Development of bimanual performance in young children with cerebral palsy.

PubMed

Klevberg, Gunvor L; Elvrum, Ann-Kristin G; Zucknick, Manuela; Elkjaer, Sonja; Østensjø, Sigrid; Krumlinde-Sundholm, Lena; Kjeken, Ingvild; Jahnsen, Reidun

2018-05-01

To describe the development of bimanual performance among young children with unilateral or bilateral cerebral palsy (CP). A population-based sample of 102 children (53 males, 49 females), median age 28.5 months (interquartile range [IQR] 16mo) at first assessment and 47 months (IQR 18mo) at last assessment, was assessed half-yearly with the Assisting Hand Assessment (AHA) or the Both Hands Assessment (BoHA) for a total of 329 assessments. Developmental limits and rates were estimated by nonlinear mixed-effects models. Developmental trajectories were compared between levels of manual ability (Mini-Manual Ability Classification System [Mini-MACS] and MACS) and AHA or BoHA performance at 18 months of age (AHA-18/BoHA-18) for both CP subgroups, and additionally between children with bilateral CP with symmetric or asymmetric hand use. For both CP subgroups, children classified in Mini-MACS/MACS level I, and those with high AHA-18 or BoHA-18 reached the highest limits of performance. For children with bilateral CP the developmental change was small, and children with symmetric hand use reached the highest limits. Mini-MACS/MACS levels and AHA-18 or BoHA-18 distinguished between various developmental trajectories both for children with unilateral and bilateral CP. Children with bilateral CP changed their performance to a smaller extent than children with unilateral CP. Manual Ability Classification System levels and Assisting Hand Assessment/Both Hands Assessment performance at 18 months are important predictors of hand use development in cerebral palsy (CP). Children with bilateral CP improved less than those with unilateral CP. Children with bilateral CP and symmetric hand use reached higher limits than those with asymmetry. © 2018 Mac Keith Press.

Validation of the questionnaire on hand function assessment in leprosy.

PubMed

Ferreira, Telma Leonel; Alvarez, Rosicler Rocha Aiza; Virmond, Marcos da Cunha Lopes

2012-06-01

To validate the psychometric properties of the questionnaire on hand function assessment in leprosy. Study conducted with a convenience sample of 101 consecutive patients in Brasília (Central-Western Brazil), from June 2008 to July 2009. The individuals were adults affected by leprosy, with impairment of the ulnar, median and radial nerves. Interobservers and intraobserver reproducibility was analyzed through successive interviews, and construct validity was analyzed through association between age, clinical form of leprosy, duration of nerve injury, grip and pinch strength measured with a dynamometer, sensibility test performed with Semmes-Weinstein monofilaments and manual ability assessment using the Jebsen test of hand function. Pondered kappa coefficient was calculated and a Bland-Altman plot was constructed to assess the reproducibility of the instrument. For internal consistency, Cronbach's alpha coefficient was utilized. Pearson's correlation coefficient was calculated and a multiple regression model was used. The pondered kappa values for interobservers and intraobserver assessments ranged from 0.86 to 0.97 and from 0.85 to 0.97, respectively. The value of Cronbach's alpha coefficient was 0.967. Pearson's correlation coefficient showed an association (p < 0.001) among duration of nerve injury, grip and pinch strength, cutaneous sensibility and mean score in the Jebsen Test. The mean score of the questionnaire on hand functional assessment in leprosy was associated with operational classification of leprosy, duration of nerve injury, grip strength, cutaneous sensibility and manual ability (p < 0.0001 for the model as a whole). The questionnaire on hand functional assessment in leprosy presents almost perfect interobservers and intraobserver reproducibility, high internal consistency and correlation with operational classification of leprosy, duration of nerve injury, grip strength, cutaneous sensibility in the hands and manual ability.

HPMCD: the database of human microbial communities from metagenomic datasets and microbial reference genomes.

PubMed

Forster, Samuel C; Browne, Hilary P; Kumar, Nitin; Hunt, Martin; Denise, Hubert; Mitchell, Alex; Finn, Robert D; Lawley, Trevor D

2016-01-04

The Human Pan-Microbe Communities (HPMC) database (http://www.hpmcd.org/) provides a manually curated, searchable, metagenomic resource to facilitate investigation of human gastrointestinal microbiota. Over the past decade, the application of metagenome sequencing to elucidate the microbial composition and functional capacity present in the human microbiome has revolutionized many concepts in our basic biology. When sufficient high quality reference genomes are available, whole genome metagenomic sequencing can provide direct biological insights and high-resolution classification. The HPMC database provides species level, standardized phylogenetic classification of over 1800 human gastrointestinal metagenomic samples. This is achieved by combining a manually curated list of bacterial genomes from human faecal samples with over 21000 additional reference genomes representing bacteria, viruses, archaea and fungi with manually curated species classification and enhanced sample metadata annotation. A user-friendly, web-based interface provides the ability to search for (i) microbial groups associated with health or disease state, (ii) health or disease states and community structure associated with a microbial group, (iii) the enrichment of a microbial gene or sequence and (iv) enrichment of a functional annotation. The HPMC database enables detailed analysis of human microbial communities and supports research from basic microbiology and immunology to therapeutic development in human health and disease. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Improving crop classification through attention to the timing of airborne radar acquisitions

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Protz, R.

1984-01-01

Radar remote sensors may provide valuable input to crop classification procedures because of (1) their independence of weather conditions and solar illumination, and (2) their ability to respond to differences in crop type. Manual classification of multidate synthetic aperture radar (SAR) imagery resulted in an overall accuracy of 83 percent for corn, forest, grain, and 'other' cover types. Forests and corn fields were identified with accuracies approaching or exceeding 90 percent. Grain fields and 'other' fields were often confused with each other, resulting in classification accuracies of 51 and 66 percent, respectively. The 83 percent correct classification represents a 10 percent improvement when compared to similar SAR data for the same area collected at alternate time periods in 1978. These results demonstrate that improvements in crop classification accuracy can be achieved with SAR data by synchronizing data collection times with crop growth stages in order to maximize differences in the geometric and dielectric properties of the cover types of interest.

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,...

Feasibility of a Day-Camp Model of Modified Constraint-Induced Movement Therapy with and without Botulinum Toxin A Injection for Children with Hemiplegia

ERIC Educational Resources Information Center

Eliasson, Ann-Christin; Shaw, Karin; Ponten, Eva; Boyd, Roslyn; Krumlinde-Sundholm, Lena

2009-01-01

The objective of the study was to investigate the feasibility of modified constraint-induced (CI) therapy provided in a 2-week day-camp model with and without intramuscular botulinum toxin type A (BoNT-A) injections for children with congenital cerebral palsy. Sixteen children with congenital hemiplegia, Manual Ability Classification System (MACS)…

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.

Autism according to diagnostic and statistical manual of mental disorders 5(th) edition: The need for further improvements.

PubMed

Posar, Annio; Resca, Federica; Visconti, Paola

2015-01-01

The fifth edition of the diagnostic and statistical manual of mental disorders (DSM-5) introduced significant changes in the classification of autism spectrum disorders (ASD), including the abolition of the diagnostic subcategories proposed by DSM-IV-Text Revision. DSM-5 describes three levels of increasing severity of ASD. The authors report two explanatory cases with ASD (verbal boys, aged about 7 and a half years, without intellectual disability). According to DSM-5, both cases fall into the lowest severity level of ASD. However, their neuropsychological and neurobehavioral profile varies significantly. While the first boy showed a prevalent impairment of visuoconstructional and visuoperceptual abilities, the second one presented a predominant involvement of verbal functions, with qualitative impairments in communication. A further step forward in the definition and classification of ASD, taking into account both intensity and quality of symptoms, is recommended in order to formulate a reliable prognosis, plan an individualized treatment and monitor the clinical course over time.

The Eating and Drinking Ability Classification System: concurrent validity and reliability in children with cerebral palsy.

PubMed

Tschirren, Lea; Bauer, Susanne; Hanser, Chiara; Marsico, Petra; Sellers, Diane; van Hedel, Hubertus J A

2018-06-01

As there is little evidence for concurrent validity of the Eating and Drinking Ability Classification System (EDACS), this study aimed to determine its concurrent validity and reliability in children and adolescents with cerebral palsy (CP). After an extensive translation procedure, we applied the German language version to 52 participants with CP (30 males, 22 females, mean age 9y 7mo [SD 4y 2mo]). We correlated (Kendall's tau or K τ ) the EDACS levels with the Bogenhausener Dysphagiescore (BODS), and the EDACS level of assistance with the Manual Ability Classification System (MACS) and the item 'eating' of the Functional Independence Measure for Children (WeeFIM). We further quantified the interrater reliability between speech and language therapists (SaLTs) and between SaLTs and parents with Kappa (κ). The EDACS levels correlated highly with the BODS (K τ =0.79), and the EDACS level of assistance correlated highly with the MACS (K τ =0.73) and WeeFIM eating item (K τ =-0.80). Interrater reliability proved almost perfect between SaLTs (EDACS: κ=0.94; EDACS level of assistance: κ=0.89) and SaLTs and parents (EDACS: κ=0.82; EDACS level of assistance: κ=0.89). The EDACS levels and level of assistance seem valid and showed almost perfect interrater reliability when classifying eating and drinking problems in children and adolescents with CP. The Eating and Drinking Ability Classification System (EDACS) correlates well with a dysphagia score. The EDACS level of assistance proves valid. The German version of EDACS is highly reliable. EDACS correlates moderately to highly with other classification systems. © 2018 Mac Keith Press.

Yarn-dyed fabric defect classification based on convolutional neural network

NASA Astrophysics Data System (ADS)

Jing, Junfeng; Dong, Amei; Li, Pengfei; Zhang, Kaibing

2017-09-01

Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

From Classification to Epilepsy Ontology and Informatics

PubMed Central

Zhang, Guo-Qiang; Sahoo, Satya S; Lhatoo, Samden D

2012-01-01

Summary The 2010 International League Against Epilepsy (ILAE) classification and terminology commission report proposed a much needed departure from previous classifications to incorporate advances in molecular biology, neuroimaging, and genetics. It proposed an interim classification and defined two key requirements that need to be satisfied. The first is the ability to classify epilepsy in dimensions according to a variety of purposes including clinical research, patient care, and drug discovery. The second is the ability of the classification system to evolve with new discoveries. Multi-dimensionality and flexibility are crucial to the success of any future classification. In addition, a successful classification system must play a central role in the rapidly growing field of epilepsy informatics. An epilepsy ontology, based on classification, will allow information systems to facilitate data-intensive studies and provide a proven route to meeting the two foregoing key requirements. Epilepsy ontology will be a structured terminology system that accommodates proposed and evolving ILAE classifications, the NIH/NINDS Common Data Elements, the ICD systems and explicitly specifies all known relationships between epilepsy concepts in a proper framework. This will aid evidence based epilepsy diagnosis, investigation, treatment and research for a diverse community of clinicians and researchers. Benefits range from systematization of electronic patient records to multi-modal data repositories for research and training manuals for those involved in epilepsy care. Given the complexity, heterogeneity and pace of research advances in the epilepsy domain, such an ontology must be collaboratively developed by key stakeholders in the epilepsy community and experts in knowledge engineering and computer science. PMID:22765502

International Classification of Impairments, Disabilities, and Handicaps: A Manual of Classification Relating to the Consequences of Disease.

ERIC Educational Resources Information Center

World Health Organization, Geneva (Switzerland).

The manual contains three classifications (impairments, disabilities, and handicaps), each relating to a different plane of experience consequent upon disease. Section 1 attempts to clarify the nature of health related experiences by addressing reponse to acute and chronic illness; the unifying framework for classification (principle events in the…

Automated classification of Acid Rock Drainage potential from Corescan drill core imagery

NASA Astrophysics Data System (ADS)

Cracknell, M. J.; Jackson, L.; Parbhakar-Fox, A.; Savinova, K.

2017-12-01

Classification of the acid forming potential of waste rock is important for managing environmental hazards associated with mining operations. Current methods for the classification of acid rock drainage (ARD) potential usually involve labour intensive and subjective assessment of drill core and/or hand specimens. Manual methods are subject to operator bias, human error and the amount of material that can be assessed within a given time frame is limited. The automated classification of ARD potential documented here is based on the ARD Index developed by Parbhakar-Fox et al. (2011). This ARD Index involves the combination of five indicators: A - sulphide content; B - sulphide alteration; C - sulphide morphology; D - primary neutraliser content; and E - sulphide mineral association. Several components of the ARD Index require accurate identification of sulphide minerals. This is achieved by classifying Corescan Red-Green-Blue true colour images into the presence or absence of sulphide minerals using supervised classification. Subsequently, sulphide classification images are processed and combined with Corescan SWIR-based mineral classifications to obtain information on sulphide content, indices representing sulphide textures (disseminated versus massive and degree of veining), and spatially associated minerals. This information is combined to calculate ARD Index indicator values that feed into the classification of ARD potential. Automated ARD potential classifications of drill core samples associated with a porphyry Cu-Au deposit are compared to manually derived classifications and those obtained by standard static geochemical testing and X-ray diffractometry analyses. Results indicate a high degree of similarity between automated and manual ARD potential classifications. Major differences between approaches are observed in sulphide and neutraliser mineral percentages, likely due to the subjective nature of manual estimates of mineral content. The automated approach presented here for the classification of ARD potential offers rapid, repeatable and accurate outcomes comparable to manually derived classifications. Methods for automated ARD classifications from digital drill core data represent a step-change for geoenvironmental management practices in the mining industry.

ICD-11 and DSM-5 personality trait domains capture categorical personality disorders: Finding a common ground.

PubMed

Bach, Bo; Sellbom, Martin; Skjernov, Mathias; Simonsen, Erik

2018-05-01

The five personality disorder trait domains in the proposed International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition are comparable in terms of Negative Affectivity, Detachment, Antagonism/Dissociality and Disinhibition. However, the International Classification of Diseases, 11th edition model includes a separate domain of Anankastia, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model includes an additional domain of Psychoticism. This study examined associations of International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domains, simultaneously, with categorical personality disorders. Psychiatric outpatients ( N = 226) were administered the Structured Clinical Interview for DSM-IV Axis II Personality Disorders Interview and the Personality Inventory for DSM-5. International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domain scores were obtained using pertinent scoring algorithms for the Personality Inventory for DSM-5. Associations between categorical personality disorders and trait domains were examined using correlation and multiple regression analyses. Both the International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models showed relevant continuity with categorical personality disorders and captured a substantial amount of their information. As expected, the International Classification of Diseases, 11th edition model was superior in capturing obsessive-compulsive personality disorder, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model was superior in capturing schizotypal personality disorder. These preliminary findings suggest that little information is 'lost' in a transition to trait domain models and potentially adds to narrowing the gap between Diagnostic and Statistical Manual of Mental Disorders, 5th edition and the proposed International Classification of Diseases, 11th edition model. Accordingly, the International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models may be used to delineate one another as well as features of familiar categorical personality disorder types. A preliminary category-to-domain 'cross walk' is provided in the article.

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

PubMed

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

2012-01-01

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

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.

Classification and mensuration of LACIE segments

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Bizzell, R. M.; Quirein, J. A.; Abotteen, K. M.; Sumner, C. A. (Principal Investigator)

1979-01-01

The theory of classification methods and the functional steps in the manual training process used in the three phases of LACIE are discussed. The major problems that arose in using a procedure for manually training a classifier and a method of machine classification are discussed to reveal the motivation that led to a redesign for the third LACIE phase.

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

PubMed

Sinha, S K; Karray, F

2002-01-01

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

The Classification and Evolution of Enzyme Function

PubMed Central

Martínez Cuesta, Sergio; Rahman, Syed Asad; Furnham, Nicholas; Thornton, Janet M.

2015-01-01

Enzymes are the proteins responsible for the catalysis of life. Enzymes sharing a common ancestor as defined by sequence and structure similarity are grouped into families and superfamilies. The molecular function of enzymes is defined as their ability to catalyze biochemical reactions; it is manually classified by the Enzyme Commission and robust approaches to quantitatively compare catalytic reactions are just beginning to appear. Here, we present an overview of studies at the interface of the evolution and function of enzymes. PMID:25986631

Yarn-dyed fabric defect classification based on convolutional neural network

NASA Astrophysics Data System (ADS)

Jing, Junfeng; Dong, Amei; Li, Pengfei

2017-07-01

Considering that the manual inspection of the yarn-dyed fabric can be time consuming and less efficient, a convolutional neural network (CNN) solution based on the modified AlexNet structure for the classification of the yarn-dyed fabric defect is proposed. CNN has powerful ability of feature extraction and feature fusion which can simulate the learning mechanism of the human brain. In order to enhance computational efficiency and detection accuracy, the local response normalization (LRN) layers in AlexNet are replaced by the batch normalization (BN) layers. In the process of the network training, through several convolution operations, the characteristics of the image are extracted step by step, and the essential features of the image can be obtained from the edge features. And the max pooling layers, the dropout layers, the fully connected layers are also employed in the classification model to reduce the computation cost and acquire more precise features of fabric defect. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show the capability of defect classification via the modified Alexnet model and indicate its robustness.

Strategies for cloud-top phase determination: differentiation between thin cirrus clouds and snow in manual (ground truth) analyses

NASA Astrophysics Data System (ADS)

Hutchison, Keith D.; Etherton, Brian J.; Topping, Phillip C.

1996-12-01

Quantitative assessments on the performance of automated cloud analysis algorithms require the creation of highly accurate, manual cloud, no cloud (CNC) images from multispectral meteorological satellite data. In general, the methodology to create ground truth analyses for the evaluation of cloud detection algorithms is relatively straightforward. However, when focus shifts toward quantifying the performance of automated cloud classification algorithms, the task of creating ground truth images becomes much more complicated since these CNC analyses must differentiate between water and ice cloud tops while ensuring that inaccuracies in automated cloud detection are not propagated into the results of the cloud classification algorithm. The process of creating these ground truth CNC analyses may become particularly difficult when little or no spectral signature is evident between a cloud and its background, as appears to be the case when thin cirrus is present over snow-covered surfaces. In this paper, procedures are described that enhance the researcher's ability to manually interpret and differentiate between thin cirrus clouds and snow-covered surfaces in daytime AVHRR imagery. The methodology uses data in up to six AVHRR spectral bands, including an additional band derived from the daytime 3.7 micron channel, which has proven invaluable for the manual discrimination between thin cirrus clouds and snow. It is concluded that while the 1.6 micron channel remains essential to differentiate between thin ice clouds and snow. However, this capability that may be lost if the 3.7 micron data switches to a nighttime-only transmission with the launch of future NOAA satellites.

Mastery motivation as a predictor of occupational performance following upper limb intervention for school-aged children with congenital hemiplegia.

PubMed

Miller, Laura; Ziviani, Jenny; Ware, Robert S; Boyd, Roslyn N

2014-10-01

To determine the extent to which children's mastery motivation predicts occupational performance outcomes following upper limb intervention (ULI). In this cohort study, participants received 45 hours of ULI, either in an intensive group-based or distributed individualized model. The Dimensions of Mastery Questionnaire (DMQ) measured mastery motivation at baseline. Occupational performance outcomes were assessed at baseline and 13 weeks' post-intervention using the Canadian Occupational Performance Measure (COPM). Multivariable models determined the contribution of mastery motivation to COPM outcome irrespective of group membership. Forty-two children with congenital hemiplegia (29 males, 13 females; mean age 7y 8mo [SD 2y 2mo]; range 5y 1mo-12y 8mo; Manual Ability Classification System [MACS] I=20 and II=22; predominant motor type unilateral spastic n=41) participated in the study. Significant gains were seen in COPM performance and satisfaction scores (p<0.001) post-intervention with no between group differences. Children who had greater persistence with object-oriented tasks (p=0.02) and better manual ability (p=0.03) achieved higher COPM performance scores at 13 weeks. Children's persistence on object-oriented tasks was the strongest predictor of COPM satisfaction (p=0.01). Children's persistence with object-oriented tasks as well as manual abilities needs to be considered when undertaking ULI. Predetermining children's motivational predispositions can assist clinicians to tailor therapy sessions individually based on children's strengths, contributing to effective engagement in ULI. © 2014 Mac Keith Press.

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.

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.

A detailed comparison of analysis processes for MCC-IMS data in disease classification—Automated methods can replace manual peak annotations

PubMed Central

Horsch, Salome; Kopczynski, Dominik; Kuthe, Elias; Baumbach, Jörg Ingo; Rahmann, Sven

2017-01-01

Motivation Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column—ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process. Method We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios. Results The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology. PMID:28910313

Purification of Training Samples Based on Spectral Feature and Superpixel Segmentation

NASA Astrophysics Data System (ADS)

Guan, X.; Qi, W.; He, J.; Wen, Q.; Chen, T.; Wang, Z.

2018-04-01

Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.

The Classification and Evolution of Enzyme Function.

