Soybean kinome: functional classification and gene expression patterns

PubMed Central

Liu, Jinyi; Chen, Nana; Grant, Joshua N.; Cheng, Zong-Ming (Max); Stewart, C. Neal; Hewezi, Tarek

2015-01-01

The protein kinase (PK) gene family is one of the largest and most highly conserved gene families in plants and plays a role in nearly all biological functions. While a large number of genes have been predicted to encode PKs in soybean, a comprehensive functional classification and global analysis of expression patterns of this large gene family is lacking. In this study, we identified the entire soybean PK repertoire or kinome, which comprised 2166 putative PK genes, representing 4.67% of all soybean protein-coding genes. The soybean kinome was classified into 19 groups, 81 families, and 122 subfamilies. The receptor-like kinase (RLK) group was remarkably large, containing 1418 genes. Collinearity analysis indicated that whole-genome segmental duplication events may have played a key role in the expansion of the soybean kinome, whereas tandem duplications might have contributed to the expansion of specific subfamilies. Gene structure, subcellular localization prediction, and gene expression patterns indicated extensive functional divergence of PK subfamilies. Global gene expression analysis of soybean PK subfamilies revealed tissue- and stress-specific expression patterns, implying regulatory functions over a wide range of developmental and physiological processes. In addition, tissue and stress co-expression network analysis uncovered specific subfamilies with narrow or wide interconnected relationships, indicative of their association with particular or broad signalling pathways, respectively. Taken together, our analyses provide a foundation for further functional studies to reveal the biological and molecular functions of PKs in soybean. PMID:25614662

Structural classification of proteins using texture descriptors extracted from the cellular automata image.

PubMed

Kavianpour, Hamidreza; Vasighi, Mahdi

2017-02-01

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.

Aperiodic topological order in the domain configurations of functional materials

NASA Astrophysics Data System (ADS)

Huang, Fei-Ting; Cheong, Sang-Wook

2017-03-01

In numerous functional materials, such as steels, ferroelectrics and magnets, new functionalities can be achieved through the engineering of the domain structures, which are associated with the ordering of certain parameters within the material. The recent progress in technologies that enable imaging at atomic-scale spatial resolution has transformed our understanding of domain topology, revealing that, along with simple stripe-like or irregularly shaped domains, intriguing vortex-type topological domain configurations also exist. In this Review, we present a new classification scheme of 'Zm Zn domains with Zl vortices' for 2D macroscopic domain structures with m directional variants and n translational antiphases. This classification, together with the concepts of topological protection and topological charge conservation, can be applied to a wide range of materials, such as multiferroics, improper ferroelectrics, layered transition metal dichalcogenides and magnetic superconductors, as we discuss using selected examples. The resulting topological considerations provide a new basis for the understanding of the formation, kinetics, manipulation and property optimization of domains and domain boundaries in functional materials.

Metagenomics of an Alkaline Hot Spring in Galicia (Spain): Microbial Diversity Analysis and Screening for Novel Lipolytic Enzymes.

PubMed

López-López, Olalla; Knapik, Kamila; Cerdán, Maria-Esperanza; González-Siso, María-Isabel

2015-01-01

A fosmid library was constructed with the metagenomic DNA from the water of the Lobios hot spring (76°C, pH = 8.2) located in Ourense (Spain). Metagenomic sequencing of the fosmid library allowed the assembly of 9722 contigs ranging in size from 500 to 56,677 bp and spanning ~18 Mbp. 23,207 ORFs (Open Reading Frames) were predicted from the assembly. Biodiversity was explored by taxonomic classification and it revealed that bacteria were predominant, while the archaea were less abundant. The six most abundant bacterial phyla were Deinococcus-Thermus, Proteobacteria, Firmicutes, Acidobacteria, Aquificae, and Chloroflexi. Within the archaeal superkingdom, the phylum Thaumarchaeota was predominant with the dominant species "Candidatus Caldiarchaeum subterraneum." Functional classification revealed the genes associated to one-carbon metabolism as the most abundant. Both taxonomic and functional classifications showed a mixture of different microbial metabolic patterns: aerobic and anaerobic, chemoorganotrophic and chemolithotrophic, autotrophic and heterotrophic. Remarkably, the presence of genes encoding enzymes with potential biotechnological interest, such as xylanases, galactosidases, proteases, and lipases, was also revealed in the metagenomic library. Functional screening of this library was subsequently done looking for genes encoding lipolytic enzymes. Six genes conferring lipolytic activity were identified and one was cloned and characterized. This gene was named LOB4Est and it was expressed in a yeast mesophilic host. LOB4Est codes for a novel esterase of family VIII, with sequence similarity to β-lactamases, but with unusual wide substrate specificity. When the enzyme was purified from the mesophilic host it showed half-life of 1 h and 43 min at 50°C, and maximal activity at 40°C and pH 7.5 with p-nitrophenyl-laurate as substrate. Interestingly, the enzyme retained more than 80% of maximal activity in a broad range of pH from 6.5 to 8.

Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis.

PubMed

Ortiz, Andrés; Munilla, Jorge; Álvarez-Illán, Ignacio; Górriz, Juan M; Ramírez, Javier

2015-01-01

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but we also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method.

Effects of gross motor function and manual function levels on performance-based ADL motor skills of children with spastic cerebral palsy.

PubMed

Park, Myoung-Ok

2017-02-01

[Purpose] The purpose of this study was to determine effects of Gross Motor Function Classification System and Manual Ability Classification System levels on performance-based motor skills of children with spastic cerebral palsy. [Subjects and Methods] Twenty-three children with cerebral palsy were included. The Assessment of Motor and Process Skills was used to evaluate performance-based motor skills in daily life. Gross motor function was assessed using Gross Motor Function Classification Systems, and manual function was measured using the Manual Ability Classification System. [Results] Motor skills in daily activities were significantly different on Gross Motor Function Classification System level and Manual Ability Classification System level. According to the results of multiple regression analysis, children categorized as Gross Motor Function Classification System level III scored lower in terms of performance based motor skills than Gross Motor Function Classification System level I children. Also, when analyzed with respect to Manual Ability Classification System level, level II was lower than level I, and level III was lower than level II in terms of performance based motor skills. [Conclusion] The results of this study indicate that performance-based motor skills differ among children categorized based on Gross Motor Function Classification System and Manual Ability Classification System levels of cerebral palsy.

Age group estimation in free-ranging African elephants based on acoustic cues of low-frequency rumbles

PubMed Central

Stoeger, Angela S.; Zeppelzauer, Matthias; Baotic, Anton

2015-01-01

Animal vocal signals are increasingly used to monitor wildlife populations and to obtain estimates of species occurrence and abundance. In the future, acoustic monitoring should function not only to detect animals, but also to extract detailed information about populations by discriminating sexes, age groups, social or kin groups, and potentially individuals. Here we show that it is possible to estimate age groups of African elephants (Loxodonta africana) based on acoustic parameters extracted from rumbles recorded under field conditions in a National Park in South Africa. Statistical models reached up to 70 % correct classification to four age groups (infants, calves, juveniles, adults) and 95 % correct classification when categorising into two groups (infants/calves lumped into one group versus adults). The models revealed that parameters representing absolute frequency values have the most discriminative power. Comparable classification results were obtained by fully automated classification of rumbles by high-dimensional features that represent the entire spectral envelope, such as MFCC (75 % correct classification) and GFCC (74 % correct classification). The reported results and methods provide the scientific foundation for a future system that could potentially automatically estimate the demography of an acoustically monitored elephant group or population. PMID:25821348

Esophageal motor disorders in subjects with incidentally discovered Chagas disease: a study using high-resolution manometry and the Chicago classification.

PubMed

Remes-Troche, J M; Torres-Aguilera, M; Antonio-Cruz, K A; Vazquez-Jimenez, G; De-La-Cruz-Patiño, E

2014-08-01

In patients with chronic indeterminate Chagas disease, conventional manometry has shown that 25-48% had esophageal motor disorders. Recently, esophageal high-resolution manometry (HRM) has revolutionized the assessment of esophageal motor function. In this study, we performed esophageal HRM in a group of subjects with incidentally positive serological findings for Trypanosoma cruzi. In this prospective observational study, we evaluated subjects who had positive serological tests for Chagas disease detected during a screening evaluation for blood donation. All subjects underwent symptomatic evaluation and esophageal HRM with a 36 solid-state catheter. Esophageal abnormalities were classified using the Chicago classification. Forty-two healthy subjects (38 males) aged 18-61 years (mean age, 40.7 years) were included. When specific symptoms questionnaire was applied, 14 (33%) subjects had esophageal symptoms. Esophageal high-resolution manometry revealed that 28 (66%) of the subjects had an esophageal motility disorder according to the Chicago classification. Most common findings were hypocontractile disorders in 18 subjects (43%) and esophagogastric junction (EGJ) outflow obstruction in 6 (15%). Esophageal high-resolution manometry reveals that up to two thirds of the subjects with an incidental diagnosis of Chagas disease have esophageal abnormalities. This technology increases the detection and allows a more complete assessment of esophageal motor function in subjects infected with T. cruzi even in the early stages of the disease. © 2012 Copyright the Authors. Journal compilation © 2012, Wiley Periodicals, Inc. and the International Society for Diseases of the Esophagus.

Influence of chronic back pain on kinematic reactions to unpredictable arm pulls.

PubMed

Götze, Martin; Ernst, Michael; Koch, Markus; Blickhan, Reinhard

2015-03-01

There is evidence that muscle reflexes are delayed in patients with chronic low back pain in response to perturbations. It is still unrevealed whether these delays accompanied by an altered kinematic or compensated by adaption of other muscle parameters. The aim of this study was to investigate whether chronic low back pain patients show an altered kinematic reaction and if such data are reliable for the classification of chronic low back pain. In an experiment involving 30 females, sudden lateral perturbations were applied to the arm of a subject in an upright, standing position. Kinematics was used to distinguish between chronic low back pain patients and healthy controls. A calculated model of a stepwise discriminant function analysis correctly predicted 100% of patients and 80% of healthy controls. The estimation of the classification error revealed a constant rate for the classification of the healthy controls and a slightly decreased rate for the patients. Observed reflex delays and identified kinematic differences inside and outside the region of pain during impaired movement indicated that chronic low back pain patients have an altered motor control that is not restricted to the lumbo-pelvic region. This applied paradigm of external perturbations can be used to detect chronic low back pain patients and also persons without chronic low back pain but with an altered motor control. Further investigations are essential to reveal whether healthy persons with changes in motor function have an increased potential to develop chronic back pain. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tellings, Retellings, and Tellings Within Tellings: The Structuring and Organization of Narrative in Kuna Indian Discourse. Sociolinguistic Working Paper Number 83.

ERIC Educational Resources Information Center

Sherzer, Joel

A system of classification for tellings and retellings by Panama's Kuna Indians reveals the dimensions of their structure and function, textually, contextually, strategically, and ethnographically. Kuna verbal life can be characterized in terms of three distinct ritual-ceremonial traditions marked by three distinct languages, settings, sets of…

Functional traits, convergent evolution, and periodic tables of niches.

PubMed

Winemiller, Kirk O; Fitzgerald, Daniel B; Bower, Luke M; Pianka, Eric R

2015-08-01

Ecology is often said to lack general theories sufficiently predictive for applications. Here, we examine the concept of a periodic table of niches and feasibility of niche classification schemes from functional trait and performance data. Niche differences and their influence on ecological patterns and processes could be revealed effectively by first performing data reduction/ordination analyses separately on matrices of trait and performance data compiled according to logical associations with five basic niche 'dimensions', or aspects: habitat, life history, trophic, defence and metabolic. Resultant patterns then are integrated to produce interpretable niche gradients, ordinations and classifications. Degree of scheme periodicity would depend on degrees of niche conservatism and convergence causing species clustering across multiple niche dimensions. We analysed a sample data set containing trait and performance data to contrast two approaches for producing niche schemes: species ordination within niche gradient space, and niche categorisation according to trait-value thresholds. Creation of niche schemes useful for advancing ecological knowledge and its applications will depend on research that produces functional trait and performance datasets directly related to niche dimensions along with criteria for data standardisation and quality. As larger databases are compiled, opportunities will emerge to explore new methods for data reduction, ordination and classification. © 2015 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

Inter-Relationships of Functional Status in Cerebral Palsy: Analyzing Gross Motor Function, Manual Ability, and Communication Function Classification Systems in Children

ERIC Educational Resources Information Center

Hidecker, Mary Jo Cooley; Ho, Nhan Thi; Dodge, Nancy; Hurvitz, Edward A.; Slaughter, Jaime; Workinger, Marilyn Seif; Kent, Ray D.; Rosenbaum, Peter; Lenski, Madeleine; Messaros, Bridget M.; Vanderbeek, Suzette B.; Deroos, Steven; Paneth, Nigel

2012-01-01

Aim: To investigate the relationships among the Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), and Communication Function Classification System (CFCS) in children with cerebral palsy (CP). Method: Using questionnaires describing each scale, mothers reported GMFCS, MACS, and CFCS levels in 222…

Variations in the Intragene Methylation Profiles Hallmark Induced Pluripotency

PubMed Central

Druzhkov, Pavel; Zolotykh, Nikolay; Meyerov, Iosif; Alsaedi, Ahmed; Shutova, Maria; Ivanchenko, Mikhail; Zaikin, Alexey

2015-01-01

We demonstrate the potential of differentiating embryonic and induced pluripotent stem cells by the regularized linear and decision tree machine learning classification algorithms, based on a number of intragene methylation measures. The resulting average accuracy of classification has been proven to be above 95%, which overcomes the earlier achievements. We propose a constructive and transparent method of feature selection based on classifier accuracy. Enrichment analysis reveals statistically meaningful presence of stemness group and cancer discriminating genes among the selected best classifying features. These findings stimulate the further research on the functional consequences of these differences in methylation patterns. The presented approach can be broadly used to discriminate the cells of different phenotype or in different state by their methylation profiles, identify groups of genes constituting multifeature classifiers, and assess enrichment of these groups by the sets of genes with a functionality of interest. PMID:26618180

Evolution, substrate specificity and subfamily classification of glycoside hydrolase family 5 (GH5).

PubMed

Aspeborg, Henrik; Coutinho, Pedro M; Wang, Yang; Brumer, Harry; Henrissat, Bernard

2012-09-20

The large Glycoside Hydrolase family 5 (GH5) groups together a wide range of enzymes acting on β-linked oligo- and polysaccharides, and glycoconjugates from a large spectrum of organisms. The long and complex evolution of this family of enzymes and its broad sequence diversity limits functional prediction. With the objective of improving the differentiation of enzyme specificities in a knowledge-based context, and to obtain new evolutionary insights, we present here a new, robust subfamily classification of family GH5. About 80% of the current sequences were assigned into 51 subfamilies in a global analysis of all publicly available GH5 sequences and associated biochemical data. Examination of subfamilies with catalytically-active members revealed that one third are monospecific (containing a single enzyme activity), although new functions may be discovered with biochemical characterization in the future. Furthermore, twenty subfamilies presently have no characterization whatsoever and many others have only limited structural and biochemical data. Mapping of functional knowledge onto the GH5 phylogenetic tree revealed that the sequence space of this historical and industrially important family is far from well dispersed, highlighting targets in need of further study. The analysis also uncovered a number of GH5 proteins which have lost their catalytic machinery, indicating evolution towards novel functions. Overall, the subfamily division of GH5 provides an actively curated resource for large-scale protein sequence annotation for glycogenomics; the subfamily assignments are openly accessible via the Carbohydrate-Active Enzyme database at http://www.cazy.org/GH5.html.

The prediction of swimming performance in competition from behavioral information.

PubMed

Rushall, B S; Leet, D

1979-06-01

The swimming performances of the Canadian Team at the 1976 Olympic Games were categorized as being improved or worse than previous best times in the events contested. The two groups had been previously assessed on the Psychological Inventories for Competitive Swimmers. A stepwise multiple-discriminant analysis of the inventory responses revealed that 13 test questions produced a perfect discrimination of group membership. The resultant discriminant functions for predicting performance classification were applied to the test responses of 157 swimmers at the 1977 Canadian Winter National Swimming Championships. Using the same performance classification criteria the accuracy of prediction was not better than chance in three of four sex by performance classifications. This yielded a failure to locate a set of behavioral factors which determine swimming performance improvements in elite competitive circumstances. The possibility of sets of factors which do not discriminate between performances in similar environments or between similar groups of swimmers was raised.

14 CFR Section 9 - Functional Classification-Operating Revenues

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 4 2010-01-01 2010-01-01 false Functional Classification-Operating... AIR CARRIERS Profit and Loss Classification Section 9 Functional Classification—Operating Revenues 3900Transport Revenues. This classification is prescribed for all air carrier groups and shall include all...

A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects.

PubMed

Doborjeh, Maryam Gholami; Wang, Grace Y; Kasabov, Nikola K; Kydd, Robert; Russell, Bruce

2016-09-01

This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. the method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. this paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.

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).

Clinical classification and neuro-vestibular evaluation in chronic dizziness.

PubMed

Oh, Sun-Young; Kim, Do-Hyung; Yang, Tae-Ho; Shin, Byoung-Soo; Jeong, Seul-Ki

2015-01-01

This study attempts to clarify the clinical characteristics of chronic dizziness and its relationships with specific vestibular, oculomotor, autonomic and psychiatric dysfunctions. 73 Patients with idiopathic chronic dizziness were recruited and classified based on history taking and clinical examination into the following four clinical subgroups; vestibular migraine (VM), dysautonomia, psychogenic, and unspecified groups. They were also evaluated using oculomotor, otolithic and autonomic function tests, and psychologic investigation. Patients in the VM group showed a high proportion of abnormality on smooth pursuit and otolithic function testing compared to the other groups. The dysautonomia group revealed significant abnormalities in sympathetic and cardiovagal autonomic function, while the psychogenic group had a high frequency of abnormality in sympathetic autonomic testing and in Beck's anxiety inventory scale. The unspecified group showed abnormalities on saccade, smooth pursuit and autonomic function testing. Clinical classification of patients with chronic dizziness was relevant and they showed a correlation with disease-specific abnormal results in oculomotor, otolithic, autonomic function and psychology testing. Appropriate diagnostic investigation based on precise clinical diagnosis of chronic dizziness reduces the need for extensive laboratory testing, neuroimaging, and other low-yield tests. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Random forests-based differential analysis of gene sets for gene expression data.

PubMed

Hsueh, Huey-Miin; Zhou, Da-Wei; Tsai, Chen-An

2013-04-10

In DNA microarray studies, gene-set analysis (GSA) has become the focus of gene expression data analysis. GSA utilizes the gene expression profiles of functionally related gene sets in Gene Ontology (GO) categories or priori-defined biological classes to assess the significance of gene sets associated with clinical outcomes or phenotypes. Many statistical approaches have been proposed to determine whether such functionally related gene sets express differentially (enrichment and/or deletion) in variations of phenotypes. However, little attention has been given to the discriminatory power of gene sets and classification of patients. In this study, we propose a method of gene set analysis, in which gene sets are used to develop classifications of patients based on the Random Forest (RF) algorithm. The corresponding empirical p-value of an observed out-of-bag (OOB) error rate of the classifier is introduced to identify differentially expressed gene sets using an adequate resampling method. In addition, we discuss the impacts and correlations of genes within each gene set based on the measures of variable importance in the RF algorithm. Significant classifications are reported and visualized together with the underlying gene sets and their contribution to the phenotypes of interest. Numerical studies using both synthesized data and a series of publicly available gene expression data sets are conducted to evaluate the performance of the proposed methods. Compared with other hypothesis testing approaches, our proposed methods are reliable and successful in identifying enriched gene sets and in discovering the contributions of genes within a gene set. The classification results of identified gene sets can provide an valuable alternative to gene set testing to reveal the unknown, biologically relevant classes of samples or patients. In summary, our proposed method allows one to simultaneously assess the discriminatory ability of gene sets and the importance of genes for interpretation of data in complex biological systems. The classifications of biologically defined gene sets can reveal the underlying interactions of gene sets associated with the phenotypes, and provide an insightful complement to conventional gene set analyses. Copyright © 2012 Elsevier B.V. All rights reserved.

Using complex networks for text classification: Discriminating informative and imaginative documents

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-01-01

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts.

Basis of Criminalistic Classification of a Person in Republic Kazakhstan and Republic Mongolia

ERIC Educational Resources Information Center

Abdilov, Kanat S.; Zusbaev, Baurzan T.; Naurysbaev, Erlan A.; Nukiev, Berik A.; Nurkina, Zanar B.; Myrzahanov, Erlan N.; Urazalin, Galym T.

2016-01-01

In this article reviewed problems of the criminalistic classification building of a person. In the work were used legal formal, logical, comparative legal methods. The author describes classification kinds. Reveal the meaning of classification in criminalistic systematics. Shows types of grounds of criminalistic classification of a person.…

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

PubMed

Bouts, Mark J R J; Möller, Christiane; Hafkemeijer, Anne; van Swieten, John C; Dopper, Elise; van der Flier, Wiesje M; Vrenken, Hugo; Wink, Alle Meije; Pijnenburg, Yolande A L; Scheltens, Philip; Barkhof, Frederik; Schouten, Tijn M; de Vos, Frank; Feis, Rogier A; van der Grond, Jeroen; de Rooij, Mark; Rombouts, Serge A R B

2018-01-01

Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

School Refusal Behavior: Classification, Assessment, and Treatment Issues.

ERIC Educational Resources Information Center

Lee, Marcella I.; Miltenberger, Raymond G.

1996-01-01

Discusses diagnostic and functional classification, assessment, and treatment approaches for school refusal behavior. Diagnostic classification focuses on separation anxiety disorder, specific phobia, social phobia, depression, and truancy. Functional classification focuses on the maintaining consequences of the behavior, such as avoidance of…

Internal representations for face detection: an application of noise-based image classification to BOLD responses.

PubMed

Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

2013-11-01

What basic visual structures underlie human face detection and how can we extract such structures directly from the amplitude of neural responses elicited by face processing? Here, we address these issues by investigating an extension of noise-based image classification to BOLD responses recorded in high-level visual areas. First, we assess the applicability of this classification method to such data and, second, we explore its results in connection with the neural processing of faces. To this end, we construct luminance templates from white noise fields based on the response of face-selective areas in the human ventral cortex. Using behaviorally and neurally-derived classification images, our results reveal a family of simple but robust image structures subserving face representation and detection. Thus, we confirm the role played by classical face selective regions in face detection and we help clarify the representational basis of this perceptual function. From a theory standpoint, our findings support the idea of simple but highly diagnostic neurally-coded features for face detection. At the same time, from a methodological perspective, our work demonstrates the ability of noise-based image classification in conjunction with fMRI to help uncover the structure of high-level perceptual representations. Copyright © 2012 Wiley Periodicals, Inc.

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

PubMed

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

2016-01-01

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

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval

PubMed Central

Bonnici, Heidi M.; Richter, Franziska R.; Yazar, Yasemin

2016-01-01

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. SIGNIFICANCE STATEMENT Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering. PMID:27194327

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval.

PubMed

Bonnici, Heidi M; Richter, Franziska R; Yazar, Yasemin; Simons, Jon S

2016-05-18

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering. Copyright © 2016 Bonnici, Richter, et al.

Assessment of pedophilia using hemodynamic brain response to sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Beier, Klaus; Neutze, Janina; Deuschl, Günther; Mehdorn, Hubertus; Siebner, Hartwig; Bosinski, Hartmut

2012-02-01

Accurately assessing sexual preference is important in the treatment of child sex offenders. Phallometry is the standard method to identify sexual preference; however, this measure has been criticized for its intrusiveness and limited reliability. To evaluate whether spatial response pattern to sexual stimuli as revealed by a change in the blood oxygen level-dependent signal facilitates the identification of pedophiles. During functional magnetic resonance imaging, pedophilic and nonpedophilic participants were briefly exposed to same- and opposite-sex images of nude children and adults. We calculated differences in blood oxygen level-dependent signals to child and adult sexual stimuli for each participant. The corresponding contrast images were entered into a group analysis to calculate whole-brain difference maps between groups. We calculated an expression value that corresponded to the group result for each participant. These expression values were submitted to 2 different classification algorithms: Fisher linear discriminant analysis and κ -nearest neighbor analysis. This classification procedure was cross-validated using the leave-one-out method. Section of Sexual Medicine, Medical School, Christian Albrechts University of Kiel, Kiel, Germany. We recruited 24 participants with pedophilia who were sexually attracted to either prepubescent girls (n = 11) or prepubescent boys (n = 13) and 32 healthy male controls who were sexually attracted to either adult women (n = 18) or adult men (n = 14). Sensitivity and specificity scores of the 2 classification algorithms. The highest classification accuracy was achieved by Fisher linear discriminant analysis, which showed a mean accuracy of 95% (100% specificity, 88% sensitivity). Functional brain response patterns to sexual stimuli contain sufficient information to identify pedophiles with high accuracy. The automatic classification of these patterns is a promising objective tool to clinically diagnose pedophilia.

Multifunctional energy landscape for a DNA G-quadruplex: An evolved molecular switch

NASA Astrophysics Data System (ADS)

Cragnolini, Tristan; Chakraborty, Debayan; Šponer, Jiří; Derreumaux, Philippe; Pasquali, Samuela; Wales, David J.

2017-10-01

We explore the energy landscape for a four-fold telomere repeat, obtaining interconversion pathways between six experimentally characterised G-quadruplex topologies. The results reveal a multi-funnel system, with a variety of intermediate configurations and misfolded states. This organisation is identified with the intrinsically multi-functional nature of the system, suggesting a new paradigm for the classification of such biomolecules and clarifying issues regarding apparently conflicting experimental results.

Deep Molecular Diversity of Mammalian Synapses: Why It Matters and How to Measure It

PubMed Central

O’Rourke, Nancy A.; Weiler, Nick C.; Micheva, Kristina D.; Smith, Stephen J

2013-01-01

Summary Pioneering studies during the middle of the twentieth century revealed substantial diversity amongst mammalian chemical synapses and led to a widely accepted synapse type classification based on neurotransmitter molecule identity. Subsequently, powerful new physiological, genetic and structural methods have enabled the discovery of much deeper functional and molecular diversity within each traditional neurotransmitter type. Today, this deep diversity continues to pose both daunting challenges and exciting new opportunities for neuroscience. Our growing understanding of deep synapse diversity may transform how we think about and study neural circuit development, structure and function. PMID:22573027

23 CFR 470.105 - Urban area boundaries and highway functional classification.

Code of Federal Regulations, 2010 CFR

2010-04-01

... classification. 470.105 Section 470.105 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION... criteria and procedures are provided in the FHWA publication “Highway Functional Classification—Concepts... functional classification shall be mapped and submitted to the Federal Highway Administration (FHWA) for...

Novel Functional Properties of Drosophila CNS Glutamate Receptors

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

Li, Yan; Dharkar, Poorva; Han, Tae-Hee

Phylogenetic analysis reveals AMPA, kainate, and NMDA receptor families in insect genomes, suggesting conserved functional properties corresponding to their vertebrate counterparts. However, heterologous expression of the Drosophila kainate receptor DKaiR1D and the AMPA receptor DGluR1A revealed novel ligand selectivity at odds with the classification used for vertebrate glutamate receptor ion channels (iGluRs). DKaiR1D forms a rapidly activating and desensitizing receptor that is inhibited by both NMDA and the NMDA receptor antagonist AP5; crystallization of the KaiR1D ligand-binding domain reveals that these ligands stabilize open cleft conformations, explaining their action as antagonists. Surprisingly, the AMPA receptor DGluR1A shows weak activation bymore » its namesake agonist AMPA and also by quisqualate. Crystallization of the DGluR1A ligand-binding domain reveals amino acid exchanges that interfere with binding of these ligands. The unexpected ligand-binding profiles of insect iGluRs allows classical tools to be used in novel approaches for the study of synaptic regulation.« less

Novel Functional Properties of Drosophila CNS Glutamate Receptors.

PubMed

Li, Yan; Dharkar, Poorva; Han, Tae-Hee; Serpe, Mihaela; Lee, Chi-Hon; Mayer, Mark L

2016-12-07

Phylogenetic analysis reveals AMPA, kainate, and NMDA receptor families in insect genomes, suggesting conserved functional properties corresponding to their vertebrate counterparts. However, heterologous expression of the Drosophila kainate receptor DKaiR1D and the AMPA receptor DGluR1A revealed novel ligand selectivity at odds with the classification used for vertebrate glutamate receptor ion channels (iGluRs). DKaiR1D forms a rapidly activating and desensitizing receptor that is inhibited by both NMDA and the NMDA receptor antagonist AP5; crystallization of the KaiR1D ligand-binding domain reveals that these ligands stabilize open cleft conformations, explaining their action as antagonists. Surprisingly, the AMPA receptor DGluR1A shows weak activation by its namesake agonist AMPA and also by quisqualate. Crystallization of the DGluR1A ligand-binding domain reveals amino acid exchanges that interfere with binding of these ligands. The unexpected ligand-binding profiles of insect iGluRs allows classical tools to be used in novel approaches for the study of synaptic regulation. VIDEO ABSTRACT. Published by Elsevier Inc.

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.

The distance function effect on k-nearest neighbor classification for medical datasets.

PubMed

Hu, Li-Yu; Huang, Min-Wei; Ke, Shih-Wen; Tsai, Chih-Fong

2016-01-01

K-nearest neighbor (k-NN) classification is conventional non-parametric classifier, which has been used as the baseline classifier in many pattern classification problems. It is based on measuring the distances between the test data and each of the training data to decide the final classification output. Since the Euclidean distance function is the most widely used distance metric in k-NN, no study examines the classification performance of k-NN by different distance functions, especially for various medical domain problems. Therefore, the aim of this paper is to investigate whether the distance function can affect the k-NN performance over different medical datasets. Our experiments are based on three different types of medical datasets containing categorical, numerical, and mixed types of data and four different distance functions including Euclidean, cosine, Chi square, and Minkowsky are used during k-NN classification individually. The experimental results show that using the Chi square distance function is the best choice for the three different types of datasets. However, using the cosine and Euclidean (and Minkowsky) distance function perform the worst over the mixed type of datasets. In this paper, we demonstrate that the chosen distance function can affect the classification accuracy of the k-NN classifier. For the medical domain datasets including the categorical, numerical, and mixed types of data, K-NN based on the Chi square distance function performs the best.

[Proposal of new trace elements classification to be used in nutrition, oligotherapy and other therapeutics strategies].

PubMed

Ramírez Hernández, Javier; Bonete Pérez, María José; Martínez Espinosa, Rosa María

2014-12-17

1) to propose a new classification of the trace elements based on a study of the recently reported research; 2) to offer detailed and actualized information about trace elements. the analysis of the research results recently reported reveals that the advances of the molecular analysis techniques point out the importance of certain trace elements in human health. A detailed analysis of the catalytic function related to several elements not considered essential o probably essentials up to now is also offered. To perform the integral analysis of the enzymes containing trace elements informatics tools have been used. Actualized information about physiological role, kinetics, metabolism, dietetic sources and factors promoting trace elements scarcity or toxicity is also presented. Oligotherapy uses catalytic active trace elements with therapeutic proposals. The new trace element classification here presented will be of high interest for different professional sectors: doctors and other professions related to medicine; nutritionist, pharmaceutics, etc. Using this new classification and approaches, new therapeutic strategies could be designed to mitigate symptomatology related to several pathologies, particularly carential and metabolic diseases. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Rebelling for a Reason: Protein Structural “Outliers”

PubMed Central

Arumugam, Gandhimathi; Nair, Anu G.; Hariharaputran, Sridhar; Ramanathan, Sowdhamini

2013-01-01

Analysis of structural variation in domain superfamilies can reveal constraints in protein evolution which aids protein structure prediction and classification. Structure-based sequence alignment of distantly related proteins, organized in PASS2 database, provides clues about structurally conserved regions among different functional families. Some superfamily members show large structural differences which are functionally relevant. This paper analyses the impact of structural divergence on function for multi-member superfamilies, selected from the PASS2 superfamily alignment database. Functional annotations within superfamilies, with structural outliers or ‘rebels’, are discussed in the context of structural variations. Overall, these data reinforce the idea that functional similarities cannot be extrapolated from mere structural conservation. The implication for fold-function prediction is that the functional annotations can only be inherited with very careful consideration, especially at low sequence identities. PMID:24073209

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2014 CFR

2014-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2013 CFR

2013-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2011 CFR

2011-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2012 CFR

2012-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

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.

A Summary of Precipitation Characteristics from the 2006–11 Northern Australian Wet Seasons as Revealed by ARM Disdrometer Research Facilities (Darwin, Australia)

DOE PAGES

Giangrande, Scott E.; Bartholomew, Mary Jane; Pope, Mick; ...

2014-05-09

The variability of rainfall and drop size distributions (DSDs) as a function of large-scale atmospheric conditions and storm characteristics is investigated using measurements from the Atmospheric Radiation Measurement (ARM) program facility at Darwin, Australia. Observations are obtained from an impact disdrometer with a near continuous record of operation over five consecutive wet seasons (2006-2011). We partition bulk rainfall characteristics according to diurnal accumulation, convective and stratiform precipitation classifications, objective monsoonal regime and MJO phase. Our findings support previous Darwin studies suggesting a significant diurnal and DSD parameter signal associated with both convective-stratiform and wet season monsoonal regime classification. Negligible MJOmore » phase influence is determined for cumulative disdrometric statistics over the Darwin location.« less

DIF Trees: Using Classification Trees to Detect Differential Item Functioning

ERIC Educational Resources Information Center

Vaughn, Brandon K.; Wang, Qiu

2010-01-01

A nonparametric tree classification procedure is used to detect differential item functioning for items that are dichotomously scored. Classification trees are shown to be an alternative procedure to detect differential item functioning other than the use of traditional Mantel-Haenszel and logistic regression analysis. A nonparametric…

Relationship between Functional Classification Levels and Anaerobic Performance of Wheelchair Basketball Athletes

ERIC Educational Resources Information Center

Molik, Bartosz; Laskin, James J.; Kosmol, Andrzej; Skucas, Kestas; Bida, Urszula

2010-01-01

Wheelchair basketball athletes are classified using the International Wheelchair Basketball Federation (IWBF) functional classification system. The purpose of this study was to evaluate the relationship between upper extremity anaerobic performance (AnP) and all functional classification levels in wheelchair basketball. Ninety-seven male athletes…

Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks

PubMed Central

Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

2016-01-01

We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

[Landscape classification: research progress and development trend].

PubMed

Liang, Fa-Chao; Liu, Li-Ming

2011-06-01

Landscape classification is the basis of the researches on landscape structure, process, and function, and also, the prerequisite for landscape evaluation, planning, protection, and management, directly affecting the precision and practicability of landscape research. This paper reviewed the research progress on the landscape classification system, theory, and methodology, and summarized the key problems and deficiencies of current researches. Some major landscape classification systems, e. g. , LANMAP and MUFIC, were introduced and discussed. It was suggested that a qualitative and quantitative comprehensive classification based on the ideology of functional structure shape and on the integral consideration of landscape classification utility, landscape function, landscape structure, physiogeographical factors, and human disturbance intensity should be the major research directions in the future. The integration of mapping, 3S technology, quantitative mathematics modeling, computer artificial intelligence, and professional knowledge to enhance the precision of landscape classification would be the key issues and the development trend in the researches of landscape classification.

Naïve Bayes classification in R.

PubMed

Zhang, Zhongheng

2016-06-01

Naïve Bayes classification is a kind of simple probabilistic classification methods based on Bayes' theorem with the assumption of independence between features. The model is trained on training dataset to make predictions by predict() function. This article introduces two functions naiveBayes() and train() for the performance of Naïve Bayes classification.

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

Cognitive and motor function of neurologically impaired extremely low birth weight children.

PubMed

Bernardo, Janine; Friedman, Harriet; Minich, Nori; Taylor, H Gerry; Wilson-Costello, Deanne; Hack, Maureen

2015-01-01

Rates of neurological impairment among extremely low birth weight children (ELBW [<1 kg]) have decreased since 2000; however, their functioning is unexamined. To compare motor and cognitive functioning of ELBW children with neurological impairment, including cerebral palsy and severe hypotonia/hypertonia, between two periods: 1990 to 1999 (n=83) and 2000 to 2005 (n=34). Measures of function at 20 months corrected age included the Mental and Psychomotor Developmental Indexes of the Bayley Scales of Infant Development and the Gross Motor Functional Classification System as primary outcomes and individual motor function items as secondary outcomes. Analysis failed to reveal significant differences for the primary outcomes, although during 2000 to 2005, sitting significantly improved in children with neurological impairment (P=0.003). Decreases in rates of neurological impairment among ELBW children have been accompanied by a suggestion of improved motor function, although cognitive function has not changed.

Functional Communication Profiles in Children with Cerebral Palsy in Relation to Gross Motor Function and Manual and Intellectual Ability.

PubMed

Choi, Ja Young; Park, Jieun; Choi, Yoon Seong; Goh, Yu Ra; Park, Eun Sook

2018-07-01

The aim of the present study was to investigate communication function using classification systems and its association with other functional profiles, including gross motor function, manual ability, intellectual functioning, and brain magnetic resonance imaging (MRI) characteristics in children with cerebral palsy (CP). This study recruited 117 individuals with CP aged from 4 to 16 years. The Communication Function Classification System (CFCS), Viking Speech Scale (VSS), Speech Language Profile Groups (SLPG), Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), and intellectual functioning were assessed in the children along with brain MRI categorization. Very strong relationships were noted among the VSS, CFCS, and SLPG, although these three communication systems provide complementary information, especially for children with mid-range communication impairment. These three communication classification systems were strongly related with the MACS, but moderately related with the GMFCS. Multiple logistic regression analysis indicated that manual ability and intellectual functioning were significantly related with VSS and CFCS function, whereas only intellectual functioning was significantly related with SLPG functioning in children with CP. Communication function in children with a periventricular white matter lesion (PVWL) varied widely. In the cases with a PVWL, poor functioning was more common on the SLPG, compared to the VSS and CFCS. Very strong relationships were noted among three communication classification systems that are closely related with intellectual ability. Compared to gross motor function, manual ability seemed more closely related with communication function in these children. © Copyright: Yonsei University College of Medicine 2018.

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

PubMed

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

2017-12-01

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

Railroad Classification Yard Technology : An Introductory Analysis of Functions and Operations

DOT National Transportation Integrated Search

1975-05-01

A review of the basic operating characteristics and functions of railroad classification yards is presented. Introductory descriptions of terms, concepts, and problems of railroad operations involving classification yards are included in an attempt t...

Developmental defects in zebrafish for classification of EGF pathway inhibitors

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

Pruvot, Benoist; Curé, Yoann; Djiotsa, Joachim

2014-01-15

One of the major challenges when testing drug candidates targeted at a specific pathway in whole animals is the discrimination between specific effects and unwanted, off-target effects. Here we used the zebrafish to define several developmental defects caused by impairment of Egf signaling, a major pathway of interest in tumor biology. We inactivated Egf signaling by genetically blocking Egf expression or using specific inhibitors of the Egf receptor function. We show that the combined occurrence of defects in cartilage formation, disturbance of blood flow in the trunk and a decrease of myelin basic protein expression represent good indicators for impairmentmore » of Egf signaling. Finally, we present a classification of known tyrosine kinase inhibitors according to their specificity for the Egf pathway. In conclusion, we show that developmental indicators can help to discriminate between specific effects on the target pathway from off-target effects in molecularly targeted drug screening experiments in whole animal systems. - Highlights: • We analyze the functions of Egf signaling on zebrafish development. • Genetic blocking of Egf expression causes cartilage, myelin and circulatory defects. • Chemical inhibition of Egf receptor function causes similar defects. • Developmental defects can reveal the specificity of Egf pathway inhibitors.« less

Outcome measures in older persons with acquired joint contractures: a systematic review and content analysis using the ICF (International Classification of Functioning, Disability and Health) as a reference.

PubMed

Bartoszek, Gabriele; Fischer, Uli; Müller, Martin; Strobl, Ralf; Grill, Eva; Nadolny, Stephan; Meyer, Gabriele

2016-02-09

Joint contractures are a common health problem in older persons with significant impact on activities of daily living. We aimed to retrieve outcome measures applied in studies on older persons with joint contractures and to identify and categorise the concepts contained in these outcome measures using the ICF (International Classification of Functioning, Disability and Health) as a reference. Electronic searches of Medline, EMBASE, CINAHL, Pedro and the Cochrane Library were conducted (1/2002-8/2012). We included studies in the geriatric rehabilitation and nursing home settings with participants aged ≥ 65 years and with acquired joint contractures. Two independent reviewers extracted the outcome measures and transferred them to concepts using predefined conceptual frameworks. Concepts were subsequently linked to the ICF categories. From the 1057 abstracts retrieved, 60 studies met the inclusion criteria. We identified 52 single outcome measures and 24 standardised assessment instruments. A total of 1353 concepts were revealed from the outcome measures; 96.2% could be linked to 50 ICF categories in the 2nd level; 3.8% were not categorised. Fourteen of the 50 categories (28%) belonged to the component Body Functions, 4 (8%) to the component Body Structures, 26 (52%) to the component Activities and Participation, and 6 (12%) to the component Environmental Factors. The ICF is a valuable reference for identifying and quantifying the concepts of outcome measures on joint contractures in older people. The revealed ICF categories remain to be validated in populations with joint contractures in terms of clinical relevance and personal impact.

Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer’s disease

PubMed Central

Berchtold, Nicole C.; Coleman, Paul D.; Cribbs, David H.; Rogers, Joseph; Gillen, Daniel L.; Cotman, Carl W.

2014-01-01

Synapses are essential for transmitting, processing, and storing information, all of which decline in aging and Alzheimer’s disease (AD). Because synapse loss only partially accounts for the cognitive declines seen in aging and AD, we hypothesized that existing synapses might undergo molecular changes that reduce their functional capacity. Microarrays were used to evaluate expression profiles of 340 synaptic genes in aging (20–99 years) and AD across 4 brain regions from 81 cases. The analysis revealed an unexpectedly large number of significant expression changes in synapse-related genes in aging, with many undergoing progressive downregulation across aging and AD. Functional classification of the genes showing altered expression revealed that multiple aspects of synaptic function are affected, notably synaptic vesicle trafficking and release, neurotransmitter receptors and receptor trafficking, postsynaptic density scaffolding, cell adhesion regulating synaptic stability, and neuromodulatory systems. The widespread declines in synaptic gene expression in normal aging suggests that function of existing synapses might be impaired, and that a common set of synaptic genes are vulnerable to change in aging and AD. PMID:23273601

The Importance of Motor Functional Levels from the Activity Limitation Perspective of ICF in Children with Cerebral Palsy

ERIC Educational Resources Information Center

Mutlu, Akmer

2010-01-01

Our purpose in this study was to evaluate performance and capacity as defined by Gross Motor Function Classification System (GMFCS) and Manual Ability Classification System (MACS) from the "activity limitation" perspective of International Classification of Functioning, Disability, and Health (ICF) and to investigate the relationship between the…

Gene Set−Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer

PubMed Central

Chang, Chia-Ming; Chuang, Chi-Mu; Wang, Mong-Lien; Yang, Yi-Ping; Chuang, Jen-Hua; Yang, Ming-Jie; Yen, Ming-Shyen; Chiou, Shih-Hwa; Chang, Cheng-Chang

2016-01-01

Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis. PMID:27527159

The phosphoproteome of Aspergillus nidulans reveals functional association with cellular processes involved in morphology and secretion.

PubMed

Ramsubramaniam, Nikhil; Harris, Steven D; Marten, Mark R

2014-11-01

We describe the first phosphoproteome of the model filamentous fungus Aspergillus nidulans. Phosphopeptides were enriched using titanium dioxide, separated using a convenient ultra-long reverse phase gradient, and identified using a "high-high" strategy (high mass accuracy on the parent and fragment ions) with higher-energy collisional dissociation. Using this approach 1801 phosphosites, from 1637 unique phosphopeptides, were identified. Functional classification revealed phosphoproteins were overrepresented under GO categories related to fungal morphogenesis: "sites of polar growth," "vesicle mediated transport," and "cytoskeleton organization." In these same GO categories, kinase-substrate analysis of phosphoproteins revealed the majority were target substrates of CDK and CK2 kinase families, indicating these kinase families play a prominent role in fungal morphogenesis. Kinase-substrate analysis also identified 57 substrates for kinases known to regulate secretion of hydrolytic enzymes (e.g. PkaA, SchA, and An-Snf1). Altogether this data will serve as a benchmark that can be used to elucidate regulatory networks functionally associated with fungal morphogenesis and secretion. All MS data have been deposited in the ProteomeXchange with identifier PXD000715 (http://proteomecentral.proteomexchange.org/dataset/PXD000715). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Non-Gaussian Distributions Affect Identification of Expression Patterns, Functional Annotation, and Prospective Classification in Human Cancer Genomes

PubMed Central

Marko, Nicholas F.; Weil, Robert J.

2012-01-01

Introduction Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. Methods We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. Results Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. Conclusions Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. PMID:23118863

Identification of functional parameters for the classification of older female fallers and prediction of ‘first-time’ fallers

PubMed Central

König, N.; Taylor, W. R.; Armbrecht, G.; Dietzel, R.; Singh, N. B.

2014-01-01

Falls remain a challenge for ageing societies. Strong evidence indicates that a previous fall is the strongest single screening indicator for a subsequent fall and the need for assessing fall risk without accounting for fall history is therefore imperative. Testing in three functional domains (using a total 92 measures) were completed in 84 older women (60–85 years of age), including muscular control, standing balance, and mean and variability of gait. Participants were retrospectively classified as fallers (n = 38) or non-fallers (n = 42) and additionally in a prospective manner to identify first-time fallers (FTFs) (n = 6) within a 12-month follow-up period. Principal component analysis revealed that seven components derived from the 92 functional measures are sufficient to depict the spectrum of functional performance. Inclusion of only three components, related to mean and temporal variability of walking, allowed classification of fallers and non-fallers with a sensitivity and specificity of 74% and 76%, respectively. Furthermore, the results indicate that FTFs show a tendency towards the performance of fallers, even before their first fall occurs. This study suggests that temporal variability and mean spatial parameters of gait are the only functional components among the 92 measures tested that differentiate fallers from non-fallers, and could therefore show efficacy in clinical screening programmes for assessing risk of first-time falling. PMID:24898021

Free classification of regional dialects of American English.

PubMed

Clopper, Cynthia G; Pisoni, David B

2007-07-01

Recent studies have found that naïve listeners perform poorly in forced-choice dialect categorization tasks. However, the listeners' error patterns in these tasks reveal systematic confusions between phonologically similar dialects. In the present study, a free classification procedure was used to measure the perceptual similarity structure of regional dialect variation in the United States. In two experiments, participants listened to a set of short English sentences produced by male talkers only (Experiment 1) and by male and female talkers (Experiment 2). The listeners were instructed to group the talkers by regional dialect into as many groups as they wanted with as many talkers in each group as they wished. Multidimensional scaling analyses of the data revealed three primary dimensions of perceptual similarity (linguistic markedness, geography, and gender). In addition, a comparison of the results obtained from the free classification task to previous results using the same stimulus materials in six-alternative forced-choice categorization tasks revealed that response biases in the six-alternative task were reduced or eliminated in the free classification task. Thus, the results obtained with the free classification task in the current study provided further evidence that the underlying structure of perceptual dialect category representations reflects important linguistic and sociolinguistic factors.

Rapid assessment of urban wetlands: Do hydrogeomorpic classification and reference criteria work?

EPA Science Inventory

The Hydrogeomorphic (HGM) functional assessment method is predicated on the ability of a wetland classification method based on hydrology (HGM classification) and a visual assessment of disturbance and alteration to provide reference standards against which functions in individua...

Comparison of World Health Organization and Asia-Pacific body mass index classifications in COPD patients.

