is made using Al2O3 contents, and a tertiary division is defined using K contents. Such divisions and subdivisions yield a classification containing 12 categories, of which six are accounted for by the existing Apollo and Luna collections. A variety of postmagma-generation such as fractional crystallization, either alone or combined with wallrock assimilation, are invoked to explain the compositional ranges of the various mare basalt suites. High-Ti mare basalts are found at Apollo 1 and Apollo 17 sites; the A-11 basalts contain lower TiO2 abundances, a considerably larger range in trace-element contents, and the only occurrence of high-Ti/high-K mare basalts. The low-Ti basalts exhibit a wide range of major-and trace-element compositions and require source heterogeneity, fractional crystallization, and some assimilation.

Clouds and Climate Change. Understanding Global Change: Earth Science and Human Impacts. Global Change Instruction Program.

ERIC Educational Resources Information Center

Shaw, Glenn E.

The Global Change Instruction Program was designed by college professors to fill a need for interdisciplinary materials on the emerging science of global change. This instructional module introduces the basic features and classifications of clouds and cloud cover, and explains how clouds form, what they are made of, what roles they play in…

Animal Classification. Animal Life in Action[TM]. Schlessinger Science Library. [Videotape].

ERIC Educational Resources Information Center

2000

This 23-minute videotape for grades 5-8, presents the myriad of animal life that exists on the planet. Students can view and perform experiments and investigations that help explain animal traits and habits. They learn what the terms "kingdom", "phylum", and "order" mean, and discover how the 3.5 million-plus organisms found on Earth fit into…

Some crucial corona and prominence observations

NASA Technical Reports Server (NTRS)

Tandberg-Hanssen, E. A.

1986-01-01

A number of theories and hypotheses are currently being developed to explain the often complex behavior of corona and prominence plasmas. In order to test the theories and hypotheses certain crucial observations are necessary. Some of these observations are examined and a few conclusions are drawn. Corona mass balance, corona and prominence classifications, prominence formation and stability, and coronal mass ejection are dicussed.

Case-mix groups for VA hospital-based home care.

PubMed

Smith, M E; Baker, C R; Branch, L G; Walls, R C; Grimes, R M; Karklins, J M; Kashner, M; Burrage, R; Parks, A; Rogers, P

1992-01-01

The purpose of this study is to group hospital-based home care (HBHC) patients homogeneously by their characteristics with respect to cost of care to develop alternative case mix methods for management and reimbursement (allocation) purposes. Six Veterans Affairs (VA) HBHC programs in Fiscal Year (FY) 1986 that maximized patient, program, and regional variation were selected, all of which agreed to participate. All HBHC patients active in each program on October 1, 1987, in addition to all new admissions through September 30, 1988 (FY88), comprised the sample of 874 unique patients. Statistical methods include the use of classification and regression trees (CART software: Statistical Software; Lafayette, CA), analysis of variance, and multiple linear regression techniques. The resulting algorithm is a three-factor model that explains 20% of the cost variance (R2 = 20%, with a cross validation R2 of 12%). Similar classifications such as the RUG-II, which is utilized for VA nursing home and intermediate care, the VA outpatient resource allocation model, and the RUG-HHC, utilized in some states for reimbursing home health care in the private sector, explained less of the cost variance and, therefore, are less adequate for VA home care resource allocation.

Mediating Factors Explaining the Association Between Sexual Minority Status and Dating Violence.

PubMed

Martin-Storey, Alexa; Fromme, Kim

2017-08-01

Dating violence presents a serious threat for individual health and well-being. A growing body of literature suggests that starting in adolescence, individuals with sexual minority identities (e.g., individuals who identify as gay, lesbian, or bisexual) may be at an increased risk for dating violence compared with heterosexuals. Research has not, however, identified the mechanisms that explain this vulnerability. Using a diverse sample of young adults ( n = 2,474), the current study explored how minority stress theory, revictimization theory, sex of sexual partners, and risky sexual behavior explained differences in dating violence between sexual minority and heterosexual young adults. Initial analyses suggested higher rates of dating violence among individuals who identified as bisexual, and individuals who identified as gay or lesbian when compared with heterosexuals, and further found that these associations failed to differ across gender. When mediating and control variables were included in the analyses, however, the association between both sexual minority identities and higher levels of dating violence became nonsignificant. Of particular interest was the role of discrimination, which mediated the association between bisexual identity and dating violence. Other factors, including sex and number of sexual partners, alcohol use, and childhood maltreatment, were associated with higher rates of dating violence but did not significantly explain vulnerability among sexual minority individuals compared with their heterosexual peers. These findings suggest the importance of minority stress theory in explaining vulnerability to dating violence victimization among bisexuals in particular, and generally support the importance of sexual-minority specific variables in understanding risk for dating violence within this vulnerable population.

Functional gastroduodenal disorders

PubMed Central

Talley, N; Stanghellini, V; Heading, R; Koch, K; Malagelada, J; Tytgat, G

1999-01-01

While widely used in research, the 1991 Rome criteria for the gastroduodenal disorders, especially symptom subgroups in dyspepsia, remain contentious. After a comprehensive literature search, a consensus-based approach was applied, supplemented by input from international experts who reviewed the report. Three functional gastroduodenal disorders are defined. Functional dyspepsia is persistent or recurrent pain or discomfort centered in the upper abdomen; evidence of organic disease likely to explain the symptoms is absent, including at upper endoscopy. Discomfort refers to a subjective, negative feeling that may be characterized by or associated with a number of non-painful symptoms including upper abdominal fullness, early satiety, bloating, or nausea. A dyspepsia subgroup classification is proposed for research purposes, based on the predominant (most bothersome) symptom: (a) ulcer-like dyspepsia when pain (from mild to severe) is the predominant symptom, and (b) dysmotility-like dyspepsia when discomfort (not pain) is the predominant symptom. This classification is supported by recent evidence suggesting that predominant symptoms, but not symptom clusters, identify subgroups with distinct underlying pathophysiological disturbances and responses to treatment. Aerophagia is an unusual complaint characterized by air swallowing that is objectively observed and troublesome repetitive belching. Functional vomiting refers to frequent episodes of recurrent vomiting that is not self-induced nor medication induced, and occurs in the absence of eating disorders, major psychiatric diseases, abnormalities in the gut or central nervous system, or metabolic diseases that can explain the symptom. The current classification requires careful validation but the criteria should be of value in future research.


Keywords: dyspepsia; functional dyspepsia; aerophagia; psychogenic vomiting; Rome II PMID:10457043

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints.

PubMed

Keitel, Anne; Gross, Joachim

2016-06-01

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles ("fingerprints"), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease.

The Fracturing of China? Ethnic Separatism and Political Violence in the Xinjiang Uyghur Autonomous Region

DTIC Science & Technology

2007-09-01

deprivation, rational choice 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18 . SECURITY CLASSIFICATION OF THIS PAGE...Prescribed by ANSI Std. 239- 18 ii THIS PAGE INTENTIONALLY LEFT BLANK iii Approved for public release; distribution is unlimited. THE...psychological, or erotic in nature.10 This argument purports that when individuals participate within a group for the advancement of collective good, they

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

Relationship between risk classifications used to organize the demand for oral health in a small city of São Paulo, Brazil.

PubMed

Peres, João; Mendes, Karine Laura Cortellazzi; Wada, Ronaldo Seichi; Sousa, Maria da Luz Rosario de

2017-06-01

Oral health teams can work with both information of the people related to the family context as individual epidemiological through risk ratings, considering equity and service organization. The purpose of our study was to evaluate the association between tools that classify individual and family risk. The study group consisted of students from the age group of 5-6 years and 11-12 years who were classified regarding risk of caries and whether their parents had periodontal disease, in addition to the family risk. There was an association between the risk rating for decay in children (n = 128) and family risk classification with Coef C = 0.338 and p = 0.01, indicating that the higher the family's risk, the higher the risk of caries. Similarly, the association between the risk classification for periodontal disease in parents and family risk classification with Coef C = 0.5503 and p = 0.03 indicated that the higher the family risk, the higher the risk of periodontal disease. It can be concluded that the use of family risk rating tool is indicated as a possibility of ordering actions of the dental service, organizing their demand with greater equity, in this access door.

FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud.

PubMed

Márquez, Cristina; López, M Isabel; Ruisánchez, Itziar; Callao, M Pilar

2016-12-01

Two data fusion strategies (high- and mid-level) combined with a multivariate classification approach (Soft Independent Modelling of Class Analogy, SIMCA) have been applied to take advantage of the synergistic effect of the information obtained from two spectroscopic techniques: FT-Raman and NIR. Mid-level data fusion consists of merging some of the previous selected variables from the spectra obtained from each spectroscopic technique and then applying the classification technique. High-level data fusion combines the SIMCA classification results obtained individually from each spectroscopic technique. Of the possible ways to make the necessary combinations, we decided to use fuzzy aggregation connective operators. As a case study, we considered the possible adulteration of hazelnut paste with almond. Using the two-class SIMCA approach, class 1 consisted of unadulterated hazelnut samples and class 2 of samples adulterated with almond. Models performance was also studied with samples adulterated with chickpea. The results show that data fusion is an effective strategy since the performance parameters are better than the individual ones: sensitivity and specificity values between 75% and 100% for the individual techniques and between 96-100% and 88-100% for the mid- and high-level data fusion strategies, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

NASA Astrophysics Data System (ADS)

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification.

PubMed

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-12-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value.