PubMed

Martínez Cuesta, Sergio; Rahman, Syed Asad; Furnham, Nicholas; Thornton, Janet M

2015-09-15

Enzymes are the proteins responsible for the catalysis of life. Enzymes sharing a common ancestor as defined by sequence and structure similarity are grouped into families and superfamilies. The molecular function of enzymes is defined as their ability to catalyze biochemical reactions; it is manually classified by the Enzyme Commission and robust approaches to quantitatively compare catalytic reactions are just beginning to appear. Here, we present an overview of studies at the interface of the evolution and function of enzymes. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.

A new classification system for lesbians: the Dyke Diagnostic Manual.

PubMed

Eliason, Michele J

2010-01-01

There has been a long-standing need for a diagnostic manual that documents the unique pathological behaviors of lesbians. The Dyke Diagnostic Manual (DDM) is meant to supplement mainstream classification systems used to identify problematic behaviors in heterosexuals. This article presents thirteen uniquely lesbian conditions that are nowhere to be found in heterosexist diagnostic systems. The DDM may help to reduce the pain and suffering found in many lesbian relationships where one or both partners are afflicted.

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

PubMed

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

2017-09-01

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

Training sample selection based on self-training for liver cirrhosis classification using ultrasound images

NASA Astrophysics Data System (ADS)

Fujita, Yusuke; Mitani, Yoshihiro; Hamamoto, Yoshihiko; Segawa, Makoto; Terai, Shuji; Sakaida, Isao

2017-03-01

Ultrasound imaging is a popular and non-invasive tool used in the diagnoses of liver disease. Cirrhosis is a chronic liver disease and it can advance to liver cancer. Early detection and appropriate treatment are crucial to prevent liver cancer. However, ultrasound image analysis is very challenging, because of the low signal-to-noise ratio of ultrasound images. To achieve the higher classification performance, selection of training regions of interest (ROIs) is very important that effect to classification accuracy. The purpose of our study is cirrhosis detection with high accuracy using liver ultrasound images. In our previous works, training ROI selection by MILBoost and multiple-ROI classification based on the product rule had been proposed, to achieve high classification performance. In this article, we propose self-training method to select training ROIs effectively. Evaluation experiments were performed to evaluate effect of self-training, using manually selected ROIs and also automatically selected ROIs. Experimental results show that self-training for manually selected ROIs achieved higher classification performance than other approaches, including our conventional methods. The manually ROI definition and sample selection are important to improve classification accuracy in cirrhosis detection using ultrasound images.

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.

Exclusion of overlapping symptoms in DSM-5 mixed features specifier: heuristic diagnostic and treatment implications.

PubMed

Malhi, Gin S; Byrow, Yulisha; Outhred, Tim; Fritz, Kristina

2017-04-01

This article focuses on the controversial decision to exclude the overlapping symptoms of distractibility, irritability, and psychomotor agitation (DIP) with the introduction of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) mixed features specifier. In order to understand the placement of mixed states within the current classification system, we first review the evolution of mixed states. Then, using Kraepelin's original classification of mixed states, we compare and contrast his conceptualization with modern day definitions. The DSM-5 workgroup excluded DIP symptoms, arguing that they lack the ability to differentiate between manic and depressive states; however, accumulating evidence suggests that DIP symptoms may be core features of mixed states. We suggest a return to a Kraepelinian approach to classification-with mood, ideation, and activity as key axes-and reintegration of DIP symptoms as features that are expressed across presentations. An inclusive definition of mixed states is urgently needed to resolve confusion in clinical practice and to redirect future research efforts.

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.

21 CFR 892.5650 - Manual radionuclide applicator system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... system. (a) Identification. A manual radionuclide applicator system is a manually operated device... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Manual radionuclide applicator system. 892.5650... planning computer programs, and accessories. (b) Classification. Class I (general controls). The device is...

21 CFR 892.5650 - Manual radionuclide applicator system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... system. (a) Identification. A manual radionuclide applicator system is a manually operated device... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Manual radionuclide applicator system. 892.5650... planning computer programs, and accessories. (b) Classification. Class I (general controls). The device is...

21 CFR 892.5650 - Manual radionuclide applicator system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... system. (a) Identification. A manual radionuclide applicator system is a manually operated device... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Manual radionuclide applicator system. 892.5650... planning computer programs, and accessories. (b) Classification. Class I (general controls). The device is...

21 CFR 892.5650 - Manual radionuclide applicator system.

Code of Federal Regulations, 2014 CFR

2014-04-01

... system. (a) Identification. A manual radionuclide applicator system is a manually operated device... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Manual radionuclide applicator system. 892.5650... planning computer programs, and accessories. (b) Classification. Class I (general controls). The device is...

21 CFR 892.5650 - Manual radionuclide applicator system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... system. (a) Identification. A manual radionuclide applicator system is a manually operated device... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Manual radionuclide applicator system. 892.5650... planning computer programs, and accessories. (b) Classification. Class I (general controls). The device is...

Combined Edition of Family Planning Library Manual and Family Planning Classification.

ERIC Educational Resources Information Center

Planned Parenthood--World Population, New York, NY. Katherine Dexter McCormick Library.

This edition combines two previous publications of the Katharine Dexter McCormick Library into one volume: the Family Planning Library Manual, a guide for starting a family planning and population library or information center, and the Family Planning Classification, a coding system for organizing book and non-book materials so that they can be…

Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma.

PubMed

Mansberger, Steven L; Menda, Shivali A; Fortune, Brad A; Gardiner, Stuart K; Demirel, Shaban

2017-02-01

To characterize the error of optical coherence tomography (OCT) measurements of retinal nerve fiber layer (RNFL) thickness when using automated retinal layer segmentation algorithms without manual refinement. Cross-sectional study. This study was set in a glaucoma clinical practice, and the dataset included 3490 scans from 412 eyes of 213 individuals with a diagnosis of glaucoma or glaucoma suspect. We used spectral domain OCT (Spectralis) to measure RNFL thickness in a 6-degree peripapillary circle, and exported the native "automated segmentation only" results. In addition, we exported the results after "manual refinement" to correct errors in the automated segmentation of the anterior (internal limiting membrane) and the posterior boundary of the RNFL. Our outcome measures included differences in RNFL thickness and glaucoma classification (i.e., normal, borderline, or outside normal limits) between scans with automated segmentation only and scans using manual refinement. Automated segmentation only resulted in a thinner global RNFL thickness (1.6 μm thinner, P < .001) when compared to manual refinement. When adjusted by operator, a multivariate model showed increased differences with decreasing RNFL thickness (P < .001), decreasing scan quality (P < .001), and increasing age (P < .03). Manual refinement changed 298 of 3486 (8.5%) of scans to a different global glaucoma classification, wherein 146 of 617 (23.7%) of borderline classifications became normal. Superior and inferior temporal clock hours had the largest differences. Automated segmentation without manual refinement resulted in reduced global RNFL thickness and overestimated the classification of glaucoma. Differences increased in eyes with a thinner RNFL thickness, older age, and decreased scan quality. Operators should inspect and manually refine OCT retinal layer segmentation when assessing RNFL thickness in the management of patients with glaucoma. Copyright © 2016 Elsevier Inc. All rights reserved.

Can manual ability be measured with a generic ABILHAND scale? A cross-sectional study conducted on six diagnostic groups

PubMed Central

Arnould, Carlyne; Vandervelde, Laure; Batcho, Charles Sèbiyo; Penta, Massimo; Thonnard, Jean-Louis

2012-01-01

Objectives Several ABILHAND Rasch-built manual ability scales were previously developed for chronic stroke (CS), cerebral palsy (CP), rheumatoid arthritis (RA), systemic sclerosis (SSc) and neuromuscular disorders (NMD). The present study aimed to explore the applicability of a generic manual ability scale unbiased by diagnosis and to study the nature of manual ability across diagnoses. Design Cross-sectional study. Setting Outpatient clinic homes (CS, CP, RA), specialised centres (CP), reference centres (CP, NMD) and university hospitals (SSc). Participants 762 patients from six diagnostic groups: 103 CS adults, 113 CP children, 112 RA adults, 156 SSc adults, 124 NMD children and 124 NMD adults. Primary and secondary outcome measures Manual ability as measured by the ABILHAND disease-specific questionnaires, diagnosis and nature (ie, uni-manual or bi-manual involvement and proximal or distal joints involvement) of the ABILHAND manual activities. Results The difficulties of most manual activities were diagnosis dependent. A principal component analysis highlighted that 57% of the variance in the item difficulty between diagnoses was explained by the symmetric or asymmetric nature of the disorders. A generic scale was constructed, from a metric point of view, with 11 items sharing a common difficulty among diagnoses and 41 items displaying a category-specific location (asymmetric: CS, CP; and symmetric: RA, SSc, NMD). This generic scale showed that CP and NMD children had significantly less manual ability than RA patients, who had significantly less manual ability than CS, SSc and NMD adults. However, the generic scale was less discriminative and responsive to small deficits than disease-specific instruments. Conclusions Our finding that most of the manual item difficulties were disease-dependent emphasises the danger of using generic scales without prior investigation of item invariance across diagnostic groups. Nevertheless, a generic manual ability scale could be developed by adjusting and accounting for activities perceived differently in various disorders. PMID:23117570

Flexible and composite structures for premium pavements. Volume 2, Design manual

DOT National Transportation Integrated Search

1980-11-01

This design manual presents the results of a detailed study to identify and design flexible and composite pavement configurations which will perform as premium or "zero-maintenance" pavements. This manual includes identification and classification of...

Correlations between risk factors and functional evolution in patients with spastic quadriplegia

PubMed Central

Rogoveanu, OC; Tuțescu, NC; Kamal, D; Alexandru, DO; Kamal, C; Streba, L; Trăistaru, MR

2016-01-01

Cerebral palsy is the most common cause of developing neuro-motor disability in children, in many cases, the triggering cause remaining unknown. Quadriplegia is the most severe spastic cerebral palsy, characterized by severe mental retardation and bi-pyramidal syndrome. The purpose of this paper was to demonstrate the importance of knowing the risk factors and the psychosomatic ones, determining to what extent they influence the functional evolution in patients diagnosed with spastic quadriplegia. 23 children diagnosed with spastic quadriplegia were included in the study, being aged between 1 year and half and 12 years. Patients were assessed at baseline (T1), at one year (T2) and after two years at the end of the study (T3). Patients received a comprehensive rehabilitation program for the motor and sensory deficits throughout the study. Initially, a comprehensive evaluation (etiopathogenic, clinical and functional) that started from a thorough medical history of children (the older ones), was conducted but chose parents to identify the risk factors, and a complete physical exam. At each assessment, joint and muscle balance was conducted. To assess functionality, the gross motor function classification systems (GMFCS) and manual ability (MACS) were used. Many risk factors that were classified according to the timeline in prenatal factors, perinatal and postnatal, were identified from a thorough history. A direct correlation was noticed between the decrease of coarse functionality and manual ability, both initially and in dynamic and low APGAR scores, low gestational age, low birth weight and a higher body mass index of the mother. A direct link was observed between the gross motor function and the manual ability. A significant improvement in the MACS score was noticed in patients with a better GMFCS score. PMID:27453749

Correlations between risk factors and functional evolution in patients with spastic quadriplegia.

PubMed

Rogoveanu, O C; Tuțescu, N C; Kamal, D; Alexandru, D O; Kamal, C; Streba, L; Trăistaru, M R

2016-01-01

Cerebral palsy is the most common cause of developing neuro-motor disability in children, in many cases, the triggering cause remaining unknown. Quadriplegia is the most severe spastic cerebral palsy, characterized by severe mental retardation and bi-pyramidal syndrome. The purpose of this paper was to demonstrate the importance of knowing the risk factors and the psychosomatic ones, determining to what extent they influence the functional evolution in patients diagnosed with spastic quadriplegia. 23 children diagnosed with spastic quadriplegia were included in the study, being aged between 1 year and half and 12 years. Patients were assessed at baseline (T1), at one year (T2) and after two years at the end of the study (T3). Patients received a comprehensive rehabilitation program for the motor and sensory deficits throughout the study. Initially, a comprehensive evaluation (etiopathogenic, clinical and functional) that started from a thorough medical history of children (the older ones), was conducted but chose parents to identify the risk factors, and a complete physical exam. At each assessment, joint and muscle balance was conducted. To assess functionality, the gross motor function classification systems (GMFCS) and manual ability (MACS) were used. Many risk factors that were classified according to the timeline in prenatal factors, perinatal and postnatal, were identified from a thorough history. A direct correlation was noticed between the decrease of coarse functionality and manual ability, both initially and in dynamic and low APGAR scores, low gestational age, low birth weight and a higher body mass index of the mother. A direct link was observed between the gross motor function and the manual ability. A significant improvement in the MACS score was noticed in patients with a better GMFCS score.

Policy Agenda for the Next Decade: Creating a Path for Graceful Evolution and Harmonized Classifications and Terminologies Used for Encoding Health Information in Electronic Environments

PubMed Central

Foley, Margaret M; Glenn, Regina M; Meli, Peggy L; Scichilone, Rita A

2009-01-01

Introduction Health information management (HIM) professionals' involvement with disease classification and nomenclature in the United States can be traced back to the early 20th century. In 1914, Grace Whiting Myers, the founder of the association known today as the American Health Information Management Association (AHIMA), served on the Committee on Uniform Nomenclature, which developed a disease classification system based upon etiological groupings. The profession's expertise and leadership in the collection, classification, and reporting of health data has continued since then. For example, in the early 1960s, another HIM professional (a medical record librarian) served as the associate editor of the fifth edition of the Standard Nomenclature of Disease (SNDO), a forerunner of the widely used clinical terminology, Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT). During the same period in history, the medical record professionals working in hospitals throughout the country were responsible for manually collecting and reporting disease and procedure information from medical records using SNDO.1 Because coded data have played a pivotal role in the ability to record and share health information through the years, creating the appropriate policy framework for the graceful evolution and harmonization of classification systems and clinical terminologies is essential. PMID:20169015

Classification of video sequences into chosen generalized use classes of target size and lighting level.

PubMed

Leszczuk, Mikołaj; Dudek, Łukasz; Witkowski, Marcin

The VQiPS (Video Quality in Public Safety) Working Group, supported by the U.S. Department of Homeland Security, has been developing a user guide for public safety video applications. According to VQiPS, five parameters have particular importance influencing the ability to achieve a recognition task. They are: usage time-frame, discrimination level, target size, lighting level, and level of motion. These parameters form what are referred to as Generalized Use Classes (GUCs). The aim of our research was to develop algorithms that would automatically assist classification of input sequences into one of the GUCs. Target size and lighting level parameters were approached. The experiment described reveals the experts' ambiguity and hesitation during the manual target size determination process. However, the automatic methods developed for target size classification make it possible to determine GUC parameters with 70 % compliance to the end-users' opinion. Lighting levels of the entire sequence can be classified with an efficiency reaching 93 %. To make the algorithms available for use, a test application has been developed. It is able to process video files and display classification results, the user interface being very simple and requiring only minimal user interaction.

Unsupervised active learning based on hierarchical graph-theoretic clustering.

PubMed

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

PubMed

Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

2007-08-01

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

Automated detection of feeding strikes by larval fish using continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events.

PubMed

Shamur, Eyal; Zilka, Miri; Hassner, Tal; China, Victor; Liberzon, Alex; Holzman, Roi

2016-06-01

Using videography to extract quantitative data on animal movement and kinematics constitutes a major tool in biomechanics and behavioral ecology. Advanced recording technologies now enable acquisition of long video sequences encompassing sparse and unpredictable events. Although such events may be ecologically important, analysis of sparse data can be extremely time-consuming and potentially biased; data quality is often strongly dependent on the training level of the observer and subject to contamination by observer-dependent biases. These constraints often limit our ability to study animal performance and fitness. Using long videos of foraging fish larvae, we provide a framework for the automated detection of prey acquisition strikes, a behavior that is infrequent yet critical for larval survival. We compared the performance of four video descriptors and their combinations against manually identified feeding events. For our data, the best single descriptor provided a classification accuracy of 77-95% and detection accuracy of 88-98%, depending on fish species and size. Using a combination of descriptors improved the accuracy of classification by ∼2%, but did not improve detection accuracy. Our results indicate that the effort required by an expert to manually label videos can be greatly reduced to examining only the potential feeding detections in order to filter false detections. Thus, using automated descriptors reduces the amount of manual work needed to identify events of interest from weeks to hours, enabling the assembly of an unbiased large dataset of ecologically relevant behaviors. © 2016. Published by The Company of Biologists Ltd.

Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches

PubMed Central

Hauschild, Anne-Christin; Kopczynski, Dominik; D’Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-01-01

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME). We manually generated a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors’ results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications. PMID:24957992

Peak detection method evaluation for ion mobility spectrometry by using machine learning approaches.

PubMed

Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-04-16

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors' results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.

76 FR 17794 - Post Office Organization and Administration: Establishment, Classification, and Discontinuance

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-31

... Organization and Administration: Establishment, Classification, and Discontinuance AGENCY: Postal Service... classification of affected facilities as Post Offices, stations, or branches. The conversion of an independent... classification system for Post Offices in accordance with Postal Operations Manual (POM) 123.11. The change in...

Preliminary clinical evaluation of semi-automated nailfold capillaroscopy in the assessment of patients with Raynaud's phenomenon.

PubMed

Murray, Andrea K; Feng, Kaiyan; Moore, Tonia L; Allen, Phillip D; Taylor, Christopher J; Herrick, Ariane L

2011-08-01

  Nailfold capillaroscopy is well established in screening patients with Raynaud's phenomenon for underlying SSc-spectrum disorders, by identifying abnormal capillaries. Our aim was to compare semi-automatic feature measurement from newly developed software with manual measurements, and determine the degree to which semi-automated data allows disease group classification.   Images from 46 healthy controls, 21 patients with PRP and 49 with SSc were preprocessed, and semi-automated measurements of intercapillary distance and capillary width, tortuosity, and derangement were performed. These were compared with manual measurements. Features were used to classify images into the three subject groups.   Comparison of automatic and manual measures for distance, width, tortuosity, and derangement had correlations of r=0.583, 0.624, 0.495 (p<0.001), and 0.195 (p=0.040). For automatic measures, correlations were found between width and intercapillary distance, r=0.374, and width and tortuosity, r=0.573 (p<0.001). Significant differences between subject groups were found for all features (p<0.002). Overall, 75% of images correctly matched clinical classification using semi-automated features, compared with 71% for manual measurements.   Semi-automatic and manual measurements of distance, width, and tortuosity showed moderate (but statistically significant) correlations. Correlation for derangement was weaker. Semi-automatic measurements are faster than manual measurements. Semi-automatic parameters identify differences between groups, and are as good as manual measurements for between-group classification. © 2011 John Wiley & Sons Ltd.

A compressed sensing method with analytical results for lidar feature classification

NASA Astrophysics Data System (ADS)

Allen, Josef D.; Yuan, Jiangbo; Liu, Xiuwen; Rahmes, Mark

2011-04-01

We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models (DSM). This technology reduces manual editing while being cost effective for large scale automated global scene modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show Probability of Detection and False Alarm of buildings vs. vegetation classification. Histograms are shown with sample size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting's unique ability to minimize or eliminate undesirable terrain data artifacts.

Transcranial direct current stimulation combined with upper limb functional training in children with spastic, hemiparetic cerebral palsy: study protocol for a randomized controlled trial.

PubMed

Moura, Renata Calhes Franco; Santos, Cibele Almeida; Grecco, Luanda André Collange; Lazzari, Roberta Delasta; Dumont, Arislander Jonathan Lopes; Duarte, Natalia Carvalho de Almeida; Braun, Luiz Alfredo; Lopes, Jamile Benite Palma; Santos, Ligia Abram Dos; Rodrigues, Eliane Lopes Souza; Albertini, Giorgio; Cimolin, Veronica; Galli, Manuela; Oliveira, Claudia Santos

2016-08-17

The aim of the proposed study is to perform a comparative analysis of functional training effects for the paretic upper limb with and without transcranial direct current stimulation over the primary motor cortex in children with spastic hemiparetic cerebral palsy. The sample will comprise 34 individuals with spastic hemiparetic cerebral palsy, 6 to 16 years old, classified at level I, II, or III of the Manual Ability Classification System. Participants will be randomly allocated to two groups: (1) functional training of the paretic upper limb combined with anodic transcranial stimulation; (2) functional training of the paretic upper limb combined with sham transcranial stimulation. Evaluation will involve three-dimensional movement analysis and electromyography using the SMART-D 140® system (BTS Engineering) and the FREEEMG® system (BTS Engineering), the Quality of Upper Extremity Skills Test, to assess functional mobility, the Portable Device and Ashworth Scale, to measure movement resistance and spasticity, and the Pediatric Evaluation of Disability Inventory, to evaluate performance. Functional reach training of the paretic upper limb will include a range of manual activities using educational toys associated with an induced constraint of the non-paretic limb during the training. Training will be performed in five weekly 20-minute sessions for two weeks. Transcranial stimulation over the primary motor cortex will be performed during the training sessions at an intensity of 1 mA. Findings will be analyzed statistically considering a 5 % significance level (P ≤ 0.05). This paper presents a detailed description of a prospective, randomized, controlled, double-blind, clinical trial designed to demonstrate the effects of combining transcranial direct current stimulation over the primary motor cortex and functional training of the paretic limb in children with cerebral palsy classified at level I, II, or III of the Manual Ability Classification System. The results will be published and evidence found may contribute to the use of transcranial stimulation for this population. ReBEC RBR-6V4Y3K . Registered on 11 February 2015.