PubMed

Lim, Jeong Uk; Lee, Jae Ha; Kim, Ju Sang; Hwang, Yong Il; Kim, Tae-Hyung; Lim, Seong Yong; Yoo, Kwang Ha; Jung, Ki-Suck; Kim, Young Kyoon; Rhee, Chin Kook

2017-01-01

A low body mass index (BMI) is associated with increased mortality and low health-related quality of life in patients with COPD. The Asia-Pacific classification of BMI has a lower cutoff for overweight and obese categories compared to the World Health Organization (WHO) classification. The present study assessed patients with COPD among different BMI categories according to two BMI classification systems: WHO and Asia-Pacific. Patients with COPD aged 40 years or older from the Korean COPD Subtype Study cohort were selected for evaluation. We enrolled 1,462 patients. Medical history including age, sex, St George's Respiratory Questionnaire (SGRQ-C), the modified Medical Research Council (mMRC) dyspnea scale, and post-bronchodilator forced expiratory volume in 1 second (FEV 1 ) were evaluated. Patients were categorized into different BMI groups according to the two BMI classification systems. FEV 1 and the diffusing capacity of the lung for carbon monoxide (DLCO) percentage revealed an inverse "U"-shaped pattern as the BMI groups changed from underweight to obese when WHO cutoffs were applied. When Asia-Pacific cutoffs were applied, FEV 1 and DLCO (%) exhibited a linearly ascending relationship as the BMI increased, and the percentage of patients in the overweight and obese groups linearly decreased with increasing severity of the Global Initiative for Chronic Obstructive Lung Disease criteria. From the underweight to the overweight groups, SGRQ-C and mMRC had a decreasing relationship in both the WHO and Asia-Pacific classifications. The prevalence of comorbidities in the different BMI groups showed similar trends in both BMI classifications systems. The present study demonstrated that patients with COPD who have a high BMI have better pulmonary function and health-related quality of life and reduced dyspnea symptoms. Furthermore, the Asia-Pacific BMI classification more appropriately reflects the correlation of obesity and disease manifestation in Asian COPD patients than the WHO classification.

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.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

The Influence of Hindu Epistemology on Ranganathan's Colon Classification.

ERIC Educational Resources Information Center

Maurer, Bradley Gerald

This study attempted to determine the influence of Hindu epistemology on Ranganathan's Colon Classification. Only the epistemological schools of Hindu philosophy and the Idea Plane element of Colon Classification were included. A literature search revealed that, although there is significant literature on each side of the problem, no bridges exist…

Acoustic detection of biosonar activity of deep diving odontocetes at Josephine Seamount High Seas Marine Protected Area.

PubMed

Giorli, Giacomo; Au, Whitlow W L; Ou, Hui; Jarvis, Susan; Morrissey, Ronald; Moretti, David

2015-05-01

The temporal occurrence of deep diving cetaceans in the Josephine Seamount High Seas Marine Protected Area (JSHSMPA), south-west Portugal, was monitored using a passive acoustic recorder. The recorder was deployed on 13 May 2010 at a depth of 814 m during the North Atlantic Treaty Organization Centre for Maritime Research and Experimentation cruise "Sirena10" and recovered on 6 June 2010. The recorder was programmed to record 40 s of data every 2 min. Acoustic data analysis, for the detection and classification of echolocation clicks, was performed using automatic detector/classification systems: M3R (Marine Mammal Monitoring on Navy Ranges), a custom matlab program, and an operator-supervised custom matlab program to assess the classification performance of the detector/classification systems. M3R CS-SVM algorithm contains templates to detect beaked whales, sperm whales, blackfish (pilot and false killer whales), and Risso's dolphins. The detections of each group of odontocetes was monitored as a function of time. Blackfish and Risso's dolphins were detected every day, while beaked whales and sperm whales were detected almost every day. The hourly distribution of detections reveals that blackfish and Risso's dolphins were more active at night, while beaked whales and sperm whales were more active during daylight hours.

Functional outcomes in children and young people with dyskinetic cerebral palsy.

PubMed

Monbaliu, Elegast; De La Peña, Mary-Grace; Ortibus, Els; Molenaers, Guy; Deklerck, Jan; Feys, Hilde

2017-06-01

This cross-sectional study aimed to map the functional profile of individuals with dyskinetic cerebral palsy (CP), to determine interrelationships between the functional classification systems, and to investigate the relationship of functional abilities with dystonia and choreoathetosis severity. Fifty-five children (<15y) and young people (15-22y) (30 males, 25 females; mean age 14y 6mo, standard deviation 4y 1mo) with dyskinetic CP were assessed using the Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), Communication Function Classification System (CFCS), Eating and Drinking Ability Classification System (EDACS), and Viking Speech Scale (VSS), as well as the Dyskinesia Impairment Scale. Over 50 per cent of the participants exhibited the highest limitation levels in GMFCS, MACS, and VSS. Better functional abilities were seen in EDACS and CFCS. Moderate to excellent interrelationship was found among the classification scales. All scales had significant correlation (r s =0.65 - 0.81) with dystonia severity except for CFCS in the young people group. Finally, only MACS (r s =0.40) and EDACS (r s =0.55) in the young people group demonstrated significant correlation with choreoathetosis severity. The need for inclusion of speech, eating, and drinking in the functional assessment of dyskinetic CP is highlighted. The study further supports the strategy of managing dystonia in particular at a younger age followed by choreoathetosis in a later stage. © 2017 Mac Keith Press.

The Brief Illness Perceptions Questionnaire identifies 3 classes of people seeking rehabilitation for mechanical neck pain.

PubMed

Walton, David M; Lefebvre, Andy; Reynolds, Darcy

2015-06-01

Illness representations pertain to the ways in which an individual constructs and understands the experience of a health condition. The Brief Illness Perceptions Questionnaire (BIPQ) comprises 9 items intended to capture the key components of the Illness Representations Model. The purpose of this paper was to explore the utility of the BIPQ for evaluating and classifying uncomplicated mechanical neck pain in the rehabilitation setting. A convenience sample of 198 subjects presenting to physiotherapy for neck pain problems were used in this study. In the first step, 183 subjects completed the BIPQ and a series of related cognitive measures. Latent class analysis (LCA) was used to explore the number of identifiable classes amongst the sample based on BIPQ response patterns. A regression equation was created to facilitate classification. In the second step, an independent sample of 15 subjects were classified using the equation established in step 1, and they were followed over a 3 month period. The LCA revealed 3 classes of subjects with optimal fit statistics: mildly affected, moderately affected, and severely affected. Inter-group comparisons of the secondary cognitive measures supported these labels. Classification accuracy of a regression equation was high (94.5%). Applying the equation to the independent longitudinal sample revealed that it functioned equally well and that the classes may have prognostic value. The BIPQ may be a useful clinical tool for classification of neck pain. Copyright © 2014 Elsevier Ltd. All rights reserved.

Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

PubMed

Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

2016-04-01

Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases. Copyright © 2016 Elsevier Inc. All rights reserved.

Should the Gross Motor Function Classification System be used for children who do not have cerebral palsy?

PubMed

Towns, Megan; Rosenbaum, Peter; Palisano, Robert; Wright, F Virginia

2018-02-01

This literature review addressed four questions. (1) In which populations other than cerebral palsy (CP) has the Gross Motor Function Classification System (GMFCS) been applied? (2) In what types of study, and why was it used? (3) How was it modified to facilitate these applications? (4) What justifications and evidence of psychometric adequacy were used to support its application? A search of PubMed, MEDLINE, and Embase databases (January 1997 to April 2017) using the terms: 'GMFCS' OR 'Gross Motor Function Classification System' yielded 2499 articles. 118 met inclusion criteria and reported children/adults with 133 health conditions/clinical descriptions other than CP. Three broad GMFCS applications were observed: as a categorization tool, independent variable, or outcome measure. While the GMFCS is widely used for children with health conditions/clinical description other than CP, researchers rarely provided adequate justification for these uses. We offer recommendations for development/validation of other condition-specific classification systems and discuss the potential need for a generic gross motor function classification system. The Gross Motor Function Classification System should not be used outside cerebral palsy or as an outcome measure. The authors provide recommendations for development and validation of condition-specific or generic classification systems. © 2017 Mac Keith Press.

Repair-oriented classification of aortic insufficiency: impact on surgical techniques and clinical outcomes.

PubMed

Boodhwani, Munir; de Kerchove, Laurent; Glineur, David; Poncelet, Alain; Rubay, Jean; Astarci, Parla; Verhelst, Robert; Noirhomme, Philippe; El Khoury, Gébrine

2009-02-01

Valve repair for aortic insufficiency requires a tailored surgical approach determined by the leaflet and aortic disease. Over the past decade, we have developed a functional classification of AI, which guides repair strategy and can predict outcome. In this study, we analyze our experience with a systematic approach to aortic valve repair. From 1996 to 2007, 264 patients underwent elective aortic valve repair for aortic insufficiency (mean age - 54 +/- 16 years; 79% male). AV was tricuspid in 171 patients bicuspid in 90 and quadricuspid in 3. One hundred fifty three patients had type I dysfunction (aortic dilatation), 134 had type II (cusp prolapse), and 40 had type III (restrictive). Thirty six percent (96/264) of the patients had more than one identified mechanism. In-hospital mortality was 1.1% (3/264). Six patients experienced early repair failure; 3 underwent re-repair. Functional classification predicted the necessary repair techniques in 82-100% of patients, with adjunctive techniques being employed in up to 35% of patients. Mid-term follow up (median [interquartile range]: 47 [29-73] months) revealed a late mortality rate of 4.2% (11/261, 10 cardiac). Five year overall survival was 95 +/- 3%. Ten patients underwent aortic valve reoperation (1 re-repair). Freedoms from recurrent Al (>2+) and from AV reoperation at 5 years was 88 +/- 3% and 92 +/- 4% respectively and patients with type I (82 +/- 9%; 93 +/- 5%) or II (95 +/- 5%; 94 +/- 6%) had better outcomes compared to type III (76 +/- 17%; 84 +/- 13%). Aortic valve repair is an acceptable therapeutic option for patients with aortic insufficiency. This functional classification allows a systematic approach to the repair of Al and can help to predict the surgical techniques required as well as the durability of repair. Restrictive cusp motion (type III), due to fibrosis or calcification, is an important predictor for recurrent Al following AV repair.

European validation of The Comprehensive International Classification of Functioning, Disability and Health Core Set for Osteoarthritis from the perspective of patients with osteoarthritis of the knee or hip.

PubMed

Weigl, Martin; Wild, Heike

2017-09-15

To validate the International Classification of Functioning, Disability and Health Comprehensive Core Set for Osteoarthritis from the patient perspective in Europe. This multicenter cross-sectional study involved 375 patients with knee or hip osteoarthritis. Trained health professionals completed the Comprehensive Core Set, and patients completed the Short-Form 36 questionnaire. Content validity was evaluated by calculating prevalences of impairments in body function and structures, limitations in activities and participation and environmental factors, which were either barriers or facilitators. Convergent construct validity was evaluated by correlating the International Classification of Functioning, Disability and Health categories with the Short-Form 36 Physical Component Score and the SF-36 Mental Component Score in a subgroup of 259 patients. The prevalences of all body function, body structure and activities and participation categories were >40%, >32% and >20%, respectively, and all environmental factors were relevant for >16% of patients. Few categories showed relevant differences between knee and hip osteoarthritis. All body function categories and all but two activities and participation categories showed significant correlations with the Physical Component Score. Body functions from the ICF chapter Mental Functions showed higher correlations with the Mental Component Score than with the Physical Component Score. This study supports the validity of the International Classification of Functioning, Disability and Health Comprehensive Core Set for Osteoarthritis. Implications for Rehabilitation Comprehensive International Classification of Functioning, Disability and Health Core Sets were developed as practical tools for application in multidisciplinary assessments. The validity of the Comprehensive International Classification of Functioning, Disability and Health Core Set for Osteoarthritis in this study supports its application in European patients with osteoarthritis. The differences in results between this Europe validation study and a previous Singaporean validation study underscore the need to validate the International Classification of Functioning, Disability and Health Core Sets in different regions of the world.

Comparisons of severity classification systems for oropharyngeal dysfunction in children with cerebral palsy: Relations with other functional profiles.

PubMed

Goh, Yu-Ra; Choi, Ja Young; Kim, Seon Ah; Park, Jieun; Park, Eun Sook

2018-01-01

This study aimed to investigate the relationships between various classification systems assessing the severity of oropharyngeal dysphagia and communication function and other functional profiles in children with cerebral palsy (CP). This is a prospective, cross-sectional, study in a university-affiliated, tertiary-care hospital. We recruited 151 children with CP (mean age 6.11 years, SD 3.42, range 3-18yr). The Eating and Drinking Ability Classification System (EDACS) and the dysphagia scales of Functional Oral Intake Scale (FOIS), Swallow Function Scales (SFS), and Food Intake Level Scale (FILS) were used. The Communication Function Classification System (CFCS) and Viking Speech Scale (VSS) were employed to classify communication function and speech intelligibility, respectively. The Pediatric Evaluation of Disability Inventory (PEDI) with the Gross Motor Function Classification System (GFMCS) and the Manual Ability Classification System (MACS) level were also assessed. Spearman correlation analysis to investigate the associations between measures and univariate and multivariate logistic regression models to identify significant factors were used. Median GMFCS level of participants was III (interquartile range II-IV). Significant dysphagia based on EDACS level III-V was noted in 23 children (15.2%). There were strong to very strong relationships between the EDACS level with the dysphagia scales. The EDACS presented strong associations with MACS, CFCS, and VSS, a moderate association with GMFCS level, and a moderate to strong association with each domain of the PEDI. In multivariate analysis, poor functioning in EDACS were associated with poor functioning in gross motor and communication functions. Copyright © 2017. Published by Elsevier Ltd.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease.

PubMed

Schouten, Tijn M; Koini, Marisa; de Vos, Frank; Seiler, Stephan; van der Grond, Jeroen; Lechner, Anita; Hafkemeijer, Anne; Möller, Christiane; Schmidt, Reinhold; de Rooij, Mark; Rombouts, Serge A R B

2016-01-01

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

Sleep in patients with disorders of consciousness characterized by means of machine learning

PubMed Central

Lechinger, Julia; Wislowska, Malgorzata; Blume, Christine; Ott, Peter; Wegenkittl, Stefan; del Giudice, Renata; Heib, Dominik P. J.; Mayer, Helmut A.; Laureys, Steven; Pichler, Gerald; Schabus, Manuel

2018-01-01

Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continues to be challenging given severely altered brain oscillations, frequent and extended artifacts in clinical recordings and the absence of established staging criteria. In the present study, we try to address these issues and investigate the usefulness of a multivariate machine learning technique based on permutation entropy, a complexity measure. Specifically, we used long-term polysomnography (PSG), along with video recordings in day and night periods in a sample of 23 DOC; 12 patients were diagnosed as Unresponsive Wakefulness Syndrome (UWS) and 11 were diagnosed as Minimally Conscious State (MCS). Eight hour PSG recordings of healthy sleepers (N = 26) were additionally used for training and setting parameters of supervised and unsupervised model, respectively. In DOC, the supervised classification (wake, N1, N2, N3 or REM) was validated using simultaneous videos which identified periods with prolonged eye opening or eye closure.The supervised classification revealed that out of the 23 subjects, 11 patients (5 MCS and 6 UWS) yielded highly accurate classification with an average F1-score of 0.87 representing high overlap between the classifier predicting sleep (i.e. one of the 4 sleep stages) and closed eyes. Furthermore, the unsupervised approach revealed a more complex pattern of sleep-wake stages during the night period in the MCS group, as evidenced by the presence of several distinct clusters. In contrast, in UWS patients no such clustering was found. Altogether, we present a novel data-driven method, based on machine learning that can be used to gain new and unambiguous insights into sleep organization and residual brain functioning of patients with DOC. PMID:29293607

An ontology-based exploration of the concepts and relationships in the activities and participation component of the international classification of functioning, disability and health.

PubMed

Della Mea, Vincenzo; Simoncello, Andrea

2012-02-28

The International Classification of Functioning, Disability and Health (ICF) is a classification of health and health-related issues, aimed at describing and measuring health and disability at both individual and population levels. Here we discuss a preliminary qualitative and quantitative analysis of the relationships used in the Activities and Participation component of ICF, and a preliminary mapping to SUMO (Suggested Upper Merged Ontology) concepts. The aim of the analysis is to identify potential logical problems within this component of ICF, and to understand whether activities and participation might be defined more formally than in the current version of ICF. In the relationship analysis, we used four predicates among those available in SUMO for processes (Patient, Instrument, Agent, and subProcess). While at the top level subsumption was used in most cases (90%), at the lower levels the percentage of other relationships rose to 41%. Chapters were heterogeneous in the relationships used and some of the leaves of the tree seemed to represent properties or parts of the parent concept rather than subclasses. Mapping of ICF to SUMO proved partially feasible, with the activity concepts being mapped mostly (but not totally) under the IntentionalProcess concept in SUMO. On the other hand, the participation concept has not been mapped to any upper level concept. Our analysis of the relationships within ICF revealed issues related to confusion between classes and their properties, incorrect classifications, and overemphasis on subsumption, confirming what already observed by other researchers. However, it also suggested some properties for Activities that could be included in a more formal model: number of agents involved, the instrument used to carry out the activity, the object of the activity, complexity of the task, and an enumeration of relevant subtasks.

A reaction limited in vivo dissolution model for the study of drug absorption: Towards a new paradigm for the biopharmaceutic classification of drugs.

PubMed

Macheras, Panos; Iliadis, Athanassios; Melagraki, Georgia

2018-05-30

The aim of this work is to develop a gastrointestinal (GI) drug absorption model based on a reaction limited model of dissolution and consider its impact on the biopharmaceutic classification of drugs. Estimates for the fraction of dose absorbed as a function of dose, solubility, reaction/dissolution rate constant and the stoichiometry of drug-GI fluids reaction/dissolution were derived by numerical solution of the model equations. The undissolved drug dose and the reaction/dissolution rate constant drive the dissolution rate and determine the extent of absorption when high-constant drug permeability throughout the gastrointestinal tract is assumed. Dose is an important element of drug-GI fluids reaction/dissolution while solubility exclusively acts as an upper limit for drug concentrations in the lumen. The 3D plots of fraction of dose absorbed as a function of dose and reaction/dissolution rate constant for highly soluble and low soluble drugs for different "stoichiometries" (0.7, 1.0, 2.0) of the drug-reaction/dissolution with the GI fluids revealed that high extent of absorption was found assuming high drug- reaction/dissolution rate constant and high drug solubility. The model equations were used to simulate in vivo supersaturation and precipitation phenomena. The model developed provides the theoretical basis for the interpretation of the extent of drug's absorption on the basis of the parameters associated with the drug-GI fluids reaction/dissolution. A new paradigm emerges for the biopharmaceutic classification of drugs, namely, a model independent biopharmaceutic classification scheme of four drug categories based on either the fulfillment or not of the current dissolution criteria and the high or low % drug metabolism. Copyright © 2018. Published by Elsevier B.V.

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

PubMed

Dey, Soumyabrata; Rao, A Ravishankar; Shah, Mubarak

2014-01-01

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Comparison of Xenon-Enhanced Area-Detector CT and Krypton Ventilation SPECT/CT for Assessment of Pulmonary Functional Loss and Disease Severity in Smokers.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Takenaka, Daisuke; Kaminaga, Shigeo; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2018-02-01

The objective of this study was to compare the capability of xenon-enhanced area-detector CT (ADCT) performed with a subtraction technique and coregistered 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity in smokers. Forty-six consecutive smokers (32 men and 14 women; mean age, 67.0 years) underwent prospective unenhanced and xenon-enhanced ADCT, 81m Kr-ventilation SPECT/CT, and pulmonary function tests. Disease severity was evaluated according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. CT-based functional lung volume (FLV), the percentage of wall area to total airway area (WA%), and ventilated FLV on xenon-enhanced ADCT and SPECT/CT were calculated for each smoker. All indexes were correlated with percentage of forced expiratory volume in 1 second (%FEV 1 ) using step-wise regression analyses, and univariate and multivariate logistic regression analyses were performed. In addition, the diagnostic accuracy of the proposed model was compared with that of each radiologic index by means of McNemar analysis. Multivariate logistic regression showed that %FEV 1 was significantly affected (r = 0.77, r 2 = 0.59) by two factors: the first factor, ventilated FLV on xenon-enhanced ADCT (p < 0.0001); and the second factor, WA% (p = 0.004). Univariate logistic regression analyses indicated that all indexes significantly affected GOLD classification (p < 0.05). Multivariate logistic regression analyses revealed that ventilated FLV on xenon-enhanced ADCT and CT-based FLV significantly influenced GOLD classification (p < 0.0001). The diagnostic accuracy of the proposed model was significantly higher than that of ventilated FLV on SPECT/CT (p = 0.03) and WA% (p = 0.008). Xenon-enhanced ADCT is more effective than 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity.

Intervention for an Adolescent With Cerebral Palsy During Period of Accelerated Growth.

PubMed

Reubens, Rebecca; Silkwood-Sherer, Debbie J

2016-01-01

The purpose of this case report was to describe changes in body functions and structures, activities, and participation after a biweekly 10-week program of home physical therapy and hippotherapy using a weighted compressor belt. A 13-year-old boy with spastic diplegic cerebral palsy, Gross Motor Function Classification System level II, was referred because of accelerated growth and functional impairments that limited daily activities. The Modified Ashworth Scale, passive range of motion, 1-Minute Walk Test, Timed Up and Down Stairs, Pediatric Balance Scale, Pediatric Evaluation of Disability Inventory Computer Adaptive Test, and Dimensions of Mastery Questionnaire 17 were examined at baseline, 5, and 10 weeks. Data at 5 and 10 weeks demonstrated positive changes in passive range of motion, balance, strength, functional activities, and motivation, with additional improvements in endurance and speed after 10 weeks. This report reveals enhanced body functions and structures and activities and improved participation and motivation.

Free classification of regional dialects of American English

PubMed Central

Clopper, Cynthia G.; Pisoni, David B.

2011-01-01

Recent studies have found that naïve listeners perform poorly in forced-choice dialect categorization tasks. However, the listeners' error patterns in these tasks reveal systematic confusions between phonologically similar dialects. In the present study, a free classification procedure was used to measure the perceptual similarity structure of regional dialect variation in the United States. In two experiments, participants listened to a set of short English sentences produced by male talkers only (Experiment 1) and by male and female talkers (Experiment 2). The listeners were instructed to group the talkers by regional dialect into as many groups as they wanted with as many talkers in each group as they wished. Multidimensional scaling analyses of the data revealed three primary dimensions of perceptual similarity (linguistic markedness, geography, and gender). In addition, a comparison of the results obtained from the free classification task to previous results using the same stimulus materials in six-alternative forced-choice categorization tasks revealed that response biases in the six-alternative task were reduced or eliminated in the free classification task. Thus, the results obtained with the free classification task in the current study provided further evidence that the underlying structure of perceptual dialect category representations reflects important linguistic and sociolinguistic factors. PMID:21423862

Unsupervised classification of major depression using functional connectivity MRI.

PubMed

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

2014-04-01

The current diagnosis of psychiatric disorders including major depressive disorder based largely on self-reported symptoms and clinical signs may be prone to patients' behaviors and psychiatrists' bias. This study aims at developing an unsupervised machine learning approach for the accurate identification of major depression based on single resting-state functional magnetic resonance imaging scans in the absence of clinical information. Twenty-four medication-naive patients with major depression and 29 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. We first clustered the voxels within the perigenual cingulate cortex into two subregions, a subgenual region and a pregenual region, according to their distinct resting-state functional connectivity patterns and showed that a maximum margin clustering-based unsupervised machine learning approach extracted sufficient information from the subgenual cingulate functional connectivity map to differentiate depressed patients from healthy controls with a group-level clustering consistency of 92.5% and an individual-level classification consistency of 92.5%. It was also revealed that the subgenual cingulate functional connectivity network with the highest discriminative power primarily included the ventrolateral and ventromedial prefrontal cortex, superior temporal gyri and limbic areas, indicating that these connections may play critical roles in the pathophysiology of major depression. The current study suggests that subgenual cingulate functional connectivity network signatures may provide promising objective biomarkers for the diagnosis of major depression and that maximum margin clustering-based unsupervised machine learning approaches may have the potential to inform clinical practice and aid in research on psychiatric disorders. Copyright © 2013 Wiley Periodicals, Inc.

Computerized measurement of facial expression of emotions in schizophrenia.

PubMed

Alvino, Christopher; Kohler, Christian; Barrett, Frederick; Gur, Raquel E; Gur, Ruben C; Verma, Ragini

2007-07-30

Deficits in the ability to express emotions characterize several neuropsychiatric disorders and are a hallmark of schizophrenia, and there is need for a method of quantifying expression, which is currently done by clinical ratings. This paper presents the development and validation of a computational framework for quantifying emotional expression differences between patients with schizophrenia and healthy controls. Each face is modeled as a combination of elastic regions, and expression changes are modeled as a deformation between a neutral face and an expressive face. Functions of these deformations, known as the regional volumetric difference (RVD) functions, form distinctive quantitative profiles of expressions. Employing pattern classification techniques, we have designed expression classifiers for the four universal emotions of happiness, sadness, anger and fear by training on RVD functions of expression changes. The classifiers were cross-validated and then applied to facial expression images of patients with schizophrenia and healthy controls. The classification score for each image reflects the extent to which the expressed emotion matches the intended emotion. Group-wise statistical analysis revealed this score to be significantly different between healthy controls and patients, especially in the case of anger. This score correlated with clinical severity of flat affect. These results encourage the use of such deformation based expression quantification measures for research in clinical applications that require the automated measurement of facial affect.

Genomic landscape of gastric cancer: molecular classification and potential targets.

PubMed

Guo, Jiawei; Yu, Weiwei; Su, Hui; Pang, Xiufeng

2017-02-01

Gastric cancer imposes a considerable health burden worldwide, and its mortality ranks as the second highest for all types of cancers. The limited knowledge of the molecular mechanisms underlying gastric cancer tumorigenesis hinders the development of therapeutic strategies. However, ongoing collaborative sequencing efforts facilitate molecular classification and unveil the genomic landscape of gastric cancer. Several new drivers and tumorigenic pathways in gastric cancer, including chromatin remodeling genes, RhoA-related pathways, TP53 dysregulation, activation of receptor tyrosine kinases, stem cell pathways and abnormal DNA methylation, have been revealed. These newly identified genomic alterations await translation into clinical diagnosis and targeted therapies. Considering that loss-of-function mutations are intractable, synthetic lethality could be employed when discussing feasible therapeutic strategies. Although many challenges remain to be tackled, we are optimistic regarding improvements in the prognosis and treatment of gastric cancer in the near future.

A discrimlnant function approach to ecological site classification in northern New England

Treesearch

James M. Fincher; Marie-Louise Smith

1994-01-01

Describes one approach to ecologically based classification of upland forest community types of the White and Green Mountain physiographic regions. The classification approach is based on an intensive statistical analysis of the relationship between the communities and soil-site factors. Discriminant functions useful in distinguishing between types based on soil-site...

Application of the International Classification of Functioning, Disability and Health system to symptoms of the Duchenne and Becker muscular dystrophies.

PubMed

Conway, Kristin M; Ciafaloni, Emma; Matthews, Dennis; Westfield, Chris; James, Kathy; Paramsothy, Pangaja; Romitti, Paul A

2018-07-01

Duchenne and Becker muscular dystrophies, collectively referred to as dystrophinopathies, are X-linked recessive diseases that affect dystrophin production resulting in compromised muscle function across multiple systems. The International Classification of Functioning, Disability and Health provides a systematic classification scheme from which body functions affected by a dystrophinopathy can be identified and used to examine functional health. The infrastructure of the Muscular Dystrophy Surveillance, Tracking, and Research Network was used to identify commonly affected body functions and link selected functions to clinical surveillance data collected through medical record abstraction. Seventy-one (24 second-, 41 third- and 7 fourth-level) body function categories were selected via clinician review and consensus. Of these, 15 of 24 retained second-level categories were linked to data elements from the Muscular Dystrophy Surveillance, Tracking, and Research Network surveillance database. Our findings support continued development of a core set of body functions from the International Classification of Functioning, Disability and Health system that are representative of disease progression in dystrophinopathies and the incorporation of these functions in standardized evaluations of functional health and implementation of individualized rehabilitation care plans. Implications for Rehabilitation Duchenne and Becker muscular dystrophies, collectively referred to as dystrophinopathies, are X-linked recessive disorders that affect the production of dystrophin resulting in compromised muscle function across multiple systems. The severity and progressive nature of dystrophinopathies can have considerable impact on a patient's participation in activities across multiple life domains. Our findings support continued development of an International Classification of Functioning, Disability and Health core set for childhood-onset dystrophinopathies. A standardized dystrophinopathy International Classification of Functioning, Disability and Health documentation form can be used as a screening tool by rehabilitation professionals and for patient goal setting when developing rehabilitation plans. Patient reports of perceived functional health should be incorporated into the rehabilitation plan and therapeutic progress monitored by a standardized form.

Discriminative analysis of non-linear brain connectivity for leukoaraiosis with resting-state fMRI

NASA Astrophysics Data System (ADS)

Lai, Youzhi; Xu, Lele; Yao, Li; Wu, Xia

2015-03-01

Leukoaraiosis (LA) describes diffuse white matter abnormalities on CT or MR brain scans, often seen in the normal elderly and in association with vascular risk factors such as hypertension, or in the context of cognitive impairment. The mechanism of cognitive dysfunction is still unclear. The recent clinical studies have revealed that the severity of LA was not corresponding to the cognitive level, and functional connectivity analysis is an appropriate method to detect the relation between LA and cognitive decline. However, existing functional connectivity analyses of LA have been mostly limited to linear associations. In this investigation, a novel measure utilizing the extended maximal information coefficient (eMIC) was applied to construct non-linear functional connectivity in 44 LA subjects (9 dementia, 25 mild cognitive impairment (MCI) and 10 cognitively normal (CN)). The strength of non-linear functional connections for the first 1% of discriminative power increased in MCI compared with CN and dementia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the non-linear and linear connectivity have similar but not completely the same spatial distribution in human brain. In the multivariate pattern analysis with multiple classifiers, the non-linear functional connectivity mostly identified dementia, MCI and CN from LA with a relatively higher accuracy rate than the linear measure. Our findings revealed the non-linear functional connectivity provided useful discriminative power in classification of LA, and the spatial distributed changes between the non-linear and linear measure may indicate the underlying mechanism of cognitive dysfunction in LA.

A bayesian hierarchical model for classification with selection of functional predictors.

PubMed

Zhu, Hongxiao; Vannucci, Marina; Cox, Dennis D

2010-06-01

In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis-Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.

It is time to bring borderline intellectual functioning back into the main fold of classification systems

PubMed Central

Wieland, Jannelien; Zitman, Frans G.

2016-01-01

Borderline intellectual functioning is an important and frequently unrecognised comorbid condition relevant to the diagnosis and treatment of any and all psychiatric disorders. In the DSM-IV-TR, it is defined by IQ in the 71–84 range. In DSM-5, IQ boundaries are no longer part of the classification, leaving the concept without a clear definition. This modification is one of the least highlighted changes in DSM-5. In this article we describe the history of the classification of borderline intellectual functioning. We provide information about it and on the importance of placing it in the right context and in the right place in future DSM editions and other classification systems such as the International Classification of Diseases. PMID:27512590

Rapid river classification using GIS-delineated functional process zones

EPA Science Inventory

Traditional classification of rivers does not take into consideration how rivers function within the ecosystem. Using factors such as hydrology and geomorphology that directly affect ecosystem structure and function, provides a means of classifying river systems into hydrogeomorp...

On-board multispectral classification study

NASA Technical Reports Server (NTRS)

Ewalt, D.

1979-01-01

The factors relating to onboard multispectral classification were investigated. The functions implemented in ground-based processing systems for current Earth observation sensors were reviewed. The Multispectral Scanner, Thematic Mapper, Return Beam Vidicon, and Heat Capacity Mapper were studied. The concept of classification was reviewed and extended from the ground-based image processing functions to an onboard system capable of multispectral classification. Eight different onboard configurations, each with varying amounts of ground-spacecraft interaction, were evaluated. Each configuration was evaluated in terms of turnaround time, onboard processing and storage requirements, geometric and classification accuracy, onboard complexity, and ancillary data required from the ground.

Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

PubMed

Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

2012-12-05

Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (P<0.05). Correlation analysis between feature importance and the statistical significance of metrics was investigated, and the results revealed a strong positive correlation between them. Overall, the current study demonstrated that major depressive disorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

Disability and Profiles of Functioning of Patients with Parkinson's Disease Described with ICF Classification

ERIC Educational Resources Information Center

Raggi, Alberto; Leonardi, Matilde; Ajovalasit, Daniela; Carella, Francesco; Soliveri, Paola; Albanese, Alberto; Romito, Luigi

2011-01-01

The objective of this study was to describe the functional profiles of patients with Parkinson's disease (PD), and the relationships between impairment in body functions, limitations in activities, and environmental factors, using the World Health Organization's International Classification of Functioning, Disability, and Health (ICF). Patients…

Perspectives on the International Classification of Functioning, Disability, and Health: Child and Youth Version (ICF-CY) and Occupational Therapy Practice

ERIC Educational Resources Information Center

Cramm, Heidi; Aiken, Alice B.; Stewart, Debra

2012-01-01

Classifying disability for children and youth has typically meant describing a diagnosis or developmental lag. The publication of the "International Classification of Functioning, Disability and Health: Child & Youth" version (ICF-CY) marks a global paradigm shift in the conceptualization and classification of childhood disability. Knowledge and…

Exploring Children's Thinking. Part 1: The Development of Classification (Preschool - Third Grade).

ERIC Educational Resources Information Center

Alward, Keith R.

This unit of the Flexible Learning System (FLS), the first of a 3-volume series on children's thinking, discusses the development of classification in children between 3 and 8 years of age. The series is based on the application of Jean Piaget's work to early childhood education. The development of classification is revealed in the way children…

An efficient ensemble learning method for gene microarray classification.

PubMed

Osareh, Alireza; Shadgar, Bita

2013-01-01

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

PubMed Central

Meszlényi, Regina J.; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network. PMID:29089883

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

PubMed

Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

Research on evaluating water resource resilience based on projection pursuit classification model

NASA Astrophysics Data System (ADS)

Liu, Dong; Zhao, Dan; Liang, Xu; Wu, Qiuchen

2016-03-01

Water is a fundamental natural resource while agriculture water guarantees the grain output, which shows that the utilization and management of water resource have a significant practical meaning. Regional agricultural water resource system features with unpredictable, self-organization, and non-linear which lays a certain difficulty on the evaluation of regional agriculture water resource resilience. The current research on water resource resilience remains to focus on qualitative analysis and the quantitative analysis is still in the primary stage, thus, according to the above issues, projection pursuit classification model is brought forward. With the help of artificial fish-swarm algorithm (AFSA), it optimizes the projection index function, seeks for the optimal projection direction, and improves AFSA with the application of self-adaptive artificial fish step and crowding factor. Taking Hongxinglong Administration of Heilongjiang as the research base and on the basis of improving AFSA, it established the evaluation of projection pursuit classification model to agriculture water resource system resilience besides the proceeding analysis of projection pursuit classification model on accelerating genetic algorithm. The research shows that the water resource resilience of Hongxinglong is the best than Raohe Farm, and the last 597 Farm. And the further analysis shows that the key driving factors influencing agricultural water resource resilience are precipitation and agriculture water consumption. The research result reveals the restoring situation of the local water resource system, providing foundation for agriculture water resource management.

Effects of two classification strategies on a Benthic Community Index for streams in the Northern Lakes and Forests Ecoregion

USGS Publications Warehouse

Butcher, Jason T.; Stewart, Paul M.; Simon, Thomas P.

2003-01-01

Ninety-four sites were used to analyze the effects of two different classification strategies on the Benthic Community Index (BCI). The first, a priori classification, reflected the wetland status of the streams; the second, a posteriori classification, used a bio-environmental analysis to select classification variables. Both classifications were examined by measuring classification strength and testing differences in metric values with respect to group membership. The a priori (wetland) classification strength (83.3%) was greater than the a posteriori (bio-environmental) classification strength (76.8%). Both classifications found one metric that had significant differences between groups. The original index was modified to reflect the wetland classification by re-calibrating the scoring criteria for percent Crustacea and Mollusca. A proposed refinement to the original Benthic Community Index is suggested. This study shows the importance of using hypothesis-driven classifications, as well as exploratory statistical analysis, to evaluate alternative ways to reveal environmental variability in biological assessment tools.

Computer-aided diagnosis in phase contrast imaging X-ray computed tomography for quantitative characterization of ex vivo human patellar cartilage.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismuller, Axel

2013-10-01

Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

What Images Reveal: a Comparative Study of Science Images between Australian and Taiwanese Junior High School Textbooks

NASA Astrophysics Data System (ADS)

Ge, Yun-Ping; Unsworth, Len; Wang, Kuo-Hua; Chang, Huey-Por

2017-07-01

From a social semiotic perspective, image designs in science textbooks are inevitably influenced by the sociocultural context in which the books are produced. The learning environments of Australia and Taiwan vary greatly. Drawing on social semiotics and cognitive science, this study compares classificational images in Australian and Taiwanese junior high school science textbooks. Classificational images are important kinds of images, which can represent taxonomic relations among objects as reported by Kress and van Leeuwen (Reading images: the grammar of visual design, 2006). An analysis of the images from sample chapters in Australian and Taiwanese high school science textbooks showed that the majority of the Taiwanese images are covert taxonomies, which represent hierarchical relations implicitly. In contrast, Australian classificational images included diversified designs, but particularly types with a tree structure which depicted overt taxonomies, explicitly representing hierarchical super-ordinate and subordinate relations. Many of the Taiwanese images are reminiscent of the specimen images in eighteenth century science texts representing "what truly is", while more Australian images emphasize structural objectivity. Moreover, Australian images support cognitive functions which facilitate reading comprehension. The relationships between image designs and learning environments are discussed and implications for textbook research and design are addressed.

Why women use makeup: implication of psychological traits in makeup functions.

PubMed

Korichi, Rodolphe; Pelle-de-Queral, Delphine; Gazano, Germaine; Aubert, Arnaud

2008-01-01

Makeup acts and stimulates three of our senses: touch (which encompasses all sensations from the body surface), smell (fragrance), and sight (the process of becoming and looking beautiful). The positive stimulation of these senses by makeup can induce sensory as well as psychological pleasure. In order to understand the relationship of women to their makeup, we interviewed different groups of women on their quality of life and makeup habits. Then, through four standard well-validated psychometric self-questionnaires, we examined the possible relation between the need to make up oneself and specific psychological features. Our first results clearly showed that makeup could support two opposite "up" functions, i.e., "camouflage" vs "seduction." Concerning their psychological profiles, results showed that women of the functional class "camouflage" are more anxious, defensive, and emotionally unstable compared to those of the functional class "seduction," who appear to be more sociable, assertive, and extroverted. Further analyses revealed a division of the two classes into subclasses of volunteers with opposed personality and psychological profiles. This new classification allowed us to define more precisely the relations existing within the subjective experience of women during the makeup process. In conclusion, our study revealed that beyond the simple application of colorful products on the face, makeup has two major functional implications depending on specific psychological profiles of women.

The roles of teachers' science talk in revealing language demands within diverse elementary school classrooms: a study of teaching heat and temperature in Singapore

NASA Astrophysics Data System (ADS)

Seah, Lay Hoon; Yore, Larry D.

2017-01-01

This study of three science teachers' lessons on heat and temperature seeks to characterise classroom talk that highlighted the ways language is used and to examine the nature of the language demands revealed in constructing, negotiating, arguing and communicating science ideas. The transcripts from the entire instructional units for these teachers' four culturally and linguistically diverse Grade 4 classes (10 years old) with English as the language of instruction constitute the data for this investigation. Analysis of these transcripts focused on teachers' talk that made explicit reference to the form or function of the language of science and led to the inductive development of the 'Attending to Language Demands in Science' analytical framework. This framework in turn revealed that the major foregrounding purposes of teachers' talk include labelling, explaining, differentiating, selecting and constructing. Further classification of the instances within these categories revealed the extensive and contextualised nature of the language demands. The results challenge the conventional assumption that basic literacy skills dominate over disciplinary literacy skills in primary school science. Potential uses of the analytical framework that could further expand our understanding of the forms, functions and demands of language used in elementary school science are also discussed.

Low back related leg pain: an investigation of construct validity of a new classification system.

PubMed

Schäfer, Axel G M; Hall, Toby M; Rolke, Roman; Treede, Rolf-Detlef; Lüdtke, Kerstin; Mallwitz, Joachim; Briffa, Kathryn N

2014-01-01

Leg pain is associated with back pain in 25-65% of all cases and classified as somatic referred pain or radicular pain. However, distinction between the two may be difficult as different pathomechanisms may cause similar patterns of pain. Therefore a pathomechanism based classification system was proposed, with four distinct hierarchical and mutually exclusive categories: Neuropathic Sensitization (NS) comprising major features of neuropathic pain with sensory sensitization; Denervation (D) arising from significant axonal compromise; Peripheral Nerve Sensitization (PNS) with marked nerve trunk mechanosensitivity; and Musculoskeletal (M) with pain referred from musculoskeletal structures. To investigate construct validity of the classification system. Construct validity was investigated by determining the relationship of nerve functioning with subgroups of patients and asymptomatic controls. Thus somatosensory profiles of subgroups of patients with low back related leg pain (LBRLP) and healthy controls were determined by a comprehensive quantitative sensory test (QST) protocol. It was hypothesized that subgroups of patients and healthy controls would show differences in QST profiles relating to underlying pathomechanisms. 77 subjects with LBRLP were recruited and classified in one of the four groups. Additionally, 18 age and gender matched asymptomatic controls were measured. QST revealed signs of pain hypersensitivity in group NS and sensory deficits in group D whereas Groups PNS and M showed no significant differences when compared to the asymptomatic group. These findings support construct validity for two of the categories of the new classification system, however further research is warranted to achieve construct validation of the classification system as a whole.

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

NASA Astrophysics Data System (ADS)

Jürgens, Björn; Herrero-Solana, Victor

2017-04-01

Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

Functions in Biological Kind Classification

ERIC Educational Resources Information Center

Lombrozo, Tania; Rehder, Bob

2012-01-01

Biological traits that serve functions, such as a zebra's coloration (for camouflage) or a kangaroo's tail (for balance), seem to have a special role in conceptual representations for biological kinds. In five experiments, we investigate whether and why functional features are privileged in biological kind classification. Experiment 1…

46 CFR 8.250 - Acceptance of standards and functions delegated under existing regulations.

Code of Federal Regulations, 2011 CFR

2011-10-01

... APPLICABLE TO THE PUBLIC VESSEL INSPECTION ALTERNATIVES Recognition of a Classification Society § 8.250 Acceptance of standards and functions delegated under existing regulations. (a) Classification society class... society has received authorization to conduct a related delegated function. (b) A recognized...

46 CFR 8.250 - Acceptance of standards and functions delegated under existing regulations.

Code of Federal Regulations, 2010 CFR

2010-10-01

... APPLICABLE TO THE PUBLIC VESSEL INSPECTION ALTERNATIVES Recognition of a Classification Society § 8.250 Acceptance of standards and functions delegated under existing regulations. (a) Classification society class... society has received authorization to conduct a related delegated function. (b) A recognized...

Atmospheric correction analysis on LANDSAT data over the Amazon region. [Manaus, Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Dias, L. A. V.; Dossantos, J. R.; Formaggio, A. R.

1983-01-01

The Amazon Region natural resources were studied in two ways and compared. A LANDSAT scene and its attributes were selected, and a maximum likelihood classification was made. The scene was atmospherically corrected, taking into account Amazonic peculiarities revealed by (ground truth) of the same area, and the subsequent classification. Comparison shows that the classification improves with the atmospherically corrected images.

Spectrally based mapping of riverbed composition

USGS Publications Warehouse

Legleiter, Carl; Stegman, Tobin K.; Overstreet, Brandon T.

2016-01-01

Remote sensing methods provide an efficient means of characterizing fluvial systems. This study evaluated the potential to map riverbed composition based on in situ and/or remote measurements of reflectance. Field spectra and substrate photos from the Snake River, Wyoming, USA, were used to identify different sediment facies and degrees of algal development and to quantify their optical characteristics. We hypothesized that accounting for the effects of depth and water column attenuation to isolate the reflectance of the streambed would enhance distinctions among bottom types and facilitate substrate classification. A bottom reflectance retrieval algorithm adapted from coastal research yielded realistic spectra for the 450 to 700 nm range; but bottom reflectance-based substrate classifications, generated using a random forest technique, were no more accurate than classifications derived from above-water field spectra. Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. Accounting for the effects of depth might have eliminated a key distinction among substrates and thus reduced discriminatory power. Although further, more systematic study across a broader range of fluvial environments is needed to substantiate our initial results, this case study suggests that bed composition in shallow, clear-flowing rivers potentially could be mapped remotely.

a Gsa-Svm Hybrid System for Classification of Binary Problems

NASA Astrophysics Data System (ADS)

Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan

2011-06-01

This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.

Maxillectomy defects: a suggested classification scheme.

PubMed

Akinmoladun, V I; Dosumu, O O; Olusanya, A A; Ikusika, O F

2013-06-01

The term "maxillectomy" has been used to describe a variety of surgical procedures for a spectrum of diseases involving a diverse anatomical site. Hence, classifications of maxillectomy defects have often made communication difficult. This article highlights this problem, emphasises the need for a uniform system of classification and suggests a classification system which is simple and comprehensive. Articles related to this subject, especially those with specified classifications of maxillary surgical defects were sourced from the internet through Google, Scopus and PubMed using the search terms maxillectomy defects classification. A manual search through available literature was also done. The review of the materials revealed many classifications and modifications of classifications from the descriptive, reconstructive and prosthodontic perspectives. No globally acceptable classification exists among practitioners involved in the management of diseases in the mid-facial region. There were over 14 classifications of maxillary defects found in the English literature. Attempts made to address the inadequacies of previous classifications have tended to result in cumbersome and relatively complex classifications. A single classification that is based on both surgical and prosthetic considerations is most desirable and is hereby proposed.

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions.

PubMed

Mai, Te-Lun; Hu, Geng-Ming; Chen, Chi-Ming

2016-07-01

Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences.

On the nature of global classification

NASA Technical Reports Server (NTRS)

Wheelis, M. L.; Kandler, O.; Woese, C. R.

1992-01-01

Molecular sequencing technology has brought biology into the era of global (universal) classification. Methodologically and philosophically, global classification differs significantly from traditional, local classification. The need for uniformity requires that higher level taxa be defined on the molecular level in terms of universally homologous functions. A global classification should reflect both principal dimensions of the evolutionary process: genealogical relationship and quality and extent of divergence within a group. The ultimate purpose of a global classification is not simply information storage and retrieval; such a system should also function as an heuristic representation of the evolutionary paradigm that exerts a directing influence on the course of biology. The global system envisioned allows paraphyletic taxa. To retain maximal phylogenetic information in these cases, minor notational amendments in existing taxonomic conventions should be adopted.

Quantitative proteomic study of Aspergillus Fumigatus secretome revealed deamidation of secretory enzymes.