Shifting syndromes: Sex chromosome variations and intersex classifications

PubMed Central

Griffiths, David Andrew

2018-01-01

The 2006 ‘Consensus statement on management of intersex disorders’ recommended moving to a new classification of intersex variations, framed in terms of ‘disorders of sex development’ or DSD. Part of the rationale for this change was to move away from associations with gender, and to increase clarity by grounding the classification system in genetics. While the medical community has largely accepted the move, some individuals from intersex activist communities have condemned it. In addition, people both inside and outside the medical community have disagreed about what should be covered by the classification system, in particular whether sex chromosome variations and the related diagnoses of Turner and Klinefelter’s syndromes should be included. This article explores initial descriptions of Turner and Klinefelter’s syndromes and their subsequent inclusion in intersex classifications, which were increasingly grounded in scientific understandings of sex chromosomes that emerged in the 1950s. The article questions the current drive to stabilize and ‘sort out’ intersex classifications through a grounding in genetics. Alternative social and historical definitions of intersex – such as those proposed by the intersex activists – have the potential to do more justice to the lived experience of those affected by such classifications and their consequences. PMID:29424285

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

PubMed Central

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-01-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

IRIS COLOUR CLASSIFICATION SCALES – THEN AND NOW

PubMed Central

Grigore, Mariana; Avram, Alina

2015-01-01

Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual’s eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale. PMID:27373112

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

PubMed Central

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

2008-01-01

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

First branchial cleft fistula presenting with internal opening on the Eustachian tube: Illustrated cases and literature review.

PubMed

Liu, Yuhe; Li, Tiancheng; Xue, Junfang; Jia, Jun; Xiao, Shuifang; Zhao, Enmin

2012-05-01

Two cases of first branchial cleft fistula with internal opening on the Eustachian tube are reported and the diagnosis, management and embryological hypothesis are discussed. Retrospective study and review of the literature. Both patients were young boys with first branchial cleft anomaly clearly identified by computed tomography fistulography scan and direct Methylene Blue dye injection. In both cases, surgical removal revealed a fistula with internal opening located on the Eustachian tube near the nasopharynx. The main embryological theories and classification are reviewed. A connection between the theories of first branchial apparatus development and the classification by Work might explain the reported clinical association. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Unusual association of congenital middle ear cholesteatoma and first branchial cleft anomaly: management and embryological concepts.

PubMed

Nicollas, R; Tardivet, L; Bourlière-Najean, B; Sudre-Levillain, I; Triglia, J M

2005-02-01

To report two cases of an undescribed association of first branchial cleft fistula and middle ear congenital cholesteatoma and to discuss management and embryological hypothesis. Retrospective study and review of the literature Both patients were young girls free of past medical or surgical history. Surgical removal of the first cleft anomaly found in the two cases a fistula routing underneath the facial nerve. Both cholesteatomas were located in the hypotympanum, mesotympanum. In one case, an anatomical link between the two malformations was clearly identified with CT scan. The main embryological theories and classification are reviewed. A connection between Aimi's and Michaels' theories (congenital cholesteatoma) and Work classification might explain the reported clinical association.

Autoclass: An automatic classification system

NASA Technical Reports Server (NTRS)

Stutz, John; Cheeseman, Peter; Hanson, Robin

1991-01-01

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

Development of a brain MRI-based hidden Markov model for dementia recognition

PubMed Central

2013-01-01

Background Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Methods Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. Results The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. Conclusion The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia. PMID:24564961

Relations between Epistemological Beliefs and Culture Classifications

ERIC Educational Resources Information Center

Sulimma, Maren

2009-01-01

Purpose: Epistemological beliefs, defined as individuals' beliefs about the nature of knowledge and the process of knowing, are assumed to serve an important function in regulating the application of individuals' learning behaviour. Previous research has mainly been shaped by the framework of results of white, well-educated people from North…

12 CFR 1207.23 - Annual reports-format and contents.

Code of Federal Regulations, 2011 CFR

2011-01-01

... and gender the number of individuals applying for employment with the regulated entity or the Office... year; (4) Data showing by minority and gender the number of individuals hired for employment with the... during the reporting year; (5) Data showing by minority, gender and disability classification, and...

Inclusion in CTE--What Works and What Needs Fixin'

ERIC Educational Resources Information Center

Casale-Giannola, Diane

2011-01-01

No Child Left Behind (NCLB) and the Individuals with Disability Education Act (IDEA) of 1997 and 2004 have brought more students with special education classifications into general education courses and made general education teachers accountable for the performance of those students. They are expected to participate in individual education plan…

Open Dataset for the Automatic Recognition of Sedentary Behaviors.

PubMed

Possos, William; Cruz, Robinson; Cerón, Jesús D; López, Diego M; Sierra-Torres, Carlos H

2017-01-01

Sedentarism is associated with the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Therefore, the identification of specific sedentary behaviors (TV viewing, sitting at work, driving, relaxing, etc.) is especially relevant for planning personalized prevention programs. To build and evaluate a public a dataset for the automatic recognition (classification) of sedentary behaviors. The dataset included data from 30 subjects, who performed 23 sedentary behaviors while wearing a commercial wearable on the wrist, a smartphone on the hip and another in the thigh. Bluetooth Low Energy (BLE) beacons were used in order to improve the automatic classification of different sedentary behaviors. The study also compared six well know data mining classification techniques in order to identify the more precise method of solving the classification problem of the 23 defined behaviors. A better classification accuracy was obtained using the Random Forest algorithm and when data were collected from the phone on the hip. Furthermore, the use of beacons as a reference for obtaining the symbolic location of the individual improved the precision of the classification.

Lung texture classification using bag of visual words

NASA Astrophysics Data System (ADS)

Asherov, Marina; Diamant, Idit; Greenspan, Hayit

2014-03-01

Interstitial lung diseases (ILD) refer to a group of more than 150 parenchymal lung disorders. High-Resolution Computed Tomography (HRCT) is the most essential imaging modality of ILD diagnosis. Nonetheless, classification of various lung tissue patterns caused by ILD is still regarded as a challenging task. The current study focuses on the classification of five most common categories of lung tissues of ILD in HRCT images: normal, emphysema, ground glass, fibrosis and micronodules. The objective of the research is to classify an expert-given annotated region of interest (AROI) using a bag of visual words (BoVW) framework. The images are divided into small patches and a collection of representative patches are defined as visual words. This procedure, termed dictionary construction, is performed for each individual lung texture category. The assumption is that different lung textures are represented by a different visual word distribution. The classification is performed using an SVM classifier with histogram intersection kernel. In the experiments, we use a dataset of 1018 AROIs from 95 patients. Classification using a leave-one-patient-out cross validation (LOPO CV) is used. Current classification accuracy obtained is close to 80%.

The Chicago classification of motility disorders: an update.

PubMed

Roman, Sabine; Gyawali, C Prakash; Xiao, Yinglian; Pandolfino, John E; Kahrilas, Peter J

2014-10-01

The Chicago Classification defines esophageal motility disorders in high resolution manometry. This is based on individual scoring of 10 swallows performed in supine position. Disorders of esophago-gastric junction (EGJ) outflow obstruction are defined by a median integrated relaxation pressure above the limit of normal and divided into 3 achalasia subtypes and EGJ outflow obstruction. Major motility disorders (aperistalsis, distal esophageal spasm, and hypercontractile esophagus) are patterns not encountered in controls in the context of normal EGJ relaxation. Finally with the latest version of the Chicago Classification, only two minor motor disorders are considered: ineffective esophageal motility and fragmented peristalsis. Copyright © 2014 Elsevier Inc. All rights reserved.

Professional classifications of American nurses, 1910 to 1935.

PubMed

Lusk, B

1997-04-01

Nursing's claim to professional status is debatable. The purpose of this historical study is to describe the official classifications of American nurses as professionals or nonprofessionals, from 1910 to 1935. Labor legislation before World War I, military decisions during that war, and federal mandates during the Great Depression resulted in differing professional classifications of nurses. Although nurse leaders aspired to traditional criteria of professionalism, such as individual responsibility and a deep, distinct body of knowledge, these criteria were subsumed by political, financial, and gender issues. This study demonstrates that professional status cannot be assured by attainment of professional criteria alone, but is defined by more diverse and complex issues.

Automatic breast density classification using a convolutional neural network architecture search procedure

NASA Astrophysics Data System (ADS)

Fonseca, Pablo; Mendoza, Julio; Wainer, Jacques; Ferrer, Jose; Pinto, Joseph; Guerrero, Jorge; Castaneda, Benjamin

2015-03-01

Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

Linking the integrated management of childhood illness (IMCI) and health information system (HIS) classifications: issues and options.

PubMed Central

Rowe, A. K.; Hirnschall, G.; Lambrechts, T.; Bryce, J.

1999-01-01

Differences in the terms used to classify diseases in the Integrated Management of Childhood Illness (IMCI) guidelines and for health information system (HIS) disease surveillance could easily create confusion among health care workers. If the equivalent terms in the two classifications are not clear to health workers who are following the guidelines, they may have problems in performing the dual activities of case management and disease surveillance. These difficulties could adversely affect an individual's performance as well as the overall effectiveness of the IMCI strategy or HIS surveillance, or both. We interviewed key informants to determine the effect of these differences between the IMCI and HIS classifications on the countries that were implementing the IMCI guidelines. Four general approaches for addressing the problem were identified: translating the IMCI classifications into HIS classifications; changing the HIS list to include the IMCI classifications; using both the IMCI and HIS classification systems at the time of consultations; and doing nothing. No single approach can satisfy the needs of all countries. However, if the short-term or medium-term goal of IMCI planners is to find a solution that will reduce the problem for health workers and is also easy to implement, the approach most likely to succeed is translation of IMCI classifications into HIS classifications. Where feasible, a modification of the health information system to include the IMCI classifications may also be considered. PMID:10680246

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

PubMed

Cho, Youngsang; Seong, Joon-Kyung; Jeong, Yong; Shin, Sung Yong

2012-02-01

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

Hydrological Climate Classification: Can We Improve on Köppen-Geiger?