21 CFR 886.1770 - Manual refractor.

Code of Federal Regulations, 2010 CFR

2010-04-01

... DEVICES OPHTHALMIC DEVICES Diagnostic Devices § 886.1770 Manual refractor. (a) Identification. A manual refractor is a device that is a set of lenses of varous dioptric powers intended to measure the refractive error of the eye. (b) Classification. Class I (general controls). The device is exempt from the...

Determining the saliency of feature measurements obtained from images of sedimentary organic matter for use in its classification

NASA Astrophysics Data System (ADS)

Weller, Andrew F.; Harris, Anthony J.; Ware, J. Andrew; Jarvis, Paul S.

2006-11-01

The classification of sedimentary organic matter (OM) images can be improved by determining the saliency of image analysis (IA) features measured from them. Knowing the saliency of IA feature measurements means that only the most significant discriminating features need be used in the classification process. This is an important consideration for classification techniques such as artificial neural networks (ANNs), where too many features can lead to the 'curse of dimensionality'. The classification scheme adopted in this work is a hybrid of morphologically and texturally descriptive features from previous manual classification schemes. Some of these descriptive features are assigned to IA features, along with several others built into the IA software (Halcon) to ensure that a valid cross-section is available. After an image is captured and segmented, a total of 194 features are measured for each particle. To reduce this number to a more manageable magnitude, the SPSS AnswerTree Exhaustive CHAID (χ 2 automatic interaction detector) classification tree algorithm is used to establish each measurement's saliency as a classification discriminator. In the case of continuous data as used here, the F-test is used as opposed to the published algorithm. The F-test checks various statistical hypotheses about the variance of groups of IA feature measurements obtained from the particles to be classified. The aim is to reduce the number of features required to perform the classification without reducing its accuracy. In the best-case scenario, 194 inputs are reduced to 8, with a subsequent multi-layer back-propagation ANN recognition rate of 98.65%. This paper demonstrates the ability of the algorithm to reduce noise, help overcome the curse of dimensionality, and facilitate an understanding of the saliency of IA features as discriminators for sedimentary OM classification.

The influence of spatial ability and experience on performance during spaceship rendezvous and docking.

PubMed

Du, Xiaoping; Zhang, Yijing; Tian, Yu; Huang, Weifen; Wu, Bin; Zhang, Jingyu

2015-01-01

Manual rendezvous and docking (manual RVD) is a challenging space task for astronauts. Previous research showed a correlation between spatial ability and manual RVD skills among participants at early stages of training, but paid less attention to experts. Therefore, this study tried to explore the role of spatial ability in manual RVD skills in two groups of trainees, one relatively inexperienced and the other experienced operators. Additionally, mental rotation has been proven essential in RVD and was tested in this study among 27 male participants, 15 novices, and 12 experts. The participants performed manual RVD tasks in a high fidelity simulator. Results showed that experience moderated the relation between mental rotation ability and manual RVD performance. On one hand, novices with high mental rotation ability tended to perform that RVD task more successfully; on the other hand, experts with high mental rotation ability showed not only no performance advantage in the final stage of the RVD task, but had certain disadvantages in their earlier processes. Both theoretical and practical implications were discussed.

The influence of spatial ability and experience on performance during spaceship rendezvous and docking

PubMed Central

Du, Xiaoping; Zhang, Yijing; Tian, Yu; Huang, Weifen; Wu, Bin; Zhang, Jingyu

2015-01-01

Manual rendezvous and docking (manual RVD) is a challenging space task for astronauts. Previous research showed a correlation between spatial ability and manual RVD skills among participants at early stages of training, but paid less attention to experts. Therefore, this study tried to explore the role of spatial ability in manual RVD skills in two groups of trainees, one relatively inexperienced and the other experienced operators. Additionally, mental rotation has been proven essential in RVD and was tested in this study among 27 male participants, 15 novices, and 12 experts. The participants performed manual RVD tasks in a high fidelity simulator. Results showed that experience moderated the relation between mental rotation ability and manual RVD performance. On one hand, novices with high mental rotation ability tended to perform that RVD task more successfully; on the other hand, experts with high mental rotation ability showed not only no performance advantage in the final stage of the RVD task, but had certain disadvantages in their earlier processes. Both theoretical and practical implications were discussed. PMID:26236252

Coordination of Local Road Classification with the State Highway System Classification: Impact and Clarification of Related Language in the LVR Manual

DOT National Transportation Integrated Search

1996-02-01

This study reviewed the low volume road (LVR) classifications in Kansas in conjunction with the State A, B, C, D, E road classification system and addressed alignment of these differences. As an extension to the State system, an F, G, H classificatio...

HAMAP in 2013, new developments in the protein family classification and annotation system

PubMed Central

Pedruzzi, Ivo; Rivoire, Catherine; Auchincloss, Andrea H.; Coudert, Elisabeth; Keller, Guillaume; de Castro, Edouard; Baratin, Delphine; Cuche, Béatrice A.; Bougueleret, Lydie; Poux, Sylvain; Redaschi, Nicole; Xenarios, Ioannis; Bridge, Alan

2013-01-01

HAMAP (High-quality Automated and Manual Annotation of Proteins—available at http://hamap.expasy.org/) is a system for the classification and annotation of protein sequences. It consists of a collection of manually curated family profiles for protein classification, and associated annotation rules that specify annotations that apply to family members. HAMAP was originally developed to support the manual curation of UniProtKB/Swiss-Prot records describing microbial proteins. Here we describe new developments in HAMAP, including the extension of HAMAP to eukaryotic proteins, the use of HAMAP in the automated annotation of UniProtKB/TrEMBL, providing high-quality annotation for millions of protein sequences, and the future integration of HAMAP into a unified system for UniProtKB annotation, UniRule. HAMAP is continuously updated by expert curators with new family profiles and annotation rules as new protein families are characterized. The collection of HAMAP family classification profiles and annotation rules can be browsed and viewed on the HAMAP website, which also provides an interface to scan user sequences against HAMAP profiles. PMID:23193261

Acquisition of dental skills in preclinical technique courses: influence of spatial and manual abilities.

PubMed

Schwibbe, Anja; Kothe, Christian; Hampe, Wolfgang; Konradt, Udo

2016-10-01

Sixty years of research have not added up to a concordant evaluation of the influence of spatial and manual abilities on dental skill acquisition. We used Ackerman's theory of ability determinants of skill acquisition to explain the influence of spatial visualization and manual dexterity on the task performance of dental students in two consecutive preclinical technique courses. We measured spatial and manual abilities of applicants to Hamburg Dental School by means of a multiple choice test on Technical Aptitude and a wire-bending test, respectively. Preclinical dental technique tasks were categorized as consistent-simple and inconsistent-complex based on their contents. For analysis, we used robust regression to circumvent typical limitations in dental studies like small sample size and non-normal residual distributions. We found that manual, but not spatial ability exhibited a moderate influence on the performance in consistent-simple tasks during dental skill acquisition in preclinical dentistry. Both abilities revealed a moderate relation with the performance in inconsistent-complex tasks. These findings support the hypotheses which we had postulated on the basis of Ackerman's work. Therefore, spatial as well as manual ability are required for the acquisition of dental skills in preclinical technique courses. These results support the view that both abilities should be addressed in dental admission procedures in addition to cognitive measures.

Visual and tactile interfaces for bi-directional human robot communication

NASA Astrophysics Data System (ADS)

Barber, Daniel; Lackey, Stephanie; Reinerman-Jones, Lauren; Hudson, Irwin

2013-05-01

Seamless integration of unmanned and systems and Soldiers in the operational environment requires robust communication capabilities. Multi-Modal Communication (MMC) facilitates achieving this goal due to redundancy and levels of communication superior to single mode interaction using auditory, visual, and tactile modalities. Visual signaling using arm and hand gestures is a natural method of communication between people. Visual signals standardized within the U.S. Army Field Manual and in use by Soldiers provide a foundation for developing gestures for human to robot communication. Emerging technologies using Inertial Measurement Units (IMU) enable classification of arm and hand gestures for communication with a robot without the requirement of line-of-sight needed by computer vision techniques. These devices improve the robustness of interpreting gestures in noisy environments and are capable of classifying signals relevant to operational tasks. Closing the communication loop between Soldiers and robots necessitates them having the ability to return equivalent messages. Existing visual signals from robots to humans typically require highly anthropomorphic features not present on military vehicles. Tactile displays tap into an unused modality for robot to human communication. Typically used for hands-free navigation and cueing, existing tactile display technologies are used to deliver equivalent visual signals from the U.S. Army Field Manual. This paper describes ongoing research to collaboratively develop tactile communication methods with Soldiers, measure classification accuracy of visual signal interfaces, and provides an integration example including two robotic platforms.

Automated Essay Grading using Machine Learning Algorithm

NASA Astrophysics Data System (ADS)

Ramalingam, V. V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu

2018-04-01

Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator’s time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.

Classification of Instructional Programs, 1990 Edition.

ERIC Educational Resources Information Center

Morgan, Robert L.; And Others

This document, the Department of Education's standard educational program classification system for secondary and postsecondary schools, supersedes all previous editions. The manual is divided into seven chapters, each of which contains, in numerical order, the complete list of currently active Classification of Instructional Programs (CIP)…

7 CFR 28.908 - Samples.

Code of Federal Regulations, 2012 CFR

2012-01-01

..., TESTING, AND STANDARDS Cotton Classification and Market News Service for Producers Sampling § 28.908... submitted for classification under this subpart. This does not prohibit the submission of an additional sample from a bale for review classification if the producer so desires. (b) Drawing of samples manual...

7 CFR 28.908 - Samples.

Code of Federal Regulations, 2014 CFR

2014-01-01

..., TESTING, AND STANDARDS Cotton Classification and Market News Service for Producers Sampling § 28.908... submitted for classification under this subpart. This does not prohibit the submission of an additional sample from a bale for review classification if the producer so desires. (b) Drawing of samples manual...

7 CFR 28.908 - Samples.

Code of Federal Regulations, 2013 CFR

2013-01-01

..., TESTING, AND STANDARDS Cotton Classification and Market News Service for Producers Sampling § 28.908... submitted for classification under this subpart. This does not prohibit the submission of an additional sample from a bale for review classification if the producer so desires. (b) Drawing of samples manual...

7 CFR 28.908 - Samples.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., TESTING, AND STANDARDS Cotton Classification and Market News Service for Producers Sampling § 28.908... submitted for classification under this subpart. This does not prohibit the submission of an additional sample from a bale for review classification if the producer so desires. (b) Drawing of samples manual...

21 CFR 864.6160 - Manual blood cell counting device.

Code of Federal Regulations, 2012 CFR

2012-04-01

... blood cell counting device. (a) Identification. A manual blood cell counting device is a device used to count red blood cells, white blood cells, or blood platelets. (b) Classification. Class I (general... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Manual blood cell counting device. 864.6160...

21 CFR 864.6160 - Manual blood cell counting device.

Code of Federal Regulations, 2013 CFR

2013-04-01

... blood cell counting device. (a) Identification. A manual blood cell counting device is a device used to count red blood cells, white blood cells, or blood platelets. (b) Classification. Class I (general... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Manual blood cell counting device. 864.6160...

21 CFR 864.6160 - Manual blood cell counting device.

Code of Federal Regulations, 2011 CFR

2011-04-01

... blood cell counting device. (a) Identification. A manual blood cell counting device is a device used to count red blood cells, white blood cells, or blood platelets. (b) Classification. Class I (general... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Manual blood cell counting device. 864.6160...

21 CFR 864.6160 - Manual blood cell counting device.

Code of Federal Regulations, 2014 CFR

2014-04-01

... blood cell counting device. (a) Identification. A manual blood cell counting device is a device used to count red blood cells, white blood cells, or blood platelets. (b) Classification. Class I (general... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Manual blood cell counting device. 864.6160...

21 CFR 864.6160 - Manual blood cell counting device.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Manual blood cell counting device. 864.6160... blood cell counting device. (a) Identification. A manual blood cell counting device is a device used to count red blood cells, white blood cells, or blood platelets. (b) Classification. Class I (general...

Acquisition of Dental Skills in Preclinical Technique Courses: Influence of Spatial and Manual Abilities

ERIC Educational Resources Information Center

Schwibbe, Anja; Kothe, Christian; Hampe, Wolfgang; Konradt, Udo

2016-01-01

Sixty years of research have not added up to a concordant evaluation of the influence of spatial and manual abilities on dental skill acquisition. We used Ackerman's theory of ability determinants of skill acquisition to explain the influence of spatial visualization and manual dexterity on the task performance of dental students in two…

Structured reporting platform improves CAD-RADS assessment.

PubMed

Szilveszter, Bálint; Kolossváry, Márton; Karády, Júlia; Jermendy, Ádám L; Károlyi, Mihály; Panajotu, Alexisz; Bagyura, Zsolt; Vecsey-Nagy, Milán; Cury, Ricardo C; Leipsic, Jonathon A; Merkely, Béla; Maurovich-Horvat, Pál

2017-11-01

Structured reporting in cardiac imaging is strongly encouraged to improve quality through consistency. The Coronary Artery Disease - Reporting and Data System (CAD-RADS) was recently introduced to facilitate interdisciplinary communication of coronary CT angiography (CTA) results. We aimed to assess the agreement between manual and automated CAD-RADS classification using a structured reporting platform. Five readers prospectively interpreted 500 coronary CT angiographies using a structured reporting platform that automatically calculates the CAD-RADS score based on stenosis and plaque parameters manually entered by the reader. In addition, all readers manually assessed CAD-RADS blinded to the automatically derived results, which was used as the reference standard. We evaluated factors influencing reader performance including CAD-RADS training, clinical load, time of the day and level of expertise. Total agreement between manual and automated classification was 80.2%. Agreement in stenosis categories was 86.7%, whereas the agreement in modifiers was 95.8% for "N", 96.8% for "S", 95.6% for "V" and 99.4% for "G". Agreement for V improved after CAD-RADS training (p = 0.047). Time of the day and clinical load did not influence reader performance (p > 0.05 both). Less experienced readers had a higher total agreement as compared to more experienced readers (87.0% vs 78.0%, respectively; p = 0.011). Even though automated CAD-RADS classification uses data filled in by the readers, it outperforms manual classification by preventing human errors. Structured reporting platforms with automated calculation of the CAD-RADS score might improve data quality and support standardization of clinical decision making. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

SCOPe: Manual Curation and Artifact Removal in the Structural Classification of Proteins - extended Database.

PubMed

Chandonia, John-Marc; Fox, Naomi K; Brenner, Steven E

2017-02-03

SCOPe (Structural Classification of Proteins-extended, http://scop.berkeley.edu) is a database of relationships between protein structures that extends the Structural Classification of Proteins (SCOP) database. SCOP is an expert-curated ordering of domains from the majority of proteins of known structure in a hierarchy according to structural and evolutionary relationships. SCOPe classifies the majority of protein structures released since SCOP development concluded in 2009, using a combination of manual curation and highly precise automated tools, aiming to have the same accuracy as fully hand-curated SCOP releases. SCOPe also incorporates and updates the ASTRAL compendium, which provides several databases and tools to aid in the analysis of the sequences and structures of proteins classified in SCOPe. SCOPe continues high-quality manual classification of new superfamilies, a key feature of SCOP. Artifacts such as expression tags are now separated into their own class, in order to distinguish them from the homology-based annotations in the remainder of the SCOPe hierarchy. SCOPe 2.06 contains 77,439 Protein Data Bank entries, double the 38,221 structures classified in SCOP. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Unbiased classification of spatial strategies in the Barnes maze.

PubMed

Illouz, Tomer; Madar, Ravit; Clague, Charlotte; Griffioen, Kathleen J; Louzoun, Yoram; Okun, Eitan

2016-11-01

Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze. Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis. Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application. eitan.okun@biu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Parents' Experiences and Perceptions when Classifying their Children with Cerebral Palsy: Recommendations for Service Providers.

PubMed

Scime, Natalie V; Bartlett, Doreen J; Brunton, Laura K; Palisano, Robert J

2017-08-01

This study investigated the experiences and perceptions of parents of children with cerebral palsy (CP) when classifying their children using the Gross Motor Function Classification System (GMFCS), the Manual Ability Classification System (MACS), and the Communication Function Classification System (CFCS). The second aim was to collate parents' recommendations for service providers on how to interact and communicate with families. A purposive sample of seven parents participating in the On Track study was recruited. Semi-structured interviews were conducted orally and were audiotaped, transcribed, and coded openly. A descriptive interpretive approach within a pragmatic perspective was used during analysis. Seven themes encompassing parents' experiences and perspectives reflect a process of increased understanding when classifying their children, with perceptions of utility evident throughout this process. Six recommendations for service providers emerged, including making the child a priority and being a dependable resource. Knowledge of parents' experiences when using the GMFCS, MACS, and CFCS can provide useful insight for service providers collaborating with parents to classify function in children with CP. Using the recommendations from these parents can facilitate family-provider collaboration for goal setting and intervention planning.

Unraveling cognitive traits using the Morris water maze unbiased strategy classification (MUST-C) algorithm.

PubMed

Illouz, Tomer; Madar, Ravit; Louzon, Yoram; Griffioen, Kathleen J; Okun, Eitan

2016-02-01

The assessment of spatial cognitive learning in rodents is a central approach in neuroscience, as it enables one to assess and quantify the effects of treatments and genetic manipulations from a broad perspective. Although the Morris water maze (MWM) is a well-validated paradigm for testing spatial learning abilities, manual categorization of performance in the MWM into behavioral strategies is subject to individual interpretation, and thus to biases. Here we offer a support vector machine (SVM) - based, automated, MWM unbiased strategy classification (MUST-C) algorithm, as well as a cognitive score scale. This model was examined and validated by analyzing data obtained from five MWM experiments with changing platform sizes, revealing a limitation in the spatial capacity of the hippocampus. We have further employed this algorithm to extract novel mechanistic insights on the impact of members of the Toll-like receptor pathway on cognitive spatial learning and memory. The MUST-C algorithm can greatly benefit MWM users as it provides a standardized method of strategy classification as well as a cognitive scoring scale, which cannot be derived from typical analysis of MWM data. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE.

PubMed

Castellana, Stefano; Fusilli, Caterina; Mazzoccoli, Gianluigi; Biagini, Tommaso; Capocefalo, Daniele; Carella, Massimo; Vescovi, Angelo Luigi; Mazza, Tommaso

2017-06-01

24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability.

Surgical manual of the Korean Gynecologic Oncology Group: classification of hysterectomy and lymphadenectomy

PubMed Central

Choi, Chel Hun; Chun, Yi Kyeong

2017-01-01

The Surgery Treatment Modality Committee of the Korean Gynecologic Oncologic Group (KGOG) has determined to develop a surgical manual to facilitate clinical trials and to improve communication between investigators by standardizing and precisely describing operating procedures. The literature on anatomic terminology, identification of surgical components, and surgical techniques were reviewed and discussed in depth to develop a surgical manual for gynecologic oncology. The surgical procedures provided here represent the minimum requirements for participating in a clinical trial. These procedures should be described in the operation record form, and the pathologic findings obtained from the procedures should be recorded in the pathologic report form. Here, we focused on radical hysterectomy and lymphadenectomy, and we developed a KGOG classification for those conditions. PMID:27670259

Surgical manual of the Korean Gynecologic Oncology Group: classification of hysterectomy and lymphadenectomy.

PubMed

Lee, Maria; Choi, Chel Hun; Chun, Yi Kyeong; Kim, Yun Hwan; Lee, Kwang Beom; Lee, Shin Wha; Shim, Seung Hyuk; Song, Yong Jung; Roh, Ju Won; Chang, Suk Joon; Lee, Jong Min

2017-01-01

The Surgery Treatment Modality Committee of the Korean Gynecologic Oncologic Group (KGOG) has determined to develop a surgical manual to facilitate clinical trials and to improve communication between investigators by standardizing and precisely describing operating procedures. The literature on anatomic terminology, identification of surgical components, and surgical techniques were reviewed and discussed in depth to develop a surgical manual for gynecologic oncology. The surgical procedures provided here represent the minimum requirements for participating in a clinical trial. These procedures should be described in the operation record form, and the pathologic findings obtained from the procedures should be recorded in the pathologic report form. Here, we focused on radical hysterectomy and lymphadenectomy, and we developed a KGOG classification for those conditions.

Introduction to Personnel Management: Participants' Manual.