PubMed

Adav, Sunil S; Ravindran, Anita; Sze, Siu Kwan

2015-04-24

Aspergillus sp. plays an essential role in lignocellulosic biomass recycling and is also exploited as cell factories for the production of industrial enzymes. This study profiled the secretome of Aspergillus fumigatus when grown with cellulose, xylan and starch by high throughput quantitative proteomics using isobaric tags for relative and absolute quantification (iTRAQ). Post translational modifications (PTMs) of proteins play a critical role in protein functions. However, our understanding of the PTMs in secretory proteins is limited. Here, we present the identification of PTMs such as deamidation of secreted proteins of A. fumigatus. This study quantified diverse groups of extracellular secreted enzymes and their functional classification revealed cellulases and glycoside hydrolases (32.9%), amylases (0.9%), hemicellulases (16.2%), lignin degrading enzymes (8.1%), peptidases and proteases (11.7%), chitinases, lipases and phosphatases (7.6%), and proteins with unknown function (22.5%). The comparison of quantitative iTRAQ results revealed that cellulose and xylan stimulates expression of specific cellulases and hemicellulases, and their abundance level as a function of substrate. In-depth data analysis revealed deamidation as a major PTM of key cellulose hydrolyzing enzymes like endoglucanases, cellobiohydrolases and glucosidases. Hemicellulose degrading endo-1,4-beta-xylanase, monosidases, xylosidases, lignin degrading laccase, isoamyl alcohol oxidase and oxidoreductases were also found to be deamidated. The filamentous fungi play an essential role in lignocellulosic biomass recycling and fungal strains belonging to Aspergillus were also exploited as cell factories for the production of organic acids, pharmaceuticals, and industrially important enzymes. In this study, extracellular proteins secreted by thermophilic A. fumigatus when grown with cellulose, xylan and starch were profiled using isobaric tags for relative and absolute quantification (iTRAQ) by adopting liquid chromatography tandem mass spectrometry. The comparison of quantitative iTRAQ results revealed that cellulose and xylan stimulate expression of specific cellulases and hemicellulases, and expression level as a function of substrate. Post translational modifications revealed deamidation of key cellulases including endoglucanases, cellobiohydrolases and glucosidases; and hemicellulases and lignin degrading enzymes. The knowledge on deamidated enzymes along with specific sites of modifications could be crucial information for further functional studies of these enzymes of A. fumigatus. Copyright © 2015 Elsevier B.V. All rights reserved.

Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

NASA Astrophysics Data System (ADS)

Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

2014-03-01

A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

Disability and Functional Profiles of Patients with Migraine Measured with ICF Classification

ERIC Educational Resources Information Center

Raggi, Alberto

2010-01-01

To describe the functional profiles of patients with migraine, and the relationships between symptoms, activities and environmental factors, using WHO's International Classification of Functioning (ICF). Patients were consecutively enrolled at the Besta Institute of Milan. The ICF checklist was administered and two count-based indexes developed:…

Resting-State Functional Magnetic Resonance Imaging for Language Preoperative Planning

PubMed Central

Branco, Paulo; Seixas, Daniela; Deprez, Sabine; Kovacs, Silvia; Peeters, Ronald; Castro, São L.; Sunaert, Stefan

2016-01-01

Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way. PMID:26869899

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

PubMed

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

2011-08-02

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

Evaluation of cognitive and social functioning in patients requiring long-term inpatient psychiatric care using the International Classification of Functioning, Disability, and Health: a large-scale, multi-institutional observational study.

PubMed

Kawaguchi, Hideaki; Taguchi, Masamoto; Sukigara, Masune; Sakuragi, Shoji; Sugiyama, Naoya; Chiba, Hisomu; Kawasaki, Tatsuhito

2017-06-15

We comprehensively evaluated cognitive and social functioning in patients requiring long-term inpatient psychiatric care using the International Classification of Functioning, Disability, and Health. We surveyed 1967 patients receiving long-term inpatient psychiatric care. Patients were further categorized into an old long-stay group (n = 892, >5 years in hospitals) and a new long-stay group (n = 1075, 1-5 years in hospitals). We obtained responses for all the International Classification of Functioning, Disability, and Health items in domain b (Body Functions) and domain d (Activities and Participation). We estimated weighted means for each item using the propensity score to adjust for confounding factors. Responses were received from 307 hospitals (response rate of hospitals: 25.5%). Cognitive and social functioning in the old long-stay group was more severely impaired than in the new long-stay group. No statistically significant differences were observed regarding the International Classification of Functioning, Disability, and Health items associated with basic activities of daily living between the two groups. Combined therapy consisting of cognitive remediation and rehabilitation on social functioning for this patient population should be started from the early stage of hospitalization. Non-restrictive, independent environments may also be optimal for this patient population. Implications for rehabilitation Rehabilitation of cognitive and social functioning for patients requiring long-term inpatient psychiatric care should be started in the early stages of hospitalization. In psychiatric fields, the International Classification of Functioning, Disability, and Health checklist could facilitate individualized rehabilitation planning by allowing healthcare professionals to visually assess the comprehensive functioning of each patient using graphics such as radar charts.

Local functional connectivity density is closely associated with the response of electroconvulsive therapy in major depressive disorder.

PubMed

Wang, Jiaojian; Wei, Qiang; Yuan, Xinru; Jiang, Xiaoyan; Xu, Jinping; Zhou, Xiaoqin; Tian, Yanghua; Wang, Kai

2018-01-01

Electroconvulsive therapy (ECT) has been demonstrated to be an effective treatment of major depressive disorder (MDD). However, the neuroanatomical basis of response to ECT is still largely unknown. In present study, we used functional connectivity density (FCD) and resting-state functional connectivity (RSFC) to identify the relationship between the changes of resting-state activities and ECT responses in 23 MDD patients before and after ECT. In addition, the identified neural indices as classification characteristics were entered into multivariate pattern analysis using linear support vector machine (SVM) to classify 23 MDD patients before ECT from 25 gender, age and years of education matched healthy controls. We found that the changes of local FCD (lFCD), not long-range FCD, of the left pre-/postcentral gyrus (Pre-/postCG), left superior temporal gyrus (STG), and right STG were significantly correlated with the changes of Hamilton Rating Scale for Depression (HRSD) scores in MDD patients before and after ECT. The subsequent functional connectivity analysis revealed significantly decreased functional connectivity between right STG and right intraparietal sulcus (IPS) in MDD after ECT in spite of no correlation with HRSD scores. Finally, SVM-based classification achieved an accuracy of 72.92% with a sensitivity of 73.91% and a specificity of 72% by leave-one-out cross-validation. Our findings indicated that Pre-/postCG and bilateral STG play an important role in response of ECT in MDD patients, and the lFCD in these areas may serve as a biomarker for predicting ECT response. Copyright © 2017 Elsevier B.V. All rights reserved.

New KF-PP-SVM classification method for EEG in brain-computer interfaces.

PubMed

Yang, Banghua; Han, Zhijun; Zan, Peng; Wang, Qian

2014-01-01

Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

Incorporating spatial context into statistical classification of multidimensional image data

NASA Technical Reports Server (NTRS)

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

1981-01-01

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

The Bellevue Classification System: nursing's voice upon the library shelves*†

PubMed Central

Mages, Keith C

2011-01-01

This article examines the inspiration, construction, and meaning of the Bellevue Classification System (BCS), created during the 1930s for use in the Bellevue School of Nursing Library. Nursing instructor Ann Doyle, with assistance from librarian Mary Casamajor, designed the BCS after consulting with library leaders and examining leading contemporary classification systems, including the Dewey Decimal Classification and Library of Congress, Ballard, and National Health Library classification systems. A close textual reading of the classes, subclasses, and subdivisions of these classification systems against those of the resulting BCS, reveals Doyle's belief that the BCS was created not only to organize the literature, but also to promote the burgeoning intellectualism and professionalism of early twentieth-century American nursing. PMID:21243054

Bimanual Capacity of Children With Cerebral Palsy: Intra- and Interrater Reliability of a Revised Edition of the Bimanual Fine Motor Function Classification.

PubMed

Elvrum, Ann-Kristin G; Beckung, Eva; Sæther, Rannei; Lydersen, Stian; Vik, Torstein; Himmelmann, Kate

2017-08-01

To develop a revised edition of the Bimanual Fine Motor Function (BFMF 2), as a classification of fine motor capacity in children with cerebral palsy (CP), and establish intra- and interrater reliability of this edition. The content of the original BFMF was discussed by an expert panel, resulting in a revised edition comprising the original description of the classification levels, but in addition including figures with specific explanatory text. Four professionals classified fine motor function of 79 children (3-17 years; 45 boys) who represented all subtypes of CP and Manual Ability Classification levels (I-V). Intra- and inter-rater reliability was assessed using overall intra-class correlation coefficient (ICC), and Cohen's quadratic weighted kappa. The overall ICC was 0.86. Cohen's weighted kappa indicated high intra-rater (к w : >0.90) and inter-rater (к w : >0.85) reliability. The revised BFMF 2 had high intra- and interrater reliability. The classification levels could be determined from short video recordings (<5 minutes), using the figures and precise descriptions of the fine motor function levels included in the BFMF 2. Thus, the BFMF 2 may be a feasible and useful classification of fine motor capacity both in research and in clinical practice.

Teaching Methods, Intelligence, and Gender Factors in Pupil Achievement on a Classification Task

ERIC Educational Resources Information Center

Ryman, Don

1977-01-01

Reports on twelve year-old students instructed in Nuffield Project and in "traditional" classrooms. A division of the subjects into two groups based on intelligence revealed significant differences on classification ability. Interaction effects were also observed. (CP)

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.

DockQ: A Quality Measure for Protein-Protein Docking Models

PubMed Central

Basu, Sankar

2016-01-01

The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (<3.0Å). This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure prediction, and DockQ should be useful in a similar development in the protein docking field. DockQ is available at http://github.com/bjornwallner/DockQ/ PMID:27560519

A machine-learned computational functional genomics-based approach to drug classification.

PubMed

Lötsch, Jörn; Ultsch, Alfred

2016-12-01

The public accessibility of "big data" about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug discovery and repurposing. This paper compares the performance of a functional genomics-based criterion to the traditional drug target-based classification. Knowledge discovery in the DrugBank and Gene Ontology databases allowed the construction of a "drug target versus biological process" matrix as a combination of "drug versus genes" and "genes versus biological processes" matrices. As a canonical example, such matrices were constructed for classical analgesic drugs. These matrices were projected onto a toroid grid of 50 × 82 artificial neurons using a self-organizing map (SOM). The distance, respectively, cluster structure of the high-dimensional feature space of the matrices was visualized on top of this SOM using a U-matrix. The cluster structure emerging on the U-matrix provided a correct classification of the analgesics into two main classes of opioid and non-opioid analgesics. The classification was flawless with both the functional genomics and the traditional target-based criterion. The functional genomics approach inherently included the drugs' modulatory effects on biological processes. The main pharmacological actions known from pharmacological science were captures, e.g., actions on lipid signaling for non-opioid analgesics that comprised many NSAIDs and actions on neuronal signal transmission for opioid analgesics. Using machine-learned techniques for computational drug classification in a comparative assessment, a functional genomics-based criterion was found to be similarly suitable for drug classification as the traditional target-based criterion. This supports a utility of functional genomics-based approaches to computational system pharmacology for drug discovery and repurposing.

Disability and Functional Profiles of Patients with Myasthenia Gravis Measured with ICF Classification

ERIC Educational Resources Information Center

Leonardi, Matilde; Raggi, Alberto; Antozzi, Carlo; Confalonieri, Paolo; Maggi, Lorenzo; Cornelio, Ferdinando; Mantegazza, Renato

2009-01-01

The objective of this study is to describe functional profiles of patients with myasthenia gravis (MG), and the relationships among symptoms, activities and environmental factors (EF), by using WHO's International Classification of Functioning Disability and Health (ICF). Patients were consecutively enrolled at the Besta Institute of Milan, Italy.…

Aligning Physical Activity Measures with the International Classification of Functioning, Disability and Health Framework for Childhood Disability

ERIC Educational Resources Information Center

Ross, Samantha Mae; Case, Layne; Leung, Willie

2016-01-01

The introduction of the International Classification of Functioning, Disability and Health has placed emphasis on framing health behavior as a multidimensional construct. In relation to childhood physical activity, this encompasses dimensions of functional performance, activity attendance, and subjective perceptions of involvement and enjoyment…

Use of DAVID algorithms for gene functional classification in a non-model organism, rainbow trout

USDA-ARS?s Scientific Manuscript database

Gene functional clustering is essential in transcriptome data analysis but software programs are not always suitable for use with non-model species. The DAVID Gene Functional Classification Tool has been widely used for soft clustering in model species, but requires adaptations for use in non-model ...

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

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

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

Linking plant and ecosystem functional biogeography.

PubMed

Reichstein, Markus; Bahn, Michael; Mahecha, Miguel D; Kattge, Jens; Baldocchi, Dennis D

2014-09-23

Classical biogeographical observations suggest that ecosystems are strongly shaped by climatic constraints in terms of their structure and function. On the other hand, vegetation function feeds back on the climate system via biosphere-atmosphere exchange of matter and energy. Ecosystem-level observations of this exchange reveal very large functional biogeographical variation of climate-relevant ecosystem functional properties related to carbon and water cycles. This variation is explained insufficiently by climate control and a classical plant functional type classification approach. For example, correlations between seasonal carbon-use efficiency and climate or environmental variables remain below 0.6, leaving almost 70% of variance unexplained. We suggest that a substantial part of this unexplained variation of ecosystem functional properties is related to variations in plant and microbial traits. Therefore, to progress with global functional biogeography, we should seek to understand the link between organismic traits and flux-derived ecosystem properties at ecosystem observation sites and the spatial variation of vegetation traits given geoecological covariates. This understanding can be fostered by synergistic use of both data-driven and theory-driven ecological as well as biophysical approaches.

Linking plant and ecosystem functional biogeography

PubMed Central

Reichstein, Markus; Bahn, Michael; Mahecha, Miguel D.; Kattge, Jens; Baldocchi, Dennis D.

2014-01-01

Classical biogeographical observations suggest that ecosystems are strongly shaped by climatic constraints in terms of their structure and function. On the other hand, vegetation function feeds back on the climate system via biosphere–atmosphere exchange of matter and energy. Ecosystem-level observations of this exchange reveal very large functional biogeographical variation of climate-relevant ecosystem functional properties related to carbon and water cycles. This variation is explained insufficiently by climate control and a classical plant functional type classification approach. For example, correlations between seasonal carbon-use efficiency and climate or environmental variables remain below 0.6, leaving almost 70% of variance unexplained. We suggest that a substantial part of this unexplained variation of ecosystem functional properties is related to variations in plant and microbial traits. Therefore, to progress with global functional biogeography, we should seek to understand the link between organismic traits and flux-derived ecosystem properties at ecosystem observation sites and the spatial variation of vegetation traits given geoecological covariates. This understanding can be fostered by synergistic use of both data-driven and theory-driven ecological as well as biophysical approaches. PMID:25225392

[Application of target restoration space quantity and quantitative relation in precise esthetic prosthodontics].

PubMed

Haiyang, Yu; Tian, Luo

2016-06-01

Target restoration space (TRS) is the most precise space required for designing optimal prosthesis. TRS consists of an internal or external tooth space to confirm the esthetics and function of the final restoration. Therefore, assisted with quantitive analysis transfer, TRS quantitative analysis is a significant improvement for minimum tooth preparation. This article presents TRS quantity-related measurement, analysis, transfer, and internal relevance of three TR. classifications. Results reveal the close bond between precision and minimally invasive treatment. This study can be used to improve the comprehension and execution of precise esthetic prosthodontics.

Burke-Fahn-Marsden dystonia severity, Gross Motor, Manual Ability, and Communication Function Classification scales in childhood hyperkinetic movement disorders including cerebral palsy: a 'Rosetta Stone' study.

PubMed

Elze, Markus C; Gimeno, Hortensia; Tustin, Kylee; Baker, Lesley; Lumsden, Daniel E; Hutton, Jane L; Lin, Jean-Pierre S-M

2016-02-01

Hyperkinetic movement disorders (HMDs) can be assessed using impairment-based scales or functional classifications. The Burke-Fahn-Marsden Dystonia Rating Scale-movement (BFM-M) evaluates dystonia impairment, but may not reflect functional ability. The Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), and Communication Function Classification System (CFCS) are widely used in the literature on cerebral palsy to classify functional ability, but not in childhood movement disorders. We explore the concordance of these three functional scales in a large sample of paediatric HMDs and the impact of dystonia severity on these scales. Children with HMDs (n=161; median age 10y 3mo, range 2y 6mo-21y) were assessed using the BFM-M, GMFCS, MACS, and CFCS from 2007 to 2013. This cross-sectional study contrasts the information provided by these scales. All four scales were strongly associated (all Spearman's rank correlation coefficient rs >0.72, p<0.001), with worse dystonia severity implying worse function. Secondary dystonias had worse dystonia and less function than primary dystonias (p<0.001). A longer proportion of life lived with dystonia is associated with more severe dystonia (rs =0.42, p<0.001). The BFM-M is strongly linked with the GMFCS, MACS, and CFCS, irrespective of aetiology. Each scale offers interrelated but complementary information and is applicable to all aetiologies. Movement disorders including cerebral palsy can be effectively evaluated using these scales. © 2015 Mac Keith Press.

Identification of Novel Functional Inhibitors of Acid Sphingomyelinase

PubMed Central

Trapp, Stefan; Pechmann, Stefanie; Friedl, Astrid; Reichel, Martin; Mühle, Christiane; Terfloth, Lothar; Groemer, Teja W.; Spitzer, Gudrun M.; Liedl, Klaus R.; Gulbins, Erich; Tripal, Philipp

2011-01-01

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans. PMID:21909365

Imagine All the People: How the Brain Creates and Uses Personality Models to Predict Behavior

PubMed Central

Hassabis, Demis; Spreng, R. Nathan; Rusu, Andrei A.; Robbins, Clifford A.; Mar, Raymond A.; Schacter, Daniel L.

2014-01-01

The behaviors of other people are often central to envisioning the future. The ability to accurately predict the thoughts and actions of others is essential for successful social interactions, with far-reaching consequences. Despite its importance, little is known about how the brain represents people in order to predict behavior. In this functional magnetic resonance imaging study, participants learned the unique personality of 4 protagonists and imagined how each would behave in different scenarios. The protagonists' personalities were composed of 2 traits: Agreeableness and Extraversion. Which protagonist was being imagined was accurately inferred based solely on activity patterns in the medial prefrontal cortex using multivariate pattern classification, providing novel evidence that brain activity can reveal whom someone is thinking about. Lateral temporal and posterior cingulate cortex discriminated between different degrees of agreeableness and extraversion, respectively. Functional connectivity analysis confirmed that regions associated with trait-processing and individual identities were functionally coupled. Activity during the imagination task, and revealed by functional connectivity, was consistent with the default network. Our results suggest that distinct regions code for personality traits, and that the brain combines these traits to represent individuals. The brain then uses this “personality model” to predict the behavior of others in novel situations. PMID:23463340

Railroad Classification Yard Technology Manual: Volume II : Yard Computer Systems

DOT National Transportation Integrated Search

1981-08-01

This volume (Volume II) of the Railroad Classification Yard Technology Manual documents the railroad classification yard computer systems methodology. The subjects covered are: functional description of process control and inventory computer systems,...

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

Predicted singers' vocal fold lengths and voice classification-a study of x-ray morphological measures.

PubMed

Roers, Friederike; Mürbe, Dirk; Sundberg, Johan

2009-07-01

Students admitted to the solo singing education at the University of Music Dresden, Germany have been submitted to a detailed physical examination of a variety of factors with relevance to voice function since 1959. In the years 1959-1991, this scheme of examinations included X-ray profiles of the singers' vocal tracts. This material of 132 X-rays of voice professionals was used to investigate different laryngeal morphological measures and their relation to vocal fold length. Further, the study aimed to investigate if there are consistent anatomical differences between singers of different voice classifications. The study design used was a retrospective analysis. Vocal fold length could be measured in 29 of these singer subjects directly. These data showed a strong correlation with the anterior-posterior diameter of the subglottis and the trachea as well as with the distance from the anterior contour of the thyroid cartilage to the anterior contour of the spine. These relations were used in an attempt to predict the 132 singers' vocal fold lengths. The results revealed a clear covariation between predicted vocal fold length and voice classification. Anterior-posterior subglottic-tracheal diameter yielded mean vocal fold lengths of 14.9, 16.0, 16.6, 18.4, 19.5, and 20.9mm for sopranos, mezzo-sopranos, altos, tenors, baritones, and basses, respectively. The data support the assumption that there are consistent anatomical laryngeal differences between singers of different voice classifications, which are of relevance to pitch range and timbre of the voice.

Functional Assessment of Genetic Variants with Outcomes Adapted to Clinical Decision-Making

PubMed Central

Thouvenot, Pierre; Ben Yamin, Barbara; Fourrière, Lou; Lescure, Aurianne; Boudier, Thomas; Del Nery, Elaine; Chauchereau, Anne; Goldgar, David E.; Stoppa-Lyonnet, Dominique; Nicolas, Alain; Millot, Gaël A.

2016-01-01

Understanding the medical effect of an ever-growing number of human variants detected is a long term challenge in genetic counseling. Functional assays, based on in vitro or in vivo evaluations of the variant effects, provide essential information, but they require robust statistical validation, as well as adapted outputs, to be implemented in the clinical decision-making process. Here, we assessed 25 pathogenic and 15 neutral missense variants of the BRCA1 breast/ovarian cancer susceptibility gene in four BRCA1 functional assays. Next, we developed a novel approach that refines the variant ranking in these functional assays. Lastly, we developed a computational system that provides a probabilistic classification of variants, adapted to clinical interpretation. Using this system, the best functional assay exhibits a variant classification accuracy estimated at 93%. Additional theoretical simulations highlight the benefit of this ready-to-use system in the classification of variants after functional assessment, which should facilitate the consideration of functional evidences in the decision-making process after genetic testing. Finally, we demonstrate the versatility of the system with the classification of siRNAs tested for human cell growth inhibition in high throughput screening. PMID:27272900

A methodology for investigation of the seasonal evolution in proglacial hydrograph form

NASA Astrophysics Data System (ADS)

Hannah, David M.; Gurnell, Angela M.; McGregor, Glenn R.

1999-11-01

This paper advances an objective method of diurnal hydrograph classification as an aid to exploring changes in the hydrological functioning of glacierized catchments over the ablation season. The temporal sequencing of different hydrograph classes allows identification of seasonal evolution in hydrograph form and also assists delimitation of hydrologically-meaningful time periods of similar diurnal discharge response. The effectiveness of this approach is illustrated by applying it to two contrasting summer discharge records for a small cirque basin. By comparing the results with patterns of surface energy receipt and glacier ablation, the seasonally transient relative influences of: (i) surface meltwater production and (ii) meltwater routing and storage conditions within the intervening glacier drainage system in determining runoff are elucidated. The method successfully characterizes distinct seasonal-scale changes in the diurnal outflow hydrograph during the ablation-dominated 1995 melt season but is also able to reveal underlying trends and short-term fluctuations in the precipitation-dominated, poorly ablation-regulated 1996 melt season. The limitations and benefits of this hydrograph classification technique are evaluated.

How musical expertise shapes speech perception: evidence from auditory classification images.

PubMed

Varnet, Léo; Wang, Tianyun; Peter, Chloe; Meunier, Fanny; Hoen, Michel

2015-09-24

It is now well established that extensive musical training percolates to higher levels of cognition, such as speech processing. However, the lack of a precise technique to investigate the specific listening strategy involved in speech comprehension has made it difficult to determine how musicians' higher performance in non-speech tasks contributes to their enhanced speech comprehension. The recently developed Auditory Classification Image approach reveals the precise time-frequency regions used by participants when performing phonemic categorizations in noise. Here we used this technique on 19 non-musicians and 19 professional musicians. We found that both groups used very similar listening strategies, but the musicians relied more heavily on the two main acoustic cues, at the first formant onset and at the onsets of the second and third formants onsets. Additionally, they responded more consistently to stimuli. These observations provide a direct visualization of auditory plasticity resulting from extensive musical training and shed light on the level of functional transfer between auditory processing and speech perception.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

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

Which method of posttraumatic stress disorder classification best predicts psychosocial function in children with traumatic brain injury?

PubMed

Iselin, Greg; Le Brocque, Robyne; Kenardy, Justin; Anderson, Vicki; McKinlay, Lynne

2010-10-01

Controversy surrounds the classification of posttraumatic stress disorder (PTSD), particularly in children and adolescents with traumatic brain injury (TBI). In these populations, it is difficult to differentiate TBI-related organic memory loss from dissociative amnesia. Several alternative PTSD classification algorithms have been proposed for use with children. This paper investigates DSM-IV-TR and alternative PTSD classification algorithms, including and excluding the dissociative amnesia item, in terms of their ability to predict psychosocial function following pediatric TBI. A sample of 184 children aged 6-14 years were recruited following emergency department presentation and/or hospital admission for TBI. PTSD was assessed via semi-structured clinical interview (CAPS-CA) with the child at 3 months post-injury. Psychosocial function was assessed using the parent report CHQ-PF50. Two alternative classification algorithms, the PTSD-AA and 2 of 3 algorithms, reached statistical significance. While the inclusion of the dissociative amnesia item increased prevalence rates across algorithms, it generally resulted in weaker associations with psychosocial function. The PTSD-AA algorithm appears to have the strongest association with psychosocial function following TBI in children and adolescents. Removing the dissociative amnesia item from the diagnostic algorithm generally results in improved validity. Copyright 2010 Elsevier Ltd. All rights reserved.

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

Seated limits-of-stability of athletes with disabilities with regard to competitive levels and sport classification.

PubMed

Santos, P B R; Vigário, P S; Mainenti, M R M; Ferreira, A S; Lemos, T

2017-12-01

In this study, we asked whether wheelchair rugby (WR) classification and competitive level influence trunk function of athletes with disabilities, in terms of seated limits-of-stability (LoS). Twenty-eight athletes were recruited from international- and national-level WR teams, with each group exhibiting marked differences in years of sports practice and training volume. Athletes were also distributed into three groups according their classification: low-point (0.5-1.5-point); mid-point (2.0-2.5-point); and high-point (3.0-3.5-point). Athletes were asked to sit on a force platform and to lean the body as far as possible in eight predefined directions. Center of pressure (COP) coordinates were calculated from the ground reaction forces acquired with the force platform. LoS were computed as the area of ellipse adjusted to maximal COP excursion achieved for the eight directions. ANOVAs reveal that LoS were not different when international- and national-level players were compared (P=.744). Nevertheless, LoS were larger in players from the high-point group than from the low-point group (P=.028), with the mid-point group being not different from both (P>.194). In summary, (i) competitive level does not impact LoS measures and (ii) LoS are remarkably distinct when comparing both extremes of the WR classification range. Our results suggest that, as a training-resistant measure, LoS could be a valid assessment of trunk impairment, potentially contributing to the development of an evidence-based WR classification. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A taxonomy has been developed for outcomes in medical research to help improve knowledge discovery.

PubMed

Dodd, Susanna; Clarke, Mike; Becker, Lorne; Mavergames, Chris; Fish, Rebecca; Williamson, Paula R

2018-04-01

There is increasing recognition that insufficient attention has been paid to the choice of outcomes measured in clinical trials. The lack of a standardized outcome classification system results in inconsistencies due to ambiguity and variation in how outcomes are described across different studies. Being able to classify by outcome would increase efficiency in searching sources such as clinical trial registries, patient registries, the Cochrane Database of Systematic Reviews, and the Core Outcome Measures in Effectiveness Trials (COMET) database of core outcome sets (COS), thus aiding knowledge discovery. A literature review was carried out to determine existing outcome classification systems, none of which were sufficiently comprehensive or granular for classification of all potential outcomes from clinical trials. A new taxonomy for outcome classification was developed, and as proof of principle, outcomes extracted from all published COS in the COMET database, selected Cochrane reviews, and clinical trial registry entries were classified using this new system. Application of this new taxonomy to COS in the COMET database revealed that 274/299 (92%) COS include at least one physiological outcome, whereas only 177 (59%) include at least one measure of impact (global quality of life or some measure of functioning) and only 105 (35%) made reference to adverse events. This outcome taxonomy will be used to annotate outcomes included in COS within the COMET database and is currently being piloted for use in Cochrane Reviews within the Cochrane Linked Data Project. Wider implementation of this standard taxonomy in trial and systematic review databases and registries will further promote efficient searching, reporting, and classification of trial outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Deep processing activates the medial temporal lobe in young but not in old adults.

PubMed

Daselaar, Sander M; Veltman, Dick J; Rombouts, Serge A R B; Raaijmakers, Jeroen G W; Jonker, Cees

2003-11-01

Age-related impairments in episodic memory have been related to a deficiency in semantic processing, based on the finding that elderly adults typically benefit less than young adults from deep, semantic as opposed to shallow, nonsemantic processing of study items. In the present study, we tested the hypothesis that elderly adults are not able to perform certain cognitive operations under deep processing conditions. We further hypothesised that this inability does not involve regions commonly associated with lexical/semantic retrieval processes, but rather involves a dysfunction of the medial temporal lobe (MTL) memory system. To this end, we used functional MRI on rather extensive groups of young and elderly adults to compare brain activity patterns obtained during a deep (living/nonliving) and a shallow (uppercase/lowercase) classification task. Common activity in relation to semantic classification was observed in regions that have been previously related to semantic retrieval, including mainly left-lateralised activity in the inferior prefrontal, middle temporal, and middle frontal/anterior cingulate gyrus. Although the young adults showed more activity in some of these areas, the finding of mainly overlapping activation patterns during semantic classification supports the idea that lexical/semantic retrieval processes are still intact in elderly adults. This received further support by the finding that both groups showed similar behavioural performances as well on the deep and shallow classification tasks. Importantly, though, the young revealed significantly more activity than the elderly adults in the left anterior hippocampus during deep relative to shallow classification. This finding is in line with the idea that age-related impairments in episodic encoding are, at least partly, due to an under-recruitment of the medial temporal lobe memory system.

Identifying gnostic predictors of the vaccine response

PubMed Central

Haining, W. Nicholas; Pulendran, Bali

2012-01-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis for their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of outcome classification. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. PMID:22633886

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

PubMed

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

2015-08-01

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

Diagnostic value of plasma morphology in patients with coronary heart disease

NASA Astrophysics Data System (ADS)

Malinova, Lidia I.; Sergeeva, Yuliya V.; Simonenko, Georgy V.; Tuchin, Valery V.; Denisova, Tatiana P.

2006-08-01

Blood plasma can be considered as a special water system with self-organization possibilities. Plasma slides as the results of wedge dehydration reflect its stereochemical interaction and their study can be used in diagnostic processes. 46 patients with coronary heart disease were studied. The main group was formed of men in age ranged from 54 to 72 years old with stable angina pectoris of II and III functional class (by Canadian classification) (n=25). The group of compare was of those who was hospitalized with diagnosis of acute coronary syndrome, men in age range 40-82. Clinical examination, basic biochemical tests and functional plasma morphology characteristics were studied. A number of qualitative and quantitative differences of blood plasma morphology of patients with chronic and acute coronary disease forms was revealed.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

Functional Class in Children with Idiopathic Dilated Cardiomyopathy. A pilot Study

PubMed Central

Tavares, Aline Cristina; Bocchi, Edimar Alcides; Guimarães, Guilherme Veiga

2016-01-01

Background Idiopathic dilated cardiomyopathy (IDCM), most common cardiac cause of pediatric deaths, mortality descriptor: a low left ventricular ejection fraction (LVEF) and low functional capacity (FC). FC is never self reported by children. Objective The aims of this study were (i) To evaluate whether functional classifications according to the children, parents and medical staff were associated. (iv) To evaluate whether there was correlation between VO2 max and Weber's classification. Method Prepubertal children with IDCM and HF (by previous IDCM and preserved LVEF) were selected, evaluated and compared. All children were assessed by testing, CPET and functional class classification. Results Chi-square test showed association between a CFm and CFp (1, n = 31) = 20.6; p = 0.002. There was no significant association between CFp and CFc (1, n = 31) = 6.7; p = 0.4. CFm and CFc were not associated as well (1, n = 31) = 1.7; p = 0.8. Weber's classification was associated to CFm (1, n = 19) = 11.8; p = 0.003, to CFp (1, n = 19) = 20.4; p = 0.0001and CFc (1, n = 19) = 6.4; p = 0.04). Conclusion Drawing were helpful for children's self NYHA classification, which were associated to Weber's stratification. PMID:27168472

Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models

PubMed Central

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

2016-01-01

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

Gene-expression signatures can distinguish gastric cancer grades and stages.

PubMed

Cui, Juan; Li, Fan; Wang, Guoqing; Fang, Xuedong; Puett, J David; Xu, Ying

2011-03-18

Microarray gene-expression data of 54 paired gastric cancer and adjacent noncancerous gastric tissues were analyzed, with the aim to establish gene signatures for cancer grades (well-, moderately-, poorly- or un-differentiated) and stages (I, II, III and IV), which have been determined by pathologists. Our statistical analysis led to the identification of a number of gene combinations whose expression patterns serve well as signatures of different grades and different stages of gastric cancer. A 19-gene signature was found to have discerning power between high- and low-grade gastric cancers in general, with overall classification accuracy at 79.6%. An expanded 198-gene panel allows the stratification of cancers into four grades and control, giving rise to an overall classification agreement of 74.2% between each grade designated by the pathologists and our prediction. Two signatures for cancer staging, consisting of 10 genes and 9 genes, respectively, provide high classification accuracies at 90.0% and 84.0%, among early-, advanced-stage cancer and control. Functional and pathway analyses on these signature genes reveal the significant relevance of the derived signatures to cancer grades and progression. To the best of our knowledge, this represents the first study on identification of genes whose expression patterns can serve as markers for cancer grades and stages.

Second-degree atrioventricular block.

PubMed

Zipes, D P

1979-09-01

1) While it is possible only one type of second-degree AV block exists electrophysiologically, the available data do not justify such a conclusion and it would seem more appropriate to remain a "splitter," and advocate separation and definition of multiple mechanisms, than to be a "lumper," and embrace a unitary concept. 2) The clinical classification of type I and type II AV block, based on present scalar electrocardiographic criteria, for the most part accurately differentiates clinically important categories of patients. Such a classification is descriptive, but serves a useful function and should be preserved, taking into account the caveats mentioned above. The site of block generally determines the clinical course for the patient. For most examples of AV block, the type I and type II classification in present use is based on the site of block. Because block in the His-Purkinje system is preceded by small or nonmeasurable increments, it is called type II AV block; but the very fact that it is preceded by small increments is because it occurs in the His-Purkinje system. Similar logic can be applied to type I AV block in the AV node. Exceptions do occur. If the site of AV block cannot be distinguished with certainity from the scalar ECG, an electrophysiologic study will generally reveal the answer.

Dissociating mental states related to doing nothing by means of fMRI pattern classification.

PubMed

Kühn, Simone; Bodammer, Nils Christian; Brass, Marcel

2010-12-01

Most juridical systems recognize intentional non-actions - the failure to render assistance - as intentional acts by regarding them as in principle culpable. This raises the fundamental question whether intentional non-actions can be distinguished from simply not doing anything. Classical GLM analysis on functional magnetic resonance imaging (fMRI) data reveals that not doing anything is associated with resting state brain areas whereas intentionally non-acting is associated with brain activity in left inferior parietal lobe and left dorsal premotor cortex. By means of pattern classification we quantify the accuracy with which we can distinguish these two mental states on the basis of brain activity. In order to identify brain regions that harbour a distributed, overlapping representation of voluntary non-actions and the decision not to act we performed pattern classification on brain areas that did not appear in the GLM contrasts. The prediction rate is not reduced and we show that the prediction relies mostly on brain areas that have been associated with action production and motor imagery as supplementary motor area, right inferior frontal gyrus and right middle temporal area (V5/MT). Hence our data support the implicit assumption of legal practice that voluntary non-action shares important features with overt voluntary action. Copyright © 2010 Elsevier Inc. All rights reserved.

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

PubMed

Wang, Rui; Li, Rui; Lei, Yanyan; Zhu, Quing

2015-01-01

Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagnostic Breast Cancer (WDBC), revealed the different accuracy of a SVM classification in terms of the number of features used and the parameters selected. A pattern recognition system is proposed by means of SVM-Recursive Feature Elimination (RFE) with the Radial Basis Function (RBF) kernel. To improve the effectiveness and robustness of the system, an optimized tuning ensemble algorithm called as SVM-RFE(C) with correlation filter was implemented to quantify feature and parameter information based on cross validation. The proposed algorithm is first demonstrated outperforming SVM-RFE on WDBC. Then the best accuracy of 94.643% and sensitivity of 94.595% were achieved when using SVM-RFE(C) to test 57 new PAT data from 19 patients. The experiment results show that the classifier constructed with SVM-RFE(C) algorithm is able to learn additional information from new data and has significant potential in ovarian cancer diagnosis.

Catchment Classification: Connecting Climate, Structure and Function

NASA Astrophysics Data System (ADS)

Sawicz, K. A.; Wagener, T.; Sivapalan, M.; Troch, P. A.; Carrillo, G. A.

2010-12-01

Hydrology does not yet possess a generally accepted catchment classification framework. Such a classification framework needs to: [1] give names to things, i.e. the main classification step, [2] permit transfer of information, i.e. regionalization of information, [3] permit development of generalizations, i.e. to develop new theory, and [4] provide a first order environmental change impact assessment, i.e., the hydrologic implications of climate, land use and land cover change. One strategy is to create a catchment classification framework based on the notion of catchment functions (partitioning, storage, and release). Results of an empirical study presented here connects climate and structure to catchment function (in the form of select hydrologic signatures), based on analyzing over 300 US catchments. Initial results indicate a wide assortment of signature relationships with properties of climate, geology, and vegetation. The uncertainty in the different regionalized signatures varies widely, and therefore there is variability in the robustness of classifying ungauged basins. This research provides insight into the controls of hydrologic behavior of a catchment, and enables a classification framework applicable to gauged and ungauged across the study domain. This study sheds light on what we can expect to achieve in mapping climate, structure and function in a top-down manner. Results of this study complement work done using a bottom-up physically-based modeling framework to generalize this approach (Carrillo et al., this session).

Comparative analysis of sugarcane bagasse metagenome reveals unique and conserved biomass-degrading enzymes among lignocellulolytic microbial communities.

PubMed

Mhuantong, Wuttichai; Charoensawan, Varodom; Kanokratana, Pattanop; Tangphatsornruang, Sithichoke; Champreda, Verawat

2015-01-01

As one of the most abundant agricultural wastes, sugarcane bagasse is largely under-exploited, but it possesses a great potential for the biofuel, fermentation, and cellulosic biorefinery industries. It also provides a unique ecological niche, as the microbes in this lignocellulose-rich environment thrive in relatively high temperatures (50°C) with varying microenvironments of aerobic surface to anoxic interior. The microbial community in bagasse thus presents a good resource for the discovery and characterization of new biomass-degrading enzymes; however, it remains largely unexplored. We have constructed a fosmid library of sugarcane bagasse and obtained the largest bagasse metagenome to date. A taxonomic classification of the bagasse metagenome reviews the predominance of Proteobacteria, which are also found in high abundance in other aerobic environments. Based on the functional characterization of biomass-degrading enzymes, we have demonstrated that the bagasse microbial community benefits from a large repertoire of lignocellulolytic enzymes, which allows them to digest different components of lignocelluoses into single molecule sugars. Comparative genomic analyses with other lignocellulolytic and non-lignocellulolytic metagenomes show that microbial communities are taxonomically separable by their aerobic "open" or anoxic "closed" environments. Importantly, a functional analysis of lignocellulose-active genes (based on the CAZy classifications) reveals core enzymes highly conserved within the lignocellulolytic group, regardless of their taxonomic compositions. Cellulases, in particular, are markedly more pronounced compared to the non-lignocellulolytic group. In addition to the core enzymes, the bagasse fosmid library also contains some uniquely enriched glycoside hydrolases, as well as a large repertoire of the newly defined auxiliary activity proteins. Our study demonstrates a conservation and diversification of carbohydrate-active genes among diverse microbial species in different biomass-degrading niches, and signifies the importance of taking a global approach to functionally investigate a microbial community as a whole, as compared to focusing on individual organisms.

SAR-based change detection using hypothesis testing and Markov random field modelling

NASA Astrophysics Data System (ADS)

Cao, W.; Martinis, S.

2015-04-01

The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the constant false alarm rate (CFAR) edge detector is reviewed and rewritten in a compact form of the incomplete beta function, which is a builtin routine in commercial scientific software such as MATLAB and IDL. Secondly, a post-classification step is introduced to optimize the noisy classification result in the previous step. Generally, an optimization problem can be formulated as a Markov random field (MRF) on which the quality of a classification is measured by an energy function. The optimal classification based on the MRF is related to the lowest energy value. Previous studies provide methods for the optimization problem using MRFs, such as the iterated conditional modes (ICM) algorithm. Recently, a novel algorithm was presented based on graph-cut theory. This method transforms a MRF to an equivalent graph and solves the optimization problem by a max-flow/min-cut algorithm on the graph. In this study this graph-cut algorithm is applied iteratively to improve the coarse classification. At each iteration the parameters of the energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). Experiments are performed using two flood events in Germany and Australia in 2011 and a forest fire on La Palma in 2009 using pre- and post-event TerraSAR-X data. The results show convincing coarse classifications and considerable improvement by the graph-cut post-classification step.

Use of collateral information to improve LANDSAT classification accuracies

NASA Technical Reports Server (NTRS)

Strahler, A. H. (Principal Investigator)

1981-01-01

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

50 CFR 540.3 - Procedures.

Code of Federal Regulations, 2013 CFR

2013-10-01

... the fact that the Commission does not have original classification authority and national security... conjunction with a transfer of functions, to the appropriate federal agency exercising original classification... contractor of the Commission originates information that is believed to require classification, the Executive...

50 CFR 540.3 - Procedures.

Code of Federal Regulations, 2014 CFR

2014-10-01

... the fact that the Commission does not have original classification authority and national security... conjunction with a transfer of functions, to the appropriate federal agency exercising original classification... contractor of the Commission originates information that is believed to require classification, the Executive...

50 CFR 540.3 - Procedures.

Code of Federal Regulations, 2012 CFR

2012-10-01

... the fact that the Commission does not have original classification authority and national security... conjunction with a transfer of functions, to the appropriate federal agency exercising original classification... contractor of the Commission originates information that is believed to require classification, the Executive...

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

PubMed

Zhang, WenJun

2007-07-01

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

Application of the International Classification of Functioning, Disability, and Health-Children and Youth Version (ICF-CY) to cleft lip and palate.

PubMed

Neumann, Sandra; Romonath, Roswitha

2012-05-01

In recent health policy discussions, the World Health Organization has urged member states to implement the International Classification of Functioning, Disability, and Health: Children and Youth Version in their clinical practice and research. The purpose of this study was to identify codes from the International Classification of Functioning, Disability, and Health: Children and Youth Version relevant for use among children with cleft lip and/or palate, thereby highlighting the potential value of these codes for interprofessional cleft palate-craniofacial teams. The scope of recent published research in the area of cleft lip and/or palate was reviewed and compared with meaningful terms identified from the International Classification of Functioning, Disability, and Health: Children and Youth Version. In a five-step procedure, a consensus-based list of terms was developed that was linked separately to International Classification of Functioning, Disability, and Health: Children and Youth Version categories and codes. This provided a first draft of a core set for use in the cleft lip and/or palate field. Adopting International Classification of Functioning, Disability, and Health: Children and Youth Version domains in cleft lip and/or palate may aid experts in identifying appropriate starting points for assessment, counseling, and therapy. When used as a clinical tool, it encourages health care professionals to go beyond treatment and outcome perspectives that are focused solely on the child and to include the children's environment and their familial/societal context. In order to establish improved, evidence-based interdisciplinary treatments for children with cleft lip and/or palate, more studies are needed that seek to identify all the influencing conditions of activities, children's participation, and barriers/facilitators in their environments.

The Psychosomatic Disorders Pertaining to Dental Practice with Revised Working Type Classification

PubMed Central

2014-01-01

Psychosomatic disorders are defined as disorders characterized by physiological changes that originate partially from emotional factors. This article aims to discuss the psychosomatic disorders of the oral cavity with a revised working type classification. The author has added one more subset to the existing classification, i.e., disorders caused by altered perception of dentofacial form and function, which include body dysmorphic disorder. The author has also inserted delusional halitosis under the miscellaneous disorders classification of psychosomatic disorders and revised the already existing classification proposed for the psychosomatic disorders pertaining to dental practice. After the inclusion of the subset (disorders caused by altered perception of dentofacial form and function), the terminology "psychosomatic disorders of the oral cavity" is modified to "psychosomatic disorders pertaining to dental practice". PMID:24478896

The psychosomatic disorders pertaining to dental practice with revised working type classification.

PubMed

Shamim, Thorakkal

2014-01-01

Psychosomatic disorders are defined as disorders characterized by physiological changes that originate partially from emotional factors. This article aims to discuss the psychosomatic disorders of the oral cavity with a revised working type classification. The author has added one more subset to the existing classification, i.e., disorders caused by altered perception of dentofacial form and function, which include body dysmorphic disorder. The author has also inserted delusional halitosis under the miscellaneous disorders classification of psychosomatic disorders and revised the already existing classification proposed for the psychosomatic disorders pertaining to dental practice. After the inclusion of the subset (disorders caused by altered perception of dentofacial form and function), the terminology "psychosomatic disorders of the oral cavity" is modified to "psychosomatic disorders pertaining to dental practice".

The Classification of Protein Domains.

PubMed

Dawson, Natalie; Sillitoe, Ian; Marsden, Russell L; Orengo, Christine A

2017-01-01

The significant expansion in protein sequence and structure data that we are now witnessing brings with it a pressing need to bring order to the protein world. Such order enables us to gain insights into the evolution of proteins, their function and the extent to which the functional repertoire can vary across the three kingdoms of life. This has lead to the creation of a wide range of protein family classifications that aim to group proteins based upon their evolutionary relationships.In this chapter we discuss the approaches and methods that are frequently used in the classification of proteins, with a specific emphasis on the classification of protein domains. The construction of both domain sequence and domain structure databases is considered and we show how the use of domain family annotations to assign structural and functional information is enhancing our understanding of genomes.

Label-noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study.

PubMed

Lee, Seokho; Shin, Hyejin; Lee, Sang Han

2016-12-01

Alzheimer's disease (AD) is usually diagnosed by clinicians through cognitive and functional performance test with a potential risk of misdiagnosis. Since the progression of AD is known to cause structural changes in the corpus callosum (CC), the CC thickness can be used as a functional covariate in AD classification problem for a diagnosis. However, misclassified class labels negatively impact the classification performance. Motivated by AD-CC association studies, we propose a logistic regression for functional data classification that is robust to misdiagnosis or label noise. Specifically, our logistic regression model is constructed by adopting individual intercepts to functional logistic regression model. This approach enables to indicate which observations are possibly mislabeled and also lead to a robust and efficient classifier. An effective algorithm using MM algorithm provides simple closed-form update formulas. We test our method using synthetic datasets to demonstrate its superiority over an existing method, and apply it to differentiating patients with AD from healthy normals based on CC from MRI. © 2016, The International Biometric Society.

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

PubMed

Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P < 0.01). A clinically useful classification tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy.

PubMed

Fang, Peng; An, Jie; Zeng, Ling-Li; Shen, Hui; Chen, Fanglin; Wang, Wensheng; Qiu, Shijun; Hu, Dewen

2015-01-01

Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE), but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right) mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

A topological approach for protein classification

DOE PAGES

Cang, Zixuan; Mu, Lin; Wu, Kedi; ...

2015-11-04

Here, protein function and dynamics are closely related to its sequence and structure. However, prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity between proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics.