NASA Astrophysics Data System (ADS)

Knoben, W.; Woods, R. A.; Freer, J. E.

2017-12-01

Classification is essential in the study of complex natural systems, yet hydrology so far has no formal way to structure the climate forcing which underlies hydrologic response. Various climate classification systems can be borrowed from other disciplines but these are based on different organizing principles than a hydrological classification might use. From gridded global data we calculate a gridded aridity index, an aridity seasonality index and a rain-vs-snow index, which we use to cluster global locations into climate groups. We then define the membership degree of nearly 1100 catchments to each of our climate groups based on each catchment's climate and investigate the extent to which streamflow responses within each climate group are similar. We compare this climate classification approach with the often-used Köppen-Geiger classification, using statistical tests based on streamflow signature values. We find that three climate indices are sufficient to distinguish 18 different climate types world-wide. Climates tend to change gradually in space and catchments can thus belong to multiple climate groups, albeit with different degrees of membership. Streamflow responses within a climate group tend to be similar, regardless of the catchments' geographical proximity. A Wilcoxon two-sample test based on streamflow signature values for each climate group shows that the new classification can distinguish different flow regimes using this classification scheme. The Köppen-Geiger approach uses 29 climate classes but is less able to differentiate streamflow regimes. Climate forcing exerts a strong control on typical hydrologic response and both change gradually in space. This makes arbitrary hard boundaries in any classification scheme difficult to defend. Any hydrological classification should thus acknowledge these gradual changes in forcing. Catchment characteristics (soil or vegetation type, land use, etc) can vary more quickly in space than climate does, which can explain streamflow differences between geographically close locations. Summarizing, this work shows that hydrology needs its own way to structure climate forcing, acknowledging that climates vary gradually on a global scale and explicitly including those climate aspects that drive seasonal changes in hydrologic regimes.

Oxytocin improves facial emotion recognition in young adults with antisocial personality disorder.

PubMed

Timmermann, Marion; Jeung, Haang; Schmitt, Ruth; Boll, Sabrina; Freitag, Christine M; Bertsch, Katja; Herpertz, Sabine C

2017-11-01

Deficient facial emotion recognition has been suggested to underlie aggression in individuals with antisocial personality disorder (ASPD). As the neuropeptide oxytocin (OT) has been shown to improve facial emotion recognition, it might also exert beneficial effects in individuals providing so much harm to the society. In a double-blind, randomized, placebo-controlled crossover trial, 22 individuals with ASPD and 29 healthy control (HC) subjects (matched for age, sex, intelligence, and education) were intranasally administered either OT (24 IU) or a placebo 45min before participating in an emotion classification paradigm with fearful, angry, and happy faces. We assessed the number of correct classifications and reaction times as indicators of emotion recognition ability. Significant group×substance×emotion interactions were found in correct classifications and reaction times. Compared to HC, individuals with ASPD showed deficits in recognizing fearful and happy faces; these group differences were no longer observable under OT. Additionally, reaction times for angry faces differed significantly between the ASPD and HC group in the placebo condition. This effect was mainly driven by longer reaction times in HC subjects after placebo administration compared to OT administration while individuals with ASPD revealed descriptively the contrary response pattern. Our data indicate an improvement of the recognition of fearful and happy facial expressions by OT in young adults with ASPD. Particularly the increased recognition of facial fear is of high importance since the correct perception of distress signals in others is thought to inhibit aggression. Beneficial effects of OT might be further mediated by improved recognition of facial happiness probably reflecting increased social reward responsiveness. Copyright © 2017. Published by Elsevier Ltd.

Illness, at-risk and resilience neural markers of early-stage bipolar disorder.

PubMed

Lin, Kangguang; Shao, Robin; Geng, Xiujuan; Chen, Kun; Lu, Rui; Gao, Yanling; Bi, Yanan; Lu, Weicong; Guan, Lijie; Kong, Jiehua; Xu, Guiyun; So, Kwok-Fai

2018-05-21

Current knowledge on objective and specific neural markers for bipolar risk and resilience-related processes is lacking, partly due to not subdividing high-risk individuals manifesting different levels of subclinical symptoms who possibly possess different levels of resilience. We delineated grey matter markers for bipolar illness, genetic high risk (endophenotype) and resilience, through comparing across 42 young non-comorbid bipolar patients, 42 healthy controls, and 72 diagnosis-free, medication-naive high-genetic-risk individuals subdivided into a combined-high-risk group who additionally manifested bipolar risk-relevant subsyndromes (N = 38), and an asymptomatic high-risk group (N = 34). Complementary analyses assessed the additional predictive and classification values of grey matter markers beyond those of clinical scores, through using logistic regression and support vector machine analyses. Illness-related effects manifested as reduced grey matter volumes of bilateral temporal limbic-striatal and cerebellar regions, which significantly differentiated bipolar patients from healthy controls and improved clinical classification specificity by 20%. Reduced bilateral cerebellar grey matter volume emerged as a potential endophenotype and (along with parieto-occipital grey matter changes) separated combined-high-risk individuals from healthy and high-risk individuals, and increased clinical classification specificity by approximately 10% and 27%, respectively, while the relatively normalized cerebellar grey matter volumes in the high-risk sample may confer resilience. The cross-validation procedure was not performed on an independent sample using independently-derived features. The BD group had different age and sex distributions than some other groups which may not be fully addressable statistically. Our framework can be applied in other measurement domains to derive complete profiles for bipolar patients and at-risk individuals, towards forming strategies for promoting resilience and preclinical intervention. Copyright © 2018 Elsevier B.V. All rights reserved.

Individually assessed boldness predicts Perca fluviatilis behaviour in shoals, but is not associated with the capture order or angling method.

PubMed

Kekäläinen, J; Podgorniak, T; Puolakka, T; Hyvärinen, P; Vainikka, A

2014-11-01

Selectivity of recreational angling on fish behaviour was studied by examining whether capture order or lure type (natural v. artificial bait) in ice-fishing could explain behavioural variation among perch Perca fluviatilis individuals. It was also tested if individually assessed personality predicts fish behaviour in groups, in the presence of natural predators. Perca fluviatilis showed individually repeatable behaviour both in individual and in group tests. Capture order, capture method, condition factor or past growth rate did not explain variation in individual behaviour. Individually determined boldness as well as fish size, however, were positively associated with first entrance to the predator zone (i.e. initial risk taking) in group behaviour tests. Individually determined boldness also explained long-term activity and total time spent in the vicinity of predators in the group. These findings suggest that individual and laboratory-based boldness tests predict boldness of P. fluviatilis in also ecologically relevant conditions, i.e. in shoals and in the presence of natural predators. The present results, however, also indicate that the above-mentioned two angling methods may not be selective for certain behavioural types in comparison to each other. © 2014 The Fisheries Society of the British Isles.

Does individualism help explain differences in employers' stigmatizing attitudes toward disability across Chinese and American cities?

PubMed

Rao, Deepa; Horton, Randall A; Tsang, Hector W H; Shi, Kan; Corrigan, Patrick W

2010-11-01

Stigmatizing attitudes toward people with disabilities can jeopardize such individuals' well-being and recovery through denial of employment and community isolation. By shaping social norms that define group membership, the construct of individualism may partially explain differences in stigmatizing attitudes across cultures. Further, widespread globalization has brought intensely individualistic social practices to certain segments of non-Western cultures. This paper examines whether the construct of individualism can help to explain cross-cultural differences in stigmatizing attitudes observed between American and Chinese employers. Employers (N = 879) from Beijing, Hong Kong, and Chicago provided information on their attitudes toward hiring people with disabilities, and path analyses were conducted to examine potential mediating relationships. Path analyses indicated that vertical individualism, along with perceived responsibility for acquiring a condition, partially mediated the relationship between culture and employers' negative attitudes about job candidates with disabilities. These results suggested that greater espousal of competitive and individualist values may drive stigmatizing attitudes across cultures. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

Does Individualism Help Explain Differences in Employers' Stigmatizing Attitudes Toward Disability Across Chinese and American Cities?

PubMed Central

Rao, Deepa; Horton, Randall A.; Tsang, Hector W.H.; Shi, Kan; Corrigan, Patrick W.

2011-01-01

Purpose Stigmatizing attitudes toward people with disabilities can jeopardize such individuals' well-being and recovery through denial of employment and community isolation. By shaping social norms that define group membership, the construct of individualism may partially explain differences in stigmatizing attitudes across cultures. Further, widespread globalization has brought intensely individualistic social practices to certain segments of non-Western cultures. This paper examines whether the construct of individualism can help to explain cross-cultural differences in stigmatizing attitudes observed between American and Chinese employers. Design Employers (N = 879) from Beijing, Hong Kong, and Chicago provided information on their attitudes toward hiring people with disabilities, and Path Analyses were conducted to examine potential mediating relationships. Results Path analyses indicated that vertical individualism, along with perceived responsibility for acquiring a condition, partially mediated the relationship between culture and employers' negative attitudes about job candidates with disabilities. Conclusion These results suggested that greater espousal of competitive and individualist values may drive stigmatizing attitudes across cultures. PMID:21171794

Cosmetics Europe compilation of historical serious eye damage/eye irritation in vivo data analysed by drivers of classification to support the selection of chemicals for development and evaluation of alternative methods/strategies: the Draize eye test Reference Database (DRD).