ERIC Educational Resources Information Center

Civil Service Commission, Denver, CO. Regional Training Center.

This manual for the introductory Federal personnel management course covers: major personnel laws and sources of information; position classification (standards and task analysis); staffing and placement (competitive appointments, temporary appointments/promotions, recruitment, and noncompetitive actions); merit promotion; qualification standards;…

HEp-2 cell image classification method based on very deep convolutional networks with small datasets

NASA Astrophysics Data System (ADS)

Lu, Mengchi; Gao, Long; Guo, Xifeng; Liu, Qiang; Yin, Jianping

2017-07-01

Human Epithelial-2 (HEp-2) cell images staining patterns classification have been widely used to identify autoimmune diseases by the anti-Nuclear antibodies (ANA) test in the Indirect Immunofluorescence (IIF) protocol. Because manual test is time consuming, subjective and labor intensive, image-based Computer Aided Diagnosis (CAD) systems for HEp-2 cell classification are developing. However, methods proposed recently are mostly manual features extraction with low accuracy. Besides, the scale of available benchmark datasets is small, which does not exactly suitable for using deep learning methods. This issue will influence the accuracy of cell classification directly even after data augmentation. To address these issues, this paper presents a high accuracy automatic HEp-2 cell classification method with small datasets, by utilizing very deep convolutional networks (VGGNet). Specifically, the proposed method consists of three main phases, namely image preprocessing, feature extraction and classification. Moreover, an improved VGGNet is presented to address the challenges of small-scale datasets. Experimental results over two benchmark datasets demonstrate that the proposed method achieves superior performance in terms of accuracy compared with existing methods.

Text Classification for Organizational Researchers

PubMed Central

Kobayashi, Vladimer B.; Mol, Stefan T.; Berkers, Hannah A.; Kismihók, Gábor; Den Hartog, Deanne N.

2017-01-01

Organizations are increasingly interested in classifying texts or parts thereof into categories, as this enables more effective use of their information. Manual procedures for text classification work well for up to a few hundred documents. However, when the number of documents is larger, manual procedures become laborious, time-consuming, and potentially unreliable. Techniques from text mining facilitate the automatic assignment of text strings to categories, making classification expedient, fast, and reliable, which creates potential for its application in organizational research. The purpose of this article is to familiarize organizational researchers with text mining techniques from machine learning and statistics. We describe the text classification process in several roughly sequential steps, namely training data preparation, preprocessing, transformation, application of classification techniques, and validation, and provide concrete recommendations at each step. To help researchers develop their own text classifiers, the R code associated with each step is presented in a tutorial. The tutorial draws from our own work on job vacancy mining. We end the article by discussing how researchers can validate a text classification model and the associated output. PMID:29881249

Railroad Classification Yard Technology Manual. Volume I : Yard Design Methods

DOT National Transportation Integrated Search

1981-02-01

This volume documents the procedures and methods associated with the design of railroad classification yards. Subjects include: site location, economic analysis, yard capacity analysis, design of flat yards, overall configuration of hump yards, hump ...

Impervious surface mapping with Quickbird imagery

PubMed Central

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio

2010-01-01

This research selects two study areas with different urban developments, sizes, and spatial patterns to explore the suitable methods for mapping impervious surface distribution using Quickbird imagery. The selected methods include per-pixel based supervised classification, segmentation-based classification, and a hybrid method. A comparative analysis of the results indicates that per-pixel based supervised classification produces a large number of “salt-and-pepper” pixels, and segmentation based methods can significantly reduce this problem. However, neither method can effectively solve the spectral confusion of impervious surfaces with water/wetland and bare soils and the impacts of shadows. In order to accurately map impervious surface distribution from Quickbird images, manual editing is necessary and may be the only way to extract impervious surfaces from the confused land covers and the shadow problem. This research indicates that the hybrid method consisting of thresholding techniques, unsupervised classification and limited manual editing provides the best performance. PMID:21643434

Unsupervised feature learning for autonomous rock image classification

NASA Astrophysics Data System (ADS)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

An Ultrasonographic Periodontal Probe

NASA Astrophysics Data System (ADS)

Bertoncini, C. A.; Hinders, M. K.

2010-02-01

Periodontal disease, commonly known as gum disease, affects millions of people. The current method of detecting periodontal pocket depth is painful, invasive, and inaccurate. As an alternative to manual probing, an ultrasonographic periodontal probe is being developed to use ultrasound echo waveforms to measure periodontal pocket depth, which is the main measure of periodontal disease. Wavelet transforms and pattern classification techniques are implemented in artificial intelligence routines that can automatically detect pocket depth. The main pattern classification technique used here, called a binary classification algorithm, compares test objects with only two possible pocket depth measurements at a time and relies on dimensionality reduction for the final determination. This method correctly identifies up to 90% of the ultrasonographic probe measurements within the manual probe's tolerance.

[Classifications in forensic medicine and their logical basis].

PubMed

Kovalev, A V; Shmarov, L A; Ten'kov, A A

2014-01-01

The objective of the present study was to characterize the main requirements for the correct construction of classifications used in forensic medicine, with special reference to the errors that occur in the relevant text-books, guidelines, and manuals and the ways to avoid them. This publication continues the series of thematic articles of the authors devoted to the logical errors in the expert conclusions. The preparation of further publications is underway to report the results of the in-depth analysis of the logical errors encountered in expert conclusions, text-books, guidelines, and manuals.

Predicting functional communication ability in children with cerebral palsy at school entry.

PubMed

Coleman, Andrea; Weir, Kelly; Ware, Robert S; Boyd, Roslyn

2015-03-01

To explore the value of demographic, environmental, and early clinical characteristics in predicting functional communication in children with cerebral palsy (CP) at school entry. Data are from an Australian prospective longitudinal study of children with CP. Children assessed at 18 to 24 and 48 to 60 months corrected age were included in the study. Functional communication was classified at 48 to 60 months using the Communication Function Classification System (CFCS). Predictive variables included communication skills at 18 to 24 months, evaluated using the Communication and Symbolic Behavioural Scales Developmental Profile (CSBS-DP) Infant-Toddler Checklist. Early Gross Motor Function Classification System (GMFCS), Manual Ability Classification System, and motor type and distribution were evaluated by two physiotherapists. Demographic and comorbid variables were obtained through parent interview with a paediatrician or rehabilitation specialist. A total of 114 children (76 males, 38 females) were included in the study. At 18 to 24 months the mean CSBS-DP was 84.9 (SD 19.0). The CFCS distribution at 48 to 60 months was I=36(32%), II=25(22%), III=20(18%), IV=19(17%), and V=14(12%). In multivariable regression analysis, only CSBS-DP (p<0.01) and GMFCS (p<0.01) at 18 to 24 months were predictors of functional communication at school entry. Body structure and function and not environmental factors impact functional communication at school entry in children with CP. This provides valuable guidance for early screening, parent education, and future planning of intervention programs to improve functional communication. © 2014 Mac Keith Press.

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

International Classification of Impairments, Disabilities, and Handicaps: A Manual of Classification Relating to the Consequences of Disease.

ERIC Educational Resources Information Center

World Health Organization, Geneva (Switzerland).

This classification system is intended to offer a conceptual framework for information; the framework is relevant to the long-term consequences of disease, injuries or disorders, and applicable both to personal health care, including early identification and prevention, and to the mitigation of environmental and societal barriers. It begins with…

Diagnostic Classification 0-3: Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood.

ERIC Educational Resources Information Center

Zero to Three: National Center for Infants, Toddlers and Families, Washington, DC.

The diagnostic framework presented in this manual seeks to address the need for a systematic, multi-disciplinary, developmentally based approach to the classification of mental health and developmental difficulties in the first 4 years of life. An introduction discusses clinical approaches to assessment and diagnosis, gives an overview of the…

Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood. Diagnostic Classification: 0-3.

ERIC Educational Resources Information Center

Wieder, Serena, Ed.

The diagnostic framework presented in this manual seeks to address the need for a systematic, multidisciplinary, developmentally based approach to the classification of mental health and developmental difficulties in the first 4 years of life. An introduction discusses clinical approaches to assessment and diagnosis, gives an overview of the…

Plate tectonics in the classification of personality disorder: shifting to a dimensional model.

PubMed

Widiger, Thomas A; Trull, Timothy J

2007-01-01

The diagnostic categories of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders were developed in the spirit of a traditional medical model that considers mental disorders to be qualitatively distinct conditions (see, e.g., American Psychiatric Association, 2000). Work is now beginning on the fifth edition of this influential diagnostic manual. It is perhaps time to consider a fundamental shift in how psychopathology is conceptualized and diagnosed. More specifically, it may be time to consider a shift to a dimensional classification of personality disorder that would help address the failures of the existing diagnostic categories as well as contribute to an integration of the psychiatric diagnostic manual with psychology's research on general personality structure. (c) 2007 APA, all rights reserved

Beyond crosswalks: reliability of exposure assessment following automated coding of free-text job descriptions for occupational epidemiology.

PubMed

Burstyn, Igor; Slutsky, Anton; Lee, Derrick G; Singer, Alison B; An, Yuan; Michael, Yvonne L

2014-05-01

Epidemiologists typically collect narrative descriptions of occupational histories because these are less prone than self-reported exposures to recall bias of exposure to a specific hazard. However, the task of coding these narratives can be daunting and prohibitively time-consuming in some settings. The aim of this manuscript is to evaluate the performance of a computer algorithm to translate the narrative description of occupational codes into standard classification of jobs (2010 Standard Occupational Classification) in an epidemiological context. The fundamental question we address is whether exposure assignment resulting from manual (presumed gold standard) coding of the narratives is materially different from that arising from the application of automated coding. We pursued our work through three motivating examples: assessment of physical demands in Women's Health Initiative observational study, evaluation of predictors of exposure to coal tar pitch volatiles in the US Occupational Safety and Health Administration's (OSHA) Integrated Management Information System, and assessment of exposure to agents known to cause occupational asthma in a pregnancy cohort. In these diverse settings, we demonstrate that automated coding of occupations results in assignment of exposures that are in reasonable agreement with results that can be obtained through manual coding. The correlation between physical demand scores based on manual and automated job classification schemes was reasonable (r = 0.5). The agreement between predictive probability of exceeding the OSHA's permissible exposure level for polycyclic aromatic hydrocarbons, using coal tar pitch volatiles as a surrogate, based on manual and automated coding of jobs was modest (Kendall rank correlation = 0.29). In the case of binary assignment of exposure to asthmagens, we observed that fair to excellent agreement in classifications can be reached, depending on presence of ambiguity in assigned job classification (κ = 0.5-0.8). Thus, the success of automated coding appears to depend on the setting and type of exposure that is being assessed. Our overall recommendation is that automated translation of short narrative descriptions of jobs for exposure assessment is feasible in some settings and essential for large cohorts, especially if combined with manual coding to both assess reliability of coding and to further refine the coding algorithm.

Machine-Learning Algorithms to Code Public Health Spending Accounts

PubMed Central

Leider, Jonathon P.; Resnick, Beth A.; Alfonso, Y. Natalia; Bishai, David

2017-01-01

Objectives: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. Methods: We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Results: Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Conclusions: Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation. PMID:28363034

Machine-Learning Algorithms to Code Public Health Spending Accounts.

PubMed

Brady, Eoghan S; Leider, Jonathon P; Resnick, Beth A; Alfonso, Y Natalia; Bishai, David

Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification. We used machine-learning algorithms to replicate the process of manually classifying state public health expenditures, using the standardized public health spending categories from the Foundational Public Health Services model and a large data set from the US Census Bureau. We obtained a data set of 1.9 million individual expenditure items from 2000 to 2013. We collapsed these data into 147 280 summary expenditure records, and we followed a standardized method of manually classifying each expenditure record as public health, maybe public health, or not public health. We then trained 9 machine-learning algorithms to replicate the manual process. We calculated recall, precision, and coverage rates to measure the performance of individual and ensembled algorithms. Compared with manual classification, the machine-learning random forests algorithm produced 84% recall and 91% precision. With algorithm ensembling, we achieved our target criterion of 90% recall by using a consensus ensemble of ≥6 algorithms while still retaining 93% coverage, leaving only 7% of the summary expenditure records unclassified. Machine learning can be a time- and cost-saving tool for estimating public health spending in the United States. It can be used with standardized public health spending categories based on the Foundational Public Health Services model to help parse public health expenditure information from other types of health-related spending, provide data that are more comparable across public health organizations, and evaluate the impact of evidence-based public health resource allocation.

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

PubMed Central

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian

2015-01-01

Background Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public’s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics. PMID:26307512

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

PubMed

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

2015-08-25

Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.

Hydrometeorological application of an extratropical cyclone classification scheme in the southern United States

NASA Astrophysics Data System (ADS)

Senkbeil, J. C.; Brommer, D. M.; Comstock, I. J.; Loyd, T.

2012-07-01

Extratropical cyclones (ETCs) in the southern United States are often overlooked when compared with tropical cyclones in the region and ETCs in the northern United States. Although southern ETCs are significant weather events, there is currently not an operational scheme used for identifying and discussing these nameless storms. In this research, we classified 84 ETCs (1970-2009). We manually identified five distinct formation regions and seven unique ETC types using statistical classification. Statistical classification employed the use of principal components analysis and two methods of cluster analysis. Both manual and statistical storm types generally showed positive (negative) relationships with El Niño (La Niña). Manual storm types displayed precipitation swaths consistent with discrete storm tracks which further legitimizes the existence of multiple modes of southern ETCs. Statistical storm types also displayed unique precipitation intensity swaths, but these swaths were less indicative of track location. It is hoped that by classifying southern ETCs into types, that forecasters, hydrologists, and broadcast meteorologists might be able to better anticipate projected amounts of precipitation at their locations.

DoD STINFO Manager Training Course. Training Manual

DTIC Science & Technology

1993-02-01

The Export Control Classification Number ( ECCN ) 2. Types of controls, e.g., COCOM 3. Requirements, such as: a. Country groups for which a validated...see Export Administration Act EAR - see Export Administration Regulations ECCN - Export Control Classification Number ELINT - Electronic

Optimization of internet content filtering-Combined with KNN and OCAT algorithms

NASA Astrophysics Data System (ADS)

Guo, Tianze; Wu, Lingjing; Liu, Jiaming

2018-04-01

The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources.

Should Social Workers Use "Diagnostic and Statistical Manual of Mental Disorders-5?"

ERIC Educational Resources Information Center

Frances, Allen; Jones, K. Dayle

2014-01-01

Up until now, social workers have depended on the "Diagnostic and Statistical Manual of Mental Disorders" ("DSM") as the primary diagnostic classification for mental disorders. However, the "DSM-5" revision includes scientifically unfounded, inadequately tested, and potentially dangerous diagnoses that may lead them…

California Community Colleges Student Attendance Accounting Manual.

ERIC Educational Resources Information Center

Cook, Gary L.; Nussbaum, Thomas J.

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

Why is the Diagnostic and Statistical Manual of Mental Disorders so hard to revise? Path-dependence and "lock-in" in classification.

PubMed

Cooper, Rachel

2015-06-01

The latest edition of the Diagnostic and Statistical Manual of Mental Disorders, the D.S.M.-5, was published in May 2013. In the lead up to publication, radical changes to the classification were anticipated; there was widespread dissatisfaction with the previous edition and it was accepted that a "paradigm shift" might be required. In the end, however, and despite huge efforts at revision, the published D.S.M.-5 differs far less than originally envisaged from its predecessor. This paper considers why it is that revising the D.S.M. has become so difficult. The D.S.M. is such an important classification that this question is worth asking in its own right. The case of the D.S.M. can also serve as a study for considering stasis in classification more broadly; why and how can classifications become resistant to change? I suggest that classifications like the D.S.M. can be thought of as forming part of the infrastructure of science, and have much in common with material infrastructure. In particular, as with material technologies, it is possible for "path dependent" development to cause a sub-optimal classification to become "locked in" and hard to replace. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas.

PubMed

Mathieu, Renaud; Aryal, Jagannath; Chong, Albert K

2007-11-20

Effective assessment of biodiversity in cities requires detailed vegetation maps.To date, most remote sensing of urban vegetation has focused on thematically coarse landcover products. Detailed habitat maps are created by manual interpretation of aerialphotographs, but this is time consuming and costly at large scale. To address this issue, wetested the effectiveness of object-based classifications that use automated imagesegmentation to extract meaningful ground features from imagery. We applied thesetechniques to very high resolution multispectral Ikonos images to produce vegetationcommunity maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and amulti-scale segmentation algorithm used to produce a hierarchical network of image objects.The upper level included four coarse strata: industrial/commercial (commercial buildings),residential (houses and backyard private gardens), vegetation (vegetation patches larger than0.8/1ha), and water. We focused on the vegetation stratum that was segmented at moredetailed level to extract and classify fifteen classes of vegetation communities. The firstclassification yielded a moderate overall classification accuracy (64%, κ = 0.52), which ledus to consider a simplified classification with ten vegetation classes. The overallclassification accuracy from the simplified classification was 77% with a κ value close tothe excellent range (κ = 0.74). These results compared favourably with similar studies inother environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spectral contrast.

Personality disorders and the DSM-5: Scientific and extra-scientific factors in the maintenance of the status quo.

PubMed

Gøtzsche-Astrup, Oluf; Moskowitz, Andrew

2016-02-01

The aim of this study was to review and discuss the evidence for dimensional classification of personality disorders and the historical and sociological bases of psychiatric nosology and research. Categorical and dimensional conceptualisations of personality disorder are reviewed, with a focus on the Diagnostic and Statistical Manual of Mental Disorders-system's categorisation and the Five-Factor Model of personality. This frames the events leading up to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, personality disorder debacle, where the implementation of a hybrid model was blocked in a last-minute intervention by the American Psychiatric Association Board of Trustees. Explanations for these events are discussed, including the existence of invisible colleges of researchers and the fear of risking a 'scientific revolution' in psychiatry. A failure to recognise extra-scientific factors at work in classification of mental illness can have a profound and long-lasting influence on psychiatric nosology. In the end it was not scientific factors that led to the failure of the hybrid model of personality disorders, but opposing forces within the mental health community in general and the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Task Force in particular. Substantial evidence has accrued over the past decades in support of a dimensional model of personality disorders. The events surrounding the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Personality and Personality Disorders Work Group show the difficulties in reconciling two different worldviews with a hybrid model. They also indicate the future of a psychiatric nosology that will be increasingly concerned with dimensional classification of mental illness. As such, the road is paved for more substantial changes to personality disorder classification in the International Classification of Diseases, 11th Revision, in 2017. © The Royal Australian and New Zealand College of Psychiatrists 2015.

Values and assumptions in the development of DSM-III and DSM-III-R: an insider's perspective and a belated response to Sadler, Hulgus, and Agich's "On values in recent American psychiatric classification".

PubMed

Spitzer, R L

2001-06-01

It is widely acknowledged that the approach taken in the development of a classification of mental disorders is guided by various values and assumptions. The author, who played a central role in the development of DSM-III (American Psychiatric Association [1980] Diagnostic and statistical manual of mental disorders, 3rd ed. Washington, DC:Author) and DSM-III-R (American Psychiatric Association [1987] Diagnostic and statistical manual of mental disorders, 3rd ed, rev. Washington, DC:Author) will explicate the basic values and assumptions that guided the development of these two diagnostic manuals. In so doing, the author will respond to the critique of DSM-III and DSM-III-R made by Sadler et al. in their 1994 paper (Sadler JZ, Hulgus YF, Agich GJ [1994] On values in recent American psychiatric classification. JMed Phil 19:261-277). The author will attempt to demonstrate that the stated goals of DSM-III and DSM-III-R are not inherently in conflict and are easily explicated by appealing to widely held values and assumptions, most of which appeared in the literature during the development of the manuals. Furthermore, we will demonstrate that it is not true that DSM-III places greater emphasis on reliability over validity and is covertly committed to a biological approach to explaining psychiatric disturbance.

Extricating Manual and Non-Manual Features for Subunit Level Medical Sign Modelling in Automatic Sign Language Classification and Recognition.

PubMed

R, Elakkiya; K, Selvamani

2017-09-22

Subunit segmenting and modelling in medical sign language is one of the important studies in linguistic-oriented and vision-based Sign Language Recognition (SLR). Many efforts were made in the precedent to focus the functional subunits from the view of linguistic syllables but the problem is implementing such subunit extraction using syllables is not feasible in real-world computer vision techniques. And also, the present recognition systems are designed in such a way that it can detect the signer dependent actions under restricted and laboratory conditions. This research paper aims at solving these two important issues (1) Subunit extraction and (2) Signer independent action on visual sign language recognition. Subunit extraction involved in the sequential and parallel breakdown of sign gestures without any prior knowledge on syllables and number of subunits. A novel Bayesian Parallel Hidden Markov Model (BPaHMM) is introduced for subunit extraction to combine the features of manual and non-manual parameters to yield better results in classification and recognition of signs. Signer independent action aims in using a single web camera for different signer behaviour patterns and for cross-signer validation. Experimental results have proved that the proposed signer independent subunit level modelling for sign language classification and recognition has shown improvement and variations when compared with other existing works.