Brain networks involved in tactile speed classification of moving dot patterns: the effects of speed and dot periodicity

PubMed Central

Yang, Jiajia; Kitada, Ryo; Kochiyama, Takanori; Yu, Yinghua; Makita, Kai; Araki, Yuta; Wu, Jinglong; Sadato, Norihiro

2017-01-01

Humans are able to judge the speed of an object’s motion by touch. Research has suggested that tactile judgment of speed is influenced by physical properties of the moving object, though the neural mechanisms underlying this process remain poorly understood. In the present study, functional magnetic resonance imaging was used to investigate brain networks that may be involved in tactile speed classification and how such networks may be affected by an object’s texture. Participants were asked to classify the speed of 2-D raised dot patterns passing under their right middle finger. Activity in the parietal operculum, insula, and inferior and superior frontal gyri was positively related to the motion speed of dot patterns. Activity in the postcentral gyrus and superior parietal lobule was sensitive to dot periodicity. Psycho-physiological interaction (PPI) analysis revealed that dot periodicity modulated functional connectivity between the parietal operculum (related to speed) and postcentral gyrus (related to dot periodicity). These results suggest that texture-sensitive activity in the primary somatosensory cortex and superior parietal lobule influences brain networks associated with tactually-extracted motion speed. Such effects may be related to the influence of surface texture on tactile speed judgment. PMID:28145505

The comparative pharmacology of some psychotropic drugs*

PubMed Central

Jacobsen, Erik

1959-01-01

Interest in the mode of action of the psychotropic drugs has been growing rapidly during the last few years and at the same time the number of compounds known to be capable of affecting the higher centres of the central nervous system has greatly increased. The diversity of psychic and somatic effects produced by these drugs, the fact that they may have similar effects on some functions but opposite effects on others, and the lack of a precise and universally accepted terminology to describe these properties have led to much confusion and thwarted attempts to arrive at a satisfactory classification. As a contribution to a clearer understanding of the relationships between the psychotropic drugs the author of this review has undertaken a systematic examination of the effects of a number of the better known compounds on the various levels of the central nervous system. The antagonistic and synergistic effects of giving the drugs in combination are also discussed, as well as the possible relationships between the central effects and certain “basic” effects on somatic functions. Although the picture revealed is a very complex one and there are still many gaps, it is possible to recognize a number of characteristic types of psychotropic drug on the basis of which a system of classification may be developed. PMID:14406391

Return to play after thigh muscle injury in elite football players: implementation and validation of the Munich muscle injury classification

PubMed Central

Ekstrand, Jan; Askling, Carl; Magnusson, Henrik; Mithoefer, Kai

2013-01-01

Background Owing to the complexity and heterogeneity of muscle injuries, a generally accepted classification system is still lacking. Aims To prospectively implement and validate a novel muscle injury classification and to evaluate its predictive value for return to professional football. Methods The recently described Munich muscle injury classification was prospectively evaluated in 31 European professional male football teams during the 2011/2012 season. Thigh muscle injury types were recorded by team medical staff and correlated to individual player exposure and resultant time-loss. Results In total, 393 thigh muscle injuries occurred. The muscle classification system was well received with a 100% response rate. Two-thirds of thigh muscle injuries were classified as structural and were associated with longer lay-off times compared to functional muscle disorders (p<0.001). Significant differences were observed between structural injury subgroups (minor partial, moderate partial and complete injuries) with increasing lay-off time associated with more severe structural injury. Median lay-off time of functional disorders was 5–8 days without significant differences between subgroups. There was no significant difference in the absence time between anterior and posterior thigh injuries. Conclusions The Munich muscle classification demonstrates a positive prognostic validity for return to play after thigh muscle injury in professional male football players. Structural injuries are associated with longer average lay-off times than functional muscle disorders. Subclassification of structural injuries correlates with return to play, while subgrouping of functional disorders shows less prognostic relevance. Functional disorders are often underestimated clinically and require further systematic study. PMID:23645834

Lagrangian submanifolds with constant angle functions of the nearly Kähler S3 ×S3

NASA Astrophysics Data System (ADS)

Bektaş, Burcu; Moruz, Marilena; Van der Veken, Joeri; Vrancken, Luc

2018-04-01

We study Lagrangian submanifolds of the nearly Kähler S3 ×S3 with respect to their so called angle functions. We show that if all angle functions are constant, then the submanifold is either totally geodesic or has constant sectional curvature and there is a classification theorem that follows from Dioos et al. (2018). Moreover, we show that if precisely one angle function is constant, then it must be equal to 0 , π/3 or 2π/3. Using then two remarkable constructions together with the classification of Lagrangian submanifolds of which the first component has nowhere maximal rank from, Bektaş et al. (2018), we obtain a classification of such Lagrangian submanifolds.

Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state.

PubMed

Dørum, Erlend S; Kaufmann, Tobias; Alnæs, Dag; Andreassen, Ole A; Richard, Geneviève; Kolskår, Knut K; Nordvik, Jan Egil; Westlye, Lars T

2017-03-01

Age-related differences in cognitive agility vary greatly between individuals and cognitive functions. This heterogeneity is partly mirrored in individual differences in brain network connectivity as revealed using resting-state functional magnetic resonance imaging (fMRI), suggesting potential imaging biomarkers for age-related cognitive decline. However, although convenient in its simplicity, the resting state is essentially an unconstrained paradigm with minimal experimental control. Here, based on the conception that the magnitude and characteristics of age-related differences in brain connectivity is dependent on cognitive context and effort, we tested the hypothesis that experimentally increasing cognitive load boosts the sensitivity to age and changes the discriminative network configurations. To this end, we obtained fMRI data from younger (n=25, mean age 24.16±5.11) and older (n=22, mean age 65.09±7.53) healthy adults during rest and two load levels of continuous multiple object tracking (MOT). Brain network nodes and their time-series were estimated using independent component analysis (ICA) and dual regression, and the edges in the brain networks were defined as the regularized partial temporal correlations between each of the node pairs at the individual level. Using machine learning based on a cross-validated regularized linear discriminant analysis (rLDA) we attempted to classify groups and cognitive load from the full set of edge-wise functional connectivity indices. While group classification using resting-state data was highly above chance (approx. 70% accuracy), functional connectivity (FC) obtained during MOT strongly increased classification performance, with 82% accuracy for the young and 95% accuracy for the old group at the highest load level. Further, machine learning revealed stronger differentiation between rest and task in young compared to older individuals, supporting the notion of network dedifferentiation in cognitive aging. Task-modulation in edgewise FC was primarily observed between attention- and sensorimotor networks; with decreased negative correlations between attention- and default mode networks in older adults. These results demonstrate that the magnitude and configuration of age-related differences in brain functional connectivity are partly dependent on cognitive context and load, which emphasizes the importance of assessing brain connectivity differences across a range of cognitive contexts beyond the resting-state. Copyright © 2017 Elsevier Inc. All rights reserved.

Effects of Extreme Prematurity on Numerical Skills and Executive Function in Kindergarten Children: An Application of Partially Ordered Classification Modeling

PubMed Central

Tatsuoka, Curtis; McGowan, Bridget; Yamada, Tomoko; Espy, Kimberly Andrews; Minich, Nori; Taylor, H. Gerry

2016-01-01

Although mathematics disabilities (MD) are common in extremely preterm/extremely low birth weight (EPT/ELBW) children, little is known about the nature of these problems. In this study partially ordered set (POSET) models were applied to classify 140 EPT/ELBW kindergarten children (gestational age <28 weeks and/or birth weight <1000 g) and 110 normal birth weight (NBW) controls into profiles of numerical and cognitive skills. Models based on five numerical skills and five executive function and processing speed skills provided a good fit to performance data. The EPT/ELBW group had poorer skills in all areas than NBW controls but the models also revealed substantial individual variability in skill profiles. Weaknesses in executive function were associated with poorer mastery of numerical skills. The findings illustrate the applicability of POSET models to research on MD and suggest distinct types of early numerical deficits in EPT/ELBW children that are related to their impairments in executive function. PMID:27818602

Effects of Extreme Prematurity on Numerical Skills and Executive Function in Kindergarten Children: An Application of Partially Ordered Classification Modeling.

PubMed

Tatsuoka, Curtis; McGowan, Bridget; Yamada, Tomoko; Espy, Kimberly Andrews; Minich, Nori; Taylor, H Gerry

2016-07-01

Although mathematics disabilities (MD) are common in extremely preterm/extremely low birth weight (EPT/ELBW) children, little is known about the nature of these problems. In this study partially ordered set (POSET) models were applied to classify 140 EPT/ELBW kindergarten children (gestational age <28 weeks and/or birth weight <1000 g) and 110 normal birth weight (NBW) controls into profiles of numerical and cognitive skills. Models based on five numerical skills and five executive function and processing speed skills provided a good fit to performance data. The EPT/ELBW group had poorer skills in all areas than NBW controls but the models also revealed substantial individual variability in skill profiles. Weaknesses in executive function were associated with poorer mastery of numerical skills. The findings illustrate the applicability of POSET models to research on MD and suggest distinct types of early numerical deficits in EPT/ELBW children that are related to their impairments in executive function.

Application of the International Classification of Functioning, Disability and Health (ICF) to people with dysphagia following non-surgical head and neck cancer management.

PubMed

Nund, Rebecca L; Scarinci, Nerina A; Cartmill, Bena; Ward, Elizabeth C; Kuipers, Pim; Porceddu, Sandro V

2014-12-01

The International Classification of Functioning, Disability, and Health (ICF) is an internationally recognized framework which allows its user to describe the consequences of a health condition on an individual in the context of their environment. With growing recognition that dysphagia can have broad ranging physical and psychosocial impacts, the aim of this paper was to identify the ICF domains and categories that describe the full functional impact of dysphagia following non-surgical head and neck cancer (HNC) management, from the perspective of the person with dysphagia. A secondary analysis was conducted on previously published qualitative study data which explored the lived experiences of dysphagia of 24 individuals with self-reported swallowing difficulties following HNC management. Categories and sub-categories identified by the qualitative analysis were subsequently mapped to the ICF using the established linking rules to develop a set of ICF codes relevant to the impact of dysphagia following HNC management. The 69 categories and sub-categories that had emerged from the qualitative analysis were successfully linked to 52 ICF codes. The distribution of these codes across the ICF framework revealed that the components of Body Functions, Activities and Participation, and Environmental Factors were almost equally represented. The findings confirm that the ICF is a valuable framework for representing the complexity and multifaceted impact of dysphagia following HNC. This list of ICF codes, which reflect the diverse impact of dysphagia associated with HNC on the individual, can be used to guide more holistic assessment and management for this population.

Positive selection and functional divergence of farnesyl pyrophosphate synthase genes in plants.

PubMed

Qian, Jieying; Liu, Yong; Chao, Naixia; Ma, Chengtong; Chen, Qicong; Sun, Jian; Wu, Yaosheng

2017-02-04

Farnesyl pyrophosphate synthase (FPS) belongs to the short-chain prenyltransferase family, and it performs a conserved and essential role in the terpenoid biosynthesis pathway. However, its classification, evolutionary history, and the forces driving the evolution of FPS genes in plants remain poorly understood. Phylogeny and positive selection analysis was used to identify the evolutionary forces that led to the functional divergence of FPS in plants, and recombinant detection was undertaken using the Genetic Algorithm for Recombination Detection (GARD) method. The dataset included 68 FPS variation pattern sequences (2 gymnosperms, 10 monocotyledons, 54 dicotyledons, and 2 outgroups). This study revealed that the FPS gene was under positive selection in plants. No recombinant within the FPS gene was found. Therefore, it was inferred that the positive selection of FPS had not been influenced by a recombinant episode. The positively selected sites were mainly located in the catalytic center and functional areas, which indicated that the 98S and 234D were important positively selected sites for plant FPS in the terpenoid biosynthesis pathway. They were located in the FPS conserved domain of the catalytic site. We inferred that the diversification of FPS genes was associated with functional divergence and could be driven by positive selection. It was clear that protein sequence evolution via positive selection was able to drive adaptive diversification in plant FPS proteins. This study provides information on the classification and positive selection of plant FPS genes, and the results could be useful for further research on the regulation of triterpenoid biosynthesis.

Virtual continuity of measurable functions and its applications

NASA Astrophysics Data System (ADS)

Vershik, A. M.; Zatitskii, P. B.; Petrov, F. V.

2014-12-01

A classical theorem of Luzin states that a measurable function of one real variable is `almost' continuous. For measurable functions of several variables the analogous statement (continuity on a product of sets having almost full measure) does not hold in general. The search for a correct analogue of Luzin's theorem leads to a notion of virtually continuous functions of several variables. This apparently new notion implicitly appears in the statements of embedding theorems and trace theorems for Sobolev spaces. In fact it reveals the nature of such theorems as statements about virtual continuity. The authors' results imply that under the conditions of Sobolev theorems there is a well-defined integration of a function with respect to a wide class of singular measures, including measures concentrated on submanifolds. The notion of virtual continuity is also used for the classification of measurable functions of several variables and in some questions on dynamical systems, the theory of polymorphisms, and bistochastic measures. In this paper the necessary definitions and properties of admissible metrics are recalled, several definitions of virtual continuity are given, and some applications are discussed. Bibliography: 24 titles.

Clinical characteristics and functional status of children with different subtypes of dyskinetic cerebral palsy.

PubMed

Sun, Dianrong; Wang, Qiang; Hou, Mei; Li, Yutang; Yu, Rong; Zhao, Jianhui; Wang, Ke

2018-05-01

Dyskinetic cerebral palsy (CP) is the second major subtype of CP. Dyskinetic CP can be classified into different subtypes, but the exact clinical characteristics of these subtypes have been poorly studied. To investigate the clinical characteristics and functional classification of dyskinetic CP from the perspective of neurologic subtypes in a hospital-based follow-up study.This was an observational study of consecutive children with dyskinetic CP treated at The Affiliated Women & Children Hospital of Qingdao University (China) from October 2005 to February 2015. The children were stratified according to their neurologic subtype and assessed with the Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), and Communication Function Classification System (CFCS). MRI scanning was conducted at 1 year of age for most children.Twenty-six participants (28.0%) had dystonic CP, 26 (28.0%) had choreoathetotic CP, and 41 (44.1%) had mixed CP. Auditory impairment and basal ganglion lesions occurred more frequently in the dystonia group (n = 8, 31%; and n = 16, 67%), while seizures, microcephaly, white matter lesions, and mixed lesions were more frequent in the mixed type (n = 14, 34%; n = 10, 24%; n = 15, 41%; n = 12, 32%). Functional classification levels were distributed unequally among the 3 subgroups (P < .01). No significant difference between GMFCS and MACS was found among the 3 subgroups (P > .05).Different subtypes of dyskinetic CP have specific comorbidities, radiological characteristics, and functional attributes according to their etiological factors and brain lesions. Children with dystonic CP have more limited functional status than children with choreoathetotic CP.

Moderators, mediators, and bidirectional relationships in the International Classification of Functioning, Disability and Health (ICF) framework: An empirical investigation using a longitudinal design and Structural Equation Modeling (SEM).

PubMed

Rouquette, Alexandra; Badley, Elizabeth M; Falissard, Bruno; Dub, Timothée; Leplege, Alain; Coste, Joël

2015-06-01

The International Classification of Functioning, Disability and Health (ICF) published in 2001 describes the consequences of health conditions with three components of impairments in body structures or functions, activity limitations and participation restrictions. Two of the new features of the conceptual model were the possibility of feedback effects between each ICF component and the introduction of contextual factors conceptualized as moderators of the relationship between the components. The aim of this longitudinal study is to provide empirical evidence of these two kinds of effect. Structural equation modeling was used to analyze data from a French population-based cohort of 548 patients with knee osteoarthritis recruited between April 2007 and March 2009 and followed for three years. Indicators of the body structure and function, activity and participation components of the ICF were derived from self-administered standardized instruments. The measurement model revealed four separate factors for body structures impairments, body functions impairments, activity limitations and participation restrictions. The classic sequence from body impairments to participation restrictions through activity limitations was found at each assessment time. Longitudinal study of the ICF component relationships showed a feedback pathway indicating that the level of participation restrictions at baseline was predictive of activity limitations three years later. Finally, the moderating role of personal (age, sex, mental health, etc.) and environmental factors (family relationships, mobility device use, etc.) was investigated. Three contextual factors (sex, family relationships and walking stick use) were found to be moderators for the relationship between the body impairments and the activity limitations components. Mental health was found to be a mediating factor of the effect of activity limitations on participation restrictions. Copyright © 2015 Elsevier Ltd. All rights reserved.

A content analysis of peripheral arterial disease patient-reported outcome measures using the International Classification of Functioning, Disability and Health.

PubMed

Osborne, Candice Lee; Kauvar, David Seth

2017-10-17

The purpose of this study was to link, classify and describe the content of peripheral arterial disease (PAD)-specific patient-reported outcome measures using the International Classification of Functioning. The results were then analyzed to determine if these assessments provide clinicians and researchers with a comprehensive understanding of the lived experience of patients with PAD. Each meaningful concept in identified PAD assessments was linked to the International Classification of Functioning, Disability and Health to determine included and excluded content areas. An overall perspective was assigned to each assessment item. Inter-rater reliability was established using a kappa statistic. The body functions component is most frequently addressed overall followed by the activities and participation component. International Classification of Functioning chapter and category distribution vary greatly between assessments and no assessment comprehensively examines community participation and relationships. The majority of the assessment items are of the health status-disability and quality of life perspectives. The results of this study suggest the need for the development of a comprehensive PAD assessment that includes a more even distribution of International Classification of Functioning topics and subtopics. A more comprehensive assessment would better capture the lived experience of this patient population. Implications for Rehabilitation A better understanding of the data collected using the current peripheral arterial disease-specific patient-reported outcome measures may contribute to the development of more comprehensive assessment tools that will ultimately lead to improved patient care. This study contributes to the preliminary foundation for the development of a peripheral arterial disease International Classification of Functioning, Disability and Health Core Set. Clinicians and researchers interested in using peripheral arterial disease-specific patient-reported outcome measures for clinical and research purposes can better understand what topics are included and excluded in the collection and what perspectives are addressed.

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

NASA Astrophysics Data System (ADS)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Categorization abilities for emotional and nonemotional stimuli in patients with alcohol-related Korsakoff syndrome.

PubMed

Labudda, Kirsten; von Rothkirch, Nadine; Pawlikowski, Mirko; Laier, Christian; Brand, Matthias

2010-06-01

To investigate whether patients with alcohol-related Korsakoff syndrome (KR) have emotion-specific or general deficits in multicategoric classification performance. Earlier studies have shown reduced performance in classifying stimuli according to their emotional valence in patients with KS. However, it is unclear whether such classification deficits are of emotion-specific nature or whether they can also occur when nonemotional classifications are demanded. In this study, we examined 35 patients with alcoholic KS and 35 healthy participants with the Emotional Picture Task (EPT) to assess valence classification performance, the Semantic Classification Task (SCT) to assess nonemotional categorizations, and an extensive neuropsychologic test battery. KS patients exhibited lower classification performance in both tasks compared with the healthy participants. EPT and SCT performance were related to each other. EPT and SCT performance correlated with general knowledge and EPT performance in addition with executive functions. Our results indicate a common underlying mechanism of the patients' reductions in emotional and nonemotional classification performance. These deficits are most probably based on problems in retrieving object and category knowledge and, partially, on executive functioning.

Prognostic value of the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification in stage IB lung adenocarcinoma.

PubMed

Xu, C-h; Wang, W; Wei, Y; Hu, H-d; Zou, J; Yan, J; Yu, L-k; Yang, R-s; Wang, Y

2015-10-01

Patients with pathological stage IB lung adenocarcinoma have a variable prognosis, even if received the same treatment. This study investigated the prognostic value of the new International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) lung adenocarcinoma classification in resected stage IB lung adenocarcinoma. We identified 276 patients with pathological stage IB adenocarcinoma who had undergone surgical resection at the Nanjing Chest Hospital between 2005 and 2010. The histological subtypes of all patients were classified according to the 2011 IASLC/ATS/ERS international multidisciplinary lung adenocarcinoma classification. Kaplan-Meier and Cox regression analyses were used to analyze the correlation between the IASLC/ATS/ERS classification and patients' prognosis. Two hundred and seventy-six patients with pathological stage IB adenocarcinoma had an 86.2% 5-year overall survival (OS) and 80.4% 5-year disease-free survival (DFS). Patients with micropapillary and solid predominant tumors had a significantly worse OS and DFS as compared to those with other subtypes predominant tumors (p = 0.003 and 0.001). Multivariate analysis revealed that the new classification was an independent prognostic factor for both OS and DFS of pathological stage IB adenocarcinoma (p = 0.009 and 0.003). Our study revealed that the new IASLC/ATS/ERS classification was an independent prognostic factor of pathological stage IB adenocarcinoma. This new classification is valuable of screening out high risk patients to receive postoperative adjuvant therapy. Copyright © 2015. Published by Elsevier Ltd.

Functional classification of rice flanking sequence tagged genes using MapMan terms and global understanding on metabolic and regulatory pathways affected by dxr mutant having defects in light response.

PubMed

Chandran, Anil Kumar Nalini; Lee, Gang-Seob; Yoo, Yo-Han; Yoon, Ung-Han; Ahn, Byung-Ohg; Yun, Doh-Won; Kim, Jin-Hyun; Choi, Hong-Kyu; An, GynHeung; Kim, Tae-Ho; Jung, Ki-Hong

2016-12-01

Rice is one of the most important food crops for humans. To improve the agronomical traits of rice, the functions of more than 1,000 rice genes have been recently characterized and summarized. The completed, map-based sequence of the rice genome has significantly accelerated the functional characterization of rice genes, but progress remains limited in assigning functions to all predicted non-transposable element (non-TE) genes, estimated to number 37,000-41,000. The International Rice Functional Genomics Consortium (IRFGC) has generated a huge number of gene-indexed mutants by using mutagens such as T-DNA, Tos17 and Ds/dSpm. These mutants have been identified by 246,566 flanking sequence tags (FSTs) and cover 65 % (25,275 of 38,869) of the non-TE genes in rice, while the mutation ratio of TE genes is 25.7 %. In addition, almost 80 % of highly expressed non-TE genes have insertion mutations, indicating that highly expressed genes in rice chromosomes are more likely to have mutations by mutagens such as T-DNA, Ds, dSpm and Tos17. The functions of around 2.5 % of rice genes have been characterized, and studies have mainly focused on transcriptional and post-transcriptional regulation. Slow progress in characterizing the function of rice genes is mainly due to a lack of clues to guide functional studies or functional redundancy. These limitations can be partially solved by a well-categorized functional classification of FST genes. To create this classification, we used the diverse overviews installed in the MapMan toolkit. Gene Ontology (GO) assignment to FST genes supplemented the limitation of MapMan overviews. The functions of 863 of 1,022 known genes can be evaluated by current FST lines, indicating that FST genes are useful resources for functional genomic studies. We assigned 16,169 out of 29,624 FST genes to 34 MapMan classes, including major three categories such as DNA, RNA and protein. To demonstrate the MapMan application on FST genes, transcriptome analysis was done from a rice mutant of 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) gene with FST. Mapping of 756 down-regulated genes in dxr mutants and their annotation in terms of various MapMan overviews revealed candidate genes downstream of DXR-mediating light signaling pathway in diverse functional classes such as the methyl-D-erythritol 4-phosphatepathway (MEP) pathway overview, photosynthesis, secondary metabolism and regulatory overview. This report provides a useful guide for systematic phenomics and further applications to enhance the key agronomic traits of rice.

Designing and Implementation of River Classification Assistant Management System

NASA Astrophysics Data System (ADS)

Zhao, Yinjun; Jiang, Wenyuan; Yang, Rujun; Yang, Nan; Liu, Haiyan

2018-03-01

In an earlier publication, we proposed a new Decision Classifier (DCF) for Chinese river classification based on their structures. To expand, enhance and promote the application of the DCF, we build a computer system to support river classification named River Classification Assistant Management System. Based on ArcEngine and ArcServer platform, this system implements many functions such as data management, extraction of river network, river classification, and results publication under combining Client / Server with Browser / Server framework.

Functional data analysis of sleeping energy expenditure.

PubMed

Lee, Jong Soo; Zakeri, Issa F; Butte, Nancy F

2017-01-01

Adequate sleep is crucial during childhood for metabolic health, and physical and cognitive development. Inadequate sleep can disrupt metabolic homeostasis and alter sleeping energy expenditure (SEE). Functional data analysis methods were applied to SEE data to elucidate the population structure of SEE and to discriminate SEE between obese and non-obese children. Minute-by-minute SEE in 109 children, ages 5-18, was measured in room respiration calorimeters. A smoothing spline method was applied to the calorimetric data to extract the true smoothing function for each subject. Functional principal component analysis was used to capture the important modes of variation of the functional data and to identify differences in SEE patterns. Combinations of functional principal component analysis and classifier algorithm were used to classify SEE. Smoothing effectively removed instrumentation noise inherent in the room calorimeter data, providing more accurate data for analysis of the dynamics of SEE. SEE exhibited declining but subtly undulating patterns throughout the night. Mean SEE was markedly higher in obese than non-obese children, as expected due to their greater body mass. SEE was higher among the obese than non-obese children (p<0.01); however, the weight-adjusted mean SEE was not statistically different (p>0.1, after post hoc testing). Functional principal component scores for the first two components explained 77.8% of the variance in SEE and also differed between groups (p = 0.037). Logistic regression, support vector machine or random forest classification methods were able to distinguish weight-adjusted SEE between obese and non-obese participants with good classification rates (62-64%). Our results implicate other factors, yet to be uncovered, that affect the weight-adjusted SEE of obese and non-obese children. Functional data analysis revealed differences in the structure of SEE between obese and non-obese children that may contribute to disruption of metabolic homeostasis.

Latent Partially Ordered Classification Models and Normal Mixtures

ERIC Educational Resources Information Center

Tatsuoka, Curtis; Varadi, Ferenc; Jaeger, Judith

2013-01-01

Latent partially ordered sets (posets) can be employed in modeling cognitive functioning, such as in the analysis of neuropsychological (NP) and educational test data. Posets are cognitively diagnostic in the sense that classification states in these models are associated with detailed profiles of cognitive functioning. These profiles allow for…

23 CFR 470.105 - Urban area boundaries and highway functional classification.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 23 Highways 1 2013-04-01 2013-04-01 false Urban area boundaries and highway functional classification. 470.105 Section 470.105 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PLANNING AND RESEARCH HIGHWAY SYSTEMS Federal-aid Highway Systems § 470.105 Urban area boundaries and...

23 CFR 470.105 - Urban area boundaries and highway functional classification.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 23 Highways 1 2011-04-01 2011-04-01 false Urban area boundaries and highway functional classification. 470.105 Section 470.105 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PLANNING AND RESEARCH HIGHWAY SYSTEMS Federal-aid Highway Systems § 470.105 Urban area boundaries and...

23 CFR 470.105 - Urban area boundaries and highway functional classification.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 23 Highways 1 2014-04-01 2014-04-01 false Urban area boundaries and highway functional classification. 470.105 Section 470.105 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PLANNING AND RESEARCH HIGHWAY SYSTEMS Federal-aid Highway Systems § 470.105 Urban area boundaries and...

23 CFR 470.105 - Urban area boundaries and highway functional classification.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 23 Highways 1 2012-04-01 2012-04-01 false Urban area boundaries and highway functional classification. 470.105 Section 470.105 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PLANNING AND RESEARCH HIGHWAY SYSTEMS Federal-aid Highway Systems § 470.105 Urban area boundaries and...

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

PubMed

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

2018-01-01

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

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

PubMed Central

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

2018-01-01

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition. PMID:29615882

Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects.

PubMed

Feder, Stephan; Sundermann, Benedikt; Wersching, Heike; Teuber, Anja; Kugel, Harald; Teismann, Henning; Heindel, Walter; Berger, Klaus; Pfleiderer, Bettina

2017-11-01

Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample. The secondary objective was to assess, if such information can improve diagnostic classification. We analyzed data from 360 patients with unipolar depression and 360 non-depressed population controls, who were subdivided into two independent subsets. Cluster analyses (unsupervised learning) of functional connectivity were used to generate hypotheses about potential patient subgroups from the first subset. The relationship of clusters with demographical and clinical measures was assessed. Subsequently, diagnostic classifiers (supervised learning), which incorporated information about these putative depression subgroups, were trained. Exploratory cluster analyses revealed two weakly separable subgroups of depressed patients. These subgroups differed in the average duration of depression and in the proportion of patients with concurrently severe depression and anxiety symptoms. The diagnostic classification models performed at chance level. It remains unresolved, if subgroups represent distinct biological subtypes, variability of continuous clinical variables or in part an overfitting of sparsely structured data. Functional connectivity in unipolar depression is associated with general disease effects. Cluster analyses provide hypotheses about potential depression subtypes. Diagnostic models did not benefit from this additional information regarding heterogeneity. Copyright © 2017 Elsevier B.V. All rights reserved.

Do Reading Experts Agree with MCAT Verbal Reasoning Item Classifications?

ERIC Educational Resources Information Center

Jackson, Evelyn W.; And Others

1994-01-01

Examined whether expert raters (n=5) could agree about classification of Medical College Admission Test (MCAT) items and whether they agreed with MCAT student manual in labeling skill being measured by each test item. Results revealed difficulties in replicating authors' labeling of skills for reading items on practice test provided with 1991 MCAT…

Test-Enhanced Learning of Natural Concepts: Effects on Recognition Memory, Classification, and Metacognition

ERIC Educational Resources Information Center

Jacoby, Larry L.; Wahlheim, Christopher N.; Coane, Jennifer H.

2010-01-01

Three experiments examined testing effects on learning of natural concepts and metacognitive assessments of such learning. Results revealed that testing enhanced recognition memory and classification accuracy for studied and novel exemplars of bird families on immediate and delayed tests. These effects depended on the balance of study and test…

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

The Ecohydrological Context of Drought and Classification of Plant Responses

NASA Astrophysics Data System (ADS)

Feng, X.; Ackerly, D.; Dawson, T. E.; Manzoni, S.; Skelton, R. P.; Vico, G.; Thompson, S. E.

2017-12-01

Many recent studies on drought-induced vegetation mortality have explored how plant functional traits, and classifications of such traits along axes of, e.g., isohydry - anisohydry, might contribute to predicting drought survival and recovery. As these studies proliferate, concerns are growing about the consistency and predictive value of such classifications. Here, we outline the basis for a systematic classification of drought strategies that accounts for both environmental conditions and functional traits. We (1) identify drawbacks of exiting isohydricity and trait-based metrics, (2) identify major axes of trait and environmental variation that determine drought mortality pathways (hydraulic failure and carbon starvation) using non-dimensional trait groups, and (3) demonstrate that these trait groupings predict physiological drought outcomes using both measured and synthetic data. In doing so we untangle some confounding effects of environment and trait variations that undermine current classification schemes, outline a pathway to progress towards a general classification of drought vulnerability, and advocate for more careful treatment of the environmental conditions within which plant drought responses occur.

Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task

NASA Astrophysics Data System (ADS)

Krumpe, Tanja; Walter, Carina; Rosenstiel, Wolfgang; Spüler, Martin

2016-08-01

Objective. In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system. Approach. A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part. Main results. Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach. Significance. The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.

Log-ratio transformed major element based multidimensional classification for altered High-Mg igneous rocks

NASA Astrophysics Data System (ADS)

Verma, Surendra P.; Rivera-Gómez, M. Abdelaly; Díaz-González, Lorena; Quiroz-Ruiz, Alfredo

2016-12-01

A new multidimensional classification scheme consistent with the chemical classification of the International Union of Geological Sciences (IUGS) is proposed for the nomenclature of High-Mg altered rocks. Our procedure is based on an extensive database of major element (SiO2, TiO2, Al2O3, Fe2O3t, MnO, MgO, CaO, Na2O, K2O, and P2O5) compositions of a total of 33,868 (920 High-Mg and 32,948 "Common") relatively fresh igneous rock samples. The database consisting of these multinormally distributed samples in terms of their isometric log-ratios was used to propose a set of 11 discriminant functions and 6 diagrams to facilitate High-Mg rock classification. The multinormality required by linear discriminant and canonical analysis was ascertained by a new computer program DOMuDaF. One multidimensional function can distinguish the High-Mg and Common igneous rocks with high percent success values of about 86.4% and 98.9%, respectively. Similarly, from 10 discriminant functions the High-Mg rocks can also be classified as one of the four rock types (komatiite, meimechite, picrite, and boninite), with high success values of about 88%-100%. Satisfactory functioning of this new classification scheme was confirmed by seven independent tests. Five further case studies involving application to highly altered rocks illustrate the usefulness of our proposal. A computer program HMgClaMSys was written to efficiently apply the proposed classification scheme, which will be available for online processing of igneous rock compositional data. Monte Carlo simulation modeling and mass-balance computations confirmed the robustness of our classification with respect to analytical errors and postemplacement compositional changes.

Classification of TP53 Mutations and HPV Predict Survival in Advanced Larynx Cancer

PubMed Central

Scheel, Adam; Bellile, Emily; McHugh, Jonathan B.; Walline, Heather M.; Prince, Mark E.; Urba, Susan; Wolf, Gregory T.; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E.; Bradford, Carol

2016-01-01

OBJECTIVE Assess TP53 functional mutations in the context of other biomarkers in advanced larynx cancer. STUDY DESIGN Prospective analysis of pretreatment tumor TP53, HPV, Bcl-xL and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. METHODS TP53 exons 4-9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl and cyclin D1 expression. RESULTS TP53 Mutations were found in 22/58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13/58 (22.4%) patients, nonsense mutations in 4/58 (6.9%), and deletions in 5/58 (8.6%). High risk HPV was found in 20/52 (38.5%) tumors. A classification based on crystal Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low risk mutations (p=0.0315). A model including this TP53 classification, HPV status, cyclin D1 and Bcl-xL staining significantly predicts survival (p=0.0017). CONCLUSION EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. PMID:27345657

Bimanual Fine Motor Function (BFMF) Classification in Children with Cerebral Palsy: Aspects of Construct and Content Validity.

PubMed

Elvrum, Ann-Kristin G; Andersen, Guro L; Himmelmann, Kate; Beckung, Eva; Öhrvall, Ann-Marie; Lydersen, Stian; Vik, Torstein

2016-01-01

The Bimanual Fine Motor Function (BFMF) is currently the principal classification of hand function recorded by the Surveillance of Cerebral Palsy in Europe (SCPE) register. The BFMF is used in a number of epidemiological studies, but has not yet been validated. To examine aspects of construct and content validity of the BFMF. Construct validity of the BFMF was assessed by comparison with the Manual Ability Classification System (MACS) using register-based data from 539 children born 1999-2003 (304 boys; 4-12 years). The high correlation with the MACS (Spearman's rho = 0.89, CI: 0.86-0.91, p<.001) supports construct validity of the BFMF. The content of the BFMF was appraised through literature review, and by using the ICF-CY as a framework to compare the BFMF and MACS. The items hold, grasp and manipulate were found to be relevant to describe increasingly advanced fine motor abilities in children with CP, but the description of the BFMF does not state whether it is a classification of fine motor capacity or performance. Our results suggest that the BFMF may provide complementary information to the MACS regarding fine motor function and actual use of the hands, particularly if used as a classification of fine motor capacity.

Metric learning with spectral graph convolutions on brain connectivity networks.

PubMed

Ktena, Sofia Ira; Parisot, Sarah; Ferrante, Enzo; Rajchl, Martin; Lee, Matthew; Glocker, Ben; Rueckert, Daniel

2018-04-01

Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP

NASA Astrophysics Data System (ADS)

Jain, Sankalp; Grandits, Melanie; Richter, Lars; Ecker, Gerhard F.

2017-06-01

The bile salt export pump (BSEP) actively transports conjugated monovalent bile acids from the hepatocytes into the bile. This facilitates the formation of micelles and promotes digestion and absorption of dietary fat. Inhibition of BSEP leads to decreased bile flow and accumulation of cytotoxic bile salts in the liver. A number of compounds have been identified to interact with BSEP, which results in drug-induced cholestasis or liver injury. Therefore, in silico approaches for flagging compounds as potential BSEP inhibitors would be of high value in the early stage of the drug discovery pipeline. Up to now, due to the lack of a high-resolution X-ray structure of BSEP, in silico based identification of BSEP inhibitors focused on ligand-based approaches. In this study, we provide a homology model for BSEP, developed using the corrected mouse P-glycoprotein structure (PDB ID: 4M1M). Subsequently, the model was used for docking-based classification of a set of 1212 compounds (405 BSEP inhibitors, 807 non-inhibitors). Using the scoring function ChemScore, a prediction accuracy of 81% on the training set and 73% on two external test sets could be obtained. In addition, the applicability domain of the models was assessed based on Euclidean distance. Further, analysis of the protein-ligand interaction fingerprints revealed certain functional group-amino acid residue interactions that could play a key role for ligand binding. Though ligand-based models, due to their high speed and accuracy, remain the method of choice for classification of BSEP inhibitors, structure-assisted docking models demonstrate reasonably good prediction accuracies while additionally providing information about putative protein-ligand interactions.

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.

A Pan-STARRS1 Proper-Motion Survey for Young Brown Dwarfs in the Nearest Star-Forming Regions and a Reddening-Free Classification Method for Ultracool Dwarfs

NASA Astrophysics Data System (ADS)

Zhang, Zhoujian; Liu, Michael C.; Best, William M. J.; Magnier, Eugene; Aller, Kimberly

2018-01-01

Young brown dwarfs are of prime importance to investigate the universality of the initial mass function (IMF). Based on photometry and proper motions from the Pan-STARRS1 (PS1) 3π survey, we are conducting the widest and deepest brown dwarf survey in the nearby star-forming regions, Taurus–Auriga (Taurus) and Upper Scorpius (USco). Our work is the first to measure proper motions, a robust proxy of membership, for brown dwarf candidates in Taurus and USco over such a large area and long time baseline (≈ 15 year) with such high precision (≈ 4 mas yr-1). Since extinction complicates spectral classification, we have developed a new approach to quantitatively determine reddening-free spectral types, extinctions, and gravity classifications for mid-M to late-L ultracool dwarfs (≈ 100–5 MJup), using low-resolution near-infrared spectra. So far, our IRTF/SpeX spectroscopic follow-up has increased the substellar and planetary-mass census of Taurus by ≈ 50% and almost doubled the substellar census of USco, constituting the largest single increases of brown dwarfs and free-floating planets found in both regions to date. Most notably, our new discoveries reveal an older (> 10 Myr) low-mass population in Taurus, in accord with recent studies of the higher-mass stellar members. In addition, the mass function appears to differ between the younger and older Taurus populations, possibly due to incompleteness of the older stellar members or different star formation processes. Upon completion, our survey will establish the most complete substellar and planetary-mass census in both Taurus and USco associations, make a significant addition to the low-mass IMF in both regions, and deliver more comprehensive pictures of star formation histories.

Regional brain volumetry and brain function in severely brain-injured patients.

PubMed

Annen, Jitka; Frasso, Gianluca; Crone, Julia Sophia; Heine, Lizette; Di Perri, Carol; Martial, Charlotte; Cassol, Helena; Demertzi, Athena; Naccache, Lionel; Laureys, Steven

2018-04-01

The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness. Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets. Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM. Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853. © 2018 American Neurological Association.

Comparisons of fish species traits from small streams to large rivers

USGS Publications Warehouse

Goldstein, R.M.; Meador, M.R.

2004-01-01

To examine the relations between fish community function and stream size, we classified 429 lotic freshwater fish species based on multiple categories within six species traits: (1) substrate preference, (2) geomorphic preference, (3) trophic ecology, (4) locomotion morphology, (5) reproductive strategy, and (6) stream size preference. Stream size categories included small streams, small, medium, and large rivers, and no size preference. The frequencies of each species trait category were determined for each stream size category based on life history information from the literature. Cluster analysis revealed the presence of covarying groups of species trait categories. One cluster (RUN) included the traits of planktivore and herbivore feeding ecology, migratory reproductive behavior and broadcast spawning, preferences for main-channel habitats, and a lack of preferences for substrate type. The frequencies of classifications for the RUN cluster varied significantly across stream size categories (P = 0.009), being greater for large rivers than for small streams and rivers. Another cluster (RIFFLE) included the traits of invertivore feeding ecology, simple nester reproductive behavior, a preference for riffles, and a preference for bedrock, boulder, and cobble-rubble substrate. No significant differences in the frequency of classifications among stream size categories were detected for the RIFFLE cluster (P = 0.328). Our results suggest that fish community function is structured by large-scale differences in habitat and is different for large rivers than for small streams and rivers. Our findings support theoretical predictions of variation in species traits among stream reaches based on ecological frameworks such as landscape filters, habitat templates, and the river continuum concept. We believe that the species trait classifications presented here provide an opportunity for further examination of fish species' relations to physical, chemical, and biological factors in lotic habitats ranging from small streams to large rivers.

Map Classification: A Comparison of Schemes with Special Reference to the Continent of Africa. Occasional Papers, Number 154.

ERIC Educational Resources Information Center

Merrett, Christopher E.

This guide to the theory and practice of map classification begins with a discussion of the filing of maps and the function of map classification based on area and theme as illustrated by four maps of Africa. The description of the various classification systems which follows is divided into book schemes with provision for maps (including Dewey…

Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin.

PubMed

Afanasyev, Pavel; Seer-Linnemayr, Charlotte; Ravelli, Raimond B G; Matadeen, Rishi; De Carlo, Sacha; Alewijnse, Bart; Portugal, Rodrigo V; Pannu, Navraj S; Schatz, Michael; van Heel, Marin

2017-09-01

Single-particle cryogenic electron microscopy (cryo-EM) can now yield near-atomic resolution structures of biological complexes. However, the reference-based alignment algorithms commonly used in cryo-EM suffer from reference bias, limiting their applicability (also known as the 'Einstein from random noise' problem). Low-dose cryo-EM therefore requires robust and objective approaches to reveal the structural information contained in the extremely noisy data, especially when dealing with small structures. A reference-free pipeline is presented for obtaining near-atomic resolution three-dimensional reconstructions from heterogeneous ('four-dimensional') cryo-EM data sets. The methodologies integrated in this pipeline include a posteriori camera correction, movie-based full-data-set contrast transfer function determination, movie-alignment algorithms, (Fourier-space) multivariate statistical data compression and unsupervised classification, 'random-startup' three-dimensional reconstructions, four-dimensional structural refinements and Fourier shell correlation criteria for evaluating anisotropic resolution. The procedures exclusively use information emerging from the data set itself, without external 'starting models'. Euler-angle assignments are performed by angular reconstitution rather than by the inherently slower projection-matching approaches. The comprehensive 'ABC-4D' pipeline is based on the two-dimensional reference-free 'alignment by classification' (ABC) approach, where similar images in similar orientations are grouped by unsupervised classification. Some fundamental differences between X-ray crystallography versus single-particle cryo-EM data collection and data processing are discussed. The structure of the giant haemoglobin from Lumbricus terrestris at a global resolution of ∼3.8 Å is presented as an example of the use of the ABC-4D procedure.

3D Complex: A Structural Classification of Protein Complexes

PubMed Central

Levy, Emmanuel D; Pereira-Leal, Jose B; Chothia, Cyrus; Teichmann, Sarah A

2006-01-01

Most of the proteins in a cell assemble into complexes to carry out their function. It is therefore crucial to understand the physicochemical properties as well as the evolution of interactions between proteins. The Protein Data Bank represents an important source of information for such studies, because more than half of the structures are homo- or heteromeric protein complexes. Here we propose the first hierarchical classification of whole protein complexes of known 3-D structure, based on representing their fundamental structural features as a graph. This classification provides the first overview of all the complexes in the Protein Data Bank and allows nonredundant sets to be derived at different levels of detail. This reveals that between one-half and two-thirds of known structures are multimeric, depending on the level of redundancy accepted. We also analyse the structures in terms of the topological arrangement of their subunits and find that they form a small number of arrangements compared with all theoretically possible ones. This is because most complexes contain four subunits or less, and the large majority are homomeric. In addition, there is a strong tendency for symmetry in complexes, even for heteromeric complexes. Finally, through comparison of Biological Units in the Protein Data Bank with the Protein Quaternary Structure database, we identified many possible errors in quaternary structure assignments. Our classification, available as a database and Web server at http://www.3Dcomplex.org, will be a starting point for future work aimed at understanding the structure and evolution of protein complexes. PMID:17112313

Compiling standardized information from clinical practice: using content analysis and ICF Linking Rules in a goal-oriented youth rehabilitation program.

PubMed

Lustenberger, Nadia A; Prodinger, Birgit; Dorjbal, Delgerjargal; Rubinelli, Sara; Schmitt, Klaus; Scheel-Sailer, Anke

2017-09-23

To illustrate how routinely written narrative admission and discharge reports of a rehabilitation program for eight youths with chronic neurological health conditions can be transformed to the International Classification of Functioning, Disability and Health. First, a qualitative content analysis was conducted by building meaningful units with text segments assigned of the reports to the five elements of the Rehab-Cycle ® : goal; assessment; assignment; intervention; evaluation. Second, the meaningful units were then linked to the ICF using the refined ICF Linking Rules. With the first step of transformation, the emphasis of the narrative reports changed to a process oriented interdisciplinary layout, revealing three thematic blocks of goals: mobility, self-care, mental, and social functions. The linked 95 unique ICF codes could be grouped in clinically meaningful goal-centered ICF codes. Between the two independent linkers, the agreement rate was improved after complementing the rules with additional agreements. The ICF Linking Rules can be used to compile standardized health information from narrative reports if prior structured. The process requires time and expertise. To implement the ICF into common practice, the findings provide the starting point for reporting rehabilitation that builds upon existing practice and adheres to international standards. Implications for Rehabilitation This study provides evidence that routinely collected health information from rehabilitation practice can be transformed to the International Classification of Functioning, Disability and Health by using the "ICF Linking Rules", however, this requires time and expertise. The Rehab-Cycle ® , including assessments, assignments, goal setting, interventions and goal evaluation, serves as feasible framework for structuring this rehabilitation program and ensures that the complexity of local practice is appropriately reflected. The refined "ICF Linking Rules" lead to a standardized transformation process of narrative text and thus a higher quality with increased transparency. As a next step, the resulting format of goal codes supplemented by goal-clarifying codes could be validated to strengthen the implementation of the International Classification of Functioning, Disability and Health into rehabilitation routine by respecting the variety of clinical practice.

A Functional Classification of Occupations.

ERIC Educational Resources Information Center

McKinlay, Donald Bruce

The need for more and better manpower information is hampered by the lack of adequate occupational data classification systems. The diversity of interests in occupations probably accounts for the absence of consensus regarding either the general outlines or the specific details of a standardized occupational classification system which would…

EUCLID: automatic classification of proteins in functional classes by their database annotations.

PubMed

Tamames, J; Ouzounis, C; Casari, G; Sander, C; Valencia, A

1998-01-01

A tool is described for the automatic classification of sequences in functional classes using their database annotations. The Euclid system is based on a simple learning procedure from examples provided by human experts. Euclid is freely available for academics at http://www.gredos.cnb.uam.es/EUCLID, with the corresponding dictionaries for the generation of three, eight and 14 functional classes. E-mail: valencia@cnb.uam.es The results of the EUCLID classification of different genomes are available at http://www.sander.ebi.ac. uk/genequiz/. A detailed description of the different applications mentioned in the text is available at http://www.gredos.cnb.uam. es/EUCLID/Full_Paper

Characterization of Different Types of Excitability in Large Somatosensory Neurons and Its Plastic Changes in Pathological Pain States

PubMed Central

Xie, Rou-Gang; Chu, Wen-Guang; Hu, San-Jue; Luo, Ceng

2018-01-01

Sensory neuron types have been distinguished by distinct morphological and transcriptional characteristics. Excitability is the most fundamental functional feature of neurons. Mathematical models described by Hodgkin have revealed three types of neuronal excitability based on the relationship between firing frequency and applied current intensity. However, whether natural sensory neurons display different functional characteristics in terms of excitability and whether this excitability type undergoes plastic changes under pathological pain states have remained elusive. Here, by utilizing whole-cell patch clamp recordings, behavioral and pharmacological assays, we demonstrated that large dorsal root ganglion (DRG) neurons can be classified into three classes and four subclasses based on their excitability patterns, which is similar to mathematical models raised by Hodgkin. Analysis of hyperpolarization-activated cation current (Ih) revealed different magnitude of Ih in different excitability types of large DRG neurons, with higher Ih in Class 2-1 than that in Class 1, 2-2 and 3. This indicates a crucial role of Ih in the determination of excitability type of large DRG neurons. More importantly, this pattern of excitability displays plastic changes and transition under pathological pain states caused by peripheral nerve injury. This study sheds new light on the functional characteristics of large DRG neurons and extends functional classification of large DRG neurons by integration of transcriptomic and morphological characteristics. PMID:29303989

Stakeholders' perspectives on social participation in preschool children with Autism Spectrum Disorder.