PubMed

Barroso, João; Pfannenbecker, Uwe; Adriaens, Els; Alépée, Nathalie; Cluzel, Magalie; De Smedt, Ann; Hibatallah, Jalila; Klaric, Martina; Mewes, Karsten R; Millet, Marion; Templier, Marie; McNamee, Pauline

2017-02-01

A thorough understanding of which of the effects assessed in the in vivo Draize eye test are responsible for driving UN GHS/EU CLP classification is critical for an adequate selection of chemicals to be used in the development and/or evaluation of alternative methods/strategies and for properly assessing their predictive capacity and limitations. For this reason, Cosmetics Europe has compiled a database of Draize data (Draize eye test Reference Database, DRD) from external lists that were created to support past validation activities. This database contains 681 independent in vivo studies on 634 individual chemicals representing a wide range of chemical classes. A description of all the ocular effects observed in vivo, i.e. degree of severity and persistence of corneal opacity (CO), iritis, and/or conjunctiva effects, was added for each individual study in the database, and the studies were categorised according to their UN GHS/EU CLP classification and the main effect driving the classification. An evaluation of the various in vivo drivers of classification compiled in the database was performed to establish which of these are most important from a regulatory point of view. These analyses established that the most important drivers for Cat 1 Classification are (1) CO mean ≥ 3 (days 1-3) (severity) and (2) CO persistence on day 21 in the absence of severity, and those for Cat 2 classification are (3) CO mean ≥ 1 and (4) conjunctival redness mean ≥ 2. Moreover, it is shown that all classifiable effects (including persistence and CO = 4) should be present in ≥60 % of the animals to drive a classification. As a consequence, our analyses suggest the need for a critical revision of the UN GHS/EU CLP decision criteria for the Cat 1 classification of chemicals. Finally, a number of key criteria are identified that should be taken into consideration when selecting reference chemicals for the development, evaluation and/or validation of alternative methods and/or strategies for serious eye damage/eye irritation testing. Most important, the DRD is an invaluable tool for any future activity involving the selection of reference chemicals.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

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

PubMed

Tang, Ping; Wang, Jianmin; Bourne, Patria

2008-04-01

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

Multiple early victimization experiences as a pathway to explain physical health disparities among sexual minority and heterosexual individuals.

PubMed

Andersen, Judith P; Zou, Christopher; Blosnich, John

2015-05-01

Prior research shows that health disparities exist between sexual minority and heterosexual individuals. We extend the literature by testing if the higher prevalence of childhood victimization experienced by sexual minority individuals accounts for lifetime health disparities. Heterosexual (n = 422) and sexual minority (n = 681) participants were recruited on-line in North America. Respondents completed surveys about their childhood victimization experiences (i.e., maltreatment by adults and peer victimization) and lifetime physician-diagnosed physical health conditions. Results showed that sexual minority individuals experienced higher prevalence of childhood victimization and lifetime physical health problems than heterosexuals. Mediation analyses indicated that maltreatment by adults and peer bullying explained the health disparities between sexual minority individuals and heterosexuals. This study is the first to show that multiple childhood victimization experiences may be one pathway to explain lifetime physical health disparities. Intervention programs reducing the perpetration of violence against sexual minority individuals are critical to reduce health care needs related to victimization experiences. Copyright © 2015 Elsevier Ltd. All rights reserved.

Individual Movement Variability Magnitudes Are Explained by Cortical Neural Variability.

PubMed

Haar, Shlomi; Donchin, Opher; Dinstein, Ilan

2017-09-13

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior-parietal cortex of individual subjects explained their movement-extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities. SIGNIFICANCE STATEMENT Neural activity and movement kinematics are remarkably variable. Although intertrial variability is rarely studied, here, we demonstrate that individual human subjects exhibit distinct magnitudes of neural and kinematic variability that are reproducible across movements to different targets and when performing these movements with either arm. Furthermore, when examining the relationship between cortical variability and movement variability, we find that cortical fMRI variability in parietal cortex of individual subjects explained their movement extent variability. This enabled us to explain why some subjects performed more variable movements than others based on their cortical variability magnitudes. Copyright © 2017 the authors 0270-6474/17/379076-10$15.00/0.

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Performance of fusion algorithms for computer-aided detection and classification of mines in very shallow water obtained from testing in navy Fleet Battle Exercise-Hotel 2000

NASA Astrophysics Data System (ADS)

Ciany, Charles M.; Zurawski, William; Kerfoot, Ian

2001-10-01

The performance of Computer Aided Detection/Computer Aided Classification (CAD/CAC) Fusion algorithms on side-scan sonar images was evaluated using data taken at the Navy's's Fleet Battle Exercise-Hotel held in Panama City, Florida, in August 2000. A 2-of-3 binary fusion algorithm is shown to provide robust performance. The algorithm accepts the classification decisions and associated contact locations form three different CAD/CAC algorithms, clusters the contacts based on Euclidian distance, and then declares a valid target when a clustered contact is declared by at least 2 of the 3 individual algorithms. This simple binary fusion provided a 96 percent probability of correct classification at a false alarm rate of 0.14 false alarms per image per side. The performance represented a 3.8:1 reduction in false alarms over the best performing single CAD/CAC algorithm, with no loss in probability of correct classification.

Stimulus and Response-Locked P3 Activity in a Dynamic Rapid Serial Visual Presentation (RSVP) Task

DTIC Science & Technology

2013-01-01

Perception and Psychophysics 1973, 14, 265–272. Touryan, J.; Gibson, L.; Horne, J. H.; Weber, P. Real-Time Classification of Neural Signals ...execution. 15. SUBJECT TERMS P300, RSVP, EEG, target recognition, reaction time, ERP 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT...applications and as an input signal in many brain computer interactive technologies (BCITs) for both patients and healthy individuals. ERPs are extracted

Improving the Selection, Classification, and Utilization of Army Enlisted Personnel: Final Report on Project A

DTIC Science & Technology

1991-08-01

being used in both current and long-range research programs that are expected to make the Army more effective in matching the requirements for first- and... make substantial improvements to the existing selection and classifi- cation system. xi IMPROVING THE SELECTION, CLASSIFICATION, AND UTILIZATION OF...basis for new methods of allocating personnel, and making near-real-time decisions on the best match between characteristics of an individual enlistee

An updated evolutionary classification of CRISPR–Cas systems

PubMed Central

Makarova, Kira S.; Wolf, Yuri I.; Alkhnbashi, Omer S.; Costa, Fabrizio; Shah, Shiraz A.; Saunders, Sita J.; Barrangou, Rodolphe; Brouns, Stan J. J.; Charpentier, Emmanuelle; Haft, Daniel H.; Horvath, Philippe; Moineau, Sylvain; Mojica, Francisco J. M.; Terns, Rebecca M.; Terns, Michael P.; White, Malcolm F.; Yakunin, Alexander F.; Garrett, Roger A.; van der Oost, John; Backofen, Rolf; Koonin, Eugene V.

2017-01-01

The evolution of CRISPR–cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR–cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR–Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized. PMID:26411297

Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network.

PubMed

Pham, Tuyen Danh; Lee, Dong Eun; Park, Kang Ryoung

2017-07-08

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods.

Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network

PubMed Central

Pham, Tuyen Danh; Lee, Dong Eun; Park, Kang Ryoung

2017-01-01

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods. PMID:28698466

Apollo 15 coarse fines (4-10 mm): Sample classification, description and inventory

NASA Technical Reports Server (NTRS)

Powell, B. N.

1972-01-01

A particle by particle binocular microscopic examination of all of the Apollo 15 4-10 mm fines samples is reported. These particles are classified according to their macroscopic lithologic features in order to provide a basis for sample allocations and future study. The relatively large size of these particles renders them too vaulable to permit treatment along with the other bulk fines, yet they are too small (and numerous) to practically receive full individual descriptive treatment as given the larger rock samples. This examination, classification and description of subgroups represents a compromise treatment. In most cases and for many types of investigation the individual particles should be large enough to permit the application of more than one type of analysis.

Sampling surface and subsurface particle-size distributions in wadable gravel-and cobble-bed streams for analyses in sediment transport, hydraulics, and streambed monitoring

Treesearch

Kristin Bunte; Steven R. Abt

2001-01-01

This document provides guidance for sampling surface and subsurface sediment from wadable gravel-and cobble-bed streams. After a short introduction to streams types and classifications in gravel-bed rivers, the document explains the field and laboratory measurement of particle sizes and the statistical analysis of particle-size distributions. Analysis of particle...

A review of data fusion techniques.

PubMed

Castanedo, Federico

2013-01-01

The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion.

Sexing adult black-legged kittiwakes by DNA, behavior, and morphology

USGS Publications Warehouse

Jodice, P.G.R.; Lanctot, Richard B.; Gill, V.A.; Roby, D.D.; Hatch, Shyla A.

2000-01-01

We sexed adult Black-legged Kittiwakes (Rissa tridactyla) using DNA-based genetic techniques, behavior and morphology and compared results from these techniques. Genetic and morphology data were collected on 605 breeding kittiwakes and sex-specific behaviors were recorded for a sub-sample of 285 of these individuals. We compared sex classification based on both genetic and behavioral techniques for this sub-sample to assess the accuracy of the genetic technique. DNA-based techniques correctly sexed 97.2% and sex-specific behaviors, 96.5% of this sub-sample. We used the corrected genetic classifications from this sub-sample and the genetic classifications for the remaining birds, under the assumption they were correct, to develop predictive morphometric discriminant function models for all 605 birds. These models accurately predicted the sex of 73-96% of individuals examined, depending on the sample of birds used and the characters included. The most accurate single measurement for determining sex was length of head plus bill, which correctly classified 88% of individuals tested. When both members of a pair were measured, classification levels improved and approached the accuracy of both behavioral observations and genetic analyses. Morphometric techniques were only slightly less accurate than genetic techniques but were easier to implement in the field and less costly. Behavioral observations, while highly accurate, required that birds be easily observable during the breeding season and that birds be identifiable. As such, sex-specific behaviors may best be applied as a confirmation of sex for previously marked birds. All three techniques thus have the potential to be highly accurate, and the selection of one or more will depend on the circumstances of any particular field study.