A Management Reporting Manual for Colleges: A System of Reporting and Accounting.

ERIC Educational Resources Information Center

Hughes, K. Scott; And Others

This manual, a revision of the 1976 publication entitled "Management Reports," is intended to assist college business officers in establishing sound accounting systems and in preparing readable and meaningful financial management reports. A detailed description of the accounting system and a new expenditure classification structure have been…

Precision of lumbar intervertebral measurements: does a computer-assisted technique improve reliability?

PubMed

Pearson, Adam M; Spratt, Kevin F; Genuario, James; McGough, William; Kosman, Katherine; Lurie, Jon; Sengupta, Dilip K

2011-04-01

Comparison of intra- and interobserver reliability of digitized manual and computer-assisted intervertebral motion measurements and classification of "instability." To determine if computer-assisted measurement of lumbar intervertebral motion on flexion-extension radiographs improves reliability compared with digitized manual measurements. Many studies have questioned the reliability of manual intervertebral measurements, although few have compared the reliability of computer-assisted and manual measurements on lumbar flexion-extension radiographs. Intervertebral rotation, anterior-posterior (AP) translation, and change in anterior and posterior disc height were measured with a digitized manual technique by three physicians and by three other observers using computer-assisted quantitative motion analysis (QMA) software. Each observer measured 30 sets of digital flexion-extension radiographs (L1-S1) twice. Shrout-Fleiss intraclass correlation coefficients for intra- and interobserver reliabilities were computed. The stability of each level was also classified (instability defined as >4 mm AP translation or 10° rotation), and the intra- and interobserver reliabilities of the two methods were compared using adjusted percent agreement (APA). Intraobserver reliability intraclass correlation coefficients were substantially higher for the QMA technique THAN the digitized manual technique across all measurements: rotation 0.997 versus 0.870, AP translation 0.959 versus 0.557, change in anterior disc height 0.962 versus 0.770, and change in posterior disc height 0.951 versus 0.283. The same pattern was observed for interobserver reliability (rotation 0.962 vs. 0.693, AP translation 0.862 vs. 0.151, change in anterior disc height 0.862 vs. 0.373, and change in posterior disc height 0.730 vs. 0.300). The QMA technique was also more reliable for the classification of "instability." Intraobserver APAs ranged from 87 to 97% for QMA versus 60% to 73% for digitized manual measurements, while interobserver APAs ranged from 91% to 96% for QMA versus 57% to 63% for digitized manual measurements. The use of QMA software substantially improved the reliability of lumbar intervertebral measurements and the classification of instability based on flexion-extension radiographs.

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.

Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.

PubMed

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

Image Classification of Human Carcinoma Cells Using Complex Wavelet-Based Covariance Descriptors

PubMed Central

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A. Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-WT) coefficients and several morphological attributes are computed. Directionally selective DT-WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html. PMID:23341908

13 CFR 121.101 - What are SBA size standards?

Code of Federal Regulations, 2011 CFR

2011-01-01

... SBA size standards? (a) SBA's size standards define whether a business entity is small and, thus... Industry Classification System (NAICS). (b) NAICS is described in the North American Industry Classification Manual-United States, which is available from the National Technical Information Service, 5285...

13 CFR 121.101 - What are SBA size standards?

Code of Federal Regulations, 2010 CFR

2010-01-01

... SBA size standards? (a) SBA's size standards define whether a business entity is small and, thus... Industry Classification System (NAICS). (b) NAICS is described in the North American Industry Classification Manual-United States, which is available from the National Technical Information Service, 5285...

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy

PubMed Central

Boyd, Roslyn N; Davies, Peter SW; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-01-01

Objectives Cerebral palsy (CP) remains the world’s most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8–12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). Methods and analyses This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006–2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. Ethics and dissemination The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5–5 then 8–12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation. Trial registration number ACTRN: 12616001488493 PMID:28706091

An advanced method for classifying atmospheric circulation types based on prototypes connectivity graph

NASA Astrophysics Data System (ADS)

Zagouras, Athanassios; Argiriou, Athanassios A.; Flocas, Helena A.; Economou, George; Fotopoulos, Spiros

2012-11-01

Classification of weather maps at various isobaric levels as a methodological tool is used in several problems related to meteorology, climatology, atmospheric pollution and to other fields for many years. Initially the classification was performed manually. The criteria used by the person performing the classification are features of isobars or isopleths of geopotential height, depending on the type of maps to be classified. Although manual classifications integrate the perceptual experience and other unquantifiable qualities of the meteorology specialists involved, these are typically subjective and time consuming. Furthermore, during the last years different approaches of automated methods for atmospheric circulation classification have been proposed, which present automated and so-called objective classifications. In this paper a new method of atmospheric circulation classification of isobaric maps is presented. The method is based on graph theory. It starts with an intelligent prototype selection using an over-partitioning mode of fuzzy c-means (FCM) algorithm, proceeds to a graph formulation for the entire dataset and produces the clusters based on the contemporary dominant sets clustering method. Graph theory is a novel mathematical approach, allowing a more efficient representation of spatially correlated data, compared to the classical Euclidian space representation approaches, used in conventional classification methods. The method has been applied to the classification of 850 hPa atmospheric circulation over the Eastern Mediterranean. The evaluation of the automated methods is performed by statistical indexes; results indicate that the classification is adequately comparable with other state-of-the-art automated map classification methods, for a variable number of clusters.

Adverse events following cervical manipulative therapy: consensus on classification among Dutch medical specialists, manual therapists, and patients.

PubMed

Kranenburg, Hendrikus A; Lakke, Sandra E; Schmitt, Maarten A; Van der Schans, Cees P

2017-12-01

To obtain consensus-based agreement on a classification system of adverse events (AE) following cervical spinal manipulation. The classification system should be comprised of clear definitions, include patients' and clinicians' perspectives, and have an acceptable number of categories. Design : A three-round Delphi study. Participants : Thirty Dutch participants (medical specialists, manual therapists, and patients) participated in an online survey. Procedure : Participants inventoried AE and were asked about their preferences for either a three- or a four-category classification system. The identified AE were classified by two analysts following the International Classification of Functioning, Disability and Health (ICF), and the International Classification of Diseases and Related Health Problems (ICD-10). Participants were asked to classify the severity for all AE in relation to the time duration. Consensus occurred in a three-category classification system. There was strong consensus for 16 AE in all severities (no, minor, and major AE) and all three time durations [hours, days, weeks]. The 16 AE included anxiety, flushing, skin rash, fainting, dizziness, coma, altered sensation, muscle tenderness, pain, increased pain during movement, radiating pain, dislocation, fracture, transient ischemic attack, stroke, and death. Mild to strong consensus was reached for 13 AE. A consensus-based classification system of AE is established which includes patients' and clinicians' perspectives and has three categories. The classification comprises a precise description of potential AE in accordance with internationally accepted classifications. After international validation, clinicians and researchers may use this AE classification system to report AE in clinical practice and research.

Classification of Living Things. A Teacher's Manual for General Level Program Development. Grades 7 and 8. Science and Society Teaching Units. Informal Series/55.

ERIC Educational Resources Information Center

Roberts, Douglas A.; And Others

This manual is one of a series designed to assist junior high school teachers in developing general level or non-academic science programs which focus on the relationship between science and society. Although designed primarily for grades 7 and 8, the content is also suitable for students in grade 6. The major portion of the manual consists of six…

Development of Subscale Fast Cookoff Test (PREPRINT)

DTIC Science & Technology

2006-09-21

The hazards classification procedures have been harmonized with both the UN Test and Criteria Manual for UN Series 1...aimed at the development of a sub-scale alternate test protocol to the external fire test currently required for final hazards classification (HC...external fire test currently required for final hazards classification (HC) of an ordnance system. The specific goal of this part of the task was

Longitudinal Development of Manual Motor Ability in Autism Spectrum Disorder from Childhood to Mid-Adulthood Relates to Adaptive Daily Living Skills

ERIC Educational Resources Information Center

Travers, Brittany G.; Bigler, Erin D.; Duffield, Tyler C.; Prigge, Molly D. B.; Froehlich, Alyson L.; Lange, Nicholas; Alexander, Andrew L.; Lainhart, Janet E.

2017-01-01

Many individuals with autism spectrum disorder (ASD) exhibit motor difficulties, but it is unknown whether manual motor skills improve, plateau, or decline in ASD in the transition from childhood into adulthood. Atypical development of manual motor skills could impact the ability to learn and perform daily activities across the life span. This…

Addressing the targeting range of the ABILHAND-56 in relapsing-remitting multiple sclerosis: A mixed methods psychometric study.

PubMed

Cleanthous, Sophie; Strzok, Sara; Pompilus, Farrah; Cano, Stefan; Marquis, Patrick; Cohan, Stanley; Goldman, Myla D; Kresa-Reahl, Kiren; Petrillo, Jennifer; Castrillo-Viguera, Carmen; Cadavid, Diego; Chen, Shih-Yin

2018-01-01

ABILHAND, a manual ability patient-reported outcome instrument originally developed for stroke patients, has been used in multiple sclerosis clinical trials; however, psychometric analyses indicated the measure's limited measurement range and precision in higher-functioning multiple sclerosis patients. The purpose of this study was to identify candidate items to expand the measurement range of the ABILHAND-56, thus improving its ability to detect differences in manual ability in higher-functioning multiple sclerosis patients. A step-wise mixed methods design strategy was used, comprising two waves of patient interviews, a combination of qualitative (concept elicitation and cognitive debriefing) and quantitative (Rasch measurement theory) analytic techniques, and consultation interviews with three clinical neurologists specializing in multiple sclerosis. Original ABILHAND was well understood in this context of use. Eighty-two new manual ability concepts were identified. Draft supplementary items were generated and refined with patient and neurologist input. Rasch measurement theory psychometric analysis indicated supplementary items improved targeting to higher-functioning multiple sclerosis patients and measurement precision. The final pool of Early Multiple Sclerosis Manual Ability items comprises 20 items. The synthesis of qualitative and quantitative methods used in this study improves the ABILHAND content validity to more effectively identify manual ability changes in early multiple sclerosis and potentially help determine treatment effect in higher-functioning patients in clinical trials.

DBCG hypo trial validation of radiotherapy parameters from a national data bank versus manual reporting.

PubMed

Brink, Carsten; Lorenzen, Ebbe L; Krogh, Simon Long; Westberg, Jonas; Berg, Martin; Jensen, Ingelise; Thomsen, Mette Skovhus; Yates, Esben Svitzer; Offersen, Birgitte Vrou

2018-01-01

The current study evaluates the data quality achievable using a national data bank for reporting radiotherapy parameters relative to the classical manual reporting method of selected parameters. The data comparison is based on 1522 Danish patients of the DBCG hypo trial with data stored in the Danish national radiotherapy data bank. In line with standard DBCG trial practice selected parameters were also reported manually to the DBCG database. Categorical variables are compared using contingency tables, and comparison of continuous parameters is presented in scatter plots. For categorical variables 25 differences between the data bank and manual values were located. Of these 23 were related to mistakes in the manual reported value whilst the remaining two were a wrong classification in the data bank. The wrong classification in the data bank was related to lack of dose information, since the two patients had been treated with an electron boost based on a manual calculation, thus data was not exported to the data bank, and this was not detected prior to comparison with the manual data. For a few database fields in the manual data an ambiguity of the parameter definition of the specific field is seen in the data. This was not the case for the data bank, which extract all data consistently. In terms of data quality the data bank is superior to manually reported values. However, there is a need to allocate resources for checking the validity of the available data as well as ensuring that all relevant data is present. The data bank contains more detailed information, and thus facilitates research related to the actual dose distribution in the patients.

The Rehabilitation Process for Clients with Specific Learning Disabilities: Trainer's Manual.

ERIC Educational Resources Information Center

Gladden, Bonnie; And Others

This document presents the manual for a training workshop for rehabilitation counselors on the delivery of services to persons with learning disabilities. It describes how counselors are trained in the workshop to: (1) use five criteria to establish eligibility; (2) apply the case management system to the classification of learning disabilities as…

Developmental Disabilities: A Summary of Major Classifications and Glossary of Terms. Parent Awareness Program, 1982-1983. Revised.

ERIC Educational Resources Information Center

Knudstrup, Katherine; And Others

Designed for use in adult education courses for parents of developmentally disabled children, this manual provides basic information about major categories of disabilities and a glossary of commonly encountered terms. After an introductory overview, the manual provides information about the characteristics and etiology of five disabling…

26 CFR 1.861-18 - Classification of transactions involving computer programs.

Code of Federal Regulations, 2014 CFR

2014-04-01

... income. In the case of a transfer of a copyrighted article, this section provides rules for determining... purposes of this paragraph (a)(3), a computer program includes any media, user manuals, documentation, data base or similar item if the media, user manuals, documentation, data base or similar item is incidental...

26 CFR 1.861-18 - Classification of transactions involving computer programs.

Code of Federal Regulations, 2013 CFR

2013-04-01

... income. In the case of a transfer of a copyrighted article, this section provides rules for determining... purposes of this paragraph (a)(3), a computer program includes any media, user manuals, documentation, data base or similar item if the media, user manuals, documentation, data base or similar item is incidental...

26 CFR 1.861-18 - Classification of transactions involving computer programs.

Code of Federal Regulations, 2012 CFR

2012-04-01

... income. In the case of a transfer of a copyrighted article, this section provides rules for determining... purposes of this paragraph (a)(3), a computer program includes any media, user manuals, documentation, data base or similar item if the media, user manuals, documentation, data base or similar item is incidental...

Using Nursing Languages in School Nursing Practice. Second Edition

ERIC Educational Resources Information Center

Denehy, Janice

2010-01-01

The purpose of this updated manual is to define and describe standardized nursing languages, highlight how nursing languages are a part of the nursing process, and illustrate through case examples how nursing languages are used in school nursing practice. This manual also summarizes the history and development of three nursing classifications, the…

Enterprise Standard Industrial Classification Manual. 1974.

ERIC Educational Resources Information Center

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

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

Diagnosis and Classification in Autism.

ERIC Educational Resources Information Center

Waterhouse, Lynn; And Others

1996-01-01

This study compared four systems for diagnosis of autism (Diagnostic and Statistical Manuals of Mental Disorders III, III-R, and IV, and the International Classification of Disabilities-10) with 2 empirically derived taxa and 3 social subgroups (aloof, passive, and active but odd) in 194 preschool children with social impairment. Findings support…

Eating Disorder Diagnoses: Empirical Approaches to Classification

ERIC Educational Resources Information Center

Wonderlich, Stephen A.; Joiner, Thomas E., Jr.; Keel, Pamela K.; Williamson, Donald A.; Crosby, Ross D.

2007-01-01

Decisions about the classification of eating disorders have significant scientific and clinical implications. The eating disorder diagnoses in the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) reflect the collective wisdom of experts in the field but are frequently not supported in…

Annotation and Classification of Argumentative Writing Revisions

ERIC Educational Resources Information Center

Zhang, Fan; Litman, Diane

2015-01-01

This paper explores the annotation and classification of students' revision behaviors in argumentative writing. A sentence-level revision schema is proposed to capture why and how students make revisions. Based on the proposed schema, a small corpus of student essays and revisions was annotated. Studies show that manual annotation is reliable with…

An automatic aerosol classification for earlinet: application and results

NASA Astrophysics Data System (ADS)

Papagiannopoulos, Nikolaos; Mona, Lucia; Amiridis, Vassilis; Binietoglou, Ioannis; D'Amico, Giuseppe; Guma-Claramunt, P.; Schwarz, Anja; Alados-Arboledas, Lucas; Amodeo, Aldo; Apituley, Arnoud; Baars, Holger; Bortoli, Daniele; Comeron, Adolfo; Guerrero-Rascado, Juan Luis; Kokkalis, Panos; Nicolae, Doina; Papayannis, Alex; Pappalardo, Gelsomina; Wandinger, Ulla; Wiegner, Matthias

2018-04-01

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.

Automating document classification for the Immune Epitope Database

PubMed Central

Wang, Peng; Morgan, Alexander A; Zhang, Qing; Sette, Alessandro; Peters, Bjoern

2007-01-01

Background The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose. Results We here report our experience in automating this process using Naïve Bayes classifiers trained on 20,910 abstracts classified by domain experts. Improvements on the basic classifier performance were made by a) utilizing information stored in PubMed beyond the abstract itself b) applying standard feature selection criteria and c) extracting domain specific feature patterns that e.g. identify peptides sequences. We have implemented the classifier into the curation process determining if abstracts are clearly relevant, clearly irrelevant, or if no certain classification can be made, in which case the abstracts are manually classified. Testing this classification scheme on an independent dataset, we achieve 95% sensitivity and specificity in the 51.1% of abstracts that were automatically classified. Conclusion By implementing text classification, we have sped up the reference selection process without sacrificing sensitivity or specificity of the human expert classification. This study provides both practical recommendations for users of text classification tools, as well as a large dataset which can serve as a benchmark for tool developers. PMID:17655769

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.

PubMed

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-10-01

To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%. A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

PubMed Central

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-01-01

Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors’ classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors’ automatic classification and manual segmentation were 91.6% ± 2.0%. Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. PMID:23039675

MRI classification system (MRICS) for children with cerebral palsy: development, reliability, and recommendations.

PubMed

Himmelmann, Kate; Horber, Veronka; De La Cruz, Javier; Horridge, Karen; Mejaski-Bosnjak, Vlatka; Hollody, Katalin; Krägeloh-Mann, Ingeborg

2017-01-01

To develop and evaluate a classification system for magnetic resonance imaging (MRI) findings of children with cerebral palsy (CP) that can be used in CP registers. The classification system was based on pathogenic patterns occurring in different periods of brain development. The MRI classification system (MRICS) consists of five main groups: maldevelopments, predominant white matter injury, predominant grey matter injury, miscellaneous, and normal findings. A detailed manual for the descriptions of these patterns was developed, including test cases (www.scpenetwork.eu/en/my-scpe/rtm/neuroimaging/cp-neuroimaging/). A literature review was performed and MRICS was compared with other classification systems. An exercise was carried out to check applicability and interrater reliability. Professionals working with children with CP or in CP registers were invited to participate in the exercise and chose to classify either 18 MRIs or MRI reports of children with CP. Classification systems in the literature were compatible with MRICS and harmonization possible. Interrater reliability was found to be good overall (k=0.69; 0.54-0.82) among the 41 participants and very good (k=0.81; 0.74-0.92) using the classification based on imaging reports. Surveillance of Cerebral Palsy in Europe (SCPE) proposes the MRICS as a reliable tool. Together with its manual it is simple to apply for CP registers. © 2016 Mac Keith Press.

Mapping ecological states in a complex environment

NASA Astrophysics Data System (ADS)

Steele, C. M.; Bestelmeyer, B.; Burkett, L. M.; Ayers, E.; Romig, K.; Slaughter, A.

2013-12-01

The vegetation of northern Chihuahuan Desert rangelands is sparse, heterogeneous and for most of the year, consists of a large proportion of non-photosynthetic material. The soils in this area are spectrally bright and variable in their reflectance properties. Both factors provide challenges to the application of remote sensing for estimating canopy variables (e.g., leaf area index, biomass, percentage canopy cover, primary production). Additionally, with reference to current paradigms of rangeland health assessment, remotely-sensed estimates of canopy variables have limited practical use to the rangeland manager if they are not placed in the context of ecological site and ecological state. To address these challenges, we created a multifactor classification system based on the USDA-NRCS ecological site schema and associated state-and-transition models to map ecological states on desert rangelands in southern New Mexico. Applying this system using per-pixel image processing techniques and multispectral, remotely sensed imagery raised other challenges. Per-pixel image classification relies upon the spectral information in each pixel alone, there is no reference to the spatial context of the pixel and its relationship with its neighbors. Ecological state classes may have direct relevance to managers but the non-unique spectral properties of different ecological state classes in our study area means that per-pixel classification of multispectral data performs poorly in discriminating between different ecological states. We found that image interpreters who are familiar with the landscape and its associated ecological site descriptions perform better than per-pixel classification techniques in assigning ecological states. However, two important issues affect manual classification methods: subjectivity of interpretation and reproducibility of results. An alternative to per-pixel classification and manual interpretation is object-based image analysis. Object-based image analysis provides a platform for classification that more closely resembles human recognition of objects within a remotely sensed image. The analysis presented here compares multiple thematic maps created for test locations on the USDA-ARS Jornada Experimental Range ranch. Three study sites in different pastures, each 300 ha in size, were selected for comparison on the basis of their ecological site type (';Clayey', ';Sandy' and a combination of both) and the degree of complexity of vegetation cover. Thematic maps were produced for each study site using (i) manual interpretation of digital aerial photography (by five independent interpreters); (ii) object-oriented, decision-tree classification of fine and moderate spatial resolution imagery (Quickbird; Landsat Thematic Mapper) and (iii) ground survey. To identify areas of uncertainty, we compared agreement in location, areal extent and class assignation between 5 independently produced, manually-digitized ecological state maps and with the map created from ground survey. Location, areal extent and class assignation of the map produced by object-oriented classification was also assessed with reference to the ground survey map.