PubMed

Germani, Tamara; Zwaigenbaum, Lonnie; Magill-Evans, Joyce; Hodgetts, Sandy; Ball, Geoff

2017-11-01

To determine (a) the essential components of social participation for preschool children with Autism Spectrum Disorder (ASD) using stakeholders' perspectives and (b) the facilitators and barriers experienced in promoting social participation. A mixed-methods, web-based survey utilizing the International Classification of Functioning, Disability and Health - Child and Youth version (ICF-CY) taxonomy was circulated across Canada through purposeful snowball sampling. Frequency analysis of the combined responses of 74 stakeholders revealed the most essential components of social participation were: (a) behavior management, (b) social interactions, and (c) various types of play. Further, content analysis revealed that stakeholders used intrinsic motivation strategies and contingency management to facilitate social participation. Stakeholders reported that the purpose of social participation was to engage the child in fun, enjoyable social activities that developed relationships between the child and peers and created a sense of belonging in the community.

Behavior Based Social Dimensions Extraction for Multi-Label Classification

PubMed Central

Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin

2016-01-01

Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

PubMed Central

Groenendyk, Derek G.; Ferré, Ty P.A.; Thorp, Kelly R.; Rice, Amy K.

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function. PMID:26121466

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

PubMed

Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

Statistical classification techniques for engineering and climatic data samples

NASA Technical Reports Server (NTRS)

Temple, E. C.; Shipman, J. R.

1981-01-01

Fisher's sample linear discriminant function is modified through an appropriate alteration of the common sample variance-covariance matrix. The alteration consists of adding nonnegative values to the eigenvalues of the sample variance covariance matrix. The desired results of this modification is to increase the number of correct classifications by the new linear discriminant function over Fisher's function. This study is limited to the two-group discriminant problem.

The Use of the International Classification of Functioning, Disability and Health, Version for Children and Youth (ICF-CY), in Portuguese Special Education Assessment and Eligibility Procedures: The Professionals' Perceptions

ERIC Educational Resources Information Center

Sanches-Ferreira, Manuela; Silveira-Maia, Mónica; Alves, Sílvia

2014-01-01

Portugal was the first country decreeing the mandatory use of the International Classification of Functioning, Disability and Health: Child and Youth (ICF-CY) framework for guiding special education assessment process and to base eligibility decision-making on students' functioning profiles--in contrast with traditional approaches centred on…

Multivariate spline methods in surface fitting

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr. (Principal Investigator); Schumaker, L. L.

1984-01-01

The use of spline functions in the development of classification algorithms is examined. In particular, a method is formulated for producing spline approximations to bivariate density functions where the density function is decribed by a histogram of measurements. The resulting approximations are then incorporated into a Bayesiaan classification procedure for which the Bayes decision regions and the probability of misclassification is readily computed. Some preliminary numerical results are presented to illustrate the method.

The protein interactome of collapsin response mediator protein-2 (CRMP2/DPYSL2) reveals novel partner proteins in brain tissue.

PubMed

Martins-de-Souza, Daniel; Cassoli, Juliana S; Nascimento, Juliana M; Hensley, Kenneth; Guest, Paul C; Pinzon-Velasco, Andres M; Turck, Christoph W

2015-10-01

Collapsin response mediator protein-2 (CRMP2) is a CNS protein involved in neuronal development, axonal and neuronal growth, cell migration, and protein trafficking. Recent studies have linked perturbations in CRMP2 function to neurodegenerative disorders such as Alzheimer's disease, neuropathic pain, and Batten disease, and to psychiatric disorders such as schizophrenia. Like most proteins, CRMP2 functions though interactions with a molecular network of proteins and other molecules. Here, we have attempted to identify additional proteins of the CRMP2 interactome to provide further leads about its roles in neurological functions. We used a combined co-immunoprecipitation and shotgun proteomic approach in order to identify CRMP2 protein partners. We identified 78 CRMP2 protein partners not previously reported in public protein interaction databases. These were involved in seven biological processes, which included cell signaling, growth, metabolism, trafficking, and immune function, according to Gene Ontology classifications. Furthermore, 32 different molecular functions were found to be associated with these proteins, such as RNA binding, ribosomal functions, transporter activity, receptor activity, serine/threonine phosphatase activity, cell adhesion, cytoskeletal protein binding and catalytic activity. In silico pathway interactome construction revealed a highly connected network with the most overrepresented functions corresponding to semaphorin interactions, along with axon guidance and WNT5A signaling. Taken together, these findings suggest that the CRMP2 pathway is critical for regulating neuronal and synaptic architecture. Further studies along these lines might uncover novel biomarkers and drug targets for use in drug discovery. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Center for Seismic Studies Final Technical Report, October 1992 through October 1993

DTIC Science & Technology

1994-02-07

SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT OF REPORT OF THIS PAGE OF ABSTRACT...Upper limit of depth error as a function of mb for estimates based on P and S waves for three netowrks : GSETr-2, ALPHA, and ALPHA + a 50 station...U 4A 4 U 4S as 1 I I I Figure 42: Upper limit of depth error as a function of mb for estimatesbased on P and S waves for three netowrk : GSETT-2o ALPHA

Pattern classification of kinematic and kinetic running data to distinguish gender, shod/barefoot and injury groups with feature ranking.

PubMed

Eskofier, Bjoern M; Kraus, Martin; Worobets, Jay T; Stefanyshyn, Darren J; Nigg, Benno M

2012-01-01

The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified. The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task. Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.

Disregarding population specificity: its influence on the sex assessment methods from the tibia.

PubMed

Kotěrová, Anežka; Velemínská, Jana; Dupej, Ján; Brzobohatá, Hana; Pilný, Aleš; Brůžek, Jaroslav

2017-01-01

Forensic anthropology has developed classification techniques for sex estimation of unknown skeletal remains, for example population-specific discriminant function analyses. These methods were designed for populations that lived mostly in the late nineteenth and twentieth centuries. Their level of reliability or misclassification is important for practical use in today's forensic practice; it is, however, unknown. We addressed the question of what the likelihood of errors would be if population specificity of discriminant functions of the tibia were disregarded. Moreover, five classification functions in a Czech sample were proposed (accuracies 82.1-87.5 %, sex bias ranged from -1.3 to -5.4 %). We measured ten variables traditionally used for sex assessment of the tibia on a sample of 30 male and 26 female models from recent Czech population. To estimate the classification accuracy and error (misclassification) rates ignoring population specificity, we selected published classification functions of tibia for the Portuguese, south European, and the North American populations. These functions were applied on the dimensions of the Czech population. Comparing the classification success of the reference and the tested Czech sample showed that females from Czech population were significantly overestimated and mostly misclassified as males. Overall accuracy of sex assessment significantly decreased (53.6-69.7 %), sex bias -29.4-100 %, which is most probably caused by secular trend and the generally high variability of body size. Results indicate that the discriminant functions, developed for skeletal series representing geographically and chronologically diverse populations, are not applicable in current forensic investigations. Finally, implications and recommendations for future research are discussed.

Prognostic value of computed tomography classification systems for intra-articular calcaneus fractures.

PubMed

Swords, Michael P; Alton, Timothy B; Holt, Sarah; Sangeorzan, Bruce J; Shank, John R; Benirschke, Stephen K

2014-10-01

There are several published computed tomography (CT) classification systems for calcaneus fractures, each validated by a different standard. The goal of this study was to measure which system would best predict clinical outcomes as measured by a widely used and validated musculoskeletal health status questionnaire. Forty-nine patients with isolated intra-articular joint depression calcaneus fractures more than 2 years after treatment were identified. All had preoperative CT studies and were treated with open reduction and plate fixation using a lateral extensile approach. Four different blinded reviewers classified injuries according to the CT classification systems of Crosby and Fitzgibbons, Eastwood, and Sanders. Functional outcomes evaluated with a Musculoskeletal Functional Assessment (MFA). The mean follow-up was 4.3 years. The mean MFA score was 15.7 (SD = 11.6), which is not significantly different from published values for midfoot injuries, hindfoot injuries, or both, 1 year after injury (mean = 22.1, SD = 18.4). The classification systems of Crosby and Fitzgibbons, Eastwood, and Sanders, the number of fragments of the posterior facet, and payer status were not significantly associated with outcome as determined by the MFA. The Sanders classification trended toward significance. Anterior process comminution and surgeon's overall impression of severity were significantly associated with functional outcome. The amount of anterior process comminution was an important determinant of functional outcome with increasing anterior process comminution significantly associated with worsened functional outcome (P = .04). In addition, the surgeon's overall impression of severity of injury was predictive of functional outcome (P = .02), as determined by MFA. Level III, comparative series. © The Author(s) 2014.

Global Proteomic Analysis in Trypanosomes Reveals Unique Proteins and Conserved Cellular Processes Impacted by Arginine Methylation

PubMed Central

Lott, Kaylen; Li, Jun; Fisk, John C.; Wang, Hao; Aletta, John M.; Qu, Jun; Read, Laurie K.

2013-01-01

Arginine methylation is a common posttranslational modification with reported functions in transcription, RNA processing and translation, and DNA repair. Trypanosomes encode five protein arginine methyltransferases, suggesting that arginine methylation exerts widespread impacts on the biology of these organisms. Here, we performed a global proteomic analysis of T. brucei to identify arginine methylated proteins and their sites of modification. Using an approach entailing two-dimensional chromatographic separation, and alternating electron transfer dissociation and collision induced dissociation, we identified 1332 methylarginines in 676 proteins. The resulting data set represents the largest compilation of arginine methylated proteins in any organism to date. Functional classification revealed numerous arginine methylated proteins involved in flagellar function, RNA metabolism, DNA replication and repair, and intracellular protein trafficking. Thus, arginine methylation has the potential to impact aspects of T. brucei gene expression, cell biology, and pathogenesis. Interestingly, pathways with known methylated proteins in higher eukaryotes were identified in this study, but often different components of the pathway were methylated in trypanosomes. Methylarginines were often identified in glycine rich contexts, although exceptions to this rule were detected. Collectively, these data inform on a multitude of aspects of trypanosome biology and serve as a guide for the identification of homologous arginine methylated proteins in higher eukaryotes. PMID:23872088

Protein partners in the life history of activated fibroblast growth factor receptors.

PubMed

Vecchione, Anna; Cooper, Helen J; Trim, Kimberley J; Akbarzadeh, Shiva; Heath, John K; Wheldon, Lee M

2007-12-01

Fibroblast growth factor receptors (FGFRs) are a family of four transmembrane (TM) receptor tyrosine kinases (RTKs) which bind to a large family of fibroblast growth factor (FGF) ligands with varying affinity and specificity. FGFR signaling regulates many physiological and pathological processes in development and tissue homeostasis. Understanding FGFR signaling processes requires the identification of partner proteins which regulate receptor function and biological outputs. In this study, we employ an epitope-tagged, covalently dimerized, and constitutively activated form of FGFR1 to identify potential protein partners by MS. By this approach, we sample candidate FGFR effectors throughout the life history of the receptor. Functional classification of the partners identified revealed specific subclasses involved in protein biosynthesis and folding; structural and regulatory components of the cytoskeleton; known signaling effectors and small GTPases implicated in endocytosis and vesicular trafficking. The kinase dependency of the interaction was determined for a subset of previously unrecognized partners by coimmunoprecipitation, Western blotting, and immunocytochemistry. From this group, the small GTPase Rab5 was selected for functional interrogation. We show that short hairpin (sh) RNA-mediated depletion of Rab5 attenuates the activation of the extracellular-regulated kinase (ERK) 1/2 pathway by FGFR signaling. The strategic approach adopted in this study has revealed bona fide novel effectors of the FGFR signaling pathway.

Profile of patients with chronic obstructive pulmonary disease classified as physically active and inactive according to different thresholds of physical activity in daily life

PubMed Central

Furlanetto, Karina C.; Pinto, Isabela F. S.; Sant’Anna, Thais; Hernandes, Nidia A.; Pitta, Fabio

2016-01-01

ABSTRACT Objective To compare the profiles of patients with chronic obstructive pulmonary disease (COPD) considered physically active or inactive according to different classifications of the level of physical activity in daily life (PADL). Method Pulmonary function, dyspnea, functional status, body composition, exercise capacity, respiratory and peripheral muscle strength, and presence of comorbidities were assessed in 104 patients with COPD. The level of PADL was quantified with a SenseWear Armband activity monitor. Three classifications were used to classify the patients as physically active or inactive: 30 minutes of activity/day with intensity >3.2 METs, if age ≥65 years, and >4 METs, if age <65 years; 30 minutes of activity/day with intensity >3.0 METs, regardless of patient age; and 80 minutes of activity/day with intensity >3.0 METs, regardless of patient age. Results In all classifications, when compared with the inactive group, the physically active group had better values of anthropometric variables (higher fat-free mass, lower body weight, body mass index and fat percentage), exercise capacity (6-minute walking distance), lung function (forced vital capacity) and functional status (personal care domain of the London Chest Activity of Daily Living). Furthermore, patients classified as physically active in two classifications also had better peripheral and expiratory muscle strength, airflow obstruction, functional status, and quality of life, as well as lower prevalence of heart disease and mortality risk. Conclusion In all classification methods, physically active patients with COPD have better exercise capacity, lung function, body composition, and functional status compared to physically inactive patients. PMID:27683835

Relationships between World Health Organization "International Classification of Functioning, Disability and Health" Constructs and Participation in Adults with Severe Mental Illness

ERIC Educational Resources Information Center

Sánchez, Jennifer; Rosenthal, David A.; Chan, Fong; Brooks, Jessica; Bezyak, Jill L.

2016-01-01

Purpose: To examine the World Health Organization "International Classification of Functioning, Disability and Health" (ICF) constructs as correlates of community participation of people with severe mental illnesses (SMI). Methods: Quantitative descriptive research design using multiple regression and correlational techniques was used to…

Implications of the International Classification of Functioning, Disability and Health (ICF) for Test Development and Use

ERIC Educational Resources Information Center

Carlson, Janet F.; Benson, Nicholas; Oakland, Thomas

2010-01-01

Implications of the International Classification of Functioning, Disability and Health (ICF) on the development and use of tests in school settings are enumerated. We predict increased demand for behavioural assessments that consider a person's activities, participation and person-environment interactions, including measures that: (a) address…

Development of the Gross Motor Function Classification System (1997)

ERIC Educational Resources Information Center

Morris, Christopher

2008-01-01

To address the need for a standardized system to classify the gross motor function of children with cerebral palsy, the authors developed a five-level classification system analogous to the staging and grading systems used in medicine. Nominal group process and Delphi survey consensus methods were used to examine content validity and revise the…

Content Validity of the Comprehensive ICF Core Set for Multiple Sclerosis from the Perspective of Speech and Language Therapists

ERIC Educational Resources Information Center

Renom, Marta; Conrad, Andrea; Bascuñana, Helena; Cieza, Alarcos; Galán, Ingrid; Kesselring, Jürg; Coenen, Michaela

2014-01-01

Background: The Comprehensive International Classification of Functioning, Disability and Health (ICF) Core Set for Multiple Sclerosis (MS) is a comprehensive framework to structure the information obtained in multidisciplinary clinical settings according to the biopsychosocial perspective of the International Classification of Functioning,…

Multiple Sclerosis and Employment: A Research Review Based on the International Classification of Function

ERIC Educational Resources Information Center

Frain, Michael P.; Bishop, Malachy; Rumrill, Phillip D., Jr.; Chan, Fong; Tansey, Timothy N.; Strauser, David; Chiu, Chung-Yi

2015-01-01

Multiple sclerosis (MS) is an unpredictable, sometimes progressive chronic illness affecting people in the prime of their working lives. This article reviews the effects of MS on employment based on the World Health Organization's International Classification of Functioning, Disability and Health model. Correlations between employment and…

Utility of the International Classification of Functioning, Disability and Health (ICF) for Educational Psychologists' Work

ERIC Educational Resources Information Center

Aljunied, Mariam; Frederickson, Norah

2014-01-01

Despite embracing a bio-psycho-social perspective, the World Health Organization's International Classification of Functioning, Disability and Health (ICF) assessment framework has had limited application to date with children who have special educational needs (SEN). This study examines its utility for educational psychologists' work with…

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.

Reconsideration of the scheme of the international classification of functioning, disability and health: incentives from the Netherlands for a global debate.

PubMed

Heerkens, Yvonne F; de Weerd, Marjolein; Huber, Machteld; de Brouwer, Carin P M; van der Veen, Sabina; Perenboom, Rom J M; van Gool, Coen H; Ten Napel, Huib; van Bon-Martens, Marja; Stallinga, Hillegonda A; van Meeteren, Nico L U

2018-03-01

The ICF (International Classification of Functioning, Disability and Health) framework (used worldwide to describe 'functioning' and 'disability'), including the ICF scheme (visualization of functioning as result of interaction with health condition and contextual factors), needs reconsideration. The purpose of this article is to discuss alternative ICF schemes. Reconsideration of ICF via literature review and discussions with 23 Dutch ICF experts. Twenty-six experts were invited to rank the three resulting alternative schemes. The literature review provided five themes: 1) societal developments; 2) health and research influences; 3) conceptualization of health; 4) models/frameworks of health and disability; and 5) ICF-criticism (e.g. position of 'health condition' at the top and role of 'contextual factors'). Experts concluded that the ICF scheme gives the impression that the medical perspective is dominant instead of the biopsychosocial perspective. Three alternative ICF schemes were ranked by 16 (62%) experts, resulting in one preferred scheme. There is a need for a new ICF scheme, better reflecting the ICF framework, for further (inter)national consideration. These Dutch schemes should be reviewed on a global scale, to develop a scheme that is more consistent with current and foreseen developments and changing ideas on health. Implications for Rehabilitation We propose policy makers on community, regional and (inter)national level to consider the use of the alternative schemes of the International Classification of Functioning, Disability and Health within their plans to promote functioning and health of their citizens and researchers and teachers to incorporate the alternative schemes into their research and education to emphasize the biopsychosocial paradigm. We propose to set up an international Delphi procedure involving citizens (including patients), experts in healthcare, occupational care, research, education and policy, and planning to get consensus on an alternative scheme of the International Classification of Functioning, Disability and Health. We recommend to discuss the alternatives for the present scheme of the International Classification of Functioning, Disability and Health in the present update and revision process within the World Health Organization as a part of the discussion on the future of the International Classification of Functioning, Disability and Health framework (including ontology, title and relation with the International Classification of Diseases). We recommend to revise the definition of personal factors and to draft a list of personal factors that can be used in policy making, clinical practice, research, and education and to put effort in the revision of the present list of environmental factors to make it more useful in, e.g., occupational health care.

Link prediction boosted psychiatry disorder classification for functional connectivity network

NASA Astrophysics Data System (ADS)

Li, Weiwei; Mei, Xue; Wang, Hao; Zhou, Yu; Huang, Jiashuang

2017-02-01

Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first `repair' the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer's Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.

[Systematization of diseases and lesions of the endometrium by etiological and pathogenetical mechanisms of development to choose an optimal treatment].

PubMed

Boroda, A M

2004-03-01

Current clinical gynecology considers pathological states of endometrium (PSE) as one of the most challenging issue of the day. Many questions of etiology, pathogenesis, diagnostics, and treatment of PSE are still under discussion. Nowadays there isn't a whole agreed classification of PSE. Morphological classification remains the most widely used one, but morphological changes occurring in the endometrium don't show a wide variety of disorders related to these pathological states. A new clinicopathogenetic classification of PSE was proposed, which is based on choosing the optimal treatment with functional state of the disease taken into account. This classification helps us to perceive the problem as a whole with choosing functionally based treatment for each patient.

The Japanese Histologic Classification and T-score in the Oxford Classification system could predict renal outcome in Japanese IgA nephropathy patients.

PubMed

Kaihan, Ahmad Baseer; Yasuda, Yoshinari; Katsuno, Takayuki; Kato, Sawako; Imaizumi, Takahiro; Ozeki, Takaya; Hishida, Manabu; Nagata, Takanobu; Ando, Masahiko; Tsuboi, Naotake; Maruyama, Shoichi

2017-12-01

The Oxford Classification is utilized globally, but has not been fully validated. In this study, we conducted a comparative analysis between the Oxford Classification and Japanese Histologic Classification (JHC) to predict renal outcome in Japanese patients with IgA nephropathy (IgAN). A retrospective cohort study including 86 adult IgAN patients was conducted. The Oxford Classification and the JHC were evaluated by 7 independent specialists. The JHC, MEST score in the Oxford Classification, and crescents were analyzed in association with renal outcome, defined as a 50% increase in serum creatinine. In multivariate analysis without the JHC, only the T score was significantly associated with renal outcome. While, a significant association was revealed only in the JHC on multivariate analysis with JHC. The JHC and T score in the Oxford Classification were associated with renal outcome among Japanese patients with IgAN. Superiority of the JHC as a predictive index should be validated with larger study population and cohort studies in different ethnicities.

Identification of Sexually Abused Female Adolescents at Risk for Suicidal Ideations: A Classification and Regression Tree Analysis

ERIC Educational Resources Information Center

Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois

2013-01-01

This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…

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

PubMed

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

2018-05-30

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

Autonomic cardiovascular control and sports classification in Paralympic athletes with spinal cord injury.

PubMed

West, Christopher R; Krassioukov, Andrei V

2017-01-01

Purpose To investigate the relationship between the classification systems used in wheelchair sports and cardiovascular function in Paralympic athletes with spinal cord injury (SCI). Methods 26 wheelchair rugby (C3-C8) and 14 wheelchair basketball (T3-L1) were assessed for their International Wheelchair Rugby and Basketball Federation sports classification. Next, athletes were assessed for resting and reflex cardiovascular and autonomic function via the change (delta) in systolic blood pressure (SBP) and heart rate (HR) in response to sit-up, and sympathetic skin responses (SSRs), respectively. Results There were no differences in supine, seated, or delta SBP and HR between different sport classes in rugby or basketball (all p > 0.23). Athletes with autonomically complete injuries (SSR score 0-1) exhibited a lower supine SBP, seated SBP and delta SBP compared to those with autonomically incomplete injuries (SSR score >1; all p < 0.010), independent of sport played. There was no association between self-report OH and measured OH (χ 2  =   1.63, p = 0.20). Conclusion We provide definitive evidence that sports specific classification is not related to the degree of remaining autonomic cardiovascular control in Paralympic athletes with SCI. We suggest that testing for remaining autonomic function, which is closely related to the degree of cardiovascular control, should be incorporated into sporting classification. Implications for Rehabilitation Spinal cord injury is a debilitating condition that affects the function of almost every physiological system. It is becoming increasingly apparent that spinal cord injury induced changes in autonomic and cardiovascular function are important determinants of sports performance in athletes with spinal cord injury. This study shows that the current sports classification systems used in wheelchair rugby and basketball do not accurately reflect autonomic and cardiovascular function and thus are placing some athletes at a distinct disadvantage/advantage within their respective sport.

Stepwise approach to curative surgery using percutaneous transhepatic cholangiodrainage and portal vein embolization for severe bile duct injury during laparoscopic cholecystectomy: a case report.

PubMed

Honmyo, Naruhiko; Kuroda, Shintaro; Kobayashi, Tsuyoshi; Ishiyama, Kohei; Ide, Kentaro; Tahara, Hiroyuki; Ohira, Masahiro; Ohdan, Hideki

2016-12-01

Laparoscopic cholecystectomy (LC) has been recently adapted to acute cholecystitis. Major bile duct injury during LC, especially Strasberg-Bismuth classification type E, can be a critical problem sometimes requiring hepatectomy. Safety and definitive treatment without further morbidities, such as posthepatectomy liver failure, is required. Here, we report a case of severe bile duct injury treated with a stepwise approach using (99m)Tc-galactosyl human serum albumin ((99m)Tc-GSA) single-photon emission computed tomography (SPECT)/CT fusion imaging to accurately estimate liver function.A 52-year-old woman diagnosed with acute cholecystitis underwent LC at another hospital and was transferred to our university hospital for persistent bile leakage on postoperative day 20. She had no jaundice or infection, although an intraperitoneal drainage tube discharged approximately 500 ml of bile per day. Recorded operation procedure showed removal of the gallbladder with a part of the common bile duct due to its misidentification, and each of the hepatic ducts and right hepatic artery was injured. Abdominal enhanced CT revealed obstructive jaundice of the left liver and arterial shunt through the hilar plate to the right liver. Magnetic resonance cholangiopancreatography revealed type E4 or more advanced bile duct injury according to the Bismuth-Strasberg classification. We planned a stepwise approach using percutaneous transhepatic cholangiodrainage (PTCD) and portal vein embolization (PVE) for secure right hemihepatectomy and biliary-jejunum reconstruction and employed (99m)Tc-GSA SPECT/CT fusion imaging to estimate future remnant liver function. The left liver function rate had changed from 26.2 % on admission to 26.3 % after PTCD and 54.5 % after PVE, while the left liver volume rate was 33.8, 33.3, and 49.6 %, respectively. The increase of liver function was higher than that of volume (28.3 vs. 15.8 %). On postoperative day 63, the curative operation, right hemihepatectomy and biliary-jejunum reconstruction, was performed, and posthepatectomy liver failure could be avoided.Careful consideration of treatment strategy for each case is necessary for severe bile duct injury with arterial injury requiring hepatectomy. The stepwise approach using PTCD and PVE could enable hemihepatectomy, and (99m)Tc-GSA SPECT/CT fusion imaging was useful to estimate heterogeneous liver function.

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

NASA Astrophysics Data System (ADS)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

Global similarity predicts dissociation of classification and recognition: evidence questioning the implicit-explicit learning distinction in amnesia.

PubMed

Jamieson, Randall K; Holmes, Signy; Mewhort, D J K

2010-11-01

Dissociation of classification and recognition in amnesia is widely taken to imply 2 functional systems: an implicit procedural-learning system that is spared in amnesia and an explicit episodic-learning system that is compromised. We argue that both tasks reflect the global similarity of probes to memory. In classification, subjects sort unstudied grammatical exemplars from lures, whereas in recognition, they sort studied grammatical exemplars from lures. Hence, global similarity is necessarily greater in recognition than in classification. Moreover, a grammatical exemplar's similarity to studied exemplars is a nonlinear function of the integrity of the data in memory. Assuming that data integrity is better for control subjects than for subjects with amnesia, the nonlinear relation combined with the advantage for recognition over classification predicts the dissociation of recognition and classification. To illustrate the dissociation of recognition and classification in healthy undergraduates, we manipulated study time to vary the integrity of the data in memory and brought the dissociation under experimental control. We argue that the dissociation reflects a general cost in memory rather than a selective impairment of separate procedural and episodic systems. (c) 2010 APA, all rights reserved

[CT morphometry for calcaneal fractures and comparison of the Zwipp and Sanders classifications].

PubMed

Andermahr, J; Jesch, A B; Helling, H J; Jubel, A; Fischbach, R; Rehm, K E

2002-01-01

The aim of the study is to correlate the CT-morphological changes of fractured calcaneus and the classifications of Zwipp and Sanders with the clinical outcome. In a retrospective clinical study, the preoperative CT scans of 75 calcaneal fractures were analysed. The morphometry of the fractures was determined by measuring height, length diameter and calcaneo-cuboidal angle in comparison to the intact contralateral side. At a mean of 38 months after trauma 44 patients were clinically followed-up. The data of CT image morphometry were correlated with the severity of fracture classified by Zwipp or Sanders as well as with the functional outcome. There was a good correlation between the fracture classifications and the morphometric data. Both fracture classifying systems have a predictive impact for functional outcome. The more exacting and accurate Zwipp classification considers the most important cofactors like involvement of the calcaneo-cuboidal joint, soft tissue damage, additional fractures etc. The Sanders classification is easier to use during clinical routine. The Zwipp classification includes more relevant cofactors (fracture of the calcaneo-cuboidal-joint, soft tissue swelling, etc.) and presents a higher correlation to the choice of therapy. Both classification systems present a prognostic impact concerning the clinical outcome.

Decoding Reveals Plasticity in V3A as a Result of Motion Perceptual Learning

PubMed Central

Shibata, Kazuhisa; Chang, Li-Hung; Kim, Dongho; Náñez, José E.; Kamitani, Yukiyasu; Watanabe, Takeo; Sasaki, Yuka

2012-01-01

Visual perceptual learning (VPL) is defined as visual performance improvement after visual experiences. VPL is often highly specific for a visual feature presented during training. Such specificity is observed in behavioral tuning function changes with the highest improvement centered on the trained feature and was originally thought to be evidence for changes in the early visual system associated with VPL. However, results of neurophysiological studies have been highly controversial concerning whether the plasticity underlying VPL occurs within the visual cortex. The controversy may be partially due to the lack of observation of neural tuning function changes in multiple visual areas in association with VPL. Here using human subjects we systematically compared behavioral tuning function changes after global motion detection training with decoded tuning function changes for 8 visual areas using pattern classification analysis on functional magnetic resonance imaging (fMRI) signals. We found that the behavioral tuning function changes were extremely highly correlated to decoded tuning function changes only in V3A, which is known to be highly responsive to global motion with human subjects. We conclude that VPL of a global motion detection task involves plasticity in a specific visual cortical area. PMID:22952849

The Effect of Involuntary Motor Activity on Myoelectric Pattern Recognition: A Case Study with Chronic Stroke Patients

PubMed Central

Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Rymer, William Zev; Zhou, Ping

2013-01-01

This study investigates the effect of involuntary motor activity of paretic-spastic muscles on classification of surface electromyography (EMG) signals. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at a relatively slow and fast speed. For each stroke subject, the degree of involuntary motor activity present in voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from slow and fast sessions. Across all tested stroke subjects, our results revealed that when involuntary surface EMG was absent or present in both training and testing datasets, high accuracies (> 96%, > 98%, respectively, averaged over all the subjects) can be achieved in classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either training or testing datasets, the classification accuracies were dramatically reduced (< 89%, < 85%, respectively). However, if both training and testing datasets contained EMG signals with presence and absence of involuntary EMG interference, high accuracies were still achieved (> 97%). The findings of this study can be used to guide appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation. PMID:23860192

Automatic classification of DMSA scans using an artificial neural network

NASA Astrophysics Data System (ADS)

Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

2014-04-01

DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

PubMed

Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier

2018-05-21

Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.

Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

PubMed

Madsen, Kristoffer H; Krohne, Laerke G; Cai, Xin-Lu; Wang, Yi; Chan, Raymond C K

2018-03-15

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.

Random forest classification of stars in the Galactic Centre

NASA Astrophysics Data System (ADS)

Plewa, P. M.

2018-05-01

Near-infrared high-angular resolution imaging observations of the Milky Way's nuclear star cluster have revealed all luminous members of the existing stellar population within the central parsec. Generally, these stars are either evolved late-type giants or massive young, early-type stars. We revisit the problem of stellar classification based on intermediate-band photometry in the K band, with the primary aim of identifying faint early-type candidate stars in the extended vicinity of the central massive black hole. A random forest classifier, trained on a subsample of spectroscopically identified stars, performs similarly well as competitive methods (F1 = 0.85), without involving any model of stellar spectral energy distributions. Advantages of using such a machine-trained classifier are a minimum of required calibration effort, a predictive accuracy expected to improve as more training data become available, and the ease of application to future, larger data sets. By applying this classifier to archive data, we are also able to reproduce the results of previous studies of the spatial distribution and the K-band luminosity function of both the early- and late-type stars.

Assessment of Patients with Intellectual Disability using the International Classification of Functioning, Disability and Health to Evaluate Dental Treatment Tolerability

ERIC Educational Resources Information Center

Maeda, S.; Kita, F.; Miyawaki, T.; Takeuchi, K.; Ishida, R.; Egusa, M.; Shimada, M.

2005-01-01

Patients with serious intellectual disability (ID) are occasionally unable to tolerate dental treatment when intravenous sedation or general anaesthesia (IVSGA) is involved. In order to make a decision regarding the application of IVSGA, the International Classification of Functioning, Disability and Health (ICF) is useful. Therefore, in this…

Free Classification as a Window on Executive Functioning in Autism Spectrum Disorders

ERIC Educational Resources Information Center

McGonigle-Chalmers, Margaret; Alderson-Day, Ben

2010-01-01

Spontaneous classification was assessed using a free serial search task in 18 school-aged children at the high functioning end of the autistic spectrum and compared with results from age-matched typically developing controls. The task required participants to touch shapes in an exhaustive non-repetitive sequence. The positions of the items varied…

The Relationship between Manual Ability and Ambulation in Adolescents with Cerebral Palsy

ERIC Educational Resources Information Center

Majnemer, Annette; Shikako-Thomas, Keiko; Shevell, Michael; Poulin, Chantal; Lach, Lucyna; Law, Mary; Schmitz, Norbert

2013-01-01

This study examined the relationship between gross motor function and manual ability in 120 adolescents with cerebral palsy (CP) (15.2, SD 2.1 years, 59.8% male). Adolescents were evaluated using the Gross Motor Function Classification System (GMFCS) and the Manual Ability Classification System (MACS). A neurologist classified CP subtype. Most…

A Handbook for Derivative Classifiers at Los Alamos National Laboratory

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

Sinkula, Barbara Jean

The Los Alamos Classification Office (within the SAFE-IP group) prepared this handbook as a resource for the Laboratory’s derivative classifiers (DCs). It contains information about United States Government (USG) classification policy, principles, and authorities as they relate to the LANL Classification Program in general, and to the LANL DC program specifically. At a working level, DCs review Laboratory documents and material that are subject to classification review requirements, while the Classification Office provides the training and resources for DCs to perform that vital function.

Classification of lymphoid neoplasms: the microscope as a tool for disease discovery

PubMed Central

Harris, Nancy Lee; Stein, Harald; Isaacson, Peter G.

2008-01-01

In the past 50 years, we have witnessed explosive growth in the understanding of normal and neoplastic lymphoid cells. B-cell, T-cell, and natural killer (NK)–cell neoplasms in many respects recapitulate normal stages of lymphoid cell differentiation and function, so that they can be to some extent classified according to the corresponding normal stage. Likewise, the molecular mechanisms involved the pathogenesis of lymphomas and lymphoid leukemias are often based on the physiology of the lymphoid cells, capitalizing on deregulated normal physiology by harnessing the promoters of genes essential for lymphocyte function. The clinical manifestations of lymphomas likewise reflect the normal function of lymphoid cells in vivo. The multiparameter approach to classification adopted by the World Health Organization (WHO) classification has been validated in international studies as being highly reproducible, and enhancing the interpretation of clinical and translational studies. In addition, accurate and precise classification of disease entities facilitates the discovery of the molecular basis of lymphoid neoplasms in the basic science laboratory. PMID:19029456

Subacute and non-acute casemix in Australia.

PubMed

Lee, L A; Eagar, K M; Smith, M C

1998-10-19

The costs of subacute care (palliative care, rehabilitation medicine, psychogeriatrics, and geriatric evaluation and management) and non-acute care (nursing home, convalescent and planned respite care) are not adequately described by existing casemix classifications. The predominant treatment goals in subacute care are enhancement of quality of life and/or improvement in functional status and, in non-acute care, maintenance of current health and functional status. A national classification system for this area has now been developed--the Australian National Sub-Acute and Non-Acute Patient Classification System (AN-SNAP). The AN-SNAP system, based on analysis of over 30,000 episodes of care, defines four case types of subacute care (palliative care, rehabilitation, psychogeriatric care, and geriatric evaluation and management and one case type of non-acute care (maintenance care), and classifies both overnight and ambulatory care. The AN-SNAP system reflects the goal of management--a change in functional status or improvement in quality of life--rather than the patient's diagnosis. It will complement the existing AN-DRG classification.

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…

Sur la classification des adverbes en -ment (On the Classification of -ment Adverbs)

ERIC Educational Resources Information Center

Mordrup, Ole

1976-01-01

Presents a classification of French "-ment" adverbs based on syntactical criteria. The major divisions, consisting of "sentence adverbs" and "adverbs of manner," are further sub-divided into functional sub-groups. (Text is in French.) Available from: Akademisk Forlag, St. Kannikestraede 6-8, DK-1169 Copenhague K Danemark. (AM)

SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

Monitoring of peri-distal gastrectomy carbohydrate antigen 19-9 level in gastric juice and its significance

PubMed Central

Xu, A-Man; Huang, Lei; Han, Wen-Xiu; Wei, Zhi-Jian

2014-01-01

Gastric carcinoma is one of the most common and deadly malignancies nowadays, and carbohydrate antigen 19-9 (CA 19-9) in gastric juice has been rarely studied. To compare peri-distal gastrectomy (DG) gastric juice and serum CA 19-9 and reveal its significance, we selected 67 patients diagnosed with gastric carcinoma who underwent DG, and collected their perioperative gastric juice whose CA 19-9 was detected, with serum CA 19-9 monitored as a comparison. We found that: gastric juice CA 19-9 pre-gastrectomy was significantly correlated with tumor TNM classification, regarding tumor size, level of gastric wall invaded, differentiated grade and number of metastatic lymph nodes as influencing factors, while serum CA 19-9 revealed little information; gastric juice CA 19-9 was significantly correlated with radical degree, and regarded number of resected lymph nodes and classification of cutting edge as impact factors; thirteen patients whose gastric juice CA 19-9 rose post-DG showed features indicating poor prognosis; the difference of gastric juice CA 19-9 between pre- and post-gastrectomy was correlated with tumor TNM classification and radical degree, and regarded tumor size, number of resected metastatic and normal lymph nodes, sum of distances from tumor to cutting edges and classification of cutting edge as influential factors. We conclude that peri-DG gastric juice CA 19-9 reveals much information about tumor and radical gastrectomy, and may indicate prognosis; while serum CA 19-9 has limited significance. PMID:24482710

Transporter taxonomy - a comparison of different transport protein classification schemes.

PubMed

Viereck, Michael; Gaulton, Anna; Digles, Daniela; Ecker, Gerhard F

2014-06-01

Currently, there are more than 800 well characterized human membrane transport proteins (including channels and transporters) and there are estimates that about 10% (approx. 2000) of all human genes are related to transport. Membrane transport proteins are of interest as potential drug targets, for drug delivery, and as a cause of side effects and drug–drug interactions. In light of the development of Open PHACTS, which provides an open pharmacological space, we analyzed selected membrane transport protein classification schemes (Transporter Classification Database, ChEMBL, IUPHAR/BPS Guide to Pharmacology, and Gene Ontology) for their ability to serve as a basis for pharmacology driven protein classification. A comparison of these membrane transport protein classification schemes by using a set of clinically relevant transporters as use-case reveals the strengths and weaknesses of the different taxonomy approaches.

Representing and organizing information to describe the lived experience of health from a personal factors perspective in the light of the International Classification of Functioning, Disability and Health (ICF): a discussion paper.

PubMed

Geyh, Szilvia; Schwegler, Urban; Peter, Claudio; Müller, Rachel

2018-03-06

To discuss the representation and organization of information describing persons' lived experience of health from a personal factors perspective in the light of the International Classification of Functioning, Disability and Health, using spinal cord injury as a case in point for disability. The scientific literature was reviewed, discussion rounds conducted, and qualitative secondary analyses of data carried out using an iterative inductive-deductive approach. Conceptual considerations are explicated that distinguish the personal factors perspective from other components of the International Classification of Functioning, Disability and Health. A representation structure is developed that organizes health-related concepts describing the internal context of functioning. Concepts are organized as individual facts, subjective experiences, and recurrent patterns of experience and behavior specifying 7 areas and 211 concept groups. The article calls for further scientific debate on the perspective of personal factors in the light of the International Classification of Functioning, Disability and Health. A structure that organizes concepts in relation to a personal factors perspective can enhance the comprehensiveness, transparency and standardization of health information, and contribute to the empowerment of persons with disabilities. Implications for rehabilitation The present study collected data from scientific literature reviews, discussion rounds and qualitative secondary analyses in order to develop a representation and organization of information describing persons' lived experience of health from a personal factors perspective in the light of the International Classification of Functioning, Disability and Health. The following representation structure for health-related information from a personal factors perspective was developed: (i) Individuals facts (i.e., socio-demographical factors, position in the immediate social and physical context, personal history and biography), (ii) subjective experience (i.e., feelings, thoughts and beliefs, motives), and (iii) recurrent patterns of experience (i.e., feelings, thoughts and beliefs) and behavior. With this study, we aim to stimulate further scientific discussion about the personal factors component in the International Classification of Functioning, Disability and Health, including its application and subsequent validation for potential implementation into clinical practice.

Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new method for spectral-spatial classification of hyperspectral images is proposed. The method is based on the integration of probabilistic classification within the hierarchical best merge region growing algorithm. For this purpose, preliminary probabilistic support vector machines classification is performed. Then, hierarchical step-wise optimization algorithm is applied, by iteratively merging regions with the smallest Dissimilarity Criterion (DC). The main novelty of this method consists in defining a DC between regions as a function of region statistical and geometrical features along with classification probabilities. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana s vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.

A Functional-Phylogenetic Classification System for Transmembrane Solute Transporters

PubMed Central

Saier, Milton H.

2000-01-01

A comprehensive classification system for transmembrane molecular transporters has been developed and recently approved by the transport panel of the nomenclature committee of the International Union of Biochemistry and Molecular Biology. This system is based on (i) transporter class and subclass (mode of transport and energy coupling mechanism), (ii) protein phylogenetic family and subfamily, and (iii) substrate specificity. Almost all of the more than 250 identified families of transporters include members that function exclusively in transport. Channels (115 families), secondary active transporters (uniporters, symporters, and antiporters) (78 families), primary active transporters (23 families), group translocators (6 families), and transport proteins of ill-defined function or of unknown mechanism (51 families) constitute distinct categories. Transport mode and energy coupling prove to be relatively immutable characteristics and therefore provide primary bases for classification. Phylogenetic grouping reflects structure, function, mechanism, and often substrate specificity and therefore provides a reliable secondary basis for classification. Substrate specificity and polarity of transport prove to be more readily altered during evolutionary history and therefore provide a tertiary basis for classification. With very few exceptions, a phylogenetic family of transporters includes members that function by a single transport mode and energy coupling mechanism, although a variety of substrates may be transported, sometimes with either inwardly or outwardly directed polarity. In this review, I provide cross-referencing of well-characterized constituent transporters according to (i) transport mode, (ii) energy coupling mechanism, (iii) phylogenetic grouping, and (iv) substrates transported. The structural features and distribution of recognized family members throughout the living world are also evaluated. The tabulations should facilitate familial and functional assignments of newly sequenced transport proteins that will result from future genome sequencing projects. PMID:10839820

Portugal's special education law: implementing the International Classification of Functioning, Disability and Health in policy and practice.

PubMed

Sanches-Ferreira, Manuela; Simeonsson, Rune J; Silveira-Maia, Mónica; Alves, Sílvia; Tavares, Ana; Pinheiro, Sara

2013-05-01

The International Classification of Functioning, Disability and Health (ICF) was introduced in Portuguese education law as the compulsory system to guide eligibility policy and practice in special education. This paper describes the implementation of the ICF and its utility in the assessment process and eligibility determination of students for special education. A study to evaluate the utility of the ICF was commissioned by the Portuguese Ministry of Education and carried out by an external evaluation team. A document analysis was made of the assessment and eligibility processes of 237 students, selected from a nationally representative sample. The results provided support for the use of the ICF in student assessment and in the multidimensional approach of generating student functioning profiles as the basis for determining eligibility. The use of the ICF contributed to the differentiation of eligible and non eligible students based on their functioning profiles. The findings demonstrate the applicability of the ICF framework and classification system for determining eligibility for special education services on the basis of student functioning rather than medical or psychological diagnose. The use of the International Classification of Functioning, Disability and Health (ICF) framework in special education policy is as follows: • The functional perspective of the ICF offers a more comprehensive, holistic assessment of student needs than medical diagnoses. • ICF-based assessment of the nature and severity of functioning can serve as the basis for determining eligibility for special education and habilitation. • Profiles of functioning can support decision making in designing appropriate educational interventions for students.

Neural Network Classification of Receiver Functions as a Step Towards Automatic Crustal Parameter Determination

NASA Astrophysics Data System (ADS)

Jemberie, A.; Dugda, M. T.; Reusch, D.; Nyblade, A.

2006-12-01

Neural networks are decision making mathematical/engineering tools, which if trained properly, can do jobs automatically (and objectively) that normally require particular expertise and/or tedious repetition. Here we explore two techniques from the field of artificial neural networks (ANNs) that seek to reduce the time requirements and increase the objectivity of quality control (QC) and Event Identification (EI) on seismic datasets. We explore to apply the multiplayer Feed Forward (FF) Artificial Neural Networks (ANN) and Self- Organizing Maps (SOM) in combination with Hk stacking of receiver functions in an attempt to test the extent of the usefulness of automatic classification of receiver functions for crustal parameter determination. Feed- forward ANNs (FFNNs) are a supervised classification tool while self-organizing maps (SOMs) are able to provide unsupervised classification of large, complex geophysical data sets into a fixed number of distinct generalized patterns or modes. Hk stacking is a methodology that is used to stack receiver functions based on the relative arrival times of P-to-S converted phase and next two reverberations to determine crustal thickness H and Vp-to-Vs ratio (k). We use receiver functions from teleseismic events recorded by the 2000- 2002 Ethiopia Broadband Seismic Experiment. Preliminary results of applying FFNN neural network and Hk stacking of receiver functions for automatic receiver functions classification as a step towards an effort of automatic crustal parameter determination look encouraging. After training a FFNN neural network, the network could classify the best receiver functions from bad ones with a success rate of about 75 to 95%. Applying H? stacking on the receiver functions classified by this FFNN as the best receiver functions, we could obtain crustal thickness and Vp/Vs ratio of 31±4 km and 1.75±0.05, respectively, for the crust beneath station ARBA in the Main Ethiopian Rift. To make comparison, we applied Hk stacking on the receiver functions which we ourselves classified as the best set and found that the crustal thickness and Vp/Vs ratio are 31±2 km and 1.75±0.02, respectively.

Influence of Structure and Composition on Dynamic Viscoelastic Property of Cartilaginous Tissue: Criteria for Classification between Hyaline Cartilage and Fibrocartilage Based on Mechanical Function

NASA Astrophysics Data System (ADS)

Miyata, Shogo; Tateishi, Tetsuya; Furukawa, Katsuko; Ushida, Takashi

Recently, many types of methodologies have been developed to regenerate articular cartilage. It is important to assess whether the reconstructed cartilaginous tissue has the appropriate mechanical functions to qualify as hyaline (articular) cartilage. In some cases, the reconstructed tissue may become fibrocartilage and not hyaline cartilage. In this study, we determined the dynamic viscoelastic properties of these two types of cartilage by using compression and shear tests, respectively. Hyaline cartilage specimens were harvested from the articular surface of bovine knee joints and fibrocartilage specimens were harvested from the meniscus tissue of the same. The results of this study revealed that the compressive energy dissipation of hyaline cartilage showed a strong dependence on testing frequency at low frequencies, while that of fibrocartilage did not. Therefore, the compressive energy dissipation that is indicated by the loss tangent could become the criterion for the in vitro assessment of the mechanical function of regenerated cartilage.

The multifaceted role of the renal mononuclear phagocyte system.