A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers

PubMed Central

Bennett, David A.; Blennow, Kaj; Carrillo, Maria C.; Feldman, Howard H.; Frisoni, Giovanni B.; Hampel, Harald; Jagust, William J.; Johnson, Keith A.; Knopman, David S.; Petersen, Ronald C.; Scheltens, Philip; Sperling, Reisa A.; Dubois, Bruno

2016-01-01

Biomarkers have become an essential component of Alzheimer disease (AD) research and because of the pervasiveness of AD pathology in the elderly, the same biomarkers are used in cognitive aging research. A number of current issues suggest that an unbiased descriptive classification scheme for these biomarkers would be useful. We propose the “A/T/N” system in which 7 major AD biomarkers are divided into 3 binary categories based on the nature of the pathophysiology that each measures. “A” refers to the value of a β-amyloid biomarker (amyloid PET or CSF Aβ42); “T,” the value of a tau biomarker (CSF phospho tau, or tau PET); and “N,” biomarkers of neurodegeneration or neuronal injury ([18F]-fluorodeoxyglucose–PET, structural MRI, or CSF total tau). Each biomarker category is rated as positive or negative. An individual score might appear as A+/T+/N−, or A+/T−/N−, etc. The A/T/N system includes the new modality tau PET. It is agnostic to the temporal ordering of mechanisms underlying AD pathogenesis. It includes all individuals in any population regardless of the mix of biomarker findings and therefore is suited to population studies of cognitive aging. It does not specify disease labels and thus is not a diagnostic classification system. It is a descriptive system for categorizing multidomain biomarker findings at the individual person level in a format that is easy to understand and use. Given the present lack of consensus among AD specialists on terminology across the clinically normal to dementia spectrum, a biomarker classification scheme will have broadest acceptance if it is independent from any one clinically defined diagnostic scheme. PMID:27371494

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

PubMed

Kim, Yong-Ku; Na, Kyoung-Sae

2018-01-03

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Statistical sensor fusion of ECG data using automotive-grade sensors

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near-infrared spectroscopy.

PubMed

Karamzadeh, Nader; Amyot, Franck; Kenney, Kimbra; Anderson, Afrouz; Chowdhry, Fatima; Dashtestani, Hadis; Wassermann, Eric M; Chernomordik, Victor; Boccara, Claude; Wegman, Edward; Diaz-Arrastia, Ramon; Gandjbakhche, Amir H

2016-11-01

We have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing the multivariate machine learning approach and introducing a novel task-related hemodynamic response detection followed by a heuristic search for optimum set of hemodynamic features. To achieve this goal, the hemodynamic response from a group of 31 healthy controls and 30 chronic TBI subjects were recorded as they performed a complexity task. To determine the optimum hemodynamic features, we considered 11 features and their combinations in characterizing TBI subjects. We investigated the significance of the features by utilizing a machine learning classification algorithm to score all the possible combinations of features according to their predictive power. The identified optimum feature elements resulted in classification accuracy, sensitivity, and specificity of 85%, 85%, and 84%, respectively. Classification improvement was achieved for TBI subject classification through feature combination. It signified the major advantage of the multivariate analysis over the commonly used univariate analysis suggesting that the features that are individually irrelevant in characterizing the data may become relevant when used in combination. We also conducted a spatio-temporal classification to identify regions within the prefrontal cortex (PFC) that contribute in distinguishing between TBI and healthy subjects. As expected, Brodmann areas (BA) 10 within the PFC were isolated as the region that healthy subjects (unlike subjects with TBI), showed major hemodynamic activity in response to the High Complexity task. Overall, our results indicate that identified temporal and spatio-temporal features from PFC's hemodynamic activity are promising biomarkers in classifying subjects with TBI.

An investigation of spatial representation of pitch in individuals with congenital amusia.

PubMed

Lu, Xuejing; Sun, Yanan; Thompson, William Forde

2017-09-01

Spatial representation of pitch plays a central role in auditory processing. However, it is unknown whether impaired auditory processing is associated with impaired pitch-space mapping. Experiment 1 examined spatial representation of pitch in individuals with congenital amusia using a stimulus-response compatibility (SRC) task. For amusic and non-amusic participants, pitch classification was faster and more accurate when correct responses involved a physical action that was spatially congruent with the pitch height of the stimulus than when it was incongruent. However, this spatial representation of pitch was not as stable in amusic individuals, revealed by slower response times when compared with control individuals. One explanation is that the SRC effect in amusics reflects a linguistic association, requiring additional time to link pitch height and spatial location. To test this possibility, Experiment 2 employed a colour-classification task. Participants judged colour while ignoring a concurrent pitch by pressing one of two response keys positioned vertically to be congruent or incongruent with the pitch. The association between pitch and space was found in both groups, with comparable response times in the two groups, suggesting that amusic individuals are only slower to respond to tasks involving explicit judgments of pitch.

Prediction of brain-computer interface aptitude from individual brain structure.

PubMed

Halder, S; Varkuti, B; Bogdan, M; Kübler, A; Rosenstiel, W; Sitaram, R; Birbaumer, N

2013-01-01

Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. This confirms that structural brain traits contribute to individual performance in BCI use.

Prediction of brain-computer interface aptitude from individual brain structure

PubMed Central

Halder, S.; Varkuti, B.; Bogdan, M.; Kübler, A.; Rosenstiel, W.; Sitaram, R.; Birbaumer, N.

2013-01-01

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use. PMID:23565083

10 CFR 71.15 - Exemption from classification as fissile material.

Code of Federal Regulations, 2012 CFR

2012-01-01

... subject to all other requirements of this part, except as noted. (a) Individual package containing 2 grams or less fissile material. (b) Individual or bulk packaging containing 15 grams or less of fissile material provided the package has at least 200 grams of solid nonfissile material for every gram of fissile...

10 CFR 71.15 - Exemption from classification as fissile material.

Code of Federal Regulations, 2010 CFR

2010-01-01

... subject to all other requirements of this part, except as noted. (a) Individual package containing 2 grams or less fissile material. (b) Individual or bulk packaging containing 15 grams or less of fissile material provided the package has at least 200 grams of solid nonfissile material for every gram of fissile...

10 CFR 71.15 - Exemption from classification as fissile material.

Code of Federal Regulations, 2013 CFR

2013-01-01

... subject to all other requirements of this part, except as noted. (a) Individual package containing 2 grams or less fissile material. (b) Individual or bulk packaging containing 15 grams or less of fissile material provided the package has at least 200 grams of solid nonfissile material for every gram of fissile...

10 CFR 71.15 - Exemption from classification as fissile material.

Code of Federal Regulations, 2011 CFR

2011-01-01

... subject to all other requirements of this part, except as noted. (a) Individual package containing 2 grams or less fissile material. (b) Individual or bulk packaging containing 15 grams or less of fissile material provided the package has at least 200 grams of solid nonfissile material for every gram of fissile...

10 CFR 71.15 - Exemption from classification as fissile material.

Code of Federal Regulations, 2014 CFR

2014-01-01

... subject to all other requirements of this part, except as noted. (a) Individual package containing 2 grams or less fissile material. (b) Individual or bulk packaging containing 15 grams or less of fissile material provided the package has at least 200 grams of solid nonfissile material for every gram of fissile...

Species rarity: definition, causes, and classification

Treesearch

Curtis H. Flather; Carolyn Hull Sieg

2007-01-01

In virtually all ecological communities around the world, most species are represented by few individuals, and most individuals come from only a few of the most common species. Why this distribution of species abundances is so regularly observed among different taxonomic sets in geographically diverse systems is a question that has received considerable theoretical and...

Biobehavioral Correlates of Depression in Reaction to Mental and Physical Challenge

DTIC Science & Technology

2007-03-07

positive effects on quality of life for individuals with depression. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same...reactivity to challenge with potential positive effects on quality of life for individuals with depression. v Biobehavioral Correlates of...Responsiveness.............................................. 22 IV. Immune System Parameters in Depression............................................ 24

Three Diagnostic Systems for Autism: DSM-III, DSM-III-R, and ICD-10.

ERIC Educational Resources Information Center

Volkmar, Fred R.; And Others

1992-01-01

This paper compared clinicians' diagnosis and DSM-III (Diagnostic and Statistical Manual), DSM-III-R (Revised), and ICD-10 (International Classification of Diseases) diagnoses of 52 individuals with autism and 62 nonautistic, developmentally disordered individuals. The DSM-III-R system overdiagnosed the presence of autism, and ICD-10 closely…

Determinants of Activity and Participation in Preschoolers with Developmental Delay

ERIC Educational Resources Information Center

Leung, Grantiana P. K.; Chan, Chetwyn C. H.; Chung, Raymond C. K.; Pang, Marco Y. C.

2011-01-01

According to the International Classification of Functioning, Disability and Health model endorsed by the World Health Organization, activity (the execution of a task or action by an individual), and participation (involvement in a life situation) are important components in the assessment of health and functioning of an individual. The purpose of…

Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search

PubMed Central

Shi, Lei; Wan, Youchuan; Gao, Xianjun

2018-01-01

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy. PMID:29581721

A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.