Toward morphological thoracic EIT: major signal sources correspond to respective organ locations in CT.

PubMed

Ferrario, Damien; Grychtol, Bartłomiej; Adler, Andy; Solà, Josep; Böhm, Stephan H; Bodenstein, Marc

2012-11-01

Lung and cardiovascular monitoring applications of electrical impedance tomography (EIT) require localization of relevant functional structures or organs of interest within the reconstructed images. We describe an algorithm for automatic detection of heart and lung regions in a time series of EIT images. Using EIT reconstruction based on anatomical models, candidate regions are identified in the frequency domain and image-based classification techniques applied. The algorithm was validated on a set of simultaneously recorded EIT and CT data in pigs. In all cases, identified regions in EIT images corresponded to those manually segmented in the matched CT image. Results demonstrate the ability of EIT technology to reconstruct relevant impedance changes at their anatomical locations, provided that information about the thoracic boundary shape (and electrode positions) are used for reconstruction.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Maslanik, J. A.; Key, J. R.

1987-01-01

A definition is undertaken of the spectral and spatial characteristics of clouds and surface conditions in the polar regions, and to the creation of calibrated, geometrically correct data sets suitable for quantitative analysis. Ways are explored in which this information can be applied to cloud classifications as new methods or as extensions to existing classification schemes. A methodology is developed that uses automated techniques to merge Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data, and to apply first-order calibration and zenith angle corrections to the AVHRR imagery. Cloud cover and surface types are manually interpreted, and manual methods are used to define relatively pure training areas to describe the textural and multispectral characteristics of clouds over several surface conditions. The effects of viewing angle and bidirectional reflectance differences are studied for several classes, and the effectiveness of some key components of existing classification schemes is tested.

Users manual for the US baseline corn and soybean segment classification procedure

NASA Technical Reports Server (NTRS)

Horvath, R.; Colwell, R. (Principal Investigator); Hay, C.; Metzler, M.; Mykolenko, O.; Odenweller, J.; Rice, D.

1981-01-01

A user's manual for the classification component of the FY-81 U.S. Corn and Soybean Pilot Experiment in the Foreign Commodity Production Forecasting Project of AgRISTARS is presented. This experiment is one of several major experiments in AgRISTARS designed to measure and advance the remote sensing technologies for cropland inventory. The classification procedure discussed is designed to produce segment proportion estimates for corn and soybeans in the U.S. Corn Belt (Iowa, Indiana, and Illinois) using LANDSAT data. The estimates are produced by an integrated Analyst/Machine procedure. The Analyst selects acquisitions, participates in stratification, and assigns crop labels to selected samples. In concert with the Analyst, the machine digitally preprocesses LANDSAT data to remove external effects, stratifies the data into field like units and into spectrally similar groups, statistically samples the data for Analyst labeling, and combines the labeled samples into a final estimate.

Effects of pressure ulcer classification system education programme on knowledge and visual differential diagnostic ability of pressure ulcer classification and incontinence-associated dermatitis for clinical nurses in Korea.

PubMed

Lee, Yun Jin; Kim, Jung Yoon

2016-03-01

The objective of this study was to evaluate the effect of pressure ulcer classification system education on clinical nurses' knowledge and visual differential diagnostic ability of pressure ulcer (PU) classification and incontinence-associated dermatitis (IAD). One group pre and post-test was used. A convenience sample of 407 nurses, participating in PU classification education programme of continuing education, were enrolled. The education programme was composed of a 50-minute lecture on PU classification and case-studies. The PU Classification system and IAD knowledge test (PUCS-KT) and visual differential diagnostic ability tool (VDDAT), consisting of 21 photographs including clinical information were used. Paired t-test was performed using SPSS/WIN 20.0. The overall mean difference of PUCS-KT (t = -11·437, P<0·001) and VDDAT (t = -21·113, P<0·001) was significantly increased after PU classification education. Overall understanding of six PU classification and IAD after education programme was increased, but lacked visual differential diagnostic ability regarding Stage III PU, suspected deep tissue injury (SDTI), and Unstageable. Continuous differentiated education based on clinical practice is needed to improve knowledge and visual differential diagnostic ability for PU classification, and comparison experiment study is required to examine effects of education programmes. © 2016 Medicalhelplines.com Inc and John Wiley & Sons Ltd.

Social communication in children with autism spectrum disorder (asd): Correlation between DSM-5 and autism classification system of functioning-social communication (ACSF:SC).

PubMed

Craig, Francesco; Fanizza, Isabella; Russo, Luigi; Lucarelli, Elisabetta; Alessandro, Lorenzo; Pasca, Maria Grazia; Trabacca, Antonio

2017-07-01

The aim of this study was to classify children with Autism Spectrum Disorder (ASD) according to Autism Classification System of Functioning: Social Communication (ACSF:SC) criteria, in order to investigate the association between social communication ability, ASD severity, adaptive functioning, cognitive abilities and psychoeducational profile. The severity of social communication impairment was specified through Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) and ACSF:SC tool. The ADOS-2, Vineland-II and PEP-3 were administered to all participants. We found a positive correlation between DSM-5 levels and ACSF:SC-Typical Performance (r = 0.35; P = 0.007) and ACSF:SC-Capacity (r = 0.31; P = 0.01) levels. Children included in the five levels of ACSF:SC (Typical Performance and Capacity) showed statistically significant differences in ADOS-2 (Social Affect), Vineland-II (Communication and Socialization), and PEP-3 (Communication, motor skills, maladaptive behavior) scores. The results of this study indicate that ACSF:SC provide a better understanding of functional profile of children with ASD based on the social communication abilities. Children with greater severity of social communication showed more difficulty in adaptive behavior and psychoeducational profiles. In conclusion, the ACSF:SC could help clinicians and therapists not only to understand the strength and weakness of preschool children with ASD but also to devise specific treatment in order to promote their social integration. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1249-1258. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Compatibility of Army Systems with Anthropometric Characteristics of Female Soldiers

DTIC Science & Technology

1997-09-01

M, Industrial Security Manual, Section 11-19 or DoD 5200.1-R, Information Security Program Regulation, Chapter IX. For Unclassified/ Limited ...of female soldiers. Participation was limited to female soldiers whose height did not exceed 5’ 5", the 5th percentile value of male soldiers’ height...CLASSIFICATION tffcMSSIFIED 18. SECURITY CLASSIFICATION uffdLÄS^FIED 19. SECURITY CLASSIFICATION ST*—FIED 20. LIMITATION OF ABSTRACT SAR

Introduction to Grassland Management. Instructor Guide, Student Reference [and] Crop and Grassland Plant Identification Manual.

ERIC Educational Resources Information Center

Suits, Susie

This packet contains an Instructor guide and student reference for a course in introduction to grassland management, as well as a crop and grassland plant identification manual. The three-unit curriculum contains the following 11 lessons: (unit I, grasslands and grassland plants): (1) an introduction to grasslands; (2) plant classification; (3)…

Overview and Analysis of the Behaviourist Criticism of the "Diagnostic and Statistical Manual of Mental Disorders (DSM)"

ERIC Educational Resources Information Center

Andersson, Gerhard; Ghaderi, Ata

2006-01-01

While a majority of cognitive behavioural researchers and clinicians adhere to the classification system provided in the "Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)," strong objections have been voiced among behaviourists who find the dichotomous allocation of patients into psychiatric diagnoses incompatible with the philosophy…

Medical Record Clerk Training Program, Course of Study; Student Manual: For Medical Record Personnel in Small Rural Hospitals in Colorado.

ERIC Educational Resources Information Center

Community Health Service (DHEW/PHS), Arlington, VA. Div. of Health Resources.

The manual provides major topics, objectives, activities and, procedures, references and materials, and assignments for the training program. The topics covered are hospital organization and community role, organization and management of a medical records department, international classification of diseases and operations, medical terminology,…

Automated quasi-3D spine curvature quantification and classification

NASA Astrophysics Data System (ADS)

Khilari, Rupal; Puchin, Juris; Okada, Kazunori

2018-02-01

Scoliosis is a highly prevalent spine deformity that has traditionally been diagnosed through measurement of the Cobb angle on radiographs. More recent technology such as the commercial EOS imaging system, although more accurate, also require manual intervention for selecting the extremes of the vertebrae forming the Cobb angle. This results in a high degree of inter and intra observer error in determining the extent of spine deformity. Our primary focus is to eliminate the need for manual intervention by robustly quantifying the curvature of the spine in three dimensions, making it consistent across multiple observers. Given the vertebrae centroids, the proposed Vertebrae Sequence Angle (VSA) estimation and segmentation algorithm finds the largest angle between consecutive pairs of centroids within multiple inflection points on the curve. To exploit existing clinical diagnostic standards, the algorithm uses a quasi-3-dimensional approach considering the curvature in the coronal and sagittal projection planes of the spine. Experiments were performed with manuallyannotated ground-truth classification of publicly available, centroid-annotated CT spine datasets. This was compared with the results obtained from manual Cobb and Centroid angle estimation methods. Using the VSA, we then automatically classify the occurrence and the severity of spine curvature based on Lenke's classification for idiopathic scoliosis. We observe that the results appear promising with a scoliotic angle lying within +/- 9° of the Cobb and Centroid angle, and vertebrae positions differing by at the most one position. Our system also resulted in perfect classification of scoliotic from healthy spines with our dataset with six cases.

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…

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

ERIC Educational Resources Information Center

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

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

Evolving forest fire burn severity classification algorithms for multispectral imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Harvey, Neal R.; Bloch, Jeffrey J.; Theiler, James P.; Perkins, Simon J.; Young, Aaron C.; Szymanski, John J.

2001-08-01

Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100% tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial color/infrared photography.

TRAFIC: fiber tract classification using deep learning

NASA Astrophysics Data System (ADS)

Ngattai Lam, Prince D.; Belhomme, Gaetan; Ferrall, Jessica; Patterson, Billie; Styner, Martin; Prieto, Juan C.

2018-03-01

We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy. With this new approach we were able to classify traced fiber tracts obtaining encouraging results. In this report we will present in detail the methods used and the results achieved with our approach.

Shortened version of the work ability index to identify workers at risk of long-term sickness absence.

PubMed

Schouten, Lianne S; Bültmann, Ute; Heymans, Martijn W; Joling, Catelijne I; Twisk, Jos W R; Roelen, Corné A M

2016-04-01

The Work Ability Index (WAI) identifies non-sicklisted workers at risk of future long-term sickness absence (LTSA). The WAI is a complicated instrument and inconvenient for use in large-scale surveys. We investigated whether shortened versions of the WAI identify non-sicklisted workers at risk of LTSA. Prospective study including two samples of non-sicklisted workers participating in occupational health checks between 2010 and 2012. A heterogeneous development sample (N= 2899) was used to estimate logistic regression coefficients for the complete WAI, a shortened WAI version without the list of diseases, and single-item Work Ability Score (WAS). These three instruments were calibrated for predictions of different (≥2, ≥4 and ≥6 weeks) LTSA durations in a validation sample of non-sicklisted workers (N= 3049) employed at a steel mill, differentiating between manual (N= 1710) and non-manual (N= 1339) workers. The discriminative ability was investigated by receiver operating characteristic analysis. All three instruments under-predicted the LTSA risks in both manual and non-manual workers. The complete WAI discriminated between individuals at high and low risk of LTSA ≥2, ≥4 and ≥6 weeks in manual and non-manual workers. Risk predictions and discrimination by the shortened WAI without the list of diseases were as good as the complete WAI. The WAS showed poorer discrimination in manual and non-manual workers. The WAI without the list of diseases is a good alternative to the complete WAI to identify non-sicklisted workers at risk of future LTSA durations ≥2, ≥4 and ≥6 weeks. © The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Caracterization of adults with cerebral palsy.

PubMed

Margre, Anna L M; Reis, Maria G L; Morais, Rosane L S

2010-01-01

cerebral Palsy (CP) is a group of permanent disorders of the development of movement and posture that cause functional limitation and are attributed to non-progressive disorders which occur in the fetal or infant brain. In recent years, with the increase in life expectancy of individuals with CP, several studies have described the impact of musculoskeletal disabilities and functional limitations over the life cycle. to characterize adults with CP through sociodemographic information, classifications, general health, associated conditions, physical complications and locomotion. twenty-two adults with CP recruited from local rehabilitation centers in an inner town of Brazil participated in this study. A questionnaire was used to collect data on sociodemographic characteristics, comorbities, and physical complications. A brief physical therapy evaluation was carried out, and the Gross Motor Function Classification System (GMFCS) and the Manual Ability Classification System (MACS) were applied. Data were analyzed through descriptive statistics. the mean age was 28.7 (SD 10.6) years, 86.4% of participants lived with parents, and 4.5% were employed. Most of the sample consisted of spastic quadriplegic subjects, corresponding to levels IV and V of the GMFCS and MACS. Different comorbidities and important physical complications such as scoliosis and muscle contractures were present. More than half of the participants were unable to walk. Most participants demonstrated important restrictions in social participation and lower educational level. Adults with CP can be affected by several physical complications and progressive limitations in gait.

Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2016-03-01

The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.

The Classification Ability with Naked Eyes According to the Understanding Level about Rocks of Pre-service Science Teachers

NASA Astrophysics Data System (ADS)

Seong, Cho Kyu; Ho, Chung Duk; Pyo, Hong Deok; Kyeong Jin, Park

2016-04-01

This study aimed to investigate the classification ability with naked eyes according to the understanding level about rocks of pre-service science teachers. We developed a questionnaire concerning misconception about minerals and rocks. The participant were 132 pre-service science teachers. Data were analyzed using Rasch model. Participants were divided into a master group and a novice group according to their understanding level. Seventeen rocks samples (6 igneous, 5 sedimentary, and 6 metamorphic rocks) were presented to pre-service science teachers to examine their classification ability, and they classified the rocks according to the criteria we provided. The study revealed three major findings. First, the pre-service science teachers mainly classified rocks according to textures, color, and grain size. Second, while they relatively easily classified igneous rocks, participants were confused when distinguishing sedimentary and metamorphic rocks from one another by using the same classification criteria. On the other hand, the understanding level of rocks has shown a statistically significant correlation with the classification ability in terms of the formation mechanism of rocks, whereas there was no statically significant relationship found with determination of correct name of rocks. However, this study found that there was a statistically significant relationship between the classification ability with regard the formation mechanism of rocks and the determination of correct name of rocks Keywords : Pre-service science teacher, Understanding level, Rock classification ability, Formation mechanism, Criterion of classification

Automatic segmentation and classification of gestational sac based on mean sac diameter using medical ultrasound image

NASA Astrophysics Data System (ADS)

Khazendar, Shan; Farren, Jessica; Al-Assam, Hisham; Sayasneh, Ahmed; Du, Hongbo; Bourne, Tom; Jassim, Sabah A.

2014-05-01

Ultrasound is an effective multipurpose imaging modality that has been widely used for monitoring and diagnosing early pregnancy events. Technology developments coupled with wide public acceptance has made ultrasound an ideal tool for better understanding and diagnosing of early pregnancy. The first measurable signs of an early pregnancy are the geometric characteristics of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from ultrasound images. The manual measurement involves multiple subjective decisions, in which dimensions are taken in three planes to establish what is known as Mean Sac Diameter (MSD). The manual measurement results in inter- and intra-observer variations, which may lead to difficulties in diagnosis. This paper proposes a fully automated diagnosis solution to accurately identify miscarriage cases in the first trimester of pregnancy based on automatic quantification of the MSD. Our study shows a strong positive correlation between the manual and the automatic MSD estimations. Our experimental results based on a dataset of 68 ultrasound images illustrate the effectiveness of the proposed scheme in identifying early miscarriage cases with classification accuracies comparable with those of domain experts using K nearest neighbor classifier on automatically estimated MSDs.

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature.

PubMed

Shigeta, Hironori; Mashita, Tomohiro; Kikuta, Junichi; Seno, Shigeto; Takemura, Haruo; Ishii, Masaru; Matsuda, Hideo

2017-10-01

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

Validation of an Improved Computer-Assisted Technique for Mining Free-Text Electronic Medical Records.

PubMed

Duz, Marco; Marshall, John F; Parkin, Tim

2017-06-29

The use of electronic medical records (EMRs) offers opportunity for clinical epidemiological research. With large EMR databases, automated analysis processes are necessary but require thorough validation before they can be routinely used. The aim of this study was to validate a computer-assisted technique using commercially available content analysis software (SimStat-WordStat v.6 (SS/WS), Provalis Research) for mining free-text EMRs. The dataset used for the validation process included life-long EMRs from 335 patients (17,563 rows of data), selected at random from a larger dataset (141,543 patients, ~2.6 million rows of data) and obtained from 10 equine veterinary practices in the United Kingdom. The ability of the computer-assisted technique to detect rows of data (cases) of colic, renal failure, right dorsal colitis, and non-steroidal anti-inflammatory drug (NSAID) use in the population was compared with manual classification. The first step of the computer-assisted analysis process was the definition of inclusion dictionaries to identify cases, including terms identifying a condition of interest. Words in inclusion dictionaries were selected from the list of all words in the dataset obtained in SS/WS. The second step consisted of defining an exclusion dictionary, including combinations of words to remove cases erroneously classified by the inclusion dictionary alone. The third step was the definition of a reinclusion dictionary to reinclude cases that had been erroneously classified by the exclusion dictionary. Finally, cases obtained by the exclusion dictionary were removed from cases obtained by the inclusion dictionary, and cases from the reinclusion dictionary were subsequently reincluded using Rv3.0.2 (R Foundation for Statistical Computing, Vienna, Austria). Manual analysis was performed as a separate process by a single experienced clinician reading through the dataset once and classifying each row of data based on the interpretation of the free-text notes. Validation was performed by comparison of the computer-assisted method with manual analysis, which was used as the gold standard. Sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and F values of the computer-assisted process were calculated by comparing them with the manual classification. Lowest sensitivity, specificity, PPVs, NPVs, and F values were 99.82% (1128/1130), 99.88% (16410/16429), 94.6% (223/239), 100.00% (16410/16412), and 99.0% (100×2×0.983×0.998/[0.983+0.998]), respectively. The computer-assisted process required few seconds to run, although an estimated 30 h were required for dictionary creation. Manual classification required approximately 80 man-hours. The critical step in this work is the creation of accurate and inclusive dictionaries to ensure that no potential cases are missed. It is significantly easier to remove false positive terms from a SS/WS selected subset of a large database than search that original database for potential false negatives. The benefits of using this method are proportional to the size of the dataset to be analyzed. ©Marco Duz, John F Marshall, Tim Parkin. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 29.06.2017.

Validation of an Improved Computer-Assisted Technique for Mining Free-Text Electronic Medical Records

PubMed Central

Marshall, John F; Parkin, Tim

2017-01-01

Background The use of electronic medical records (EMRs) offers opportunity for clinical epidemiological research. With large EMR databases, automated analysis processes are necessary but require thorough validation before they can be routinely used. Objective The aim of this study was to validate a computer-assisted technique using commercially available content analysis software (SimStat-WordStat v.6 (SS/WS), Provalis Research) for mining free-text EMRs. Methods The dataset used for the validation process included life-long EMRs from 335 patients (17,563 rows of data), selected at random from a larger dataset (141,543 patients, ~2.6 million rows of data) and obtained from 10 equine veterinary practices in the United Kingdom. The ability of the computer-assisted technique to detect rows of data (cases) of colic, renal failure, right dorsal colitis, and non-steroidal anti-inflammatory drug (NSAID) use in the population was compared with manual classification. The first step of the computer-assisted analysis process was the definition of inclusion dictionaries to identify cases, including terms identifying a condition of interest. Words in inclusion dictionaries were selected from the list of all words in the dataset obtained in SS/WS. The second step consisted of defining an exclusion dictionary, including combinations of words to remove cases erroneously classified by the inclusion dictionary alone. The third step was the definition of a reinclusion dictionary to reinclude cases that had been erroneously classified by the exclusion dictionary. Finally, cases obtained by the exclusion dictionary were removed from cases obtained by the inclusion dictionary, and cases from the reinclusion dictionary were subsequently reincluded using Rv3.0.2 (R Foundation for Statistical Computing, Vienna, Austria). Manual analysis was performed as a separate process by a single experienced clinician reading through the dataset once and classifying each row of data based on the interpretation of the free-text notes. Validation was performed by comparison of the computer-assisted method with manual analysis, which was used as the gold standard. Sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and F values of the computer-assisted process were calculated by comparing them with the manual classification. Results Lowest sensitivity, specificity, PPVs, NPVs, and F values were 99.82% (1128/1130), 99.88% (16410/16429), 94.6% (223/239), 100.00% (16410/16412), and 99.0% (100×2×0.983×0.998/[0.983+0.998]), respectively. The computer-assisted process required few seconds to run, although an estimated 30 h were required for dictionary creation. Manual classification required approximately 80 man-hours. Conclusions The critical step in this work is the creation of accurate and inclusive dictionaries to ensure that no potential cases are missed. It is significantly easier to remove false positive terms from a SS/WS selected subset of a large database than search that original database for potential false negatives. The benefits of using this method are proportional to the size of the dataset to be analyzed. PMID:28663163

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar

NASA Astrophysics Data System (ADS)

Fedrigo, Melissa; Newnham, Glenn J.; Coops, Nicholas C.; Culvenor, Darius S.; Bolton, Douglas K.; Nitschke, Craig R.