PubMed

Viehmann, Susanne F; Böhner, Alexander M C; Kurts, Christian; Brähler, Sebastian

2018-04-22

The kidney contains a large and complex network of mononuclear phagocytes, which includes dendritic cells (DCs) and macrophages (MØs). The distinction between these cell types is traditionally based on the expression of molecular markers and morphology. However, several classification systems are used in parallel to identify DCs and MØs, leading to considerable uncertainty about their identity and functional roles. The discovery that a substantial proportion of macrophages in tissues like the kidney are embryonically derived further complicates the situation. Recent studies have used newly identified transcription factors such as ZBTB46 and lineage tracing techniques for classifying mononuclear phagocytes. These approaches have shed new light on the functional specialization of these cells in health and disease, uncovered an influence of the renal microenvironment and revealed considerable cellular plasticity, especially in inflammatory situations. In this review, the current knowledge about the developmental origins and versatile functional roles of DCs and MØs in kidney homeostasis and disease is discussed. Copyright © 2018 Elsevier Inc. All rights reserved.

Hierarchically nested river landform sequences

NASA Astrophysics Data System (ADS)

Pasternack, G. B.; Weber, M. D.; Brown, R. A.; Baig, D.

2017-12-01

River corridors exhibit landforms nested within landforms repeatedly down spatial scales. In this study we developed, tested, and implemented a new way to create river classifications by mapping domains of fluvial processes with respect to the hierarchical organization of topographic complexity that drives fluvial dynamism. We tested this approach on flow convergence routing, a morphodynamic mechanism with different states depending on the structure of nondimensional topographic variability. Five nondimensional landform types with unique functionality (nozzle, wide bar, normal channel, constricted pool, and oversized) represent this process at any flow. When this typology is nested at base flow, bankfull, and floodprone scales it creates a system with up to 125 functional types. This shows how a single mechanism produces complex dynamism via nesting. Given the classification, we answered nine specific scientific questions to investigate the abundance, sequencing, and hierarchical nesting of these new landform types using a 35-km gravel/cobble river segment of the Yuba River in California. The nested structure of flow convergence routing landforms found in this study revealed that bankfull landforms are nested within specific floodprone valley landform types, and these types control bankfull morphodynamics during moderate to large floods. As a result, this study calls into question the prevailing theory that the bankfull channel of a gravel/cobble river is controlled by in-channel, bankfull, and/or small flood flows. Such flows are too small to initiate widespread sediment transport in a gravel/cobble river with topographic complexity.

Development and reliability of the Functional Communication Classification System for children with cerebral palsy.

PubMed

Barty, Elizabeth; Caynes, Katy; Johnston, Leanne M

2016-10-01

This paper describes the development, validation, and reliability of the Functional Communication Classification System (FCCS), designed to classify expressive communication skills of children with cerebral palsy (CP) aged 4 years and 5 years (between their fourth and sixth birthdays). The Functional Communication Classification System (FCCS) was developed in 2006 using a literature review, client file audit, and expert consultative committee process in order to devise scale content, structure, and check clinical validity and utility. Interrater reliability was examined between speech-language pathologists (SLPs), other allied health professionals (AHPs), and parents of 48 children with CP. The scale was revised and a clinical reasoning prompt sheet added, then trialled again for 42 children. The result was a five-level system with descriptors and decision-making guides for classification of functional expressive communication for children with CP. Overall interrater reliability was excellent for the final FCCS, intraclass correlation coefficient=0.97 (95% confidence interval 0.95 to 0.98). Kappa values were 0.94 between SLPs and AHPs, 0.59 between SLPs and parents, and 0.60 between AHPs and parents. The FCCS is a reliable tool for describing functional communication in young children with CP, appropriate for use by SLPs, other AHPs, and parents of children with CP. © 2016 Mac Keith Press.

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

PubMed Central

Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang

2017-01-01

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926

Impact of FAB classification on predicting outcome in acute myeloid leukemia, not otherwise specified, patients undergoing allogeneic stem cell transplantation in CR1: An analysis of 1690 patients from the acute leukemia working party of EBMT.

PubMed

Canaani, Jonathan; Beohou, Eric; Labopin, Myriam; Socié, Gerard; Huynh, Anne; Volin, Liisa; Cornelissen, Jan; Milpied, Noel; Gedde-Dahl, Tobias; Deconinck, Eric; Fegueux, Nathalie; Blaise, Didier; Mohty, Mohamad; Nagler, Arnon

2017-04-01

The French, American, and British (FAB) classification system for acute myeloid leukemia (AML) is extensively used and is incorporated into the AML, not otherwise specified (NOS) category in the 2016 WHO edition of myeloid neoplasm classification. While recent data proposes that FAB classification does not provide additional prognostic information for patients for whom NPM1 status is available, it is unknown whether FAB still retains a current prognostic role in predicting outcome of AML patients undergoing allogeneic stem cell transplantation. Using the European Society of Blood and Bone Marrow Transplantation registry we analyzed outcome of 1690 patients transplanted in CR1 to determine if FAB classification provides additional prognostic value. Multivariate analysis revealed that M6/M7 patients had decreased leukemia free survival (hazard ratio (HR) of 1.41, 95% confidence interval (CI), 1.01-1.99; P = .046) in addition to increased nonrelapse mortality (NRM) rates (HR, 1.79; 95% CI, 1.06-3.01; P = .028) compared with other FAB types. In the NPM1 wt AML, NOS cohort, FAB M6/M7 was also associated with increased NRM (HR, 2.17; 95% CI, 1.14-4.16; P = .019). Finally, in FLT3-ITD + patients, multivariate analyses revealed that specific FAB types were tightly associated with adverse outcome. In conclusion, FAB classification may predict outcome following transplantation in AML, NOS patients. © 2017 Wiley Periodicals, Inc.

Automatic classification of protein structures using physicochemical parameters.

PubMed

Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

2014-09-01

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

Integrative analysis of environmental sequences using MEGAN4.

PubMed

Huson, Daniel H; Mitra, Suparna; Ruscheweyh, Hans-Joachim; Weber, Nico; Schuster, Stephan C

2011-09-01

A major challenge in the analysis of environmental sequences is data integration. The question is how to analyze different types of data in a unified approach, addressing both the taxonomic and functional aspects. To facilitate such analyses, we have substantially extended MEGAN, a widely used taxonomic analysis program. The new program, MEGAN4, provides an integrated approach to the taxonomic and functional analysis of metagenomic, metatranscriptomic, metaproteomic, and rRNA data. While taxonomic analysis is performed based on the NCBI taxonomy, functional analysis is performed using the SEED classification of subsystems and functional roles or the KEGG classification of pathways and enzymes. A number of examples illustrate how such analyses can be performed, and show that one can also import and compare classification results obtained using others' tools. MEGAN4 is freely available for academic purposes, and installers for all three major operating systems can be downloaded from www-ab.informatik.uni-tuebingen.de/software/megan.

Interactomic approach for evaluating nucleophosmin-binding proteins as biomarkers for Ewing's sarcoma.

PubMed

Haga, Ayako; Ogawara, Yoko; Kubota, Daisuke; Kitabayashi, Issay; Murakami, Yasufumi; Kondo, Tadashi

2013-06-01

Nucleophosmin (NPM) is a novel prognostic biomarker for Ewing's sarcoma. To evaluate the prognostic utility of NPM, we conducted an interactomic approach to characterize the NPM protein complex in Ewing's sarcoma cells. A gene suppression assay revealed that NPM promoted cell proliferation and the invasive properties of Ewing's sarcoma cells. FLAG-tag-based affinity purification coupled with liquid chromatography-tandem mass spectrometry identified 106 proteins in the NPM protein complex. The functional classification suggested that the NPM complex participates in critical biological events, including ribosome biogenesis, regulation of transcription and translation, and protein folding, that are mediated by these proteins. In addition to JAK1, a candidate prognostic biomarker for Ewing's sarcoma, the NPM complex, includes 11 proteins known as prognostic biomarkers for other malignancies. Meta-analysis of gene expression profiles of 32 patients with Ewing's sarcoma revealed that 6 of 106 were significantly and independently associated with survival period. These observations suggest a functional role as well as prognostic value of these NPM complex proteins in Ewing's sarcoma. Further, our study suggests the potential applications of interactomics in conjunction with meta-analysis for biomarker discovery. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nonadditive protein accumulation patterns in Maize (Zea mays L.) hybrids during embryo development.

PubMed

Marcon, Caroline; Schützenmeister, André; Schütz, Wolfgang; Madlung, Johannes; Piepho, Hans-Peter; Hochholdinger, Frank

2010-12-03

Heterosis describes the superior performance of heterozygous F(1)-hybrid plants compared to their homozygous parental inbred lines. In the present study, heterosis was detected for length, weight, and the time point of seminal root primordia initiation in maize (Zea mays L.) embryos of the reciprocal F(1)-hybrids UH005xUH250 and UH250xUH005. A two-dimensional gel electrophoresis (2-DE) proteome survey of the most abundant proteins of the reciprocal hybrids and their parental inbred lines 25 and 35 days after pollination revealed that 141 of 597 detected proteins (24%) exhibited nonadditive accumulation in at least one hybrid. Approximately 44% of all nonadditively accumulated proteins displayed an expression pattern that was not distinguishable from the low parent value. Electrospray ionization-tandem mass spectrometry (ESI-MS/MS) analyses and subsequent functional classification of the 141 proteins revealed that development, protein metabolism, redox-regulation, glycolysis, and amino acid metabolism were the most prominent functional classes among nonadditively accumulated proteins. In 35-day-old embryos of the hybrid UH250xUH005, a significant up-regulation of enzymes related to glucose metabolism which often exceeded the best parent values was observed. A comparison of nonadditive protein accumulation between rice and maize embryo data sets revealed a significant overlap of nonadditively accumulated proteins suggesting conserved organ- or tissue-specific regulatory mechanisms in monocots related to heterosis.

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

PubMed Central

Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance. PMID:29209156

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

PubMed Central

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C. M.

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases. PMID:25692885

Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.

PubMed

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C M

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.

Contemporary survival of patients with pulmonary arterial hypertension and congenital systemic to pulmonary shunts

PubMed Central

Chungsomprasong, Paweena; Bositthipichet, Densiri; Ketsara, Salisa; Titaram, Yuttapon; Chanthong, Prakul; Kanjanauthai, Supaluck

2018-01-01

Objective To compare survival of patients with newly diagnosed pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) according to various clinical classifications with classifications of anatomical-pathophysiological systemic to pulmonary shunts in a single-center cohort. Methods All prevalent cases of PAH-CHD with hemodynamic confirmation by cardiac catheterization in 1995–2015 were retrospectively reviewed. Patients who were younger than three months of age, or with single ventricle following surgery were excluded. Baseline characteristics and clinical outcomes were retrieved from the database. The survival analysis was performed at the end of 2016. Prognostic factors were identified using multivariate analysis. Results A total of 366 consecutive patients (24.5 ± 17.6 years of age, 40% male) with PAH-CHD were analyzed. Most had simple shunts (85 pre-tricuspid, 105 post-tricuspid, 102 combined shunts). Patients with pre-tricuspid shunts were significantly older at diagnosis in comparison to post-tricuspid, combined, and complex shunts. Clinical classifications identified patients as having Eisenmenger syndrome (ES, 26.8%), prevalent left to right shunt (66.7%), PAH with small defect (3%), or PAH following defect correction (3.5%). At follow-up (median = 5.9 years; 0.1–20.7 years), no statistically significant differences in survival rate were seen among the anatomical-pathophysiological shunts (p = 0.1). Conversely, the clinical classifications revealed that patients with PAH-small defect had inferior survival compared to patients with ES, PAH post-corrective surgery, or PAH with prevalent left to right shunt (p = 0.01). Significant mortality risks were functional class III, age < 10 years, PAH-small defect, elevated right atrial pressure > 15 mmHg, and baseline PVR > 8 WU•m.2 Conclusion Patients with PAH-CHD had a modest long-term survival. Different anatomical-pathophysiological shunts affect the natural presentation, while clinical classifications indicate treatment strategies and survival. Contemporary therapy improves survival in deliberately selected patients. PMID:29664959

Using the International Classification of Functioning, Disability and Health in Assessment and Intervention of School-Aged Children with Language Impairments

ERIC Educational Resources Information Center

Westby, Carol; Washington, Karla N.

2017-01-01

Purpose: The aim of this tutorial is to support speech-language pathologists' (SLPs') application of the International Classification of Functioning, Disability and Health (ICF) in assessment and treatment practices with children with language impairment. Method: This tutorial reviews the framework of the ICF, describes the implications of the ICF…

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.…

Manage Work Better to Better Manage Human Resources: A Comparative Study of Two Approaches to Job Analysis.

ERIC Educational Resources Information Center

Clifford, James P.

1996-01-01

The Position Classification Questionnaire (PCQ) and task inventory method (TI) were used to study the same work functions. PCQ divided functions into 16 classes for wage/salary determination. TI divided them into 28 classifications for training purposes. The two approaches were considerably different in their approach to how work should be…

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…

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.

An extension of the receiver operating characteristic curve and AUC-optimal classification.

PubMed

Takenouchi, Takashi; Komori, Osamu; Eguchi, Shinto

2012-10-01

While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimum function are discussed. A procedure derived by maximizing a specific optimum function has high robustness, based on gross error sensitivity. Additionally, we focus on the partial AUC, which is the partial area under the ROC curve. For example, in medical screening, a high true-positive rate to the fixed lower false-positive rate is preferable and thus the partial AUC corresponding to lower false-positive rates is much more important than the remaining AUC. We extend the class of concave optimum functions for partial AUC optimality with the boosting algorithm. We investigated the validity of the proposed method through several experiments with data sets in the UCI repository.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Classification in childhood disability: focusing on function in the 21st century.

PubMed

Rosenbaum, Peter; Eliasson, Ann-Christin; Hidecker, Mary Jo Cooley; Palisano, Robert J

2014-08-01

Classification systems in health care are usually based on current understanding of the condition. They are often derived empirically and adopted applying sound principles of measurement science to assess whether they are reliable (consistent) and valid (true) for the purposes to which they are applied. In the past 15 years, the authors have developed and validated classification systems for specific aspects of everyday function in people with cerebral palsy--gross motor function, manual abilities, and communicative function. This article describes the approaches used to conceptualize each aspect of function, develop the tools, and assess their reliability and validity. We report on the utility of each system with respect to clinical applicability, use of these tools for research, and the uptake and impact that they have had around the world. We hope that readers will find these accounts interesting, relevant, and applicable to their daily work with children and youth with disabilities. © The Author(s) 2014.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis.

PubMed

Fernández-Arjona, María Del Mar; Grondona, Jesús M; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis

PubMed Central

Fernández-Arjona, María del Mar; Grondona, Jesús M.; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D.

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor. PMID:28848398

Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

ERIC Educational Resources Information Center

Fan, Xitao; Wang, Lin

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…

[The informational support of statistical observation related to children disability].

PubMed

Son, I M; Polikarpov, A V; Ogrizko, E V; Golubeva, T Yu

2016-01-01

Within the framework of the Convention on rights of the disabled the revision is specified concerning criteria of identification of disability of children and reformation of system of medical social expertise according international standards of indices of health and indices related to health. In connection with it, it is important to consider the relationship between alterations in forms of the Federal statistical monitoring in the part of registration of disabled children in the Russian Federation and classification of health indices and indices related to health applied at identification of disability. The article presents analysis of relationship between alterations in forms of the Federal statistical monitoring in the part of registration of disabled children in the Russian Federation and applied classifications used at identification of disability (International classification of impairments, disabilities and handicap (ICDH), international classification of functioning, disability and health (ICF), international classification of functioning, disability and health, version for children and youth (ICF-CY). The intersectorial interaction is considered within the framework of statistics of children disability.

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

International Classification of Functioning, Disability and Health Core Sets for cerebral palsy, autism spectrum disorder, and attention-deficit-hyperactivity disorder.

PubMed

Schiariti, Verónica; Mahdi, Soheil; Bölte, Sven

2018-05-30

Capturing functional information is crucial in childhood disability. The International Classification of Functioning, Disability and Health (ICF) Core Sets promote assessments of functional abilities and disabilities in clinical practice regarding circumscribed diagnoses. However, the specificity of ICF Core Sets for childhood-onset disabilities has been doubted. This study aimed to identify content commonalities and differences among the ICF Core Sets for cerebral palsy (CP), and the newly developed Core Sets for autism spectrum disorder (ASD) and attention-deficit-hyperactivity disorder (ADHD). The categories within each Core Set were aggregated at the ICF component and chapter levels. Content comparison was conducted using descriptive analyses. The activities and participation component of the ICF was the most covered across all Core Sets. Main differences included representation of ICF components and coverage of ICF chapters within each component. CP included all ICF components, while ADHD and ASD predominantly focused on activities and participation. Environmental factors were highly represented in the ADHD Core Sets (40.5%) compared to the ASD (28%) and CP (27%) Core Sets. International Classification of Functioning, Disability and Health Core Sets for CP, ASD, and ADHD capture both common but also unique functional information, showing the importance of creating condition-specific, ICF-based tools to build functional profiles of individuals with childhood-onset disabilities. The International Classification of Functioning, Disability and Health (ICF) Core Sets for cerebral palsy (CP), autism spectrum disorder (ASD), and attention-deficit-hyperactivity disorder (ADHD) include unique functional information. The ICF-based tools for CP, ASD, and ADHD differ in terms of representation and coverage of ICF components and ICF chapters. Representation of environmental factors uniquely influences functioning and disability across ICF Core Sets for CP, ASD and ADHD. © 2018 Mac Keith Press.

Effective Feature Selection for Classification of Promoter Sequences.

PubMed

K, Kouser; P G, Lavanya; Rangarajan, Lalitha; K, Acharya Kshitish

2016-01-01

Exploring novel computational methods in making sense of biological data has not only been a necessity, but also productive. A part of this trend is the search for more efficient in silico methods/tools for analysis of promoters, which are parts of DNA sequences that are involved in regulation of expression of genes into other functional molecules. Promoter regions vary greatly in their function based on the sequence of nucleotides and the arrangement of protein-binding short-regions called motifs. In fact, the regulatory nature of the promoters seems to be largely driven by the selective presence and/or the arrangement of these motifs. Here, we explore computational classification of promoter sequences based on the pattern of motif distributions, as such classification can pave a new way of functional analysis of promoters and to discover the functionally crucial motifs. We make use of Position Specific Motif Matrix (PSMM) features for exploring the possibility of accurately classifying promoter sequences using some of the popular classification techniques. The classification results on the complete feature set are low, perhaps due to the huge number of features. We propose two ways of reducing features. Our test results show improvement in the classification output after the reduction of features. The results also show that decision trees outperform SVM (Support Vector Machine), KNN (K Nearest Neighbor) and ensemble classifier LibD3C, particularly with reduced features. The proposed feature selection methods outperform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method) but much faster than MRMR. Such methods could be useful to categorize new promoters and explore regulatory mechanisms of gene expressions in complex eukaryotic species.

Predicting heart failure mortality in frail seniors: comparing the NYHA functional classification with the Resident Assessment Instrument (RAI) 2.0.

PubMed

Tjam, Erin Y; Heckman, George A; Smith, Stuart; Arai, Bruce; Hirdes, John; Poss, Jeff; McKelvie, Robert S

2012-02-23

Though the NYHA functional classification is recommended in clinical settings, concerns have been raised about its reliability particularly among older patients. The RAI 2.0 is a comprehensive assessment system specifically developed for frail seniors. We hypothesized that a prognostic model for heart failure (HF) developed from the RAI 2.0 would be superior to the NYHA classification. The purpose of this study was to determine whether a HF-specific prognostic model based on the RAI 2.0 is superior to the NYHA functional classification in predicting mortality in frail older HF patients. Secondary analysis of data from a prospective cohort study of a HF education program for care providers in long-term care and retirement homes. Univariate analyses identified RAI 2.0 variables predicting death at 6 months. These and the NYHA classification were used to develop logistic models. Two RAI 2.0 models were derived. The first includes six items: "weight gain of 5% or more of total body weight over 30 days", "leaving 25% or more food uneaten", "unable to lie flat", "unstable cognitive, ADL, moods, or behavioural patterns", "change in cognitive function" and "needing help to walk in room"; the C statistic was 0.866. The second includes the CHESS health instability scale and the item "requiring help walking in room"; the C statistic was 0.838. The C statistic for the NYHA scale was 0.686. These results suggest that data from the RAI 2.0, an instrument for comprehensive assessment of frail seniors, can better predict mortality than the NYHA classification. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Improving Virus Taxonomy by Recontextualizing Sequence-Based Classification with Biologically Relevant Data: the Case of the Alphacoronavirus 1 Species

PubMed Central

André, Nicole M.

2018-01-01

ABSTRACT The difficulties related to virus taxonomy have been amplified by recent advances in next-generation sequencing and metagenomics, prompting the field to revisit the question of what constitutes a useful viral classification. Here, taking a challenging classification found in coronaviruses, we argue that consideration of biological properties in addition to sequence-based demarcations is critical for generating useful taxonomy that recapitulates complex evolutionary histories. Within the Alphacoronavirus genus, the Alphacoronavirus 1 species encompasses several biologically distinct viruses. We carried out functionally based phylogenetic analysis, centered on the spike gene, which encodes the main surface antigen and primary driver of tropism and pathogenesis. Within the Alphacoronavirus 1 species, we identify clade A (encompassing serotype I feline coronavirus [FCoV] and canine coronavirus [CCoV]) and clade B (grouping serotype II FCoV and CCoV and transmissible gastroenteritis virus [TGEV]-like viruses). We propose this clade designation, along with the newly proposed Alphacoronavirus 2 species, as an improved way to classify the Alphacoronavirus genus. IMPORTANCE Our work focuses on improving the classification of the Alphacoronavirus genus. The Alphacoronavirus 1 species groups viruses of veterinary importance that infect distinct mammalian hosts and includes canine and feline coronaviruses and transmissible gastroenteritis virus. It is the prototype species of the Alphacoronavirus genus; however, it encompasses biologically distinct viruses. To better characterize this prototypical species, we performed phylogenetic analyses based on the sequences of the spike protein, one of the main determinants of tropism and pathogenesis, and reveal the existence of two subgroups or clades that fit with previously established serotype demarcations. We propose a new clade designation to better classify Alphacoronavirus 1 members. PMID:29299531

Using FDA reports to inform a classification for health information technology safety problems

PubMed Central

Ong, Mei-Sing; Runciman, William; Coiera, Enrico

2011-01-01

Objective To expand an emerging classification for problems with health information technology (HIT) using reports submitted to the US Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) database. Design HIT events submitted to MAUDE were retrieved using a standardized search strategy. Using an emerging classification with 32 categories of HIT problems, a subset of relevant events were iteratively analyzed to identify new categories. Two coders then independently classified the remaining events into one or more categories. Free-text descriptions were analyzed to identify the consequences of events. Measurements Descriptive statistics by number of reported problems per category and by consequence; inter-rater reliability analysis using the κ statistic for the major categories and consequences. Results A search of 899 768 reports from January 2008 to July 2010 yielded 1100 reports about HIT. After removing duplicate and unrelated reports, 678 reports describing 436 events remained. The authors identified four new categories to describe problems with software functionality, system configuration, interface with devices, and network configuration; the authors' classification with 32 categories of HIT problems was expanded by the addition of these four categories. Examination of the 436 events revealed 712 problems, 96% were machine-related, and 4% were problems at the human–computer interface. Almost half (46%) of the events related to hazardous circumstances. Of the 46 events (11%) associated with patient harm, four deaths were linked to HIT problems (0.9% of 436 events). Conclusions Only 0.1% of the MAUDE reports searched were related to HIT. Nevertheless, Food and Drug Administration reports did prove to be a useful new source of information about the nature of software problems and their safety implications with potential to inform strategies for safe design and implementation. PMID:21903979

Improving Virus Taxonomy by Recontextualizing Sequence-Based Classification with Biologically Relevant Data: the Case of the Alphacoronavirus 1 Species.

PubMed

Whittaker, Gary R; André, Nicole M; Millet, Jean Kaoru

2018-01-01

The difficulties related to virus taxonomy have been amplified by recent advances in next-generation sequencing and metagenomics, prompting the field to revisit the question of what constitutes a useful viral classification. Here, taking a challenging classification found in coronaviruses, we argue that consideration of biological properties in addition to sequence-based demarcations is critical for generating useful taxonomy that recapitulates complex evolutionary histories. Within the Alphacoronavirus genus, the Alphacoronavirus 1 species encompasses several biologically distinct viruses. We carried out functionally based phylogenetic analysis, centered on the spike gene, which encodes the main surface antigen and primary driver of tropism and pathogenesis. Within the Alphacoronavirus 1 species, we identify clade A (encompassing serotype I feline coronavirus [FCoV] and canine coronavirus [CCoV]) and clade B (grouping serotype II FCoV and CCoV and transmissible gastroenteritis virus [TGEV]-like viruses). We propose this clade designation, along with the newly proposed Alphacoronavirus 2 species, as an improved way to classify the Alphacoronavirus genus. IMPORTANCE Our work focuses on improving the classification of the Alphacoronavirus genus. The Alphacoronavirus 1 species groups viruses of veterinary importance that infect distinct mammalian hosts and includes canine and feline coronaviruses and transmissible gastroenteritis virus. It is the prototype species of the Alphacoronavirus genus; however, it encompasses biologically distinct viruses. To better characterize this prototypical species, we performed phylogenetic analyses based on the sequences of the spike protein, one of the main determinants of tropism and pathogenesis, and reveal the existence of two subgroups or clades that fit with previously established serotype demarcations. We propose a new clade designation to better classify Alphacoronavirus 1 members.

Immunological discrimination of Atlantic striped bass stocks

USGS Publications Warehouse

Schill, W.B.; Dorazio, R.M.

1990-01-01

Stocks of Atlantic striped bass Morone saxatilis that were assumed to be geographically isolated during spawning showed strong antigenic differences in blood serum albumin. A discriminant function was estimated from the immunologic responses of northern (Canadian and Hudson River) and southern (Chesapeake Bay and Roanoke River) stocks to two reference antisera. The function correctly classified 92% of the northern and 95% of the southern fish in the training set. Cross-validation revealed similar percentages of correct classification for fish that were of known origin but not used to estimate the discriminant function. Monte Carlo experiments were used to evaluate the ability of the discriminant function to predict the relative contribution of northern fish in samples of various size and stock composition. Averages of predicted proportions of northern fish in the samples agreed well with actual proportions. Coefficients of variation (100 × SD/mean) in the predicted proportions ranged from 1.5 to 36% for samples of 50–400 fish that contained at least 10% northern stock. In samples that contained only 2% northern stock, however, at least 1,600 fish were required to achieve similar levels of precision.

AnnoTALE: bioinformatics tools for identification, annotation, and nomenclature of TALEs from Xanthomonas genomic sequences

PubMed Central

Grau, Jan; Reschke, Maik; Erkes, Annett; Streubel, Jana; Morgan, Richard D.; Wilson, Geoffrey G.; Koebnik, Ralf; Boch, Jens

2016-01-01

Transcription activator-like effectors (TALEs) are virulence factors, produced by the bacterial plant-pathogen Xanthomonas, that function as gene activators inside plant cells. Although the contribution of individual TALEs to infectivity has been shown, the specific roles of most TALEs, and the overall TALE diversity in Xanthomonas spp. is not known. TALEs possess a highly repetitive DNA-binding domain, which is notoriously difficult to sequence. Here, we describe an improved method for characterizing TALE genes by the use of PacBio sequencing. We present ‘AnnoTALE’, a suite of applications for the analysis and annotation of TALE genes from Xanthomonas genomes, and for grouping similar TALEs into classes. Based on these classes, we propose a unified nomenclature for Xanthomonas TALEs that reveals similarities pointing to related functionalities. This new classification enables us to compare related TALEs and to identify base substitutions responsible for the evolution of TALE specificities. PMID:26876161

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.

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.

Cognitive-motivational deficits in ADHD: development of a classification system.

PubMed

Gupta, Rashmi; Kar, Bhoomika R; Srinivasan, Narayanan

2011-01-01

The classification systems developed so far to detect attention deficit/hyperactivity disorder (ADHD) do not have high sensitivity and specificity. We have developed a classification system based on several neuropsychological tests that measure cognitive-motivational functions that are specifically impaired in ADHD children. A total of 240 (120 ADHD children and 120 healthy controls) children in the age range of 6-9 years and 32 Oppositional Defiant Disorder (ODD) children (aged 9 years) participated in the study. Stop-Signal, Task-Switching, Attentional Network, and Choice Delay tests were administered to all the participants. Receiver operating characteristic (ROC) analysis indicated that percentage choice of long-delay reward best classified the ADHD children from healthy controls. Single parameters were not helpful in making a differential classification of ADHD with ODD. Multinominal logistic regression (MLR) was performed with multiple parameters (data fusion) that produced improved overall classification accuracy. A combination of stop-signal reaction time, posterror-slowing, mean delay, switch cost, and percentage choice of long-delay reward produced an overall classification accuracy of 97.8%; with internal validation, the overall accuracy was 92.2%. Combining parameters from different tests of control functions not only enabled us to accurately classify ADHD children from healthy controls but also in making a differential classification with ODD. These results have implications for the theories of ADHD.

[New International Classification of Chronic Pancreatitis (M-ANNHEIM multifactor classification system, 2007): principles, merits, and demerits].

PubMed

Tsimmerman, Ia S

2008-01-01

The new International Classification of Chronic Pancreatitis (designated as M-ANNHEIM) proposed by a group of German specialists in late 2007 is reviewed. All its sections are subjected to analysis (risk group categories, clinical stages and phases, variants of clinical course, diagnostic criteria for "established" and "suspected" pancreatitis, instrumental methods and functional tests used in the diagnosis, evaluation of the severity of the disease using a scoring system, stages of elimination of pain syndrome). The new classification is compared with the earlier classification proposed by the author. Its merits and demerits are discussed.

Wildlife management by habitat units: A preliminary plan of action

NASA Technical Reports Server (NTRS)

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

1975-01-01

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

Single-accelerometer-based daily physical activity classification.

PubMed

Long, Xi; Yin, Bin; Aarts, Ronald M

2009-01-01

In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of approximately 80%, which was comparable with that obtained by a Decision Tree classifier.

Global Transcriptional Response of Human Liver Cells to Ethanol Stress of Different Strength Reveals Hormetic Behavior.

PubMed

Schmidt-Heck, Wolfgang; Wönne, Eva C; Hiller, Thomas; Menzel, Uwe; Koczan, Dirk; Damm, Georg; Seehofer, Daniel; Knöspel, Fanny; Freyer, Nora; Guthke, Reinhard; Dooley, Steven; Zeilinger, Katrin

2017-05-01

The liver is the major site for alcohol metabolism in the body and therefore the primary target organ for ethanol (EtOH)-induced toxicity. In this study, we investigated the in vitro response of human liver cells to different EtOH concentrations in a perfused bioartificial liver device that mimics the complex architecture of the natural organ. Primary human liver cells were cultured in the bioartificial liver device and treated for 24 hours with medium containing 150 mM (low), 300 mM (medium), or 600 mM (high) EtOH, while a control culture was kept untreated. Gene expression patterns for each EtOH concentration were monitored using Affymetrix Human Gene 1.0 ST Gene chips. Scaled expression profiles of differentially expressed genes (DEGs) were clustered using Fuzzy c-means algorithm. In addition, functional classification methods, KEGG pathway mapping and also a machine learning approach (Random Forest) were utilized. A number of 966 (150 mM EtOH), 1,334 (300 mM EtOH), or 4,132 (600 mM EtOH) genes were found to be differentially expressed. Dose-response relationships of the identified clusters of co-expressed genes showed a monotonic, threshold, or nonmonotonic (hormetic) behavior. Functional classification of DEGs revealed that low or medium EtOH concentrations operate adaptation processes, while alterations observed for the high EtOH concentration reflect the response to cellular damage. The genes displaying a hormetic response were functionally characterized by overrepresented "cellular ketone metabolism" and "carboxylic acid metabolism." Altered expression of the genes BAHD1 and H3F3B was identified as sufficient to classify the samples according to the applied EtOH doses. Different pathways of metabolic and epigenetic regulation are affected by EtOH exposition and partly undergo hormetic regulation in the bioartificial liver device. Gene expression changes observed at high EtOH concentrations reflect in some aspects the situation of alcoholic hepatitis in humans. Copyright © 2017 by the Research Society on Alcoholism.

Classification of TP53 mutations and HPV predict survival in advanced larynx cancer.

PubMed

Scheel, Adam; Bellile, Emily; McHugh, Jonathan B; Walline, Heather M; Prince, Mark E; Urba, Susan; Wolf, Gregory T; Eisbruch, Avraham; Worden, Francis; Carey, Thomas E; Bradford, Carol

2016-09-01

Assess tumor suppressor p53 (TP53) functional mutations in the context of other biomarkers in advanced larynx cancer. Prospective analysis of pretreatment tumor TP53, human papillomavirus (HPV), Bcl-xL, and cyclin D1 status in stage III and IV larynx cancer patients in a clinical trial. TP53 exons 4 through 9 from 58 tumors were sequenced. Mutations were grouped using three classifications based on their expected function. Each functional group was analyzed for response to induction chemotherapy, time to surgery, survival, HPV status, p16INK4a, Bcl-xl, and cyclin D1 expression. TP53 mutations were found in 22 of 58 (37.9%) patients with advanced larynx cancer, including missense mutations in 13 of 58 (22.4%) patients, nonsense mutations in four of 58 (6.9%), and deletions in five of 58 (8.6%). High-risk HPV was found in 20 of 52 (38.5%) tumors. A classification based on Evolutionary Action score of p53 (EAp53) distinguished missense mutations with high risk for decreased survival from low-risk mutations (P = 0.0315). A model including this TP53 classification, HPV status, cyclin D1, and Bcl-xL staining significantly predicts survival (P = 0.0017). EAp53 functional classification of TP53 mutants and biomarkers predict survival in advanced larynx cancer. NA. Laryngoscope, 126:E292-E299, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Combined application of the International Classification of Functioning, Disability and Health and the NANDA-International Taxonomy II.

PubMed

Boldt, Christine; Grill, Eva; Bartholomeyczik, Sabine; Brach, Mirjam; Rauch, Alexandra; Eriks-Hoogland, Inge; Stucki, Gerold

2010-08-01

This paper presents a discussion of the conceptual and practical relationships between the International Classification of Functioning, Disability and Health and the NANDA-International Taxonomy II for nursing diagnoses, and their use in nursing practice. The ICF provides a common classification framework for all healthcare professionals, including nurses. Nursing care plans can be broadly based on NANDA-I taxonomies. No published attempt has been made to systematically compare the NANDA-I Taxonomy II to the ICF. The most recently published descriptions of both classifications and a case example presenting the combined use of both classifications. The work was carried out in 2009. There are conceptual commonalities and differences between the ICF and the NANDA-I Taxonomy II. In the case example, the overlap between the ICF categories and NANDA-I nursing diagnoses reflects the fact that the ICF, focusing on functioning and disability, and the NANDA-I Taxonomy II, with its functioning health patterns, are similar in their approaches. The NANDA-I Taxonomy II permits the fulfilment of requirements that are exclusively nursing issues. The application of the ICF is useful for nurses to communicate nursing issues with other healthcare professionals in a common language. For nurses, knowledge shared with other healthcare professionals may contribute to broader understanding of a patient's situation. The ICF and the NANDA-I Taxonomy II should be used in concert by nurses and can complement each other to enhance the quality of clinical team work and nursing practice.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Diagnostic Classification of Schizophrenia Patients on the Basis of Regional Reward-Related fMRI Signal Patterns

PubMed Central

Koch, Stefan P.; Hägele, Claudia; Haynes, John-Dylan; Heinz, Andreas; Schlagenhauf, Florian; Sterzer, Philipp

2015-01-01

Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way. PMID:25799236

ESPEN endorsed recommendations. Definition and classification of intestinal failure in adults.

PubMed

Pironi, Loris; Arends, Jann; Baxter, Janet; Bozzetti, Federico; Peláez, Rosa Burgos; Cuerda, Cristina; Forbes, Alastair; Gabe, Simon; Gillanders, Lyn; Holst, Mette; Jeppesen, Palle Bekker; Joly, Francisca; Kelly, Darlene; Klek, Stanislaw; Irtun, Øivind; Olde Damink, S W; Panisic, Marina; Rasmussen, Henrik Højgaard; Staun, Michael; Szczepanek, Kinga; Van Gossum, André; Wanten, Geert; Schneider, Stéphane Michel; Shaffer, Jon

2015-04-01

Intestinal failure (IF) is not included in the list of PubMed Mesh terms, as failure is the term describing a state of non functioning of other organs, and as such is not well recognized. No scientific society has yet devised a formal definition and classification of IF. The European Society for Clinical Nutrition and Metabolism guideline committee endorsed its "home artificial nutrition and chronic IF" and "acute IF" special interest groups to write recommendations on these issues. After a Medline Search, in December 2013, for "intestinal failure" and "review"[Publication Type], the project was developed using the Delphi round methodology. The final consensus was reached on March 2014, after 5 Delphi rounds and two live meetings. The recommendations comprise the definition of IF, a functional and a pathophysiological classification for both acute and chronic IF and a clinical classification of chronic IF. IF was defined as "the reduction of gut function below the minimum necessary for the absorption of macronutrients and/or water and electrolytes, such that intravenous supplementation is required to maintain health and/or growth". This formal definition and classification of IF, will facilitate communication and cooperation among professionals in clinical practice, organization and management, and research. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Using the International Classification of Functioning, Disability and Health Children and Youth version in education systems: a new approach to eligibility.

PubMed

Hollenweger, Judith; Moretti, Marta

2012-02-01

In developed countries, establishing eligibility for persons with disabilities is a requirement for accessing specialized services or benefits. The underlying conceptualizations of disability are often problematic because they concentrate on deficits but try to promote social participation and focus on dependence while trying to strengthen independence. In addition, such conceptualizations are unable to respond to the rights-based approach of the UN Convention on the Rights of Persons with Disabilities. The International Classification of Functioning, Disability and Health Version for Children and Youth provides a model and classification that allows relating disease- or impairment-specific information to participation in the life domains relevant for a specific policy area. Establishing eligibility in education systems needs to be compatible with the principles of inclusive education, participation, and social justice. In addition, the overall goals of education and individualized goals for a specific child with disabilities need to be taken into account. Using the International Classification of Functioning, Disability and Health Version for Children and Youth as a model and classification, the different factors influencing eligibility-related decisions (impairments, activity/participation, environment, personal factors) can be made transparent to provide the basis for a decision-making process to which parents and the child actively contribute.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures

PubMed Central

Cetin, Mustafa S.; Houck, Jon M.; Rashid, Barnaly; Agacoglu, Oktay; Stephen, Julia M.; Sui, Jing; Canive, Jose; Mayer, Andy; Aine, Cheryl; Bustillo, Juan R.; Calhoun, Vince D.

2016-01-01

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of “synchronicity” of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone. PMID:27807403

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…

The Representation of Object-Directed Action and Function Knowledge in the Human Brain

PubMed Central

Chen, Quanjing; Garcea, Frank E.; Mahon, Bradford Z.

2016-01-01

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function. PMID:25595179

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

DOE PAGES

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra; ...

2017-05-23

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splittingmore » of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.« less

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

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

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splittingmore » of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.« less

Global Similarity Predicts Dissociation of Classification and Recognition: Evidence Questioning the Implicit-Explicit Learning Distinction in Amnesia

ERIC Educational Resources Information Center

Jamieson, Randall K.; Holmes, Signy; Mewhort, D. J. K.

2010-01-01

Dissociation of classification and recognition in amnesia is widely taken to imply 2 functional systems: an implicit procedural-learning system that is spared in amnesia and an explicit episodic-learning system that is compromised. We argue that both tasks reflect the global similarity of probes to memory. In classification, subjects sort…

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

NASA Astrophysics Data System (ADS)

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

2007-11-01

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

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

PubMed

Schwartzkopf, Wade C; Bovik, Alan C; Evans, Brian L

2005-12-01

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

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

PubMed

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

1999-06-01

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

Inter- and intra-rater reliability of nasal auscultation in daycare children.

PubMed

Santos, Rita; Silva Alexandrino, Ana; Tomé, David; Melo, Cristina; Mesquita Montes, António; Costa, Daniel; Pinto Ferreira, João

2018-02-01

The aim of this study was to assess nasal auscultation's intra- and inter-rater reliability and to analyze ear and respiratory clinical condition according to nasal auscultation. Cross-sectional study performed in 125 children aged up to 3 years old attending daycare centers. Nasal auscultation, tympanometry and Paediatric Respiratory Severity Score (PRSS) were applied to all children. Nasal sounds were classified by an expert panel in order to determine nasal auscultation's intra and inter- rater reliability. The classification of nasal sounds was assessed against tympanometric and PRSS values. Nasal auscultation revealed substantial inter-rater (K=0.75) and intra-rater (K=0.69; K=0.61 and K=0.72) reliability. Children with a "non-obstructed" classification revealed a lower peak pressure (t=-3.599, P<0.001 in left ear; t=-2.258, P=0.026 in right ear) and a higher compliance (t=-2,728, P=0.007 in left ear; t=-3.830. P<0.001 in right ear) in both ears. There was an association between the classification of sounds and tympanogram types in both ears (X=11.437, P=0.003 in left ear; X=13.535, P=0.001 in right ear). Children with a "non-obstructed" classification had a healthier respiratory condition. Nasal auscultation revealed substantial intra- and inter-rater reliability. Nasal auscultation exhibited important differences according to ear and respiratory clinical conditions. Nasal auscultation in pediatrics seems to be an original topic as well as a simple method that can be used to identify early signs of nasopharyngeal obstruction.

Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy.

PubMed

Li, Zhaohua; Wang, Yuduo; Quan, Wenxiang; Wu, Tongning; Lv, Bin

2015-02-15

Based on near-infrared spectroscopy (NIRS), recent converging evidence has been observed that patients with schizophrenia exhibit abnormal functional activities in the prefrontal cortex during a verbal fluency task (VFT). Therefore, some studies have attempted to employ NIRS measurements to differentiate schizophrenia patients from healthy controls with different classification methods. However, no systematic evaluation was conducted to compare their respective classification performances on the same study population. In this study, we evaluated the classification performance of four classification methods (including linear discriminant analysis, k-nearest neighbors, Gaussian process classifier, and support vector machines) on an NIRS-aided schizophrenia diagnosis. We recruited a large sample of 120 schizophrenia patients and 120 healthy controls and measured the hemoglobin response in the prefrontal cortex during the VFT using a multichannel NIRS system. Features for classification were extracted from three types of NIRS data in each channel. We subsequently performed a principal component analysis (PCA) for feature selection prior to comparison of the different classification methods. We achieved a maximum accuracy of 85.83% and an overall mean accuracy of 83.37% using a PCA-based feature selection on oxygenated hemoglobin signals and support vector machine classifier. This is the first comprehensive evaluation of different classification methods for the diagnosis of schizophrenia based on different types of NIRS signals. Our results suggested that, using the appropriate classification method, NIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Copyright © 2014 Elsevier B.V. All rights reserved.

Principal Component Analysis reveals correlation of cavities evolution and functional motions in proteins.

PubMed

Desdouits, Nathan; Nilges, Michael; Blondel, Arnaud

2015-02-01

Protein conformation has been recognized as the key feature determining biological function, as it determines the position of the essential groups specifically interacting with substrates. Hence, the shape of the cavities or grooves at the protein surface appears to drive those functions. However, only a few studies describe the geometrical evolution of protein cavities during molecular dynamics simulations (MD), usually with a crude representation. To unveil the dynamics of cavity geometry evolution, we developed an approach combining cavity detection and Principal Component Analysis (PCA). This approach was applied to four systems subjected to MD (lysozyme, sperm whale myoglobin, Dengue envelope protein and EF-CaM complex). PCA on cavities allows us to perform efficient analysis and classification of the geometry diversity explored by a cavity. Additionally, it reveals correlations between the evolutions of the cavities and structures, and can even suggest how to modify the protein conformation to induce a given cavity geometry. It also helps to perform fast and consensual clustering of conformations according to cavity geometry. Finally, using this approach, we show that both carbon monoxide (CO) location and transfer among the different xenon sites of myoglobin are correlated with few cavity evolution modes of high amplitude. This correlation illustrates the link between ligand diffusion and the dynamic network of internal cavities. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Feature-based classification of amino acid substitutions outside conserved functional protein domains.

PubMed

Gemovic, Branislava; Perovic, Vladimir; Glisic, Sanja; Veljkovic, Nevena

2013-01-01

There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.

Discriminant analysis for fast multiclass data classification through regularized kernel function approximation.

PubMed

Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K

2010-06-01

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.

A three-way approach for protein function classification

PubMed Central

2017-01-01

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy. PMID:28234929

A three-way approach for protein function classification.

PubMed

Ur Rehman, Hafeez; Azam, Nouman; Yao, JingTao; Benso, Alfredo

2017-01-01

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy.

Access management classification and spacing standards

DOT National Transportation Integrated Search

1996-08-23

The specific objectives of this background paper are:(1)Provide an overview of : the relationship between the functional integrity of the highway system and the : access management classification system, (2)Review and evaluate the current : Oregon De...

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Less-Complex Method of Classifying MPSK

NASA Technical Reports Server (NTRS)

Hamkins, Jon

2006-01-01

An alternative to an optimal method of automated classification of signals modulated with M-ary phase-shift-keying (M-ary PSK or MPSK) has been derived. The alternative method is approximate, but it offers nearly optimal performance and entails much less complexity, which translates to much less computation time. Modulation classification is becoming increasingly important in radio-communication systems that utilize multiple data modulation schemes and include software-defined or software-controlled receivers. Such a receiver may "know" little a priori about an incoming signal but may be required to correctly classify its data rate, modulation type, and forward error-correction code before properly configuring itself to acquire and track the symbol timing, carrier frequency, and phase, and ultimately produce decoded bits. Modulation classification has long been an important component of military interception of initially unknown radio signals transmitted by adversaries. Modulation classification may also be useful for enabling cellular telephones to automatically recognize different signal types and configure themselves accordingly. The concept of modulation classification as outlined in the preceding paragraph is quite general. However, at the present early stage of development, and for the purpose of describing the present alternative method, the term "modulation classification" or simply "classification" signifies, more specifically, a distinction between M-ary and M'-ary PSK, where M and M' represent two different integer multiples of 2. Both the prior optimal method and the present alternative method require the acquisition of magnitude and phase values of a number (N) of consecutive baseband samples of the incoming signal + noise. The prior optimal method is based on a maximum- likelihood (ML) classification rule that requires a calculation of likelihood functions for the M and M' hypotheses: Each likelihood function is an integral, over a full cycle of carrier phase, of a complicated sum of functions of the baseband sample values, the carrier phase, the carrier-signal and noise magnitudes, and M or M'. Then the likelihood ratio, defined as the ratio between the likelihood functions, is computed, leading to the choice of whichever hypothesis - M or M'- is more likely. In the alternative method, the integral in each likelihood function is approximated by a sum over values of the integrand sampled at a number, 1, of equally spaced values of carrier phase. Used in this way, 1 is a parameter that can be adjusted to trade computational complexity against the probability of misclassification. In the limit as 1 approaches infinity, one obtains the integral form of the likelihood function and thus recovers the ML classification. The present approximate method has been tested in comparison with the ML method by means of computational simulations. The results of the simulations have shown that the performance (as quantified by probability of misclassification) of the approximate method is nearly indistinguishable from that of the ML method (see figure).

Novel PAX3 mutations causing Waardenburg syndrome type 1 in Tunisian patients.