PubMed

Cacha, L A; Parida, S; Dehuri, S; Cho, S-B; Poznanski, R R

2016-12-01

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

Decimated Input Ensembles for Improved Generalization

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Norvig, Peter (Technical Monitor)

1999-01-01

Recently, many researchers have demonstrated that using classifier ensembles (e.g., averaging the outputs of multiple classifiers before reaching a classification decision) leads to improved performance for many difficult generalization problems. However, in many domains there are serious impediments to such "turnkey" classification accuracy improvements. Most notable among these is the deleterious effect of highly correlated classifiers on the ensemble performance. One particular solution to this problem is generating "new" training sets by sampling the original one. However, with finite number of patterns, this causes a reduction in the training patterns each classifier sees, often resulting in considerably worsened generalization performance (particularly for high dimensional data domains) for each individual classifier. Generally, this drop in the accuracy of the individual classifier performance more than offsets any potential gains due to combining, unless diversity among classifiers is actively promoted. In this work, we introduce a method that: (1) reduces the correlation among the classifiers; (2) reduces the dimensionality of the data, thus lessening the impact of the 'curse of dimensionality'; and (3) improves the classification performance of the ensemble.

Reconsidering the classification of tick-borne encephalitis virus within the Siberian subtype gives new insights into its evolutionary history.

PubMed

Kovalev, S Y; Mukhacheva, T A

2017-11-01

Tick-borne encephalitis is widespread in Eurasia and transmitted by Ixodes ticks. Classification of its causative agent, tick-borne encephalitis virus (TBEV), includes three subtypes, namely Far-Eastern, European, and Siberian (TBEV-Sib), as well as a group of 886-84-like strains with uncertain taxonomic status. TBEV-Sib is subdivided into three phylogenetic lineages: Baltic, Asian, and South-Siberian. A reason to reconsider TBEV-Sib classification was the analysis of 186 nucleotide sequences of an E gene fragment submitted to GenBank during the last two years. Within the South-Siberian lineage, we have identified a distinct group with prototype strains Aina and Vasilchenko as an individual lineage named East-Siberian. The analysis of reclassified lineages has promoted a new model of the evolutionary history of TBEV-Sib lineages and TBEV-Sib as a whole. Moreover, we present arguments supporting separation of 886-84-like strains into an individual TBEV subtype, which we propose to name Baikalian (TBEV-Bkl). Copyright © 2017 Elsevier B.V. All rights reserved.

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

Webb-Robertson, Bobbie-Jo M.; Wiberg, Holli K.; Matzke, Melissa M.

In this review, we apply selected imputation strategies to label-free liquid chromatography–mass spectrometry (LC–MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC–MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yieldedmore » the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. In summary, on the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.« less

Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Handoko, Stephanus Daniel; Kwoh, Chee Keong; Ong, Yew Soon

Regressions has successfully been incorporated into memetic algorithm (MA) to build surrogate models for the objective or constraint landscape of optimization problems. This helps to alleviate the needs for expensive fitness function evaluations by performing local refinements on the approximated landscape. Classifications can alternatively be used to assist MA on the choice of individuals that would experience refinements. Support-vector-assisted MA were recently proposed to alleviate needs for function evaluations in the inequality-constrained optimization problems by distinguishing regions of feasible solutions from those of the infeasible ones based on some past solutions such that search efforts can be focussed on some potential regions only. For problems having equality constraints, however, the feasible space would obviously be extremely small. It is thus extremely difficult for the global search component of the MA to produce feasible solutions. Hence, the classification of feasible and infeasible space would become ineffective. In this paper, a novel strategy to overcome such limitation is proposed, particularly for problems having one and only one equality constraint. The raw constraint value of an individual, instead of its feasibility class, is utilized in this work.

Multitask SVM learning for remote sensing data classification

NASA Astrophysics Data System (ADS)

Leiva-Murillo, Jose M.; Gómez-Chova, Luis; Camps-Valls, Gustavo

2010-10-01

Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.

Validation of a new classification for periprosthetic shoulder fractures.

PubMed

Kirchhoff, Chlodwig; Beirer, Marc; Brunner, Ulrich; Buchholz, Arne; Biberthaler, Peter; Crönlein, Moritz

2018-06-01

Successful treatment of periprosthetic shoulder fractures depends on the right strategy, starting with a well-structured classification of the fracture. Unfortunately, clinically relevant factors for treatment planning are missing in the pre-existing classifications. Therefore, the aim of the present study was to describe a new specific classification system for periprosthetic shoulder fractures including a structured treatment algorithm for this important fragility fracture issue. The classification was established, focussing on five relevant items, naming the prosthesis type, the fracture localisation, the rotator cuff status, the anatomical fracture region and the stability of the implant. After considering each single item, the individual treatment concept can be assessed in one last step. To evaluate the introduced classification, a retrospective analysis of pre- and post-operative data of patients, treated with periprosthetic shoulder fractures, was conducted by two board certified trauma surgery consultants. The data of 19 patients (8 male, 11 female) with a mean age of 74 ± five years have been analysed in our study. The suggested treatment algorithm was proven to be reliable, detected by good clinical outcome in 15 of 16 (94%) cases, where the suggested treatment was maintained. Only one case resulted in poor outcome due to post-operative wound infection and had to be revised. The newly developed six-step classification is easy to utilise and extends the pre-existing classification systems in terms of clinically-relevant information. This classification should serve as a simple tool for the surgeon to consider the optimal treatment for his patients.

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.

Influence of Texture and Colour in Breast TMA Classification

PubMed Central

Fernández-Carrobles, M. Milagro; Bueno, Gloria; Déniz, Oscar; Salido, Jesús; García-Rojo, Marcial; González-López, Lucía

2015-01-01

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors. PMID:26513238

Classification of multiple sclerosis lesions using adaptive dictionary learning.

PubMed

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Classification Systems for Individual Differences in Multiple-task Performance and Subjective Estimates of Workload

NASA Technical Reports Server (NTRS)

Damos, D. L.

1984-01-01

Human factors practitioners often are concerned with mental workload in multiple-task situations. Investigations of these situations have demonstrated repeatedly that individuals differ in their subjective estimates of workload. These differences may be attributed in part to individual differences in definitions of workload. However, after allowing for differences in the definition of workload, there are still unexplained individual differences in workload ratings. The relation between individual differences in multiple-task performance, subjective estimates of workload, information processing abilities, and the Type A personality trait were examined.

Snacking now or later? Individual differences in following intentions or habits explained by time perspective.

PubMed

Onwezen, M C; Van 't Riet, J; Dagevos, H; Sijtsema, S J; Snoek, H M

2016-12-01

Even when individuals are aware of long-term health effects of their diet, and form healthy intentions, they often engage in relatively unhealthy snacking habits. Some individuals fall back on unhealthy habits more easily than others. We aim to explore whether time perspective can explain why some individuals are more prone to rely on habits and others on intentions. Study 1 (N = 1503) provides a first exploration of the role of time perspective by exploring individual differences in perception of long-term and short-term consequences. In accordance with our hypotheses, Study 1 shows that habits are associated with short-term consequences and intentions with long-term consequences. Study 2 (N = 1497) shows that the effects of habits on snacking behaviour are strengthened by a present time perspective, whereas the effects of intentions on snacking behaviour are strengthened by a future time perspective. These findings imply that there is a fundamental difference in the guiding function of intentions and habits which might explain individual differences in following intentions versus habits. Individuals with a long-term perspective are more inclined to follow intentions and individuals with a short-term perspective are more inclined to follow habits. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

NASA Astrophysics Data System (ADS)

Bangs, Corey F.; Kruse, Fred A.; Olsen, Chris R.

2013-05-01

Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were used from the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) portions of the electromagnetic spectrum. Haralick texture features (contrast, entropy, and correlation) were extracted from the average gray-level image for each of the four spectral ranges studied. A maximum likelihood classifier was trained using a set of ground truth regions of interest (ROIs) and applied separately to the spectral data, texture data, and a fused dataset containing both. Classification accuracy was measured by comparison of results to a separate verification set of test ROIs. Analysis indicates that the spectral range (source of the gray-level image) used to extract the texture feature data has a significant effect on the classification accuracy. This result applies to texture-only classifications as well as the classification of integrated spectral data and texture feature data sets. Overall classification improvement for the integrated data sets was near 1%. Individual improvement for integrated spectral and texture classification of the "Urban" class showed approximately 9% accuracy increase over spectral-only classification. Texture-only classification accuracy was highest for the "Dirt Path" class at approximately 92% for the spectral range from 947 to 1343nm. This research demonstrates the effectiveness of texture feature data for more accurate analysis of hyperspectral data and the importance of selecting the correct spectral range to be used for the gray-level image source to extract these features.

Correlational selection on personality and social plasticity: morphology and social context determine behavioural effects on mating success.