2018-02-01

Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen's kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the 'ecotone') between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Neural network classification of questionable EGRET events

NASA Astrophysics Data System (ADS)

Meetre, C. A.; Norris, J. P.

1992-02-01

High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 104 events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.

Neural network classification of questionable EGRET events

NASA Technical Reports Server (NTRS)

Meetre, C. A.; Norris, J. P.

1992-01-01

High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 10(exp 4) events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.

Individual Differences in Language Development: Relationship with Motor Skill at 21 Months

ERIC Educational Resources Information Center

Alcock, Katherine J.; Krawczyk, Kirsty

2010-01-01

Language development has long been associated with motor development, particularly manual gesture. We examined a variety of motor abilities--manual gesture including symbolic, meaningless and sequential memory, oral motor control, gross and fine motor control--in 129 children aged 21 months. Language abilities were assessed and cognitive and…

Promising Curriculum and Instructional Practices for High-Ability Learners Manual.

ERIC Educational Resources Information Center

Auld, Corrine; Brown, Jane; Duffy, Mary; Falter, Nancy; Hammond, Tom; Jensen, Dennis; Schlager, Carolyn; Senseney, Alice; Ward, Noreen

This manual is intended to assist teachers in Nebraska's schools in meeting the needs of high-ability learners in their classrooms. Chapter 1 focuses on curriculum differentiation regarding the content, process, and product. Bloom's taxonomy of thinking is discussed; a list of acceptable student projects for elementary and secondary students is…

Manual Therapy: The Historical, Current, and Future Role in the Treatment of Pain

PubMed Central

Smith, A. Russell

2007-01-01

Manual therapy has been an approach in the management of patients with various disorders dating back to ancient times and continues to play a significant role in current health care. The future role of manual therapy in health care is an important area of research. This paper reviews the history of manual therapy, examines the current literature related to the use of manual techniques (including manipulation, massage, and nerve manipulation), and discusses future research topics. The literature related to manual therapy has historically been anecdotal and theoretical, and current research tends to have a generic approach with broad definitions of manual therapy and inconsistencies in the classification of specific disorders. Systematic reviews of various types of manual therapy have differed on their conclusions regarding the effectiveness of this treatment modality. The current demand in health care for evidence-based practice necessitates a movement towards more specificity in the research of the effectiveness of manual therapy, with emphasis on specific patient signs and symptoms and specific manual techniques that result in effective care. PMID:17334604

Key-phrase based classification of public health web pages.

PubMed

Dolamic, Ljiljana; Boyer, Célia

2013-01-01

This paper describes and evaluates the public health web pages classification model based on key phrase extraction and matching. Easily extendible both in terms of new classes as well as the new language this method proves to be a good solution for text classification faced with the total lack of training data. To evaluate the proposed solution we have used a small collection of public health related web pages created by a double blind manual classification. Our experiments have shown that by choosing the adequate threshold value the desired value for either precision or recall can be achieved.

A combined Fuzzy and Naive Bayesian strategy can be used to assign event codes to injury narratives.

PubMed

Marucci-Wellman, H; Lehto, M; Corns, H

2011-12-01

Bayesian methods show promise for classifying injury narratives from large administrative datasets into cause groups. This study examined a combined approach where two Bayesian models (Fuzzy and Naïve) were used to either classify a narrative or select it for manual review. Injury narratives were extracted from claims filed with a worker's compensation insurance provider between January 2002 and December 2004. Narratives were separated into a training set (n=11,000) and prediction set (n=3,000). Expert coders assigned two-digit Bureau of Labor Statistics Occupational Injury and Illness Classification event codes to each narrative. Fuzzy and Naïve Bayesian models were developed using manually classified cases in the training set. Two semi-automatic machine coding strategies were evaluated. The first strategy assigned cases for manual review if the Fuzzy and Naïve models disagreed on the classification. The second strategy selected additional cases for manual review from the Agree dataset using prediction strength to reach a level of 50% computer coding and 50% manual coding. When agreement alone was used as the filtering strategy, the majority were coded by the computer (n=1,928, 64%) leaving 36% for manual review. The overall combined (human plus computer) sensitivity was 0.90 and positive predictive value (PPV) was >0.90 for 11 of 18 2-digit event categories. Implementing the 2nd strategy improved results with an overall sensitivity of 0.95 and PPV >0.90 for 17 of 18 categories. A combined Naïve-Fuzzy Bayesian approach can classify some narratives with high accuracy and identify others most beneficial for manual review, reducing the burden on human coders.

The role of identity in the DSM-5 classification of personality disorders

PubMed Central

2013-01-01

In the revised Diagnostic and Statistical Manual DSM-5 the definition of personality disorder diagnoses has not been changed from that in the DSM-IV-TR. However, an alternative model for diagnosing personality disorders where the construct “identity” has been integrated as a central diagnostic criterion for personality disorders has been placed in section III of the manual. The alternative model’s hybrid nature leads to the simultaneous use of diagnoses and the newly developed “Level of Personality Functioning-Scale” (a dimensional tool to define the severity of the disorder). Pathological personality traits are assessed in five broad domains which are divided into 25 trait facets. With this dimensional approach, the new classification system gives, both clinicians and researchers, the opportunity to describe the patient in much more detail than previously possible. The relevance of identity problems in assessing and understanding personality pathology is illustrated using the new classification system applied in two case examples of adolescents with a severe personality disorder. PMID:23902698

The role of identity in the DSM-5 classification of personality disorders.

PubMed

Schmeck, Klaus; Schlüter-Müller, Susanne; Foelsch, Pamela A; Doering, Stephan

2013-07-31

In the revised Diagnostic and Statistical Manual DSM-5 the definition of personality disorder diagnoses has not been changed from that in the DSM-IV-TR. However, an alternative model for diagnosing personality disorders where the construct "identity" has been integrated as a central diagnostic criterion for personality disorders has been placed in section III of the manual. The alternative model's hybrid nature leads to the simultaneous use of diagnoses and the newly developed "Level of Personality Functioning-Scale" (a dimensional tool to define the severity of the disorder). Pathological personality traits are assessed in five broad domains which are divided into 25 trait facets. With this dimensional approach, the new classification system gives, both clinicians and researchers, the opportunity to describe the patient in much more detail than previously possible. The relevance of identity problems in assessing and understanding personality pathology is illustrated using the new classification system applied in two case examples of adolescents with a severe personality disorder.

State Guidelines for Mental Retardation and Intellectual Disabilities: A Re-visitation of Previous Analyses in Light of Changes in the Field

ERIC Educational Resources Information Center

Polloway, Edward A.; Patton, James R.; Smith, J. David; Lubin, Jacqueline; Antoine, Karian

2009-01-01

In 2002, the American Association on Mental Retardation (AAMR) (Luckasson et al., 2002) revised their manual on mental retardation. It also extended the changes that had been made in the previous (1992) manual to further promote an alternative approach to definition and classification in the field. The study reported here sought to determine the…

A Labor and Delivery Patient Classification System Based on Direct Nursing Care Time

DTIC Science & Technology

1991-08-01

physician 2409 Internal or external monitoring--uterine contraction/ fetal heart tones 2410 Manual contraction assessment 2411 Pitocin induction...assisting physician 2412 Fetal heart tones, manual 2413 Fetal heart tones, doppler 2414 Fetal scalp sampling, assisting physician 241E Routine delivery room... heart tones, ultrasonic transducer 2437 Monitoring fetal heart tones, ultrasonic transducer and uterine contraction, tocotransducer 69 Appendix B: List

PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy.

PubMed

Boyd, Roslyn N; Davies, Peter Sw; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Sakzewski, Leanne; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

2017-07-12

Cerebral palsy (CP) remains the world's most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8-12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006-2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5-5 then 8-12 years of direct clinical assessment to enable prediction of outcomes and healthcare needs essential for tailoring interventions (eg, rehabilitation, orthopaedic surgery and nutritional supplements) and the projected healthcare utilisation. ACTRN: 12616001488493. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Status of Artist Upgrade

DTIC Science & Technology

1988-09-01

Autodrift, ARTIST Autoscaling , Electron Density 16. PRICE CODE Profiles 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY...FIGURES Figure No. Page 2.1 ARTIST Scaled Parameters 4 2.2 ARTIST ASCII Ionogram 6 2.3 ARTISTSV Optifont lonogram 7 2.4 Autoscaling of Es Trace Before...diagnostic programs for testing communication ports. The aforementioned contract required a performance evaluation of ARTIST . Manual and autoscaled

Automated Classification of Power Signals

DTIC Science & Technology

2008-06-01

determine when a transient occurs. The identification of this signal can then be determined by an expert classifier and a series of these...the manual identification and classification of system events. Once events were located, the characteristics were examined to determine if system... identification code, which varies depending on the system classifier that is specified. Figure 3-7 provides an example of a Linux directory containing

Review of Psychodynamic diagnostics manual (PDM).

PubMed

Moses, Ira

2008-03-01

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

[A multilevel model analysis of correlation between population characteristics and work ability of employees].

PubMed

Zhang, Lei; Huang, Chunping; Lan, Yajia; Wang, Mianzhen

2015-12-01

To analyze the correlation between population characteristics and work ability of employees with a multilevel model, to investigate the important influencing factors for work ability, and to provide a basis for improvement in work ability. Work ability index (WAI)was applied to measure the work ability of 1686 subjects from different companies (n=6). MLwi N2.0 software was applied for two-level variance component model fitting. The WAI of employees showed differences between various companies (χ2=3.378 6, P=0.0660); working years was negatively correlated with WAI (χ2=38.229 2, P=0.0001), and the WAI of the employees with 20 or more working years was 1.63 lower than that of the employees with less than 20 working years; the work ability of manual workers was lower than that of mental-manual workers (χ2=8.2726, P=0.0040), and the work ability showed no significant difference between mental workers and mental-manual workers (χ2=2.086 0, P=0.148 7). From the perspective of probability, the multilevel model analysis reveals the differences in work ability of employees between different companies, and suggests that company, work type, and working years are the important influencing factors for work ability of employees. These factors should be improved and adjusted to protect or enhance the work ability of employees.

Robotic Anesthesia – A Vision for the Future of Anesthesia

PubMed Central

Hemmerling, Thomas M; Taddei, Riccardo; Wehbe, Mohamad; Morse, Joshua; Cyr, Shantale; Zaouter, Cedrick

2011-01-01

Summary This narrative review describes a rationale for robotic anesthesia. It offers a first classification of robotic anesthesia by separating it into pharmacological robots and robots for aiding or replacing manual gestures. Developments in closed loop anesthesia are outlined. First attempts to perform manual tasks using robots are described. A critical analysis of the delayed development and introduction of robots in anesthesia is delivered. PMID:23905028

Low-cost real-time automatic wheel classification system

NASA Astrophysics Data System (ADS)

Shabestari, Behrouz N.; Miller, John W. V.; Wedding, Victoria

1992-11-01

This paper describes the design and implementation of a low-cost machine vision system for identifying various types of automotive wheels which are manufactured in several styles and sizes. In this application, a variety of wheels travel on a conveyor in random order through a number of processing steps. One of these processes requires the identification of the wheel type which was performed manually by an operator. A vision system was designed to provide the required identification. The system consisted of an annular illumination source, a CCD TV camera, frame grabber, and 386-compatible computer. Statistical pattern recognition techniques were used to provide robust classification as well as a simple means for adding new wheel designs to the system. Maintenance of the system can be performed by plant personnel with minimal training. The basic steps for identification include image acquisition, segmentation of the regions of interest, extraction of selected features, and classification. The vision system has been installed in a plant and has proven to be extremely effective. The system properly identifies the wheels correctly up to 30 wheels per minute regardless of rotational orientation in the camera's field of view. Correct classification can even be achieved if a portion of the wheel is blocked off from the camera. Significant cost savings have been achieved by a reduction in scrap associated with incorrect manual classification as well as a reduction of labor in a tedious task.

21 CFR 888.4540 - Orthopedic manual surgical instrument.

Code of Federal Regulations, 2012 CFR

2012-04-01

..., femoral neck punch, socket pusher, reamer, rongeur, scissors, screwdriver, bone skid, staple driver, bone screw starter, surgical stripper, tamp, bone tap, trephine, wire twister, and wrench. (b) Classification...

21 CFR 888.4540 - Orthopedic manual surgical instrument.

Code of Federal Regulations, 2010 CFR

2010-04-01

..., femoral neck punch, socket pusher, reamer, rongeur, scissors, screwdriver, bone skid, staple driver, bone screw starter, surgical stripper, tamp, bone tap, trephine, wire twister, and wrench. (b) Classification...

21 CFR 888.4540 - Orthopedic manual surgical instrument.

Code of Federal Regulations, 2013 CFR

2013-04-01

..., femoral neck punch, socket pusher, reamer, rongeur, scissors, screwdriver, bone skid, staple driver, bone screw starter, surgical stripper, tamp, bone tap, trephine, wire twister, and wrench. (b) Classification...

21 CFR 888.4540 - Orthopedic manual surgical instrument.

Code of Federal Regulations, 2011 CFR

2011-04-01

..., femoral neck punch, socket pusher, reamer, rongeur, scissors, screwdriver, bone skid, staple driver, bone screw starter, surgical stripper, tamp, bone tap, trephine, wire twister, and wrench. (b) Classification...

21 CFR 888.4540 - Orthopedic manual surgical instrument.

Code of Federal Regulations, 2014 CFR

2014-04-01

..., femoral neck punch, socket pusher, reamer, rongeur, scissors, screwdriver, bone skid, staple driver, bone screw starter, surgical stripper, tamp, bone tap, trephine, wire twister, and wrench. (b) Classification...

Classifying Structures in the ISM with Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

2011-01-01

The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

Operation and Maintenance Manual, Ultrasonic Fish Deterrent System

DTIC Science & Technology

1991-07-01

PAGES Fishery management--Instruments 61 Ultrsonic transducers 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY...compatible computer with a communications software package will be most convenient; however, any terminal will work. To begin operation, connect the...D. Next connect the communications cable (TC-4) between the RFPG and the terminal. An ONSET TC-4 cable must be used due to level shifting

Development of Automated Image Analysis Software for Suspended Marine Particle Classification

DTIC Science & Technology

2003-09-30

Development of Automated Image Analysis Software for Suspended Marine Particle Classification Scott Samson Center for Ocean Technology...REPORT TYPE 3. DATES COVERED 00-00-2003 to 00-00-2003 4. TITLE AND SUBTITLE Development of Automated Image Analysis Software for Suspended...objective is to develop automated image analysis software to reduce the effort and time required for manual identification of plankton images. Automated

The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

PubMed

Roberts, Kirk; Shooshan, Sonya E; Rodriguez, Laritza; Abhyankar, Swapna; Kilicoglu, Halil; Demner-Fushman, Dina

2015-12-01

This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utilize a series of support vector machine models in conjunction with manually built lexicons to classify triggers specific to each risk factor. The features used for classification were quite simple, utilizing only lexical information and ignoring higher-level linguistic information such as syntax and semantics. Instead, we incorporated high-quality data to train the models by annotating additional information on top of a standard corpus. Despite the relative simplicity of the system, it achieves the highest scores (micro- and macro-F1, and micro- and macro-recall) out of the 20 participants in the 2014 i2b2/UTHealth Shared Task. This system obtains a micro- (macro-) precision of 0.8951 (0.8965), recall of 0.9625 (0.9611), and F1-measure of 0.9276 (0.9277). Additionally, we perform a series of experiments to assess the value of the annotated data we created. These experiments show how manually-labeled negative annotations can improve information extraction performance, demonstrating the importance of high-quality, fine-grained natural language annotations. Copyright © 2015 Elsevier Inc. All rights reserved.

Classification Behavior in Children Thirty-Six Months of Age.

ERIC Educational Resources Information Center

Shimada, Shoko; Sano, Ryogoro

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

Identification the Relation between Active Basketball Classification Referees' Empathetic Tendencies and Their Problem Solving Abilities

ERIC Educational Resources Information Center

Karaçam, Aydin; Pulur, Atilla

2016-01-01

This study aims to determine the relation between basketball classification referees' problem solving ability and empathetic tendencies. Research model of the study is relational screening model. Sampling of the study is constituted by 124 male and 18 female basketball classification referees who made active refereeing within Turkish Basketball…

We Don't Train in Vain: A Dissemination Trial of Three Strategies of Training Clinicians in Cognitive–Behavioral Therapy

PubMed Central

Sholomskas, Diane E.; Syracuse-Siewert, Gia; Rounsaville, Bruce J.; Ball, Samuel A.; Nuro, Kathryn F.; Carroll, Kathleen M.

2008-01-01

There has been little research on the effectiveness of different training strategies or the impact of exposure to treatment manuals alone on clinicians' ability to effectively implement empirically supported therapies. Seventy-eight community-based clinicians were assigned to 1 of 3 training conditions: review of a cognitive–behavioral therapy (CBT) manual only, review of the manual plus access to a CBT training Web site, or review of the manual plus a didactic seminar followed by supervised casework. The primary outcome measure was the clinicians' ability to demonstrate key CBT interventions, as assessed by independent ratings of structured role plays. Statistically significant differences favoring the seminar plus supervision over the manual only condition were found for adherence and skill ratings for 2 of the 3 role plays, with intermediate scores for the Web condition. PMID:15709837

Validation of Case Finding Algorithms for Hepatocellular Cancer From Administrative Data and Electronic Health Records Using Natural Language Processing.

PubMed

Sada, Yvonne; Hou, Jason; Richardson, Peter; El-Serag, Hashem; Davila, Jessica

2016-02-01

Accurate identification of hepatocellular cancer (HCC) cases from automated data is needed for efficient and valid quality improvement initiatives and research. We validated HCC International Classification of Diseases, 9th Revision (ICD-9) codes, and evaluated whether natural language processing by the Automated Retrieval Console (ARC) for document classification improves HCC identification. We identified a cohort of patients with ICD-9 codes for HCC during 2005-2010 from Veterans Affairs administrative data. Pathology and radiology reports were reviewed to confirm HCC. The positive predictive value (PPV), sensitivity, and specificity of ICD-9 codes were calculated. A split validation study of pathology and radiology reports was performed to develop and validate ARC algorithms. Reports were manually classified as diagnostic of HCC or not. ARC generated document classification algorithms using the Clinical Text Analysis and Knowledge Extraction System. ARC performance was compared with manual classification. PPV, sensitivity, and specificity of ARC were calculated. A total of 1138 patients with HCC were identified by ICD-9 codes. On the basis of manual review, 773 had HCC. The HCC ICD-9 code algorithm had a PPV of 0.67, sensitivity of 0.95, and specificity of 0.93. For a random subset of 619 patients, we identified 471 pathology reports for 323 patients and 943 radiology reports for 557 patients. The pathology ARC algorithm had PPV of 0.96, sensitivity of 0.96, and specificity of 0.97. The radiology ARC algorithm had PPV of 0.75, sensitivity of 0.94, and specificity of 0.68. A combined approach of ICD-9 codes and natural language processing of pathology and radiology reports improves HCC case identification in automated data.

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

PubMed Central

Tsai, Yu Hsin; Stow, Douglas; Weeks, John

2013-01-01

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. PMID:24415810

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.

Comparative Effectiveness Research and Children With Cerebral Palsy: Identifying a Conceptual Framework and Specifying Measures.

PubMed

Gannotti, Mary E; Law, Mary; Bailes, Amy F; OʼNeil, Margaret E; Williams, Uzma; DiRezze, Briano

2016-01-01

A step toward advancing research about rehabilitation service associated with positive outcomes for children with cerebral palsy is consensus about a conceptual framework and measures. A Delphi process was used to establish consensus among clinicians and researchers in North America. Directors of large pediatric rehabilitation centers, clinicians from large hospitals, and researchers with expertise in outcomes participated (N = 18). Andersen's model of health care utilization framed outcomes: consumer satisfaction, activity, participation, quality of life, and pain. Measures agreed upon included Participation and Environment Measure for Children and Youth, Measure of Processes of Care, PEDI-CAT, KIDSCREEN-10, PROMIS Pediatric Pain Interference Scale, Visual Analog Scale for pain intensity, PROMIS Global Health Short Form, Family Environment Scale, Family Support Scale, and functional classification levels for gross motor, manual ability, and communication. Universal forms for documenting service use are needed. Findings inform clinicians and researchers concerned with outcome assessment.