PubMed

Trabelsi, Mediha; Nouira, Malek; Maazoul, Faouzi; Kraoua, Lilia; Meddeb, Rim; Ouertani, Ines; Chelly, Imen; Benoit, Valérie; Besbes, Ghazi; Mrad, Ridha

2017-12-01

Waardenburg syndrome (WS) is an auditory-pigmentary disease characterized by a clinical and genetic variability. WS is classified into four types depending on the presence or absence of additional symptoms: WS1, WS2, WS3 and WS4. Type 1 and 3 are mostly caused by PAX3 mutations, while type 2 and type 4 are genetically heterogeneous. The aims of this study are to confirm the diagnostic of WS1 by the sequencing of PAX3 gene and to evaluate the genotype phenotype correlation. A clinical classification was established for 14 patients WS, as proposed by the Waardenburg Consortium, and noted a predominance of type 1 and type 2 with 6 patients WS1, 7 patients WS2 and 1 patient WS3. A significant inter and intra-familial clinical heterogeneity was also observed. A sequencing of PAX3 gene in the 6 patients WS1 confirmed the diagnosis in 4 of them by revealing three novel mutations that modify two functional domains of the protein: the c.942delC; the c.933_936dupTTAC and the c.164delTCCGCCACA. These three variations are most likely responsible for the phenotype, however their pathogenic effects need to be confirmed by functional studies. The MLPA analysis of the 2 patients who were sequence negative for PAX3 gene revealed, in one of them, a heterozygous deletion of exons 5 to 9 confirming the WS1 diagnosis. Both clinical and molecular approaches led to the conclusion that there is a lack of genotype-phenotype correlation in WS1, an element that must be taken into account in genetic counseling. The absence of PAX3 mutation in one patient WS1 highlights the fact that the clinical classification is sometimes insufficient to distinguish WS1 from other types WS hence the interest of sequencing the other WS genes in this patient. Copyright © 2017 Elsevier B.V. All rights reserved.

Complex Processes from Dynamical Architectures with Time-Scale Hierarchy

PubMed Central

Perdikis, Dionysios; Huys, Raoul; Jirsa, Viktor

2011-01-01

The idea that complex motor, perceptual, and cognitive behaviors are composed of smaller units, which are somehow brought into a meaningful relation, permeates the biological and life sciences. However, no principled framework defining the constituent elementary processes has been developed to this date. Consequently, functional configurations (or architectures) relating elementary processes and external influences are mostly piecemeal formulations suitable to particular instances only. Here, we develop a general dynamical framework for distinct functional architectures characterized by the time-scale separation of their constituents and evaluate their efficiency. Thereto, we build on the (phase) flow of a system, which prescribes the temporal evolution of its state variables. The phase flow topology allows for the unambiguous classification of qualitatively distinct processes, which we consider to represent the functional units or modes within the dynamical architecture. Using the example of a composite movement we illustrate how different architectures can be characterized by their degree of time scale separation between the internal elements of the architecture (i.e. the functional modes) and external interventions. We reveal a tradeoff of the interactions between internal and external influences, which offers a theoretical justification for the efficient composition of complex processes out of non-trivial elementary processes or functional modes. PMID:21347363

Functional Heterogeneity and Convergence in the Right Temporoparietal Junction

PubMed Central

Lee, Su Mei; McCarthy, Gregory

2016-01-01

The right temporoparietal junction (rTPJ) is engaged by tasks that manipulate biological motion processing, Theory of Mind attributions, and attention reorienting. The proximity of activations elicited by these tasks raises the question of whether these tasks share common cognitive component processes that are subserved by common neural substrates. Here, we used high-resolution whole-brain functional magnetic resonance imaging in a within-subjects design to determine whether these tasks activate common regions of the rTPJ. Each participant was presented with the 3 tasks in the same imaging session. In a whole-brain analysis, we found that only the right and left TPJs were activated by all 3 tasks. Multivoxel pattern analysis revealed that the regions of overlap could still discriminate the 3 tasks. Notably, we found significant cross-task classification in the right TPJ, which suggests a shared neural process between the 3 tasks. Taken together, these results support prior studies that have indicated functional heterogeneity within the rTPJ but also suggest a convergence of function within a region of overlap. These results also call for further investigation into the nature of the function subserved in this overlap region. PMID:25477367

Exploring time- and frequency- dependent functional connectivity and brain networks during deception with single-trial event-related potentials

NASA Astrophysics Data System (ADS)

Gao, Jun-Feng; Yang, Yong; Huang, Wen-Tao; Lin, Pan; Ge, Sheng; Zheng, Hong-Mei; Gu, Ling-Yun; Zhou, Hui; Li, Chen-Hong; Rao, Ni-Ni

2016-11-01

To better characterize the cognitive processes and mechanisms that are associated with deception, wavelet coherence was employed to evaluate functional connectivity between different brain regions. Two groups of subjects were evaluated for this purpose: 32 participants were required to either tell the truth or to lie when facing certain stimuli, and their electroencephalogram signals on 12 electrodes were recorded. The experimental results revealed that deceptive responses elicited greater connectivity strength than truthful responses, particularly in the θ band on specific electrode pairs primarily involving connections between the prefrontal/frontal and central regions and between the prefrontal/frontal and left parietal regions. These results indicate that these brain regions play an important role in executing lying responses. Additionally, three time- and frequency-dependent functional connectivity networks were proposed to thoroughly reflect the functional coupling of brain regions that occurs during lying. Furthermore, the wavelet coherence values for the connections shown in the networks were extracted as features for support vector machine training. High classification accuracy suggested that the proposed network effectively characterized differences in functional connectivity between the two groups of subjects over a specific time-frequency area and hence could be a sensitive measurement for identifying deception.

Lean waste classification model to support the sustainable operational practice

NASA Astrophysics Data System (ADS)

Sutrisno, A.; Vanany, I.; Gunawan, I.; Asjad, M.

2018-04-01

Driven by growing pressure for a more sustainable operational practice, improvement on the classification of non-value added (waste) is one of the prerequisites to realize sustainability of a firm. While the use of the 7 (seven) types of the Ohno model now becoming a versatile tool to reveal the lean waste occurrence. In many recent investigations, the use of the Seven Waste model of Ohno is insufficient to cope with the types of waste occurred in industrial practices at various application levels. Intended to a narrowing down this limitation, this paper presented an improved waste classification model based on survey to recent studies discussing on waste at various operational stages. Implications on the waste classification model to the body of knowledge and industrial practices are provided.

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

PubMed

Kragel, Philip A; Labar, Kevin S

2013-08-01

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Multivariate Pattern Classification Reveals Autonomic and Experiential Representations of Discrete Emotions

PubMed Central

Kragel, Philip A.; LaBar, Kevin S.

2013-01-01

Defining the structural organization of emotions is a central unresolved question in affective science. In particular, the extent to which autonomic nervous system activity signifies distinct affective states remains controversial. Most prior research on this topic has used univariate statistical approaches in attempts to classify emotions from psychophysiological data. In the present study, electrodermal, cardiac, respiratory, and gastric activity, as well as self-report measures were taken from healthy subjects during the experience of fear, anger, sadness, surprise, contentment, and amusement in response to film and music clips. Information pertaining to affective states present in these response patterns was analyzed using multivariate pattern classification techniques. Overall accuracy for classifying distinct affective states was 58.0% for autonomic measures and 88.2% for self-report measures, both of which were significantly above chance. Further, examining the error distribution of classifiers revealed that the dimensions of valence and arousal selectively contributed to decoding emotional states from self-report, whereas a categorical configuration of affective space was evident in both self-report and autonomic measures. Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression. PMID:23527508

Non-linear molecular pattern classification using molecular beacons with multiple targets.

PubMed

Lee, In-Hee; Lee, Seung Hwan; Park, Tai Hyun; Zhang, Byoung-Tak

2013-12-01

In vitro pattern classification has been highlighted as an important future application of DNA computing. Previous work has demonstrated the feasibility of linear classifiers using DNA-based molecular computing. However, complex tasks require non-linear classification capability. Here we design a molecular beacon that can interact with multiple targets and experimentally shows that its fluorescent signals form a complex radial-basis function, enabling it to be used as a building block for non-linear molecular classification in vitro. The proposed method was successfully applied to solving artificial and real-world classification problems: XOR and microRNA expression patterns. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Revealing how different spinors can be: The Lounesto spinor classification

NASA Astrophysics Data System (ADS)

Hoff da Silva, J. M.; Cavalcanti, R. T.

2017-11-01

This paper aims to give a coordinate-based introduction to the so-called Lounesto spinorial classification scheme. Among other results, it has evinced classes of spinors which fail to satisfy Dirac equation. The underlying idea and the central aspects of such spinorial categorization are introduced in an argumentative basis, after which we delve into a commented account on recent results obtained from (and within) this branch of research.

Laccase versus Laccase-Like Multi-Copper Oxidase: A Comparative Study of Similar Enzymes with Diverse Substrate Spectra

PubMed Central

Reiss, Renate; Ihssen, Julian; Richter, Michael; Eichhorn, Eric; Schilling, Boris; Thöny-Meyer, Linda

2013-01-01

Laccases (EC 1.10.3.2) are multi-copper oxidases that catalyse the one-electron oxidation of a broad range of compounds including substituted phenols, arylamines and aromatic thiols to the corresponding radicals. Owing to their broad substrate range, copper-containing laccases are versatile biocatalysts, capable of oxidizing numerous natural and non-natural industry-relevant compounds, with water as the sole by-product. In the present study, 10 of the 11 multi-copper oxidases, hitherto considered to be laccases, from fungi, plant and bacterial origin were compared. A substrate screen of 91 natural and non-natural compounds was recorded and revealed a fairly broad but distinctive substrate spectrum amongst the enzymes. Even though the enzymes share conserved active site residues we found that the substrate ranges of the individual enzymes varied considerably. The EC classification is based on the type of chemical reaction performed and the actual name of the enzyme often refers to the physiological substrate. However, for the enzymes studied in this work such classification is not feasible, even more so as their prime substrates or natural functions are mainly unknown. The classification of multi-copper oxidases assigned as laccases remains a challenge. For the sake of simplicity we propose to introduce the term “laccase-like multi-copper oxidase” (LMCO) in addition to the term laccase that we use exclusively for the enzyme originally identified from the sap of the lacquer tree Rhus vernicifera. PMID:23755261

The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients

NASA Astrophysics Data System (ADS)

Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Zev Rymer, William; Zhou, Ping

2013-08-01

Objective. This study investigates the effect of the involuntary motor activity of paretic-spastic muscles on the classification of surface electromyography (EMG) signals. Approach. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at relatively slow and fast speeds. For each stroke subject, the degree of involuntary motor activity present in the voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from the slow and fast sessions. Main results. Across all tested stroke subjects, our results revealed that when involuntary surface EMG is absent or present in both the training and testing datasets, high accuracies (>96%, >98%, respectively, averaged over all the subjects) can be achieved in the classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either the training or testing datasets, the classification accuracies were dramatically reduced (<89%, <85%, respectively). However, if both the training and testing datasets contained EMG signals with the presence and absence of involuntary EMG interference, high accuracies were still achieved (>97%). Significance. The findings of this study can be used to guide the appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation.

Core Self-Evaluations as Personal Factors in the World Health Organization's International Classification of Functioning, Disability and Health Model: An Application in Persons with Spinal Cord Injury

ERIC Educational Resources Information Center

Yaghmanian, Rana; Smedema, Susan Miller; Thompson, Kerry

2017-01-01

Purpose: To evaluate Chan, Gelman, Ditchman, Kim, and Chiu's (2009) revised World Health Organization's International Classification of Functioning, Disability and Health (ICF) model using core self-evaluations (CSE) to account for Personal Factors in persons with spinal cord injury (SCI). Method: One hundred eighty-seven adults with SCI were…

Interobserver Agreement of the Gross Motor Function Classification System in an Ambulant Population of Children with Cerebral Palsy

ERIC Educational Resources Information Center

McDowell, Brona C.; Kerr, Claire; Parkes, Jackie

2007-01-01

Gross Motor Function Classification System (GMFCS) level was reported by three independent assessors in a population of children with cerebral palsy (CP) aged between 4 and 18 years (n=184; 112 males, 72 females; mean age 10y 10mo [SD 3y 7mo]). A software algorithm also provided a computed GMFCS level from a regional CP registry. Participants had…

Spirituality and the International Classification of Functioning, Disability and Health: content comparison of questionnaires measuring mindfulness based on the International Classification of Functioning.

PubMed

Offenbächer, Martin; Sauer, Sebastian; Hieblinger, Robin; Hufford, David J; Walach, Harald; Kohls, Niko

2011-01-01

To identify and compare the concepts contained in questionnaires measuring mindfulness using the International Classification of Functioning (ICF) as external reference. Questionnaires which are published in peer-reviewed journals and listed in Pubmed or PsycInfo were included. The questionnaires were analysed and, using a content-analytical approach, the respective items were categorised and linked to the ICF. Ten questionnaires were included. Ninety-four per cent (N = 341) of the concepts could be linked to 37 different ICF categories. One hundred and seventy-one (50.1%) concepts were linked to ICF categories of the component Body Function, 74 (21.7%) to categories of the component Activity and Participation and none to categories of the component Environmental Factors. In total, 28.2% of the linked concepts belonged to Personal factors, which are not yet classified in the ICF. The questionnaires exhibited considerable differences regarding content density (i.e. the average number of concepts per item) and content diversity (i.e. the number of ICF categories per concept). The ICF provides an useful external reference to identify and compare the concepts contained in mindfulness questionnaires. Also, mindfulness questionnaire concepts suggest potentially useful factors for classification within the ICF.

Multiprofessional evaluation in clinical practice: establishing a core set of outcome measures for children with cerebral palsy.

PubMed

Mäenpää, Helena; Autti-Rämö, Ilona; Varho, Tarja; Forsten, Wivi; Haataja, Leena

2017-03-01

To develop a national consensus on outcome measures that define functional ability in children with cerebral palsy (CP) according to the International Classification of Functioning, Disability and Health (ICF) framework. The project started in 2008 in neuropaediatric units of two university hospitals and one outpatient clinic. Each professional group selected representatives to be knowledge brokers for their own specialty. Based on the evidence, expert opinion, and the ICF framework, multiprofessional teams selected the most valid measures used in clinical practice (2009-2010). Data from 269 children with CP were analysed, classified by the Gross Motor Function Classification System, Manual Ability Classification System, and Communication Function Classification System, and evaluated. The process aimed at improving and unifying clinical practice in Finland through a national consensus on the core set of measures. The selected measures were presented by professional groups, and consensus was reached on the recommended core set of measures to be used in all hospitals treating children with CP in Finland. A national consensus on relevant and feasible measures is essential for identifying differences in the effectiveness of local practices, and for conducting multisite intervention studies. This project showed that multiprofessional rehabilitation practices can be improved through respect for and inclusion of everyone involved. © 2016 Mac Keith Press.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

The Transporter Classification Database: recent advances.

PubMed

Saier, Milton H; Yen, Ming Ren; Noto, Keith; Tamang, Dorjee G; Elkan, Charles

2009-01-01

The Transporter Classification Database (TCDB), freely accessible at http://www.tcdb.org, is a relational database containing sequence, structural, functional and evolutionary information about transport systems from a variety of living organisms, based on the International Union of Biochemistry and Molecular Biology-approved transporter classification (TC) system. It is a curated repository for factual information compiled largely from published references. It uses a functional/phylogenetic system of classification, and currently encompasses about 5000 representative transporters and putative transporters in more than 500 families. We here describe novel software designed to support and extend the usefulness of TCDB. Our recent efforts render it more user friendly, incorporate machine learning to input novel data in a semiautomatic fashion, and allow analyses that are more accurate and less time consuming. The availability of these tools has resulted in recognition of distant phylogenetic relationships and tremendous expansion of the information available to TCDB users.

Feature Selection for Ridge Regression with Provable Guarantees.

PubMed

Paul, Saurabh; Drineas, Petros

2016-04-01

We introduce single-set spectral sparsification as a deterministic sampling-based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

Improving balance, mobility, and dual-task performance in an adolescent with cerebral palsy: A case report.

PubMed

Fisher-Pipher, Sarah; Kenyon, Lisa K; Westman, Marci

2017-07-01

Improving functional mobility is often a desired outcome for adolescents with cerebral palsy (CP). Traditional neurorehabilitation approaches are frequently directed at impairments; however, improvements may not be carried over into functional mobility. The purpose of this case report was to describe the examination, intervention, and outcomes of a task-oriented physical therapy intervention program to improve dynamic balance, functional mobility, and dual-task performance in an adolescent with CP. The participant was a 15-year-old girl with spastic triplegic CP (Gross Motor Classification System Level II). Examination procedures included the Canadian Occupational Performance Measure, 6-minute walk test, Muscle Power Sprint Test, 10 x 5-meter sprint test, Timed Up and Down Stairs Test, Gross Motor Function Measure, Gillette Functional Assessment Questionnaire, and functional lower extremity strength tests. Intervention focused on task-oriented dynamic balance and mobility tasks that incorporated coordination and speed demands as well as task-specific lower extremity and trunk strengthening activities. Dual task demands were integrated into all intervention activities. Post-intervention testing revealed improvements in cardiovascular endurance, anaerobic power, agility, stair climbing, gross motor skills, and mobility. The participant appeared to benefit from a task-oriented program to improve dynamic balance, functional mobility, and dual-task performance.

Characteristics of a global classification system for perinatal deaths: a Delphi consensus study.

PubMed

Wojcieszek, Aleena M; Reinebrant, Hanna E; Leisher, Susannah Hopkins; Allanson, Emma; Coory, Michael; Erwich, Jan Jaap; Frøen, J Frederik; Gardosi, Jason; Gordijn, Sanne; Gulmezoglu, Metin; Heazell, Alexander E P; Korteweg, Fleurisca J; McClure, Elizabeth; Pattinson, Robert; Silver, Robert M; Smith, Gordon; Teoh, Zheyi; Tunçalp, Özge; Flenady, Vicki

2016-08-15

Despite the global burden of perinatal deaths, there is currently no single, globally-acceptable classification system for perinatal deaths. Instead, multiple, disparate systems are in use world-wide. This inconsistency hinders accurate estimates of causes of death and impedes effective prevention strategies. The World Health Organisation (WHO) is developing a globally-acceptable classification approach for perinatal deaths. To inform this work, we sought to establish a consensus on the important characteristics of such a system. A group of international experts in the classification of perinatal deaths were identified and invited to join an expert panel to develop a list of important characteristics of a quality global classification system for perinatal death. A Delphi consensus methodology was used to reach agreement. Three rounds of consultation were undertaken using a purpose built on-line survey. Round one sought suggested characteristics for subsequent scoring and selection in rounds two and three. The panel of experts agreed on a total of 17 important characteristics for a globally-acceptable perinatal death classification system. Of these, 10 relate to the structural design of the system and 7 relate to the functional aspects and use of the system. This study serves as formative work towards the development of a globally-acceptable approach for the classification of the causes of perinatal deaths. The list of functional and structural characteristics identified should be taken into consideration when designing and developing such a system.

2D-DIGE analysis of mango (Mangifera indica L.) fruit reveals major proteomic changes associated with ripening.

PubMed

Andrade, Jonathan de Magalhães; Toledo, Tatiana Torres; Nogueira, Silvia Beserra; Cordenunsi, Beatriz Rosana; Lajolo, Franco Maria; do Nascimento, João Roberto Oliveira

2012-06-18

A comparative proteomic investigation between the pre-climacteric and climacteric mango fruits (cv. Keitt) was performed to identify protein species with variable abundance during ripening. Proteins were phenol-extracted from fruits, cyanine-dye-labeled, and separated on 2D gels at pH 4-7. Total spot count of about 373 proteins spots was detected in each gel and forty-seven were consistently different between pre-climacteric and climacteric fruits and were subjected to LC-MS/MS analysis. Functional classification revealed that protein species involved in carbon fixation and hormone biosynthesis decreased during ripening, whereas those related to catabolism and the stress-response, including oxidative stress and abiotic and pathogen defense factors, accumulated. In relation to fruit quality, protein species putatively involved in color development and pulp softening were also identified. This study on mango proteomics provides an overview of the biological processes that occur during ripening. Copyright © 2012 Elsevier B.V. All rights reserved.

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins.

PubMed

Waks, Zeev; Weissbrod, Omer; Carmeli, Boaz; Norel, Raquel; Utro, Filippo; Goldschmidt, Yaara

2016-12-23

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

Transcriptome Sequencing of Lima Bean (Phaseolus lunatus) to Identify Putative Positive Selection in Phaseolus and Legumes

PubMed Central

Li, Fengqi; Cao, Depan; Liu, Yang; Yang, Ting; Wang, Guirong

2015-01-01

The identification of genes under positive selection is a central goal of evolutionary biology. Many legume species, including Phaseolus vulgaris (common bean) and Phaseolus lunatus (lima bean), have important ecological and economic value. In this study, we sequenced and assembled the transcriptome of one Phaseolus species, lima bean. A comparison with the genomes of six other legume species, including the common bean, Medicago, lotus, soybean, chickpea, and pigeonpea, revealed 15 and 4 orthologous groups with signatures of positive selection among the two Phaseolus species and among the seven legume species, respectively. Characterization of these positively selected genes using Non redundant (nr) annotation, gene ontology (GO) classification, GO term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that these genes are mostly involved in thylakoids, photosynthesis and metabolism. This study identified genes that may be related to the divergence of the Phaseolus and legume species. These detected genes are particularly good candidates for subsequent functional studies. PMID:26151849

Deep learning for tumor classification in imaging mass spectrometry.

PubMed

Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

2018-04-01

Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

Modified Angle's Classification for Primary Dentition.

PubMed

Chandranee, Kaushik Narendra; Chandranee, Narendra Jayantilal; Nagpal, Devendra; Lamba, Gagandeep; Choudhari, Purva; Hotwani, Kavita

2017-01-01

This study aims to propose a modification of Angle's classification for primary dentition and to assess its applicability in children from Central India, Nagpur. Modification in Angle's classification has been proposed for application in primary dentition. Small roman numbers i/ii/iii are used for primary dentition notation to represent Angle's Class I/II/III molar relationships as in permanent dentition, respectively. To assess applicability of modified Angle's classification a cross-sectional preschool 2000 children population from central India; 3-6 years of age residing in Nagpur metropolitan city of Maharashtra state were selected randomly as per the inclusion and exclusion criteria. Majority 93.35% children were found to have bilateral Class i followed by 2.5% bilateral Class ii and 0.2% bilateral half cusp Class iii molar relationships as per the modified Angle's classification for primary dentition. About 3.75% children had various combinations of Class ii relationships and 0.2% children were having Class iii subdivision relationship. Modification of Angle's classification for application in primary dentition has been proposed. A cross-sectional investigation using new classification revealed various 6.25% Class ii and 0.4% Class iii molar relationships cases in preschool children population in a metropolitan city of Nagpur. Application of the modified Angle's classification to other population groups is warranted to validate its routine application in clinical pediatric dentistry.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

A hybrid sensing approach for pure and adulterated honey classification.

PubMed

Subari, Norazian; Mohamad Saleh, Junita; Md Shakaff, Ali Yeon; Zakaria, Ammar

2012-10-17

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

PubMed Central

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Galaxy Classifications with Deep Learning

NASA Astrophysics Data System (ADS)

Lukic, Vesna; Brüggen, Marcus

2017-06-01

Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.

[LiLa classification for paediatric long bone fractures. Intraobserver and interobserver reliability].

PubMed

Kamphaus, A; Rapp, M; Wessel, L M; Buchholz, M; Massalme, E; Schneidmüller, D; Roeder, C; Kaiser, M M

2015-04-01

There are two child-specific fracture classification systems for long bone fractures: the AO classification of pediatric long-bone fractures (PCCF) and the LiLa classification of pediatric fractures of long bones (LiLa classification). Both are still not widely established in comparison to the adult AO classification for long bone fractures. During a period of 12 months all long bone fractures in children were documented and classified according to the LiLa classification by experts and non-experts. Intraobserver and interobserver reliability were calculated according to Cohen (kappa). A total of 408 fractures were classified. The intraobserver reliability for location in the skeletal and bone segment showed an almost perfect agreement (K = 0.91-0.95) and also the morphology (joint/shaft fracture) (K = 0.87-0.93). Due to different judgment of the fracture displacement in the second classification round, the intraobserver reliability of the whole classification revealed moderate agreement (K = 0.53-0.58). Interobserver reliability showed moderate agreement (K = 0.55) often due to the low quality of the X-rays. Further differences occurred due to difficulties in assigning the precise transition from metaphysis to diaphysis. The LiLa classification is suitable and in most cases user-friendly for classifying long bone fractures in children. Reliability is higher than in established fracture specific classifications and comparable to the AO classification of pediatric long bone fractures. Some mistakes were due to a low quality of the X-rays and some due to difficulties to classify the fractures themselves. Improvements include a more precise definition of the metaphysis and the kind of displacement. Overall the LiLa classification should still be considered as an alternative for classifying pediatric long bone fractures.

Using the International Classification of Functioning, Disability and Health Framework to Achieve Interprofessional Functional Outcomes for Young Children: A Speech-Language Pathology Perspective.

PubMed

McNeilly, Lemmietta G

2018-02-01

The International Classification of Functioning, Disability and Health (ICF) framework is an excellent tool to facilitate the writing of functional goals for children who exhibit communication disorders and other developmental problems that require services from professionals in multiple therapeutic areas. The holistic view of children provides each professional with an approach that integrates how one's specific health conditions and contextual factors influence a child's functioning and participation in daily activities. This allows the interprofessional team to view the child as a person, recognizing how one need influences another within his or her environment. Copyright © 2017 Elsevier Inc. All rights reserved.

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components

NASA Astrophysics Data System (ADS)

Müller-Putz, Gernot R.; Scherer, Reinhold; Brauneis, Christian; Pfurtscheller, Gert

2005-12-01

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.

PubMed

Müller-Putz, Gernot R; Scherer, Reinhold; Brauneis, Christian; Pfurtscheller, Gert

2005-12-01

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

Apocrine hidradenocarcinoma of the scalp: a classification conundrum.

PubMed

Cohen, Marc; Cassarino, David S; Shih, Hubert B; Abemayor, Elliot; St John, Maie

2009-03-01

The classification of malignant sweat gland lesions is complex. Traditionally, cutaneous sweat gland tumors have been classified by either eccrine or apocrine features. A case report of a 33-year-old Hispanic man with a left scalp mass diagnosed as a malignancy of adnexal origin preoperatively is discussed. After presentation at our multidisciplinary tumor board, excision with ipsilateral neck dissection was undertaken. Final pathology revealed an apocrine hidradenocarcinoma. The classification and behavior of this entity are discussed in this report. Apocrine hidradenocarcinoma can be viewed as an aggressive malignant lesion of cutaneous sweat glands on a spectrum that involves both eccrine and apoeccrine lesions.

Apocrine Hidradenocarcinoma of the Scalp: A Classification Conundrum

PubMed Central

Cassarino, David S.; Shih, Hubert B.; Abemayor, Elliot; John, Maie St.

2008-01-01

Introduction The classification of malignant sweat gland lesions is complex. Traditionally, cutaneous sweat gland tumors have been classified by either eccrine or apocrine features. Methods A case report of a 33-year-old Hispanic man with a left scalp mass diagnosed as a malignancy of adnexal origin preoperatively is discussed. After presentation at our multidisciplinary tumor board, excision with ipsilateral neck dissection was undertaken. Results Final pathology revealed an apocrine hidradenocarcinoma. The classification and behavior of this entity are discussed in this report. Conclusion Apocrine hidradenocarcinoma can be viewed as an aggressive malignant lesion of cutaneous sweat glands on a spectrum that involves both eccrine and apoeccrine lesions. PMID:20596988

A label distance maximum-based classifier for multi-label learning.

PubMed

Liu, Xiaoli; Bao, Hang; Zhao, Dazhe; Cao, Peng

2015-01-01

Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label Classification. The method was formulated by modifying the total error function of the standard BP by adding a penalty term, which was realized by maximizing the distance between the positive and negative labels. Extensive experiments were conducted to compare this method against state-of-the-art multi-label methods on three popular bioinformatic benchmark datasets. The results illustrated that this proposed method is more effective for bioinformatic multi-label classification compared to commonly used techniques.

The mediating effect of psychological distress on functional dependence in stroke patients.

PubMed

Huang, Hui-Chuan; Huang, Li-Kai; Hu, Chaur-Jong; Chang, Chien-Hung; Lee, Hsin-Chien; Chi, Nai-Fang; Shyu, Meei-Ling; Chang, Hsiu-Ju

2014-12-01

To explore varied forms of psychological distress and to determine the mediating influence of psychological distress on functional outcomes in stroke patients. Previous studies attest to the influence of depression on poststroke functional recovery. While there is evidence for neuropathological deficits that occur after stroke to be associated with psychological distress, few studies have explored the effect of various types of psychological distress on functional recovery. A cross-sectional study was used. Data were collected from 178 first-time stroke patients. Study variables included demographic and disease characteristics (stroke location and stroke syndrome classification), psychological distress (the Chinese language version of the Emotional and Social Dysfunction Questionnaire) and functional outcome (Barthel index). Regression and mediation models were used to evaluate the effect of psychological distress on functional outcome. Results revealed that stroke patients experience various forms of mild psychological distress, including anger, helplessness, emotional dyscontrol, indifference, inertia and euphoria, after stroke. Regression and mediation analyses further confirmed that various forms of psychological distress significantly mediated the effect of severe stroke syndromes on functional dependence. The various forms of psychological distress after stroke might play a mediating role in functional recovery and explain how stroke severity affects functional dependence. By understanding the nature of various forms of psychological distress, healthcare professionals should adopt appropriate assessment instruments and design effective interventions to help improve mental and physical function of stroke patients. © 2014 John Wiley & Sons Ltd.

Atmospheric pressure chemical ionisation mass spectrometry analysis linked with chemometrics for food classification - a case study: geographical provenance and cultivar classification of monovarietal clarified apple juices.

PubMed

Gan, Heng-Hui; Soukoulis, Christos; Fisk, Ian

2014-03-01

In the present work, we have evaluated for first time the feasibility of APCI-MS volatile compound fingerprinting in conjunction with chemometrics (PLS-DA) as a new strategy for rapid and non-destructive food classification. For this purpose 202 clarified monovarietal juices extracted from apples differing in their botanical and geographical origin were used for evaluation of the performance of APCI-MS as a classification tool. For an independent test set PLS-DA analyses of pre-treated spectral data gave 100% and 94.2% correct classification rate for the classification by cultivar and geographical origin, respectively. Moreover, PLS-DA analysis of APCI-MS in conjunction with GC-MS data revealed that masses within the spectral ACPI-MS data set were related with parent ions or fragments of alkyesters, carbonyl compounds (hexanal, trans-2-hexenal) and alcohols (1-hexanol, 1-butanol, cis-3-hexenol) and had significant discriminating power both in terms of cultivar and geographical origin. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Automatic Classification of Medical Text: The Influence of Publication Form1

PubMed Central

Cole, William G.; Michael, Patricia A.; Stewart, James G.; Blois, Marsden S.

1988-01-01

Previous research has shown that within the domain of medical journal abstracts the statistical distribution of words is neither random nor uniform, but is highly characteristic. Many words are used mainly or solely by one medical specialty or when writing about one particular level of description. Due to this regularity of usage, automatic classification within journal abstracts has proved quite successful. The present research asks two further questions. It investigates whether this statistical regularity and automatic classification success can also be achieved in medical textbook chapters. It then goes on to see whether the statistical distribution found in textbooks is sufficiently similar to that found in abstracts to permit accurate classification of abstracts based solely on previous knowledge of textbooks. 14 textbook chapters and 45 MEDLINE abstracts were submitted to an automatic classification program that had been trained only on chapters drawn from a standard textbook series. Statistical analysis of the properties of abstracts vs. chapters revealed important differences in word use. Automatic classification performance was good for chapters, but poor for abstracts.

An experimental study of interstitial lung tissue classification in HRCT images using ANN and role of cost functions

NASA Astrophysics Data System (ADS)

Dash, Jatindra K.; Kale, Mandar; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Prabhakar, Nidhi; Garg, Mandeep; Kalra, Naveen

2017-03-01

In this paper, we investigate the effect of the error criteria used during a training phase of the artificial neural network (ANN) on the accuracy of the classifier for classification of lung tissues affected with Interstitial Lung Diseases (ILD). Mean square error (MSE) and the cross-entropy (CE) criteria are chosen being most popular choice in state-of-the-art implementations. The classification experiment performed on the six interstitial lung disease (ILD) patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Micronodules, Fibrosis and Healthy from MedGIFT database. The texture features from an arbitrary region of interest (AROI) are extracted using Gabor filter. Two different neural networks are trained with the scaled conjugate gradient back propagation algorithm with MSE and CE error criteria function respectively for weight updation. Performance is evaluated in terms of average accuracy of these classifiers using 4 fold cross-validation. Each network is trained for five times for each fold with randomly initialized weight vectors and accuracies are computed. Significant improvement in classification accuracy is observed when ANN is trained by using CE (67.27%) as error function compared to MSE (63.60%). Moreover, standard deviation of the classification accuracy for the network trained with CE (6.69) error criteria is found less as compared to network trained with MSE (10.32) criteria.

Multiple kernel learning using single stage function approximation for binary classification problems

NASA Astrophysics Data System (ADS)

Shiju, S.; Sumitra, S.

2017-12-01

In this paper, the multiple kernel learning (MKL) is formulated as a supervised classification problem. We dealt with binary classification data and hence the data modelling problem involves the computation of two decision boundaries of which one related with that of kernel learning and the other with that of input data. In our approach, they are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS) as the direct sum of the RKHSs corresponding to the decision boundaries of kernel learning and input data and searching that function from the global RKHS, which can be represented as the direct sum of the decision boundaries under consideration. In our experimental analysis, the proposed model had shown superior performance in comparison with that of existing two stage function approximation formulation of MKL, where the decision functions of kernel learning and input data are found separately using two different cost functions. This is due to the fact that single stage representation helps the knowledge transfer between the computation procedures for finding the decision boundaries of kernel learning and input data, which inturn boosts the generalisation capacity of the model.

PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine.

PubMed

Hayat, Maqsood; Tahir, Muhammad

2015-08-01

Membrane protein is a central component of the cell that manages intra and extracellular processes. Membrane proteins execute a diversity of functions that are vital for the survival of organisms. The topology of transmembrane proteins describes the number of transmembrane (TM) helix segments and its orientation. However, owing to the lack of its recognized structures, the identification of TM helix and its topology through experimental methods is laborious with low throughput. In order to identify TM helix segments reliably, accurately, and effectively from topogenic sequences, we propose the PSOFuzzySVM-TMH model. In this model, evolutionary based information position specific scoring matrix and discrete based information 6-letter exchange group are used to formulate transmembrane protein sequences. The noisy and extraneous attributes are eradicated using an optimization selection technique, particle swarm optimization, from both feature spaces. Finally, the selected feature spaces are combined in order to form ensemble feature space. Fuzzy-support vector Machine is utilized as a classification algorithm. Two benchmark datasets, including low and high resolution datasets, are used. At various levels, the performance of the PSOFuzzySVM-TMH model is assessed through 10-fold cross validation test. The empirical results reveal that the proposed framework PSOFuzzySVM-TMH outperforms in terms of classification performance in the examined datasets. It is ascertained that the proposed model might be a useful and high throughput tool for academia and research community for further structure and functional studies on transmembrane proteins.

The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

NASA Astrophysics Data System (ADS)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, A. P. P. Abdul; Razali Abdullah, Mohamad; Aizzat Zakaria, Muhammad; Muaz Alim, Muhammad; Arif Mat Jizat, Jessnor; Fauzi Ibrahim, Mohamad

2018-03-01

Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models, i.e. linear and fine radial basis function (RBF) kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes.

PubMed

Roche, Kimberly E; Weinstein, Marvin; Dunwoodie, Leland J; Poehlman, William L; Feltus, Frank A

2018-05-25

We applied two state-of-the-art, knowledge independent data-mining methods - Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) - to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn't detectable with the core biomarker transcripts present. This suggests the existence of a "background classification" potential in which the pattern of gene expression after continued removal of "biomarker" transcripts could still classify tumors in agreement with the tumor type.

Multiplex profiling of tumor-associated proteolytic activity in serum of colorectal cancer patients.

PubMed

Yepes, Diego; Costina, Victor; Pilz, Lothar R; Hofheinz, Ralf; Neumaier, Michael; Findeisen, Peter

2014-06-01

The monitoring of tumor-associated protease activity in blood specimens has recently been proposed as new diagnostic tool in cancer research. In this paper, we describe the screening of a peptide library for identification of reporter peptides (RPs) that are selectively cleaved in serum specimens from colorectal cancer patients and investigate the benefits of RP multiplexing. A library of 144 RPs was constructed that contained amino acid sequences of abundant plasma proteins. Proteolytic cleavage of RPs was monitored with MS. Five RPs that were selectively cleaved in serum specimens from tumor patients were selected for further validation in serum specimens of colorectal tumor patients (n = 30) and nonmalignant controls (n = 60). RP spiking and subsequent quantification of proteolytic fragments with LC-MS showed good reproducibility with CVs always below 26%. The linear discriminant analysis and PCA revealed that a combination of RPs for diagnostic classification is superior to single markers. Classification accuracy reached 88% (79/90) when all five markers were combined. Functional protease profiling with RPs might improve the laboratory-based diagnosis, monitoring and prognosis of malignant disease, and has to be evaluated thoroughly in future studies. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Delineation of sympatric morphotypes of lake trout in Lake Superior

USGS Publications Warehouse

Moore, Seth A.; Bronte, Charles R.

2001-01-01

Three morphotypes of lake trout Salvelinus namaycush are recognized in Lake Superior: lean, siscowet, and humper. Absolute morphotype assignment can be difficult. We used a size-free, whole-body morphometric analysis (truss protocol) to determine whether differences in body shape existed among lake trout morphotypes. Our results showed discrimination where traditional morphometric characters and meristic measurements failed to detect differences. Principal components analysis revealed some separation of all three morphotypes based on head and caudal peduncle shape, but it also indicated considerable overlap in score values. Humper lake trout have smaller caudal peduncle widths to head length and depth characters than do lean or siscowet lake trout. Lean lake trout had larger head measures to caudal widths, whereas siscowet had higher caudal peduncle to head measures. Backward stepwise discriminant function analysis retained two head measures, three midbody measures, and four caudal peduncle measures; correct classification rates when using these variables were 83% for leans, 80% for siscowets, and 83% for humpers, which suggests the measures we used for initial classification were consistent. Although clear ecological reasons for these differences are not readily apparent, patterns in misclassification rates may be consistent with evolutionary hypotheses for lake trout within the Laurentian Great Lakes.

Is U.S. climatic diversity well represented within the existing federal protection network?

PubMed

Batllori, Enric; Miller, Carol; Parisien, Marc-Andre; Parks, Sean A; Moritz, Max A

Establishing protection networks to ensure that biodiversity and associated ecosystem services persist under changing environments is a major challenge for conservation planning. The potential consequences of altered climates for the structure and function of ecosystems necessitates new and complementary approaches be incorporated into traditional conservation plans. The conterminous United States of America (CONUS) has an extensive system of protected areas managed by federal agencies, but a comprehensive assessment of how this network represents CONUS climate is lacking. We present a quantitative classification of the climate space that is independent from the geographic locations to evaluate the climatic representation of the existing protected area network. We use this classification to evaluate the coverage of each agency's jurisdiction and to identify current conservation deficits. Our findings reveal that the existing network poorly represents CONUS climatic diversity. Although rare climates are generally well represented by the network, the most common climates are particularly underrepresented. Overall, 83% of the area of the CONUS corresponds to climates underrepresented by the network. The addition of some currently unprotected federal lands to the network would enhance the coverage of CONUS climates. However, to fully palliate current conservation deficits, large-scale private-land conservation initiatives will be critical.

Predicting human decisions in socioeconomic interaction using real-time functional magnetic resonance imaging (rtfMRI)

NASA Astrophysics Data System (ADS)

Hollmann, Maurice; Mönch, Tobias; Müller, Charles; Bernarding, Johannes

2009-02-01

A major field in cognitive neuroscience investigates neuronal correlates of human decision-making processes [1, 2]. Is it possible to predict a decision before it is actually revealed by the volunteer? In the presented manuscript we use a standard paradigm from economic behavioral research that proved emotional influences on human decision making: the Ultimatum Game (UG). In the UG, two players have the opportunity to split a sum of money. One player is deemed the proposer and the other, the responder. The proposer makes an offer as to how this money should be split between the two. The second player can either accept or reject this offer. If it is accepted, the money is split as proposed. If rejected, then neither player receives anything. In the presented study a real-time fMRI system was used to derive the brain activation of the responder. Using a Relevance-Vector-Machine classifier it was possible to predict if the responder will accept or reject an offer. The classification result was presented to the operator 1-2 seconds before the volunteer pressed a button to convey his decision. The classification accuracy reached about 70% averaged over six subjects.

Learning to Control Actions: Transfer Effects following a Procedural Cognitive Control Computerized Training

PubMed Central

Shahar, Nitzan; Meiran, Nachshon

2015-01-01

Few studies have addressed action control training. In the current study, participants were trained over 19 days in an adaptive training task that demanded constant switching, maintenance and updating of novel action rules. Participants completed an executive functions battery before and after training that estimated processing speed, working memory updating, set-shifting, response inhibition and fluid intelligence. Participants in the training group showed greater improvement than a no-contact control group in processing speed, indicated by reduced reaction times in speeded classification tasks. No other systematic group differences were found across the different pre-post measurements. Ex-Gaussian fitting of the reaction-time distribution revealed that the reaction time reduction observed among trained participants was restricted to the right tail of the distribution, previously shown to be related to working memory. Furthermore, training effects were only found in classification tasks that required participants to maintain novel stimulus-response rules in mind, supporting the notion that the training improved working memory abilities. Training benefits were maintained in a 10-month follow-up, indicating relatively long-lasting effects. The authors conclude that training improved action-related working memory abilities. PMID:25799443

A Spiking Neural Network System for Robust Sequence Recognition.

PubMed

Yu, Qiang; Yan, Rui; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2016-03-01

This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the system is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the system is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the system provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The system structure would be beneficial for developments in both hardware and software.

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau Functional for Classification of High-Dimensional Data

DTIC Science & Technology

2016-02-01

Modified Cheeger and Ratio Cut Methods Using the Ginzburg-Landau Functional for Classification of High-Dimensional Data Ekaterina Merkurjev*, Andrea...bertozzi@math.ucla.edu, xiaoran@isi.edu, lerman@isi.edu. Abstract Recent advances in clustering have included continuous relaxations of the Cheeger cut ...fully nonlinear Cheeger cut problem, as well as the ratio cut optimization task. Both problems are connected to total variation minimization, and the

Position Between Trunk and Pelvis During Gait Depending on the Gross Motor Function Classification System.

PubMed

Sanz-Mengibar, Jose Manuel; Altschuck, Natalie; Sanchez-de-Muniain, Paloma; Bauer, Christian; Santonja-Medina, Fernando

2017-04-01

To understand whether there is a trunk postural control threshold in the sagittal plane for the transition between the Gross Motor Function Classification System (GMFCS) levels measured with 3-dimensional gait analysis. Kinematics from 97 children with spastic bilateral cerebral palsy from spine angles according to Plug-In Gait model (Vicon) were plotted relative to their GMFCS level. Only average and minimum values of the lumbar spine segment correlated with GMFCS levels. Maximal values at loading response correlated independently with age at all functional levels. Average and minimum values were significant when analyzing age in combination with GMFCS level. There are specific postural control patterns in the average and minimum values for the position between trunk and pelvis in the sagittal plane during gait, for the transition among GMFCS I-III levels. Higher classifications of gross motor skills correlate with more extended spine angles.

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.

PubMed

Ravindran, Sindhu; Jambek, Asral Bahari; Muthusamy, Hariharan; Neoh, Siew-Chin

2015-01-01

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

A comparison of the International Classification of Functioning, Disability, and Health to the disability tax credit.

PubMed

Conti-Becker, Angela; Doralp, Samantha; Fayed, Nora; Kean, Crystal; Lencucha, Raphael; Leyshon, Rhysa; Mersich, Jackie; Robbins, Shawn; Doyle, Phillip C

2007-01-01

The Disability Tax Credit (DTC) Certification is an assessment tool used to provide Canadians with disability tax relief The International Classification of Functioning, Disability and Health (ICF) provides a universal framework for defining disability. The purpose of this study was to evaluate the DTC and familiarize occupational therapists with the process of mapping measures to the ICF classification system. Concepts within the DTC were identified and mapped to appropriate ICF codes (Cieza et al., 2005). The DTC was linked to 45 unique ICF codes (16 Body Functions, 19 Activities and Participation, and 8 Environmental Factors). The DTC encompasses various domains of the ICF; however, there is no consideration of Personal Factors, Body Structures, and key aspects of Activities and Participation. Refining the DTC to address these aspects will provide an opportunity for fair and just determinations for those who experience disability.

Mining for class-specific motifs in protein sequence classification

PubMed Central

2013-01-01

Background In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms. PMID:23496846

Classification of speech and language profiles in 4-year old children with cerebral palsy: A prospective preliminary study

PubMed Central

Hustad, Katherine C.; Gorton, Kristin; Lee, Jimin

2010-01-01

Purpose Little is known about the speech and language abilities of children with cerebral palsy (CP) and there is currently no system for classifying speech and language profiles. Such a system would have epidemiological value and would have the potential to advance the development of interventions that improve outcomes. In this study, we propose and test a preliminary speech and language classification system by quantifying how well speech and language data differentiate among children classified into different hypothesized profile groups. Method Speech and language assessment data were collected in a laboratory setting from 34 children with CP (18 males; 16 females) who were a mean age of 54 months (SD 1.8 months). Measures of interest were vowel area, speech rate, language comprehension scores, and speech intelligibility ratings. Results Canonical discriminant function analysis showed that three functions accounted for 100% of the variance among profile groups, with speech variables accounting for 93% of the variance. Classification agreement varied from 74% to 97% using four different classification paradigms. Conclusions Results provide preliminary support for the classification of speech and language abilities of children with CP into four initial profile groups. Further research is necessary to validate the full classification system. PMID:20643795

The morphology and classification of α ganglion cells in the rat retinae: a fractal analysis study.

PubMed

Jelinek, Herbert F; Ristanović, Dušan; Milošević, Nebojša T

2011-09-30

Rat retinal ganglion cells have been proposed to consist of a varying number of subtypes. Dendritic morphology is an essential aspect of classification and a necessary step toward understanding structure-function relationships of retinal ganglion cells. This study aimed at using a heuristic classification procedure in combination with the box-counting analysis to classify the alpha ganglion cells in the rat retinae based on the dendritic branching pattern and to investigate morphological changes with retinal eccentricity. The cells could be divided into two groups: cells with simple dendritic pattern (box dimension lower than 1.390) and cells with complex dendritic pattern (box dimension higher than 1.390) according to their dendritic branching pattern complexity. Both were further divided into two subtypes due to the stratification within the inner plexiform layer. In the present study we have shown that the alpha rat RCGs can be classified further by their dendritic branching complexity and thus extend those of previous reports that fractal analysis can be successfully used in neuronal classification, particularly that the fractal dimension represents a robust and sensitive tool for the classification of retinal ganglion cells. A hypothesis of possible functional significance of our classification scheme is also discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose

PubMed Central

Rahman, Mohammad Mizanur; Suksompong, Prapun; Toochinda, Pisanu; Taparugssanagorn, Attaphongse

2017-01-01

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k-nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms. PMID:28895910

A False Alarm Reduction Method for a Gas Sensor Based Electronic Nose.