PubMed

Montiglio, Pierre-Olivier; Wey, Tina W; Chang, Ann T; Fogarty, Sean; Sih, Andrew

2017-03-01

Despite a central line of research aimed at quantifying relationships between mating success and sexually dimorphic traits (e.g., ornaments), individual variation in sexually selected traits often explains only a modest portion of the variation in mating success. Another line of research suggests that a significant portion of the variation in mating success observed in animal populations could be explained by correlational selection, where the fitness advantage of a given trait depends on other components of an individual's phenotype and/or its environment. We tested the hypothesis that interactions between multiple traits within an individual (phenotype dependence) or between an individual's phenotype and its social environment (context dependence) can select for individual differences in behaviour (i.e., personality) and social plasticity. To quantify the importance of phenotype- and context-dependent selection on mating success, we repeatedly measured the behaviour, social environment and mating success of about 300 male stream water striders, Aquarius remigis. Rather than explaining individual differences in long-term mating success, we instead quantified how the combination of a male's phenotype interacted with the immediate social context to explain variation in hour-by-hour mating decisions. We suggest that this analysis captures more of the mechanisms leading to differences in mating success. Males differed consistently in activity, aggressiveness and social plasticity. The mating advantage of these behavioural traits depended on male morphology and varied with the number of rival males in the pool, suggesting mechanisms selecting for consistent differences in behaviour and social plasticity. Accounting for phenotype and context dependence improved the amount of variation in male mating success we explained statistically by 30-274%. Our analysis of the determinants of male mating success provides important insights into the evolutionary forces that shape phenotypic variation. In particular, our results suggest that sexual selection is likely to favour individual differences in behaviour, social plasticity (i.e., individuals adjusting their behaviour), niche preference (i.e., individuals dispersing to particular social conditions) or social niche construction (i.e., individuals modifying the social environment). The true effect of sexual traits can only be understood in interaction with the individual's phenotype and environment. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

Learning viewpoint invariant object representations using a temporal coherence principle.

PubMed

Einhäuser, Wolfgang; Hipp, Jörg; Eggert, Julian; Körner, Edgar; König, Peter

2005-07-01

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability" objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an--also unlabeled--distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformations.

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

PubMed Central

Zhao, Changbo; Li, Guo-Zheng; Wang, Chengjun; Niu, Jinling

2015-01-01

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification. PMID:26246834

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

PubMed

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

2009-06-01

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

A software package for interactive motor unit potential classification using fuzzy k-NN classifier.

PubMed

Rasheed, Sarbast; Stashuk, Daniel; Kamel, Mohamed

2008-01-01

We present an interactive software package for implementing the supervised classification task during electromyographic (EMG) signal decomposition process using a fuzzy k-NN classifier and utilizing the MATLAB high-level programming language and its interactive environment. The method employs an assertion-based classification that takes into account a combination of motor unit potential (MUP) shapes and two modes of use of motor unit firing pattern information: the passive and the active modes. The developed package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into motor unit potential trains (MUPTs) using assertion-based classifiers.

Land classification of south-central Iowa from computer enhanced images

NASA Technical Reports Server (NTRS)

Lucas, J. R.; Taranik, J. V.; Billingsley, F. C. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Enhanced LANDSAT imagery was most useful for land classification purposes, because these images could be photographically printed at large scales such as 1:63,360. The ability to see individual picture elements was no hindrance as long as general image patterns could be discerned. Low cost photographic processing systems for color printings have proved to be effective in the utilization of computer enhanced LANDSAT products for land classification purposes. The initial investment for this type of system was very low, ranging from $100 to $200 beyond a black and white photo lab. The technical expertise can be acquired from reading a color printing and processing manual.

Effectiveness of repeated examination to diagnose enterobiasis in nursery school groups.

PubMed

Remm, Mare; Remm, Kalle

2009-09-01

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions.

Effectiveness of Repeated Examination to Diagnose Enterobiasis in Nursery School Groups

PubMed Central

Remm, Kalle

2009-01-01

The aim of this study was to estimate the benefit from repeated examinations in the diagnosis of enterobiasis in nursery school groups, and to test the effectiveness of individual-based risk predictions using different methods. A total of 604 children were examined using double, and 96 using triple, anal swab examinations. The questionnaires for parents, structured observations, and interviews with supervisors were used to identify factors of possible infection risk. In order to model the risk of enterobiasis at individual level, a similarity-based machine learning and prediction software Constud was compared with data mining methods in the Statistica 8 Data Miner software package. Prevalence according to a single examination was 22.5%; the increase as a result of double examinations was 8.2%. Single swabs resulted in an estimated prevalence of 20.1% among children examined 3 times; double swabs increased this by 10.1%, and triple swabs by 7.3%. Random forest classification, boosting classification trees, and Constud correctly predicted about 2/3 of the results of the second examination. Constud estimated a mean prevalence of 31.5% in groups. Constud was able to yield the highest overall fit of individual-based predictions while boosting classification tree and random forest models were more effective in recognizing Enterobius positive persons. As a rule, the actual prevalence of enterobiasis is higher than indicated by a single examination. We suggest using either the values of the mean increase in prevalence after double examinations compared to single examinations or group estimations deduced from individual-level modelled risk predictions. PMID:19724696

Sex determination and disorders of sex development according to the revised nomenclature and classification in 46,XX individuals.

PubMed

Kousta, Eleni; Papathanasiou, Asteroula; Skordis, Nicos

2010-01-01

There have been considerable advances concerning understanding of the early and later stages of ovarian development; a number of genes have been implicated and their mutations have been associated with developmental abnormalities. The most important genes controlling the initial phase of gonadal development, identical in females and males, are Wilms' tumor suppressor 1 (WT1) and steroidogenic factor 1 (SF1). Four genes are likely to be involved in the subsequent stages of ovarian development (WNT4, DAX1, FOXL2 and RSPO1), but none is yet proven to be the ovarian determining factor. Changes in nomenclature and classification were recently proposed in order to incorporate genetic advances and substitute gender-based diagnostic labels in terminology. The term "disorders of sex development" (DSD) is proposed to substitute the previous term "intersex disorders". Three main categories have been used to describe DSD in the 46,XX individual: 1) disorders of gonadal (ovarian) development: ovotesticular DSD, previously named true hermaphroditism, testicular DSD, previously named XX males, and gonadal dysgenesis; 2) disorders related to androgen excess (congenital adrenal hyperplasia, aromatase deficiency and P450 oxidoreductase deficiency); and 3) other rare disorders. In this mini-review, recent advances concerning development of the genital system in 46,XX individuals and related abnormalities are discussed. Basic embryology of the ovary and molecular pathways determining ovarian development are reviewed, focusing on mutations disrupting normal ovarian development. Disorders of sex development according to the revised nomenclature and classification in 46,XX individuals are summarized, including genetic progress in the field.

Case definition terminology for paratuberculosis (Johne's disease).

PubMed

Whittington, R J; Begg, D J; de Silva, K; Purdie, A C; Dhand, N K; Plain, K M

2017-11-09

Paratuberculosis (Johne's disease) is an economically significant condition caused by Mycobacterium avium subsp. paratuberculosis. However, difficulties in diagnosis and classification of individual animals with the condition have hampered research and impeded efforts to halt its progressive spread in the global livestock industry. Descriptive terms applied to individual animals and herds such as exposed, infected, diseased, clinical, sub-clinical, infectious and resistant need to be defined so that they can be incorporated consistently into well-understood and reproducible case definitions. These allow for consistent classification of individuals in a population for the purposes of analysis based on accurate counts. The outputs might include the incidence of cases, frequency distributions of the number of cases by age class or more sophisticated analyses involving statistical comparisons of immune responses in vaccine development studies, or gene frequencies or expression data from cases and controls in genomic investigations. It is necessary to have agreed definitions in order to be able to make valid comparisons and meta-analyses of experiments conducted over time by a given researcher, in different laboratories, by different researchers, and in different countries. In this paper, terms are applied systematically in an hierarchical flow chart to enable classification of individual animals. We propose descriptive terms for different stages in the pathogenesis of paratuberculosis to enable their use in different types of studies and to enable an independent assessment of the extent to which accepted definitions for stages of disease have been applied consistently in any given study. This will assist in the general interpretation of data between studies, and will facilitate future meta-analyses.

Individual Finger Control of the Modular Prosthetic Limb using High-Density Electrocorticography in a Human Subject

PubMed Central

Fifer, Matthew S.; Johannes, Matthew S.; Katyal, Kapil D.; Para, Matthew P.; Armiger, Robert; Anderson, William S.; Thakor, Nitish V.; Wester, Brock A.; Crone, Nathan E.

2016-01-01

Objective We used native sensorimotor representations of fingers in a brain-machine interface to achieve immediate online control of individual prosthetic fingers. Approach Using high gamma responses recorded with a high-density ECoG array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: 1) if any finger was moving, and, if so, 2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory (JHU/APL) Modular Prosthetic Limb (MPL). Main Results The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Significance Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time. PMID:26863276

Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking.

PubMed

Larrañaga, Ana; Bielza, Concha; Pongrácz, Péter; Faragó, Tamás; Bálint, Anna; Larrañaga, Pedro

2015-03-01

Barking is perhaps the most characteristic form of vocalization in dogs; however, very little is known about its role in the intraspecific communication of this species. Besides the obvious need for ethological research, both in the field and in the laboratory, the possible information content of barks can also be explored by computerized acoustic analyses. This study compares four different supervised learning methods (naive Bayes, classification trees, [Formula: see text]-nearest neighbors and logistic regression) combined with three strategies for selecting variables (all variables, filter and wrapper feature subset selections) to classify Mudi dogs by sex, age, context and individual from their barks. The classification accuracy of the models obtained was estimated by means of [Formula: see text]-fold cross-validation. Percentages of correct classifications were 85.13 % for determining sex, 80.25 % for predicting age (recodified as young, adult and old), 55.50 % for classifying contexts (seven situations) and 67.63 % for recognizing individuals (8 dogs), so the results are encouraging. The best-performing method was [Formula: see text]-nearest neighbors following a wrapper feature selection approach. The results for classifying contexts and recognizing individual dogs were better with this method than they were for other approaches reported in the specialized literature. This is the first time that the sex and age of domestic dogs have been predicted with the help of sound analysis. This study shows that dog barks carry ample information regarding the caller's indexical features. Our computerized analysis provides indirect proof that barks may serve as an important source of information for dogs as well.

Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject

NASA Astrophysics Data System (ADS)

Hotson, Guy; McMullen, David P.; Fifer, Matthew S.; Johannes, Matthew S.; Katyal, Kapil D.; Para, Matthew P.; Armiger, Robert; Anderson, William S.; Thakor, Nitish V.; Wester, Brock A.; Crone, Nathan E.

2016-04-01

Objective. We used native sensorimotor representations of fingers in a brain-machine interface (BMI) to achieve immediate online control of individual prosthetic fingers. Approach. Using high gamma responses recorded with a high-density electrocorticography (ECoG) array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: (1) if any finger was moving, and, if so, (2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory modular prosthetic limb. Main results. The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Significance. Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time.

A Comparison of the Human Characteristics of Curriculum Materials Used in an Air National Guard Leadership Development Program with the Characteristics of Students Encountering the Materials: A Study Using the Annehurst Curriculum Classification System.

ERIC Educational Resources Information Center

French, Russell L.; And Others

The Annehurst Curriculum Classification System (ACCS), a tool for matching individual learners with appropriate curriculum materials, was used with a group of fifty-nine students (Air National Guard officer candidates) and their four instructor-advisors to examine two issues: (1) the applicability of the ACCS in a highly structured,…

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.

Migraine classification using magnetic resonance imaging resting-state functional connectivity data.

PubMed

Chong, Catherine D; Gaw, Nathan; Fu, Yinlin; Li, Jing; Wu, Teresa; Schwedt, Todd J

2017-08-01

Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

A novel approach to internal crown characterization for coniferous tree species classification

NASA Astrophysics Data System (ADS)

Harikumar, A.; Bovolo, F.; Bruzzone, L.

2016-10-01

The knowledge about individual trees in forest is highly beneficial in forest management. High density small foot- print multi-return airborne Light Detection and Ranging (LiDAR) data can provide a very accurate information about the structural properties of individual trees in forests. Every tree species has a unique set of crown structural characteristics that can be used for tree species classification. In this paper, we use both the internal and external crown structural information of a conifer tree crown, derived from a high density small foot-print multi-return LiDAR data acquisition for species classification. Considering the fact that branches are the major building blocks of a conifer tree crown, we obtain the internal crown structural information using a branch level analysis. The structure of each conifer branch is represented using clusters in the LiDAR point cloud. We propose the joint use of the k-means clustering and geometric shape fitting, on the LiDAR data projected onto a novel 3-dimensional space, to identify branch clusters. After mapping the identified clusters back to the original space, six internal geometric features are estimated using a branch-level analysis. The external crown characteristics are modeled by using six least correlated features based on cone fitting and convex hull. Species classification is performed using a sparse Support Vector Machines (sparse SVM) classifier.

Cesarean section trends in the Nordic Countries - a comparative analysis with the Robson classification.

PubMed

Pyykönen, Aura; Gissler, Mika; Løkkegaard, Ellen; Bergholt, Thomas; Rasmussen, Steen C; Smárason, Alexander; Bjarnadóttir, Ragnheiður I; Másdóttir, Birna B; Källén, Karin; Klungsoyr, Kari; Albrechtsen, Susanne; Skjeldestad, Finn E; Tapper, Anna-Maija

2017-05-01

The cesarean rates are low but increasing in most Nordic countries. Using the Robson classification, we analyzed which obstetric groups have contributed to the changes in the cesarean rates. Retrospective population-based registry study including all deliveries (3 398 586) between 2000 and 2011 in Denmark, Finland, Iceland, Norway and Sweden. The Robson group distribution, cesarean rate and contribution of each Robson group were analyzed nationally for four 3-year time periods. For each country, we analyzed which groups contributed to the change in the total cesarean rate. Between the first and the last time period studied, the total cesarean rates increased in Denmark (16.4 to 20.7%), Norway (14.4 to 16.5%) and Sweden (15.5 to 17.1%), but towards the end of our study, the cesarean rates stabilized or even decreased. The increase was explained mainly by increases in the absolute contribution from R5 (women with previous cesarean) and R2a (induced labor on nulliparous). In Finland, the cesarean rate decreased slightly (16.5 to 16.2%) mainly due to decrease among R5 and R6-R7 (breech presentation, nulliparous/multiparous). In Iceland, the cesarean rate decreased in all parturient groups (17.6 to 15.3%), most essentially among nulliparous women despite the increased induction rates. The increased total cesarean rates in the Nordic countries are explained by increased cesarean rates among nulliparous women, and by an increased percentage of women with previous cesarean. Meanwhile, induction rates on nulliparous increased significantly, but the impact on the total cesarean rate was unclear. The Robson classification facilitates benchmarking and targeting efforts for lowering the cesarean rates. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

Effect of Process-Oriented Guided-Inquiry Learning on Non-majors Biology Students' Understanding of Biological Classification

NASA Astrophysics Data System (ADS)

Wozniak, Breann M.

The purpose of this study was to examine the effect of process-oriented guided-inquiry learning (POGIL) on non-majors college biology students' understanding of biological classification. This study addressed an area of science instruction, POGIL in the non-majors college biology laboratory, which has yet to be qualitatively and quantitatively researched. A concurrent triangulation mixed methods approach was used. Students' understanding of biological classification was measured in two areas: scores on pre and posttests (consisting of 11 multiple choice questions), and conceptions of classification as elicited in pre and post interviews and instructor reflections. Participants were Minnesota State University, Mankato students enrolled in BIOL 100 Summer Session. One section was taught with the traditional curriculum (n = 6) and the other section in the POGIL curriculum (n = 10) developed by the researcher. Three students from each section were selected to take part in pre and post interviews. There were no significant differences within each teaching method (p < .05). There was a tendency of difference in the means. The POGIL group may have scored higher on the posttest (M = 8.830 +/- .477 vs. M = 7.330 +/- .330; z =-1.729, p = .084) and the traditional group may have scored higher on the pretest than the posttest (M = 8.333 +/- .333 vs M = 7.333 +/- .333; z = -1.650 , p = .099). Two themes emerged after the interviews and instructor reflections: 1) After instruction students had a more extensive understanding of classification in three areas: vocabulary terms, physical characteristics, and types of evidence used to classify. Both groups extended their understanding, but only POGIL students could explain how molecular evidence is used in classification. 2) The challenges preventing students from understanding classification were: familiar animal categories and aquatic habitats, unfamiliar organisms, combining and subdividing initial groupings, and the hierarchical nature of classification. The POGIL students were the only group to surpass these challenges after the teaching intervention. This study shows that POGIL is an effective technique at eliciting students' misconceptions, and addressing these misconceptions, leading to an increase in student understanding of biological classification.

Examination of Variables That May Affect the Relationship Between Cognition and Functional Status in Individuals with Mild Cognitive Impairment: A Meta-Analysis

PubMed Central

Mcalister, Courtney; Schmitter-Edgecombe, Maureen; Lamb, Richard

2016-01-01

The objective of this meta-analysis was to improve understanding of the heterogeneity in the relationship between cognition and functional status in individuals with mild cognitive impairment (MCI). Demographic, clinical, and methodological moderators were examined. Cognition explained an average of 23% of the variance in functional outcomes. Executive function measures explained the largest amount of variance (37%), whereas global cognitive status and processing speed measures explained the least (20%). Short- and long-delayed memory measures accounted for more variance (35% and 31%) than immediate memory measures (18%), and the relationship between cognition and functional outcomes was stronger when assessed with informant-report (28%) compared with self-report (21%). Demographics, sample characteristics, and type of everyday functioning measures (i.e., questionnaire, performance-based) explained relatively little variance compared with cognition. Executive functioning, particularly measured by Trails B, was a strong predictor of everyday functioning in individuals with MCI. A large proportion of variance remained unexplained by cognition. PMID:26743326

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

PubMed

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

2017-01-01

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

The challenge of monitoring elusive large carnivores: An accurate and cost-effective tool to identify and sex pumas (Puma concolor) from footprints.

PubMed

Alibhai, Sky; Jewell, Zoe; Evans, Jonah

2017-01-01

Acquiring reliable data on large felid populations is crucial for effective conservation and management. However, large felids, typically solitary, elusive and nocturnal, are difficult to survey. Tagging and following individuals with VHF or GPS technology is the standard approach, but costs are high and these methodologies can compromise animal welfare. Such limitations can restrict the use of these techniques at population or landscape levels. In this paper we describe a robust technique to identify and sex individual pumas from footprints. We used a standardized image collection protocol to collect a reference database of 535 footprints from 35 captive pumas over 10 facilities; 19 females (300 footprints) and 16 males (235 footprints), ranging in age from 1-20 yrs. Images were processed in JMP data visualization software, generating one hundred and twenty three measurements from each footprint. Data were analyzed using a customized model based on a pairwise trail comparison using robust cross-validated discriminant analysis with a Ward's clustering method. Classification accuracy was consistently > 90% for individuals, and for the correct classification of footprints within trails, and > 99% for sex classification. The technique has the potential to greatly augment the methods available for studying puma and other elusive felids, and is amenable to both citizen-science and opportunistic/local community data collection efforts, particularly as the data collection protocol is inexpensive and intuitive.

Classification of Aircraft Maneuvers for Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Clancy, Daniel (Technical Monitor)

2002-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data is a reasonable match to known examples of proper operation. In our domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. We explain where this subsystem fits into our envisioned fault detection system as well its experiments showing the promise of this classification subsystem.