PubMed

Rahman, Mohammad Mizanur; Charoenlarpnopparut, Chalie; Suksompong, Prapun; Toochinda, Pisanu; Taparugssanagorn, Attaphongse

2017-09-12

Electronic noses (E-Noses) are becoming popular for food and fruit quality assessment due to their robustness and repeated usability without fatigue, unlike human experts. An E-Nose equipped with classification algorithms and having open ended classification boundaries such as the k -nearest neighbor ( k -NN), support vector machine (SVM), and multilayer perceptron neural network (MLPNN), are found to suffer from false classification errors of irrelevant odor data. To reduce false classification and misclassification errors, and to improve correct rejection performance; algorithms with a hyperspheric boundary, such as a radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) with a Gaussian activation function in the hidden layer should be used. The simulation results presented in this paper show that GRNN has more correct classification efficiency and false alarm reduction capability compared to RBFNN. As the design of a GRNN and RBFNN is complex and expensive due to large numbers of neuron requirements, a simple hyperspheric classification method based on minimum, maximum, and mean (MMM) values of each class of the training dataset was presented. The MMM algorithm was simple and found to be fast and efficient in correctly classifying data of training classes, and correctly rejecting data of extraneous odors, and thereby reduced false alarms.

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

PubMed

Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing.

PubMed

Dottori, Martin; Sedeño, Lucas; Martorell Caro, Miguel; Alifano, Florencia; Hesse, Eugenia; Mikulan, Ezequiel; García, Adolfo M; Ruiz-Tagle, Amparo; Lillo, Patricia; Slachevsky, Andrea; Serrano, Cecilia; Fraiman, Daniel; Ibanez, Agustin

2017-06-19

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer's disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.

Genome-Wide Analyses and Functional Classification of Proline Repeat-Rich Proteins: Potential Role of eIF5A in Eukaryotic Evolution

PubMed Central

Mandal, Ajeet; Mandal, Swati; Park, Myung Hee

2014-01-01

The eukaryotic translation factor, eIF5A has been recently reported as a sequence-specific elongation factor that facilitates peptide bond formation at consecutive prolines in Saccharomyces cerevisiae, as its ortholog elongation factor P (EF-P) does in bacteria. We have searched the genome databases of 35 representative organisms from six kingdoms of life for PPP (Pro-Pro-Pro) and/or PPG (Pro-Pro-Gly)-encoding genes whose expression is expected to depend on eIF5A. We have made detailed analyses of proteome data of 5 selected species, Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, Mus musculus and Homo sapiens. The PPP and PPG motifs are low in the prokaryotic proteomes. However, their frequencies markedly increase with the biological complexity of eukaryotic organisms, and are higher in newly derived proteins than in those orthologous proteins commonly shared in all species. Ontology classifications of S. cerevisiae and human genes encoding the highest level of polyprolines reveal their strong association with several specific biological processes, including actin/cytoskeletal associated functions, RNA splicing/turnover, DNA binding/transcription and cell signaling. Previously reported phenotypic defects in actin polarity and mRNA decay of eIF5A mutant strains are consistent with the proposed role for eIF5A in the translation of the polyproline-containing proteins. Of all the amino acid tandem repeats (≥3 amino acids), only the proline repeat frequency correlates with functional complexity of the five organisms examined. Taken together, these findings suggest the importance of proline repeat-rich proteins and a potential role for eIF5A and its hypusine modification pathway in the course of eukaryotic evolution. PMID:25364902

Melanopsin expressing human retinal ganglion cells: Subtypes, distribution, and intraretinal connectivity.

PubMed

Hannibal, Jens; Christiansen, Anders Tolstrup; Heegaard, Steffen; Fahrenkrug, Jan; Kiilgaard, Jens Folke

2017-06-01

Intrinsically photosensitive retinal ganglion cells (ipRGCs) expressing the photopigment melanopsin belong to a heterogenic population of RGCs which regulate the circadian clock, masking behavior, melatonin suppression, the pupillary light reflex, and sleep/wake cycles. The different functions seem to be associated to different subtypes of melanopsin cells. In rodents, subtype classification has associated subtypes to function. In primate and human retina such classification has so far, not been applied. In the present study using antibodies against N- and C-terminal parts of human melanopsin, confocal microscopy and 3D reconstruction of melanopsin immunoreactive (-ir) RGCs, we applied the criteria used in mouse on human melanopsin-ir RGCs. We identified M1, displaced M1, M2, and M4 cells. We found two other subtypes of melanopsin-ir RGCs, which were named "gigantic M1 (GM1)" and "gigantic displaced M1 (GDM1)." Few M3 cells and no M5 subtypes were labeled. Total cell counts from one male and one female retina revealed that the human retina contains 7283 ± 237 melanopsin-ir (0.63-0.75% of the total number of RGCs). The melanopsin subtypes were unevenly distributed. Most significant was the highest density of M4 cells in the nasal retina. We identified input to the melanopsin-ir RGCs from AII amacrine cells and directly from rod bipolar cells via ribbon synapses in the innermost ON layer of the inner plexiform layer (IPL) and from dopaminergic amacrine cells and GABAergic processes in the outermost OFF layer of the IPL. The study characterizes a heterogenic population of human melanopsin-ir RGCs, which most likely are involved in different functions. © 2017 Wiley Periodicals, Inc.

Influence of microclimatic ammonia levels on productive performance of different broilers' breeds estimated with univariate and multivariate approaches.

PubMed

Soliman, Essam S; Moawed, Sherif A; Hassan, Rania A

2017-08-01

Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people's health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Ammonia levels were significantly higher (p< 0.01) in the Ross compared to the Hubbard breed farm, although no significant differences (p>0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (p< 0.01) during fall compared to winter irrespective of broiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p< 0.01), but not with the ambient temperature (r=-0.045; p>0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. The study revealed that broiler's growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers' growth performance parameters data using multivariate discriminant function analysis.

Influence of microclimatic ammonia levels on productive performance of different broilers’ breeds estimated with univariate and multivariate approaches

PubMed Central

Soliman, Essam S.; Moawed, Sherif A.; Hassan, Rania A.

2017-01-01

Background and Aim: Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people’s health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. Materials and Methods: A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Results: Ammonia levels were significantly higher (p< 0.01) in the Ross compared to the Hubbard breed farm, although no significant differences (p>0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (p< 0.01) during fall compared to winter irrespective of broiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p< 0.01), but not with the ambient temperature (r=−0.045; p>0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. Conclusion: The study revealed that broiler’s growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers’ growth performance parameters data using multivariate discriminant function analysis. PMID:28919677

GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank.

PubMed

You, Ronghui; Zhang, Zihan; Xiong, Yi; Sun, Fengzhu; Mamitsuka, Hiroshi; Zhu, Shanfeng

2018-03-07

Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only <1% of more than 70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multilabel classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-called difficult proteins, which have <60% sequence identity to proteins with annotations already. Thus the vital and challenging problem now is how to develop a method for SAFP, particularly for difficult proteins. The key of this method is to extract not only homology information but also diverse, deep- rooted information/evidence from sequence inputs and integrate them into a predictor in a both effective and efficient manner. We propose GOLabeler, which integrates five component classifiers, trained from different features, including GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, etc., in the framework of learning to rank (LTR), a paradigm of machine learning, especially powerful for multilabel classification. The empirical results obtained by examining GOLabeler extensively and thoroughly by using large-scale datasets revealed numerous favorable aspects of GOLabeler, including significant performance advantage over state-of-the-art AFP methods. http://datamining-iip.fudan.edu.cn/golabeler. zhusf@fudan.edu.cn. Supplementary data are available at Bioinformatics online.

Authorship Discovery in Blogs Using Bayesian Classification with Corrective Scaling

DTIC Science & Technology

2008-06-01

4 2.3 W. Fucks ’ Diagram of n-Syllable Word Frequencies . . . . . . . . . . . . . . 5 3.1 Confusion Matrix for All Test Documents of 500...of the books which scholars believed he had. • Wilhelm Fucks discriminated between authors using the average number of syllables per word and average...distance between equal-syllabled words [8]. Fucks , too, concluded that a study such as his reveals a “possibility of a quantitative classification

Human microbiota, blood group antigens, and disease.

PubMed

Ewald, D Rose; Sumner, Susan C J

2018-05-01

Far from being just "bugs in our guts," the microbiota interacts with the body in previously unimagined ways. Research into the genome and the microbiome has revealed that the human body and the microbiota have a long-established but only recently recognized symbiotic relationship; homeostatic balance between them regulates body function. That balance is fragile, easily disturbed, and plays a fundamental role in human health-our very survival depends on the healthy functioning of these microorganisms. Increasing rates of cardiovascular, autoimmune, and inflammatory diseases, as well as epidemics in obesity and diabetes in recent decades are believed to be explained, in part, by unintended effects on the microbiota from vaccinations, poor diets, environmental chemicals, indiscriminate antibiotic use, and "germophobia." Discovery and exploration of the brain-gut-microbiota axis have provided new insights into functional diseases of the gut, autoimmune and stress-related disorders, and the role of probiotics in treating certain affective disorders; it may even explain some aspects of autism. Research into dietary effects on the human gut microbiota led to its classification into three proposed enterotypes, but also revealed the surprising role of blood group antigens in shaping those populations. Blood group antigens have previously been associated with disease risks; their subsequent association with the microbiota may reveal mechanisms that lead to development of nutritional interventions and improved treatment modalities. Further exploration of associations between specific enteric microbes and specific metabolites will foster new dietary interventions, treatment modalities, and genetic therapies, and inevitably, their application in personalized healthcare strategies. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Translational, Genomic, and Systems Medicine > Translational Medicine Physiology > Mammalian Physiology in Health and Disease. © 2018 Wiley Periodicals, Inc.

Oregon Hydrologic Landscapes: A Classification Framework

EPA Science Inventory

There is a growing need for hydrologic classification systems that can provide a basis for broad-scale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors such as climate change. We developed a hydrologic landscape (HL) classifica...

Combining multiple decisions: applications to bioinformatics

NASA Astrophysics Data System (ADS)

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

2008-01-01

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

Free classification of American English dialects by native and non-native listeners

PubMed Central

Clopper, Cynthia G.; Bradlow, Ann R.

2009-01-01

Most second language acquisition research focuses on linguistic structures, and less research has examined the acquisition of sociolinguistic patterns. The current study explored the perceptual classification of regional dialects of American English by native and non-native listeners using a free classification task. Results revealed similar classification strategies for the native and non-native listeners. However, the native listeners were more accurate overall than the non-native listeners. In addition, the non-native listeners were less able to make use of constellations of cues to accurately classify the talkers by dialect. However, the non-native listeners were able to attend to cues that were either phonologically or sociolinguistically relevant in their native language. These results suggest that non-native listeners can use information in the speech signal to classify talkers by regional dialect, but that their lack of signal-independent cultural knowledge about variation in the second language leads to less accurate classification performance. PMID:20161400

Adaptive phase k-means algorithm for waveform classification

NASA Astrophysics Data System (ADS)

Song, Chengyun; Liu, Zhining; Wang, Yaojun; Xu, Feng; Li, Xingming; Hu, Guangmin

2018-01-01

Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis.

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…

The functional therapeutic chemical classification system.

PubMed

Croset, Samuel; Overington, John P; Rebholz-Schuhmann, Dietrich

2014-03-15

Drug repositioning is the discovery of new indications for compounds that have already been approved and used in a clinical setting. Recently, some computational approaches have been suggested to unveil new opportunities in a systematic fashion, by taking into consideration gene expression signatures or chemical features for instance. We present here a novel method based on knowledge integration using semantic technologies, to capture the functional role of approved chemical compounds. In order to computationally generate repositioning hypotheses, we used the Web Ontology Language to formally define the semantics of over 20 000 terms with axioms to correctly denote various modes of action (MoA). Based on an integration of public data, we have automatically assigned over a thousand of approved drugs into these MoA categories. The resulting new resource is called the Functional Therapeutic Chemical Classification System and was further evaluated against the content of the traditional Anatomical Therapeutic Chemical Classification System. We illustrate how the new classification can be used to generate drug repurposing hypotheses, using Alzheimers disease as a use-case. https://www.ebi.ac.uk/chembl/ftc; https://github.com/loopasam/ftc. croset@ebi.ac.uk Supplementary data are available at Bioinformatics online.

Confidence level estimation in multi-target classification problems

NASA Astrophysics Data System (ADS)

Chang, Shi; Isaacs, Jason; Fu, Bo; Shin, Jaejeong; Zhu, Pingping; Ferrari, Silvia

2018-04-01

This paper presents an approach for estimating the confidence level in automatic multi-target classification performed by an imaging sensor on an unmanned vehicle. An automatic target recognition algorithm comprised of a deep convolutional neural network in series with a support vector machine classifier detects and classifies targets based on the image matrix. The joint posterior probability mass function of target class, features, and classification estimates is learned from labeled data, and recursively updated as additional images become available. Based on the learned joint probability mass function, the approach presented in this paper predicts the expected confidence level of future target classifications, prior to obtaining new images. The proposed approach is tested with a set of simulated sonar image data. The numerical results show that the estimated confidence level provides a close approximation to the actual confidence level value determined a posteriori, i.e. after the new image is obtained by the on-board sensor. Therefore, the expected confidence level function presented in this paper can be used to adaptively plan the path of the unmanned vehicle so as to optimize the expected confidence levels and ensure that all targets are classified with satisfactory confidence after the path is executed.

RNAi-Mediated Reverse Genetic Screen Identified Drosophila Chaperones Regulating Eye and Neuromuscular Junction Morphology.

PubMed

Raut, Sandeep; Mallik, Bhagaban; Parichha, Arpan; Amrutha, Valsakumar; Sahi, Chandan; Kumar, Vimlesh

2017-07-05

Accumulation of toxic proteins in neurons has been linked with the onset of neurodegenerative diseases, which in many cases are characterized by altered neuronal function and synapse loss. Molecular chaperones help protein folding and the resolubilization of unfolded proteins, thereby reducing the protein aggregation stress. While most of the chaperones are expressed in neurons, their functional relevance remains largely unknown. Here, using bioinformatics analysis, we identified 95 Drosophila chaperones and classified them into seven different classes. Ubiquitous actin5C -Gal4-mediated RNAi knockdown revealed that ∼50% of the chaperones are essential in Drosophila Knocking down these genes in eyes revealed that ∼30% of the essential chaperones are crucial for eye development. Using neuron-specific knockdown, immunocytochemistry, and robust behavioral assays, we identified a new set of chaperones that play critical roles in the regulation of Drosophila NMJ structural organization. Together, our data present the first classification and comprehensive analysis of Drosophila chaperones. Our screen identified a new set of chaperones that regulate eye and NMJ morphogenesis. The outcome of the screen reported here provides a useful resource for further elucidating the role of individual chaperones in Drosophila eye morphogenesis and synaptic development. Copyright © 2017 Raut et al.

Transcriptomic analysis of Ruditapes philippinarum hemocytes reveals cytoskeleton disruption after in vitro Vibrio tapetis challenge.

PubMed

Brulle, Franck; Jeffroy, Fanny; Madec, Stéphanie; Nicolas, Jean-Louis; Paillard, Christine

2012-10-01

The Manila clam, Ruditapes philippinarum, is an economically-important, commercial shellfish; harvests are diminished in some European waters by a pathogenic bacterium, Vibrio tapetis, that causes Brown Ring disease. To identify molecular characteristics associated with susceptibility or resistance to Brown Ring disease, Suppression Subtractive Hybridization (SSH) analyzes were performed to construct cDNA libraries enriched in up- or down-regulated transcripts from clam immune cells, hemocytes, after a 3-h in vitro challenge with cultured V. tapetis. Nine hundred and ninety eight sequences from the two libraries were sequenced, and an in silico analysis identified 235 unique genes. BLAST and "Gene ontology" classification analyzes revealed that 60.4% of the Expressed Sequence Tags (ESTs) have high similarities with genes involved in various physiological functions, such as immunity, apoptosis and cytoskeleton organization; whereas, 39.6% remain unidentified. From the 235 unique genes, we selected 22 candidates based upon physiological function and redundancy in the libraries. Then, Real-Time PCR analysis identified 3 genes related to cytoskeleton organization showing significant variation in expression attributable to V. tapetis exposure. Disruption in regulation of these genes is consistent with the etiologic agent of Brown Ring disease in Manila clams. Copyright © 2012 Elsevier Ltd. All rights reserved.

Content Representation in the Human Medial Temporal Lobe

PubMed Central

Liang, Jackson C.; Wagner, Anthony D.

2013-01-01

Current theories of medial temporal lobe (MTL) function focus on event content as an important organizational principle that differentiates MTL subregions. Perirhinal and parahippocampal cortices may play content-specific roles in memory, whereas hippocampal processing is alternately hypothesized to be content specific or content general. Despite anatomical evidence for content-specific MTL pathways, empirical data for content-based MTL subregional dissociations are mixed. Here, we combined functional magnetic resonance imaging with multiple statistical approaches to characterize MTL subregional responses to different classes of novel event content (faces, scenes, spoken words, sounds, visual words). Univariate analyses revealed that responses to novel faces and scenes were distributed across the anterior–posterior axis of MTL cortex, with face responses distributed more anteriorly than scene responses. Moreover, multivariate pattern analyses of perirhinal and parahippocampal data revealed spatially organized representational codes for multiple content classes, including nonpreferred visual and auditory stimuli. In contrast, anterior hippocampal responses were content general, with less accurate overall pattern classification relative to MTL cortex. Finally, posterior hippocampal activation patterns consistently discriminated scenes more accurately than other forms of content. Collectively, our findings indicate differential contributions of MTL subregions to event representation via a distributed code along the anterior–posterior axis of MTL that depends on the nature of event content. PMID:22275474

Memory functioning and mental verbs acquisition in children with specific language impairment.

PubMed

Spanoudis, George C; Natsopoulos, Demetrios

2011-01-01

Memory and language operate in synergy. Recent literature stresses the importance of memory functioning in interpreting language deficits. Two groups of 50 children each, ages 8-12 were studied. The first group included children with specific language impairment, while the participants in the second group were typically developing children. The two groups, which were matched on age, nonverbal intelligence and varied significantly in verbal ability were examined, using a test battery of four memory functioning (phonological, working and long-term memory) and five mental verb measures. The statistical analyses indicated that the two groups differed significantly in all language and memory measures; a logistic regression analysis revealed that within each main group existed nested subgroups of different developmental patterns with working and long-term memory measures as the most robust discriminate markers of classification. Language impaired children had more difficulties in the acquisition of mental verbs because they are less able to process and store phonological information in working memory and long-term lexicon. Copyright © 2011 Elsevier Ltd. All rights reserved.

The clinical outcomes of deep gray matter injury in children with cerebral palsy in relation with brain magnetic resonance imaging.

PubMed

Choi, Ja Young; Choi, Yoon Seong; Rha, Dong-Wook; Park, Eun Sook

2016-08-01

In the present study we investigated the nature and extent of clinical outcomes using various classifications and analyzed the relationship between brain magnetic resonance imaging (MRI) findings and the extent of clinical outcomes in children with cerebral palsy (CP) with deep gray matter injury. The deep gray matter injuries of 69 children were classified into hypoxic ischemic encephalopathy (HIE) and kernicterus patterns. HIE patterns were divided into four groups (I-IV) based on severity. Functional classification was investigated using the gross motor function classification system-expanded and revised, manual ability classification system, communication function classification system, and tests of cognitive function, and other associated problems. The severity of HIE pattern on brain MRI was strongly correlated with the severity of clinical outcomes in these various domains. Children with a kernicterus pattern showed a wide range of clinical outcomes in these areas. Children with severe HIE are at high risk of intellectual disability (ID) or epilepsy and children with a kernicterus pattern are at risk of hearing impairment and/or ID. Grading severity of HIE pattern on brain MRI is useful for predicting overall outcomes. The clinical outcomes of children with a kernicterus pattern range widely from mild to severe. Delineation of the clinical outcomes of children with deep gray matter injury, which are a common abnormal brain MRI finding in children with CP, is necessary. The present study provides clinical outcomes for various domains in children with deep gray matter injury on brain MRI. The deep gray matter injuries were divided into two major groups; HIE and kernicterus patterns. Our study showed that severity of HIE pattern on brain MRI was strongly associated with the severity of impairments in gross motor function, manual ability, communication function, and cognition. These findings suggest that severity of HIE pattern can be useful for predicting the severity of impairments. Conversely, children with a kernicterus pattern showed a wide range of clinical outcomes in various domains. Children with severe HIE pattern are at high risk of ID or epilepsy and children with kernicterus pattern are at risk of hearing impairment or ID. The strength of our study was the assessment of clinical outcomes after 3 years of age using standardized classification systems in various domains in children with deep gray matter injury. Copyright © 2016 Elsevier Ltd. All rights reserved.

Improving Hospital-Wide Early Resource Allocation through Machine Learning.

PubMed

Gartner, Daniel; Padman, Rema

2015-01-01

The objective of this paper is to evaluate the extent to which early determination of diagnosis-related groups (DRGs) can be used for better allocation of scarce hospital resources. When elective patients seek admission, the true DRG, currently determined only at discharge, is unknown. We approach the problem of early DRG determination in three stages: (1) test how much a Naïve Bayes classifier can improve classification accuracy as compared to a hospital's current approach; (2) develop a statistical program that makes admission and scheduling decisions based on the patients' clincial pathways and scarce hospital resources; and (3) feed the DRG as classified by the Naïve Bayes classifier and the hospitals' baseline approach into the model (which we evaluate in simulation). Our results reveal that the DRG grouper performs poorly in classifying the DRG correctly before admission while the Naïve Bayes approach substantially improves the classification task. The results from the connection of the classification method with the mathematical program also reveal that resource allocation decisions can be more effective and efficient with the hybrid approach.

Di-codon Usage for Gene Classification

NASA Astrophysics Data System (ADS)

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

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

Oregon Hydrologic Landscapes: An Approach for Broadscale Hydrologic Classification

EPA Science Inventory

Gaged streams represent only a small percentage of watershed hydrologic conditions throughout the Unites States and globe, but there is a growing need for hydrologic classification systems that can serve as the foundation for broad-scale assessments of the hydrologic functions of...

On-board multispectral classification study. Volume 2: Supplementary tasks. [adaptive control

NASA Technical Reports Server (NTRS)

Ewalt, D.

1979-01-01

The operational tasks of the onboard multispectral classification study were defined. These tasks include: sensing characteristics for future space applications; information adaptive systems architectural approaches; data set selection criteria; and onboard functional requirements for interfacing with global positioning satellites.

Cognitive Precursors of Receptive vs. Expressive Language.

ERIC Educational Resources Information Center

Smolak, Linda

1982-01-01

The relationship of object permanence and classification skills to receptive and expressive language development was investigated in infants. Object permanence, classification, and parent-child verbal interaction ratings were about equally related to language comprehension functioning, while permanence was more strongly related to language…

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.

[Automation in surgery: a systematical approach].

PubMed

Strauss, G; Meixensberger, J; Dietz, A; Manzey, D

2007-04-01

Surgical assistance systems permit a misalignment from the purely manual to an assisted activity of the surgeon (automation). Automation defines a system, that partly or totally fulfils function, those was carried out before totally or partly by the user. The organization of surgical assistance systems following application (planning, simulation, intraoperative navigation and visualization) or technical configuration of the system (manipulator, robot) is not suitable for a description of the interaction between user (surgeon) and the system. The available work has the goal of providing a classification for the degree of the automation of surgical interventions and describing by examples. The presented classification orients itself at pre-working from the Human-Factors-Sciences. As a condition for an automation of a surgical intervention applies that an assumption of a task, which was alone assigned so far to the surgeon takes place via the system. For both reference objects (humans and machine) the condition passively or actively comes into consideration. Besides can be classified according to which functions are taken over during a selected function division by humans and/or the surgical assistance system. Three functional areas were differentiated: "information acquisition and -analysis", "decision making and action planning" as well as "execution of the surgical action". From this results a classification of pre- and intraoperative surgical assist systems in six categories, which represent different automation degrees. The classification pattern is described and illustrated on the basis of surgical of examples.

Functional Interactions Between c-Src and HER1 Potentiate Neoplastic Transformation: Implications for the Etiology of Human Breast Cancer

DTIC Science & Technology

2000-07-01

receptor 120 16. PRICE CODE 17. SECURITY CLASSIFICATION 18 . SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT OF REPORT OF...THIS PAGE OF ABSTRACT Unclassified Unclassified Unclassified Unlimited NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) Prescribed by ANSI Std. Z39- 18 ... 18 -26 Appended Manuscripts 3 INTRODUCTION Recent work in our laboratory has established the importance of a

Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.

PubMed

Poirazi, Panayiota; Neocleous, Costas; Pattichis, Costantinos S; Schizas, Christos N

2004-05-01

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.

Bioinformatic and Comparative Localization of Rab Proteins Reveals Functional Insights into the Uncharacterized GTPases Ypt10p and Ypt11p†

PubMed Central

Buvelot Frei, Stéphanie; Rahl, Peter B.; Nussbaum, Maria; Briggs, Benjamin J.; Calero, Monica; Janeczko, Stephanie; Regan, Andrew D.; Chen, Catherine Z.; Barral, Yves; Whittaker, Gary R.; Collins, Ruth N.

2006-01-01

A striking characteristic of a Rab protein is its steady-state localization to the cytosolic surface of a particular subcellular membrane. In this study, we have undertaken a combined bioinformatic and experimental approach to examine the evolutionary conservation of Rab protein localization. A comprehensive primary sequence classification shows that 10 out of the 11 Rab proteins identified in the yeast (Saccharomyces cerevisiae) genome can be grouped within a major subclass, each comprising multiple Rab orthologs from diverse species. We compared the locations of individual yeast Rab proteins with their localizations following ectopic expression in mammalian cells. Our results suggest that green fluorescent protein-tagged Rab proteins maintain localizations across large evolutionary distances and that the major known player in the Rab localization pathway, mammalian Rab-GDI, is able to function in yeast. These findings enable us to provide insight into novel gene functions and classify the uncharacterized Rab proteins Ypt10p (YBR264C) as being involved in endocytic function and Ypt11p (YNL304W) as being localized to the endoplasmic reticulum, where we demonstrate it is required for organelle inheritance. PMID:16980630

The International Classification of Functioning, Disability and Health (ICF) in vocational rehabilitation and disability assessment in Slovenia: state of law and users' perspective.

PubMed

Ptyushkin, Pavel; Vidmar, Gaj; Burger, Helena; Marinček, Crt; Escorpizo, Reuben

2011-01-01

This study illustrates the use of the ICF in vocational rehabilitation and disability assessment in Slovenia. A review of the Slovenian law about vocational rehabilitation was performed. A survey was developed and group and individual interviews were conducted with professionals involved in vocational rehabilitation who use the ICF. The vast majority of the respondents believe that ICF helps to create a common language for multidisciplinary communication. The main advantages of the ICF identified by the respondents are that it provides a holistic view of the person, assesses complexities of functioning, provides a unified language and offers a quick and easy insight into functioning. The disadvantages of ICF are complicated terminology and subjectivity of the assessor. A difficulty encountered by the users is that by law, only body functions of the ICF are assessed. Additional qualitative analysis of the users' understanding of ICF and its purpose revealed heterogeneity. Significant differences between public and private organisations were found. ICF is a promising tool for use in vocational rehabilitation and disability assessment in Slovenia. A major challenge is the lack of interface between ICF and policies on vocational rehabilitation in Slovenia.

Hidden order and flux attachment in symmetry-protected topological phases: A Laughlin-like approach

NASA Astrophysics Data System (ADS)

Ringel, Zohar; Simon, Steven H.

2015-05-01

Topological phases of matter are distinct from conventional ones by their lack of a local order parameter. Still in the quantum Hall effect, hidden order parameters exist and constitute the basis for the celebrated composite-particle approach. Whether similar hidden orders exist in 2D and 3D symmetry protected topological phases (SPTs) is a largely open question. Here, we introduce a new approach for generating SPT ground states, based on a generalization of the Laughlin wave function. This approach gives a simple and unifying picture of some classes of SPTs in 1D and 2D, and reveals their hidden order and flux attachment structures. For the 1D case, we derive exact relations between the wave functions obtained in this manner and group cohomology wave functions, as well as matrix product state classification. For the 2D Ising SPT, strong analytical and numerical evidence is given to show that the wave function obtained indeed describes the desired SPT. The Ising SPT then appears as a state with quasi-long-range order in composite degrees of freedom consisting of Ising-symmetry charges attached to Ising-symmetry fluxes.

In situ measurements of angular-dependent light scattering by aerosols over the contiguous United States

NASA Astrophysics Data System (ADS)

Reed Espinosa, W.; Vanderlei Martins, J.; Remer, Lorraine A.; Puthukkudy, Anin; Orozco, Daniel; Dolgos, Gergely

2018-03-01

This work provides a synopsis of aerosol phase function (F11) and polarized phase function (F12) measurements made by the Polarized Imaging Nephelometer (PI-Neph) during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and the Deep Convection Clouds and Chemistry (DC3) field campaigns. In order to more easily explore this extensive dataset, an aerosol classification scheme is developed that identifies the different aerosol types measured during the deployments. This scheme makes use of ancillary data that include trace gases, chemical composition, aerodynamic particle size and geographic location, all independent of PI-Neph measurements. The PI-Neph measurements are then grouped according to their ancillary data classifications and the resulting scattering patterns are examined in detail. These results represent the first published airborne measurements of F11 and -F12/F11 for many common aerosol types. We then explore whether PI-Neph light-scattering measurements alone are sufficient to reconstruct the results of this ancillary data classification algorithm. Principal component analysis (PCA) is used to reduce the dimensionality of the multi-angle PI-Neph scattering data and the individual measurements are examined as a function of ancillary data classification. Clear clustering is observed in the PCA score space, corresponding to the ancillary classification results, suggesting that, indeed, a strong link exists between the angular-scattering measurements and the aerosol type or composition. Two techniques are used to quantify the degree of clustering and it is found that in most cases the results of the ancillary data classification can be predicted from PI-Neph measurements alone with better than 85 % recall. This result both emphasizes the validity of the ancillary data classification as well as the PI-Neph's ability to distinguish common aerosol types without additional information.

Expected energy-based restricted Boltzmann machine for classification.

PubMed

Elfwing, S; Uchibe, E; Doya, K

2015-04-01

In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach to provide a self-contained RBM method for classification, inspired by free-energy based function approximation (FE-RBM), originally proposed for reinforcement learning. For classification, the FE-RBM method computes the output for an input vector and a class vector by the negative free energy of an RBM. Learning is achieved by stochastic gradient-descent using a mean-squared error training objective. In an earlier study, we demonstrated that the performance and the robustness of FE-RBM function approximation can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that the learning performance of RBM function approximation can be further improved by computing the output by the negative expected energy (EE-RBM), instead of the negative free energy. To create a deep learning architecture, we stack several RBMs on top of each other. We also connect the class nodes to all hidden layers to try to improve the performance even further. We validate the classification performance of EE-RBM using the MNIST data set and the NORB data set, achieving competitive performance compared with other classifiers such as standard neural networks, deep belief networks, classification RBMs, and support vector machines. The purpose of using the NORB data set is to demonstrate that EE-RBM with binary input nodes can achieve high performance in the continuous input domain. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

PubMed

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Perceptual-motor skill learning in Gilles de la Tourette syndrome. Evidence for multiple procedural learning and memory systems.

PubMed

Marsh, Rachel; Alexander, Gerianne M; Packard, Mark G; Zhu, Hongtu; Peterson, Bradley S

2005-01-01

Procedural learning and memory systems likely comprise several skills that are differentially affected by various illnesses of the central nervous system, suggesting their relative functional independence and reliance on differing neural circuits. Gilles de la Tourette syndrome (GTS) is a movement disorder that involves disturbances in the structure and function of the striatum and related circuitry. Recent studies suggest that patients with GTS are impaired in performance of a probabilistic classification task that putatively involves the acquisition of stimulus-response (S-R)-based habits. Assessing the learning of perceptual-motor skills and probabilistic classification in the same samples of GTS and healthy control subjects may help to determine whether these various forms of procedural (habit) learning rely on the same or differing neuroanatomical substrates and whether those substrates are differentially affected in persons with GTS. Therefore, we assessed perceptual-motor skill learning using the pursuit-rotor and mirror tracing tasks in 50 patients with GTS and 55 control subjects who had previously been compared at learning a task of probabilistic classifications. The GTS subjects did not differ from the control subjects in performance of either the pursuit rotor or mirror-tracing tasks, although they were significantly impaired in the acquisition of a probabilistic classification task. In addition, learning on the perceptual-motor tasks was not correlated with habit learning on the classification task in either the GTS or healthy control subjects. These findings suggest that the differing forms of procedural learning are dissociable both functionally and neuroanatomically. The specific deficits in the probabilistic classification form of habit learning in persons with GTS are likely to be a consequence of disturbances in specific corticostriatal circuits, but not the same circuits that subserve the perceptual-motor form of habit learning.

Mapping the Content of the Patient Reported Outcomes Measurement Information System (PROMIS®) Using the International Classification of Functioning, Health and Disability

PubMed Central

Tucker, Carole A; Escorpizo, Reuben; Cieza, Alarcos; Lai, Jin Shei; Stucki, Gerold; Ustun, T. Bedirhan; Kostanjsek, Nenad; Cella, David; Forrest, Christopher B.

2014-01-01

Background The Patient Reported Outcomes Measurement Information System (PROMIS®) is a U.S. National Institutes of Health initiative that has produced self-reported item banks for physical, mental, and social health. Objective To describe the content of PROMIS at the item level using the World Health Organization’s International Classification of Functioning, Disability and Health (ICF). Methods All PROMIS adult items (publicly available as of 2012) were assigned to relevant ICF concepts. The content of the PROMIS adult item banks were then described using the mapped ICF code descriptors. Results The 1006 items in the PROMIS instruments could all be mapped to ICF concepts at the second level of classification, with the exception of 3 items of global or general health that mapped across the first-level classification of ICF activity and participation component (d categories). Individual PROMIS item banks mapped from 1 to 5 separate ICF codes indicating one-to-one, one-to-many and many-to-one mappings between PROMIS item banks and ICF second level classification codes. PROMIS supports measurement of the majority of major concepts in the ICF Body Functions (b) and Activity & Participation (d) components using PROMIS item banks or subsets of PROMIS items that could, with care, be used to develop customized instruments. Given the focus of PROMIS is on measurement of person health outcomes, concepts in body structures (s) and some body functions (b), as well as many ICF environmental factor have minimal coverage in PROMIS. Discussion The PROMIS-ICF mapped items provide a basis for users to evaluate the ICF related content of specific PROMIS instruments, and to select PROMIS instruments in ICF based measurement applications. PMID:24760532

Promoting consistent use of the communication function classification system (CFCS).

PubMed

Cunningham, Barbara Jane; Rosenbaum, Peter; Hidecker, Mary Jo Cooley

2016-01-01

We developed a Knowledge Translation (KT) intervention to standardize the way speech-language pathologists working in Ontario Canada's Preschool Speech and Language Program (PSLP) used the Communication Function Classification System (CFCS). This tool was being used as part of a provincial program evaluation and standardizing its use was critical for establishing reliability and validity within the provincial dataset. Two theoretical foundations - Diffusion of Innovations and the Communication Persuasion Matrix - were used to develop and disseminate the intervention to standardize use of the CFCS among a cohort speech-language pathologists. A descriptive pre-test/post-test study was used to evaluate the intervention. Fifty-two participants completed an electronic pre-test survey, reviewed intervention materials online, and then immediately completed an electronic post-test survey. The intervention improved clinicians' understanding of how the CFCS should be used, their intentions to use the tool in the standardized way, and their abilities to make correct classifications using the tool. Findings from this work will be shared with representatives of the Ontario PSLP. The intervention may be disseminated to all speech-language pathologists working in the program. This study can be used as a model for developing and disseminating KT interventions for clinicians in paediatric rehabilitation. The Communication Function Classification System (CFCS) is a new tool that allows speech-language pathologists to classify children's skills into five meaningful levels of function. There is uncertainty and inconsistent practice in the field about the methods for using this tool. This study used combined two theoretical frameworks to develop an intervention to standardize use of the CFCS among a cohort of speech-language pathologists. The intervention effectively increased clinicians' understanding of the methods for using the CFCS, ability to make correct classifications, and intention to use the tool in the standardized way in the future.

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

PubMed

Jiao, Y; Chen, R; Ke, X; Cheng, L; Chu, K; Lu, Z; Herskovits, E H

2011-01-01

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.

Classification of debtor credit status and determination amount of credit risk by using linier discriminant function

NASA Astrophysics Data System (ADS)

Aidi, Muhammad Nur; Sari, Resty Indah

2012-05-01

A decision of credit that given by bank or another creditur must have a risk and it called credit risk. Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. The substantial of credit risk can lead to losses for the banks and the debtor. To minimize this problem need a further study to identify a potential new customer before the decision given. Identification of debtor can using various approaches analysis, one of them is by using discriminant analysis. Discriminant analysis in this study are used to classify whether belonging to the debtor's good credit or bad credit. The result of this study are two discriminant functions that can identify new debtor. Before step built the discriminant function, selection of explanatory variables should be done. Purpose of selection independent variable is to choose the variable that can discriminate the group maximally. Selection variables in this study using different test, for categoric variable selection of variable using proportion chi-square test, and stepwise discriminant for numeric variable. The result of this study are two discriminant functions that can identify new debtor. The selected variables that can discriminating two groups of debtor maximally are status of existing checking account, credit history, credit amount, installment rate in percentage of disposable income, sex, age in year, other installment plans, and number of people being liable to provide maintenance. This classification produce a classification accuracy rate is good enough, that is equal to 74,70%. Debtor classification using discriminant analysis has risk level that is small enough, and it ranged beetwen 14,992% and 17,608%. Based on that credit risk rate, using discriminant analysis on the classification of credit status can be used effectively.

The Representation of Object-Directed Action and Function Knowledge in the Human Brain.

PubMed

Chen, Quanjing; Garcea, Frank E; Mahon, Bradford Z

2016-04-01

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Obesity and Functioning Among Individuals with Chronic Obstructive Pulmonary Disease (COPD).

PubMed

Katz, Patricia; Iribarren, Carlos; Sanchez, Gabriela; Blanc, Paul D

2016-06-01

In COPD, body composition studies have focused primarily on low BMI. We examined obesity (BMI ≥ 30 kg/m(2)) as a risk factor for poor function and longitudinal functional decline. Data from a longitudinal cohort of adults with COPD (n = 1096) and an age- and sex-matched comparison group collected in two in-person visits ∼49 months apart were analyzed. Two measures of functioning were examined: six-minute walk distance (6MWD) and Short Physical Performance Battery (SPPB). Multivariate regression analyses examined relationships of obesity with functioning. Secondary analyses stratified by GOLD classification (GOLD-0/1, GOLD-2, GOLD-3/4). Obesity (53% of COPD cohort) was associated cross-sectionally with 6MWD and SPPB in COPD, and only with 6MWD in the comparison group. Obesity predicted significant functional decline in 6MWD for individuals with COPD (odds ratio (OR) for decline [95% CI] 1.8 [1.1, 2.9]), but not the comparison group. Secondary analyses revealed that the risk of decline was significant only in those with more severe COPD (GOLD 3/4, OR = 2.3 [1.0, 5.4]). Obesity was highly prevalent and was associated with poor function concurrently and with subsequent decline in 6MWD in COPD. Obesity in COPD should be considered a risk not only for more co-morbidities and greater health care use, but also for functional decline.

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.

Modified Angle's Classification for Primary Dentition

PubMed Central

Chandranee, Kaushik Narendra; Chandranee, Narendra Jayantilal; Nagpal, Devendra; Lamba, Gagandeep; Choudhari, Purva; Hotwani, Kavita

2017-01-01

Aim: This study aims to propose a modification of Angle's classification for primary dentition and to assess its applicability in children from Central India, Nagpur. Methods: Modification in Angle's classification has been proposed for application in primary dentition. Small roman numbers i/ii/iii are used for primary dentition notation to represent Angle's Class I/II/III molar relationships as in permanent dentition, respectively. To assess applicability of modified Angle's classification a cross-sectional preschool 2000 children population from central India; 3–6 years of age residing in Nagpur metropolitan city of Maharashtra state were selected randomly as per the inclusion and exclusion criteria. Results: Majority 93.35% children were found to have bilateral Class i followed by 2.5% bilateral Class ii and 0.2% bilateral half cusp Class iii molar relationships as per the modified Angle's classification for primary dentition. About 3.75% children had various combinations of Class ii relationships and 0.2% children were having Class iii subdivision relationship. Conclusions: Modification of Angle's classification for application in primary dentition has been proposed. A cross-sectional investigation using new classification revealed various 6.25% Class ii and 0.4% Class iii molar relationships cases in preschool children population in a metropolitan city of Nagpur. Application of the modified Angle's classification to other population groups is warranted to validate its routine application in clinical pediatric dentistry. PMID:29326514

Review article: A systematic review of emergency department incident classification frameworks.

PubMed

Murray, Matthew; McCarthy, Sally

2018-06-01

As in any part of the hospital system, safety incidents can occur in the ED. These incidents arguably have a distinct character, as the ED involves unscheduled flows of urgent patients who require disparate services. To aid understanding of safety issues and support risk management of the ED, a comparison of published ED specific incident classification frameworks was performed. A review of emergency medicine, health management and general medical publications, using Ovid SP to interrogate Medline (1976-2016) was undertaken to identify any type of taxonomy or classification-like framework for ED related incidents. These frameworks were then analysed and compared. The review identified 17 publications containing an incident classification framework. Comparison of factors and themes making up the classification constituent elements revealed some commonality, but no overall consistency, nor evolution towards an ideal framework. Inconsistency arises from differences in the evidential basis and design methodology of classifications, with design itself being an inherently subjective process. It was not possible to identify an 'ideal' incident classification framework for ED risk management, and there is significant variation in the selection of categories used by frameworks. The variation in classification could risk an unbalanced emphasis in findings through application of a particular framework. Design of an ED specific, ideal incident classification framework should be informed by a much wider range of theories of how organisations and systems work, in addition to clinical and human factors. © 2017 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features

PubMed Central

Song, Le; Epps, Julien

2007-01-01

Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach. PMID:18364986

Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

PubMed

Wang, Rong

2015-01-01

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

Improving oil classification quality from oil spill fingerprint beyond six sigma approach.

PubMed

Juahir, Hafizan; Ismail, Azimah; Mohamed, Saiful Bahri; Toriman, Mohd Ekhwan; Kassim, Azlina Md; Zain, Sharifuddin Md; Ahmad, Wan Kamaruzaman Wan; Wah, Wong Kok; Zali, Munirah Abdul; Retnam, Ananthy; Taib, Mohd Zaki Mohd; Mokhtar, Mazlin

2017-07-15

This study involves the use of quality engineering in oil spill classification based on oil spill fingerprinting from GC-FID and GC-MS employing the six-sigma approach. The oil spills are recovered from various water areas of Peninsular Malaysia and Sabah (East Malaysia). The study approach used six sigma methodologies that effectively serve as the problem solving in oil classification extracted from the complex mixtures of oil spilled dataset. The analysis of six sigma link with the quality engineering improved the organizational performance to achieve its objectivity of the environmental forensics. The study reveals that oil spills are discriminated into four groups' viz. diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) according to the similarity of the intrinsic chemical properties. Through the validation, it confirmed that four discriminant component, diesel, hydrocarbon fuel oil (HFO), mixture oil lubricant and fuel oil (MOLFO) and waste oil (WO) dominate the oil types with a total variance of 99.51% with ANOVA giving F stat >F critical at 95% confidence level and a Chi Square goodness test of 74.87. Results obtained from this study reveals that by employing six-sigma approach in a data-driven problem such as in the case of oil spill classification, good decision making can be expedited. Copyright © 2017. Published by Elsevier Ltd.

Classification and source determination of medium petroleum distillates by chemometric and artificial neural networks: a self organizing feature approach.

PubMed

Mat-Desa, Wan N S; Ismail, Dzulkiflee; NicDaeid, Niamh

2011-10-15

Three different medium petroleum distillate (MPD) products (white spirit, paint brush cleaner, and lamp oil) were purchased from commercial stores in Glasgow, Scotland. Samples of 10, 25, 50, 75, 90, and 95% evaporated product were prepared, resulting in 56 samples in total which were analyzed using gas chromatography-mass spectrometry. Data sets from the chromatographic patterns were examined and preprocessed for unsupervised multivariate analyses using principal component analysis (PCA), hierarchical cluster analysis (HCA), and a self organizing feature map (SOFM) artificial neural network. It was revealed that data sets comprised of higher boiling point hydrocarbon compounds provided a good means for the classification of the samples and successfully linked highly weathered samples back to their unevaporated counterpart in every case. The classification abilities of SOFM were further tested and validated for their predictive abilities where one set of weather data in each case was withdrawn from the sample set and used as a test set of the retrained network. This revealed SOFM to be an outstanding mechanism for sample discrimination and linkage over the more conventional PCA and HCA methods often suggested for such data analysis. SOFM also has the advantage of providing additional information through the evaluation of component planes facilitating the investigation of underlying variables that account for the classification. © 2011 American Chemical Society

The history of the CATH structural classification of protein domains.

PubMed

Sillitoe, Ian; Dawson, Natalie; Thornton, Janet; Orengo, Christine

2015-12-01

This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Microsurgical reconstruction of large nerve defects using autologous nerve grafts.

PubMed

Daoutis, N K; Gerostathopoulos, N E; Efstathopoulos, D G; Misitizis, D P; Bouchlis, G N; Anagnostou, S K

1994-01-01

Between 1986 and 1993, 643 patients with peripheral nerve trauma were treated in our clinic. Primary neurorraphy was performed in 431 of these patients and nerve grafting in 212 patients. We present the functional results after nerve grafting in 93 patients with large nerve defects who were followed for more than 2 years. Evaluation of function was based on the Medical Research Council (MRC) classification for motor and sensory recovery. Factors affecting functional outcome, such as age of the patient, denervation time, length of the defect, and level of the injury were noted. Good results according to the MRC classification were obtained in the majority of cases, although function remained less than that of the uninjured side.

A neural network for noise correlation classification

NASA Astrophysics Data System (ADS)

Paitz, Patrick; Gokhberg, Alexey; Fichtner, Andreas

2018-02-01

We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20 per cent or less can be achieved when the network training is performed with as little as 3.5 per cent and 16 per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35 per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.

Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

PubMed Central

Kauppi, Jukka-Pekka; Hahne, Janne; Müller, Klaus-Robert; Hyvärinen, Aapo

2015-01-01

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results. PMID:26039100

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

PubMed Central

Subari, Norazian; Saleh, Junita Mohamad; Shakaff, Ali Yeon Md; Zakaria, Ammar

2012-01-01

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data. PMID:23202033

Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA

EPA Science Inventory

Classification can allow assessments of the hydrologic functions of landscapes and their responses to stressors. Here we demonstrate the use of a hydrologic landscape (HL) approach to assess vulnerability to potential future climate change at statewide and basin scales. The HL ...

21 CFR 864.8100 - Bothrops atrox reagent.

Code of Federal Regulations, 2011 CFR

2011-04-01

...) Identification. A Bothrops atrox reagent is a device made from snake venom and used to determine blood fibrinogen... the treatment of thrombosis) or as an aid in the classification of dysfibrinogenemia (presence in the plasma of functionally defective fibrinogen). (b) Classification. Class II (performance standards). [45...

21 CFR 864.8100 - Bothrops atrox reagent.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) Identification. A Bothrops atrox reagent is a device made from snake venom and used to determine blood fibrinogen... the treatment of thrombosis) or as an aid in the classification of dysfibrinogenemia (presence in the plasma of functionally defective fibrinogen). (b) Classification. Class II (performance standards). [45...

75 FR 56015 - Vessel Inspection Alternatives

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-15

... page 88, in Sec. 8.420, paragraph (c) is revised to read as follows: Sec. 8.420 Classification society authorization to participate in the Alternate Compliance Program. * * * * * (c) A recognized classification... (2) Must have performed a delegated function related to general vessel safety assessment, as defined...