Manualization: A Blessing or a Curse?

ERIC Educational Resources Information Center

Marshall, W. L.

2009-01-01

This paper considers the issues involved in the use of manuals to guide the treatment of sexual offenders. I identify problems in the use of manuals, particularly their failure to encourage satisfactorily the implementation of therapeutic skills, the restrictions they place upon the therapist's ability to address the responsivity principle, the…

A minimum spanning forest based classification method for dedicated breast CT images

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

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei, E-mail: bfei@emory.edu

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting modelmore » used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.« less

Electrical Impedance Imaging for Early Detection of Breast Cancer for Young Women

DTIC Science & Technology

2013-12-01

replacement therapy, prior biopsy, frequency of menstrual periods 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES...separate manual remediation databases were being used (one of which was at one point a copy of the other with some overlap of corrected values), there...in file" during manual remediation) . The Visits and Exams tables both had date variables and although the Menstrual History and All Findings tables

An Evaluation of a Self-Instructional Manual for Teaching Individuals How to Administer the Revised ABLA Test

ERIC Educational Resources Information Center

Boris, Ashley L.; Awadalla, Nardeen; Martin, Toby L.; Martin, Garry L.; Kaminski, Lauren; Miljkovic, Morena

2015-01-01

The Assessment of Basic Learning Abilities (ABLA) is a tool that is used to assess the learning ability of individuals with intellectual disability (ID) and children with autism. The ABLA was recently revised and is now referred to as the ABLA-Revised (ABLA-R). A self-instructional manual was prepared to teach individuals how to administer the…

Semi-Automated Classification of Seafloor Data Collected on the Delmarva Inner Shelf

NASA Astrophysics Data System (ADS)

Sweeney, E. M.; Pendleton, E. A.; Brothers, L. L.; Mahmud, A.; Thieler, E. R.

2017-12-01

We tested automated classification methods on acoustic bathymetry and backscatter data collected by the U.S. Geological Survey (USGS) and National Oceanic and Atmospheric Administration (NOAA) on the Delmarva inner continental shelf to efficiently and objectively identify sediment texture and geomorphology. Automated classification techniques are generally less subjective and take significantly less time than manual classification methods. We used a semi-automated process combining unsupervised and supervised classification techniques to characterize seafloor based on bathymetric slope and relative backscatter intensity. Statistical comparison of our automated classification results with those of a manual classification conducted on a subset of the acoustic imagery indicates that our automated method was highly accurate (95% total accuracy and 93% Kappa). Our methods resolve sediment ridges, zones of flat seafloor and areas of high and low backscatter. We compared our classification scheme with mean grain size statistics of samples collected in the study area and found that strong correlations between backscatter intensity and sediment texture exist. High backscatter zones are associated with the presence of gravel and shells mixed with sand, and low backscatter areas are primarily clean sand or sand mixed with mud. Slope classes further elucidate textural and geomorphologic differences in the seafloor, such that steep slopes (>0.35°) with high backscatter are most often associated with the updrift side of sand ridges and bedforms, whereas low slope with high backscatter correspond to coarse lag or shell deposits. Low backscatter and high slopes are most often found on the downdrift side of ridges and bedforms, and low backscatter and low slopes identify swale areas and sand sheets. We found that poor acoustic data quality was the most significant cause of inaccurate classification results, which required additional user input to mitigate. Our method worked well along the primarily sandy Delmarva inner continental shelf, and outlines a method that can be used to efficiently and consistently produce surficial geologic interpretations of the seafloor from ground-truthed geophysical or hydrographic data.

Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

PubMed Central

Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

2013-01-01

Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts. PMID:24236224

Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

NASA Astrophysics Data System (ADS)

Perry, Daniel; Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

2012-03-01

Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts.

Application of DNA Profiling in Resolving Aviation Forensic Toxicology Issues

DTIC Science & Technology

2009-10-01

National Technical Information Service, Springfield, VA 22161 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21 ...J,. Schumm. JW ..Development. of. highly. polymorphic.pentanucleotide.tandem.repeat.loci. with.low.stutter ..Profiles in DNA ..1998;2:3–6 . 21 ... PowerPlex ™ 16 System, Technical Manual No. D012 ..Madison,.WI:.Promega.Cor- poration;. 2000. (Available. at:. www .cstl .nist .gov/ strbase/images

Computer Center Reference Manual. Volume 1

DTIC Science & Technology

1990-09-30

Unlimited o- 0 0 91o1 UNCLASSI FI ED SECURITY CLASSIFICATION OF THIS PAGE REPORT DOCUMENTATION PAGE la . REPORT SECURITY CLASSIFICATION lb. RESTRICTIVE...with connection to INTERNET ) (host tables allow transfer to some other networks) OASYS - the DTRC Office Automation System The following can be reached...and buffers, two windows, and some word processing commands. Advanced editing commands are entered through the use of a command line. EVE las its own

Automated lidar-derived canopy height estimates for the Upper Mississippi River System

USGS Publications Warehouse

Hlavacek, Enrika

2015-01-01

Land cover/land use (LCU) classifications serve as important decision support products for researchers and land managers. The LCU classifications produced by the U.S. Geological Survey’s Upper Midwest Environmental Sciences Center (UMESC) include canopy height estimates that are assigned through manual aerial photography interpretation techniques. In an effort to improve upon these techniques, this project investigated the use of high-density lidar data for the Upper Mississippi River System to determine canopy height. An ArcGIS tool was developed to automatically derive height modifier information based on the extent of land cover features for forest classes. The measurement of canopy height included a calculation of the average height from lidar point cloud data as well as the inclusion of a local maximum filter to identify individual tree canopies. Results were compared to original manually interpreted height modifiers and to field survey data from U.S. Forest Service Forest Inventory and Analysis plots. This project demonstrated the effectiveness of utilizing lidar data to more efficiently assign height modifier attributes to LCU classifications produced by the UMESC.

Classification in Australia.

ERIC Educational Resources Information Center

McKinlay, John

Despite some inroads by the Library of Congress Classification and short-lived experimentation with Universal Decimal Classification and Bliss Classification, Dewey Decimal Classification, with its ability in recent editions to be hospitable to local needs, remains the most widely used classification system in Australia. Although supplemented at…

The effect of call libraries and acoustic filters on the identification of bat echolocation.

PubMed

Clement, Matthew J; Murray, Kevin L; Solick, Donald I; Gruver, Jeffrey C

2014-09-01

Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys.

The effect of call libraries and acoustic filters on the identification of bat echolocation

PubMed Central

Clement, Matthew J; Murray, Kevin L; Solick, Donald I; Gruver, Jeffrey C

2014-01-01

Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys. PMID:25535563

The effect of call libraries and acoustic filters on the identification of bat echolocation

USGS Publications Warehouse

Clement, Matthew; Murray, Kevin L; Solick, Donald I; Gruver, Jeffrey C

2014-01-01

Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys.

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

NASA Technical Reports Server (NTRS)

Key, J. R.; Maslanik, J. A.; Barry, R. G.

1988-01-01

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Readers on the Prowl: Florida Library Youth Program Manual.

ERIC Educational Resources Information Center

Johnson, Margie, Comp.; Cook, Ann, Comp.; O'Reilly, Lesley, Comp.

This manual is designed to accompany the 1999 Florida Library Youth Program, focusing on the theme of the library as a destination. This introductory section of the manual contains: an overview of the program that discusses basic philosophy, outreach to children in poverty, age and ability levels, and materials and incentives; a schedule of…

Calibration of a system to collect visible-light polarization data for classification of geosynchronous satellites

NASA Astrophysics Data System (ADS)

Speicher, Andy; Matin, Mohammad; Tippets, Roger; Chun, Francis

2014-09-01

In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. The objective of this study was to calibrate a system to exploit the optical signature of unresolved geosynchronous satellite images by collecting polarization data in the visible wavelengths for the purpose of revealing discriminating features. These features may lead to positive identification or classification of each satellite. The system was calibrated with an algorithm and process that takes raw observation data from a two-channel polarimeter and converts it to Stokes parameters S0 and S1. This instrumentation is a new asset for the United States Air Force Academy (USAFA) Department of Physics and consists of one 20-inch Ritchey-Chretien telescope and a dual focal plane system fed with a polarizing beam splitter. This study calibrated the system and collected preliminary polarization data on five geosynchronous satellites to validate performance. Preliminary data revealed that each of the five satellites had a different polarization signature that could potentially lead to identification in future studies.

Comparing Levels of Mastery Motivation in Children with Cerebral Palsy (CP) and Typically Developing Children.

PubMed

Salavati, Mahyar; Vameghi, Roshanak; Hosseini, Seyed Ali; Saeedi, Ahmad; Gharib, Masoud

2018-02-01

The present study aimed to compare motivation in school-age children with CP and typically developing children. 229 parents of children with cerebral palsy and 212 parents of typically developing children participated in the present cross sectional study and completed demographic and DMQ18 forms. The rest of information was measured by an occupational therapist. Average age was equal to 127.12±24.56 months for children with cerebral palsy (CP) and 128.08±15.90 for typically developing children. Independent t-test used to compare two groups; and Pearson correlation coefficient by SPSS software applied to study correlation with other factors. There were differences between DMQ subscales of CP and typically developing groups in terms of all subscales ( P <0.05). The lowest motivation scores of subscales obtained in gross motor persistence (2.4870±.81047) and cognitive-oriented persistence (2.8529±.84223) in children with CP. Motivation was correlated with Gross Motor function Classification System (r= -0.831, P<0.001), Manual ability classification system (r=-0.782, P<0.001) and cognitive impairment (r=-0.161, P<0.05). Children with CP had lower mastery motivation than typically developing children. Rehabilitation efforts should take to enhance motivation, so that children felt empowered to do tasks or practices.

Implementation of a Cognitive Skills Training Program in ROTC: The Leadership Enrichment Program

DTIC Science & Technology

1987-03-01

IMPLEMENTATION ..................... B-i C. LEP BRIDGING MANUAL . ............... ..... C-I D. ROTC INSTRUCTORS TRAINED IN IE ............... D-1 E. BASIC...cognitive ability. The importance of thinking ability is em- phasized throughout the leadership field manual , FM-22-100, particularly in the sections...haplamtation * Qonfeaumos Calls, * Gonctmetr Site Visits * instrutor 4-5/85 questionnaires Got Feeback an * Conference Camll * Ref ruskur Session 2/85 Frolow

Railroad Classification Yard Technology Manual. Volume III. Freight Car Rollability

DOT National Transportation Integrated Search

1981-07-01

The report presents a survey of rolling resistance research, histograms of rolling resistance from five yards, a statistical regression analysis of causal factors affecting rolling resistance, procedures for constructing a rolling resistance histogra...

RMP*eSubmit User's Manual

EPA Pesticide Factsheets

RMP*eSubmit facilitates secure online Risk Management Plan updates/resubmissions, required at least every 5 years. Reporting requirements have not changed since 2004, but the 2012 version of North American Industry Classification System has been integrated

Vietnamese Document Representation and Classification

NASA Astrophysics Data System (ADS)

Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

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

PubMed

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

2015-03-01

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

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

PubMed

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

2017-03-01

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

Computerized Classification Testing under the One-Parameter Logistic Response Model with Ability-Based Guessing

ERIC Educational Resources Information Center

Wang, Wen-Chung; Huang, Sheng-Yun

2011-01-01

The one-parameter logistic model with ability-based guessing (1PL-AG) has been recently developed to account for effect of ability on guessing behavior in multiple-choice items. In this study, the authors developed algorithms for computerized classification testing under the 1PL-AG and conducted a series of simulations to evaluate their…

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Instruction manual for the ILAE 2017 operational classification of seizure types.

PubMed

Fisher, Robert S; Cross, J Helen; D'Souza, Carol; French, Jacqueline A; Haut, Sheryl R; Higurashi, Norimichi; Hirsch, Edouard; Jansen, Floor E; Lagae, Lieven; Moshé, Solomon L; Peltola, Jukka; Roulet Perez, Eliane; Scheffer, Ingrid E; Schulze-Bonhage, Andreas; Somerville, Ernest; Sperling, Michael; Yacubian, Elza Márcia; Zuberi, Sameer M

2017-04-01

This companion paper to the introduction of the International League Against Epilepsy (ILAE) 2017 classification of seizure types provides guidance on how to employ the classification. Illustration of the classification is enacted by tables, a glossary of relevant terms, mapping of old to new terms, suggested abbreviations, and examples. Basic and extended versions of the classification are available, depending on the desired degree of detail. Key signs and symptoms of seizures (semiology) are used as a basis for categories of seizures that are focal or generalized from onset or with unknown onset. Any focal seizure can further be optionally characterized by whether awareness is retained or impaired. Impaired awareness during any segment of the seizure renders it a focal impaired awareness seizure. Focal seizures are further optionally characterized by motor onset signs and symptoms: atonic, automatisms, clonic, epileptic spasms, or hyperkinetic, myoclonic, or tonic activity. Nonmotor-onset seizures can manifest as autonomic, behavior arrest, cognitive, emotional, or sensory dysfunction. The earliest prominent manifestation defines the seizure type, which might then progress to other signs and symptoms. Focal seizures can become bilateral tonic-clonic. Generalized seizures engage bilateral networks from onset. Generalized motor seizure characteristics comprise atonic, clonic, epileptic spasms, myoclonic, myoclonic-atonic, myoclonic-tonic-clonic, tonic, or tonic-clonic. Nonmotor (absence) seizures are typical or atypical, or seizures that present prominent myoclonic activity or eyelid myoclonia. Seizures of unknown onset may have features that can still be classified as motor, nonmotor, tonic-clonic, epileptic spasms, or behavior arrest. This "users' manual" for the ILAE 2017 seizure classification will assist the adoption of the new system. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Scientific Forum on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V)-An Invitation.

PubMed

Aboraya, Ahmed

2010-11-01

The publication of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) is anticipated in May 2013 with many new additions and changes. In this article, the author summarizes the phases of psychiatric classification from the turn of the 20th century until today. Psychiatry 2010 offers a DSM-V Scientific Forum and invites readers to submit comments, recommendations, and articles to Psychiatry 2010 and DSM-V Task Force.

Classification of calcium in intravascular OCT images for the purpose of intervention planning

NASA Astrophysics Data System (ADS)

Shalev, Ronny; Bezerra, Hiram G.; Ray, Soumya; Prabhu, David; Wilson, David L.

2016-03-01

The presence of extensive calcification is a primary concern when planning and implementing a vascular percutaneous intervention such as stenting. If the balloon does not expand, the interventionalist must blindly apply high balloon pressure, use an atherectomy device, or abort the procedure. As part of a project to determine the ability of Intravascular Optical Coherence Tomography (IVOCT) to aid intervention planning, we developed a method for automatic classification of calcium in coronary IVOCT images. We developed an approach where plaque texture is modeled by the joint probability distribution of a bank of filter responses where the filter bank was chosen to reflect the qualitative characteristics of the calcium. This distribution is represented by the frequency histogram of filter response cluster centers. The trained algorithm was evaluated on independent ex-vivo image data accurately labeled using registered 3D microscopic cryo-image data which was used as ground truth. In this study, regions for extraction of sub-images (SI's) were selected by experts to include calcium, fibrous, or lipid tissues. We manually optimized algorithm parameters such as choice of filter bank, size of the dictionary, etc. Splitting samples into training and testing data, we achieved 5-fold cross validation calcium classification with F1 score of 93.7+/-2.7% with recall of >=89% and a precision of >=97% in this scenario with admittedly selective data. The automated algorithm performed in close-to-real-time (2.6 seconds per frame) suggesting possible on-line use. This promising preliminary study indicates that computational IVOCT might automatically identify calcium in IVOCT coronary artery images.

Developing a classification system of social communication functioning of preschool children with autism spectrum disorder.

PubMed

Di Rezze, Briano; Rosenbaum, Peter; Zwaigenbaum, Lonnie; Hidecker, Mary Jo Cooley; Stratford, Paul; Cousins, Martha; Camden, Chantal; Law, Mary

2016-09-01

Impairments in social communication are the hallmark of autism spectrum disorder (ASD). Operationalizing 'severity' in ASD has been challenging; thus, stratifying by functioning has not been possible. The purpose of this study is to describe the development of the Autism Classification System of Functioning: Social Communication (ACSF:SC) and to evaluate its consistency within and between parent and professional ratings. (1) ACSF:SC development based on focus groups and surveys involving parents, educators, and clinicians familiar with preschoolers with ASD; and (2) evaluation of the intra- and interrater agreement of the ACSF:SC using weighted kappa (кw ). Seventy-six participants were involved in the development process. Core characteristics of social communication were ascertained: communicative intent; communicative skills and reciprocity; and impact of environment. Five ACSF:SC levels were created and content-validated across participants. Best capacity and typical performance agreement ratings varied as follows: intrarater agreement on 41 children was кw =0.61 to 0.69 for parents, and кw =0.71 to 0.95 for professionals; interrater agreement between professionals was кw =0.47 to 0.61, and between parents and professionals was кw =0.33 to 0.53. Perspectives from parents and professionals informed ACSF:SC development, providing common descriptions of the levels of everyday communicative abilities of children with ASD to complement the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Rater agreement demonstrates that the ACSF:SC can be used with acceptable consistency compared with other functional classification systems. © 2016 Mac Keith Press.

One Small Step for Manuals: Computer-Assisted Training in Twelve-Step Facilitation*

PubMed Central

Sholomskas, Diane E.; Carroll, Kathleen M.

2008-01-01

Objective The burgeoning number of empirically validated therapies has not been met with systematic evaluation of practical, inexpensive means of teaching large numbers of clinicians to use these treatments effectively. An interactive, computer-assisted training program that sought to impart skills associated with the Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) Twelve-Step Facilitation (TSF) manual was developed to address this need. Method Twenty-five community-based substance use-treatment clinicians were randomized to one of two training conditions: (1) access to the computer-assisted training program plus the TSF manual or (2) access to the manual only. The primary outcome measure was change from pre- to posttraining in the clinicians' ability to demonstrate key TSF skills. Results The data suggested that the clinicians' ability to implement TSF, as assessed by independent ratings of adherence and skill for the key TSF interventions, was significantly higher after training for those who had access to the computerized training condition than those who were assigned to the manual-only condition. Those assigned to the computer-assisted training condition also demonstrated greater gains in a knowledge test assessing familiarity with concepts presented in the TSF manual. Conclusions Computer-based training may be a feasible and effective means of training larger numbers of clinicians in empirically supported, manual-guided therapies. PMID:17061013

One small step for manuals: Computer-assisted training in twelve-step facilitation.

PubMed

Sholomskas, Diane E; Carroll, Kathleen M

2006-11-01

The burgeoning number of empirically validated therapies has not been met with systematic evaluation of practical, inexpensive means of teaching large numbers of clinicians to use these treatments effectively. An interactive, computer-assisted training program that sought to impart skills associated with the Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) Twelve-Step Facilitation (TSF) manual was developed to address this need. Twenty-five community-based substance use-treatment clinicians were randomized to one of two training conditions: (1) access to the computer- assisted training program plus the TSF manual or (2) access to the manual only. The primary outcome measure was change from preto posttraining in the clinicians' ability to demonstrate key TSF skills. The data suggested that the clinicians' ability to implement TSF, as assessed by independent ratings of adherence and skill for the key TSF interventions, was significantly higher after training for those who had access to the computerized training condition than those who were assigned to the manual-only condition. Those assigned to the computer-assisted training condition also demonstrated greater gains in a knowledge test assessing familiarity with concepts presented in the TSF manual. Computer-based training may be a feasible and effective means of training larger numbers of clinicians in empirically supported, manual-guided therapies.

Toward accelerating landslide mapping with interactive machine learning techniques

NASA Astrophysics Data System (ADS)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also included an experimental evaluation of the uncertainties of manual mappings from multiple experts and demonstrated strong relationships between the uncertainty of the experts and the machine learning model.

A graduated food addiction classification approach significantly differentiates obesity among people with type 2 diabetes.

PubMed

Raymond, Karren-Lee; Kannis-Dymand, Lee; Lovell, Geoff P

2016-10-01

This study examined a graduated severity level approach to food addiction classification against associations with World Health Organization obesity classifications (body mass index, kg/m 2 ) among 408 people with type 2 diabetes. A survey including the Yale Food Addiction Scale and several demographic questions demonstrated four distinct Yale Food Addiction Scale symptom severity groups (in line with Diagnostic and Statistical Manual of Mental Disorders (5th ed.) severity indicators): non-food addiction, mild food addiction, moderate food addiction and severe food addiction. Analysis of variance with post hoc tests demonstrated each severity classification group was significantly different in body mass index, with each grouping being associated with increased World Health Organization obesity classifications. These findings have implications for diagnosing food addiction and implementing treatment and prevention methodologies of obesity among people with type 2 diabetes.

Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints.

PubMed

Zhang, Jiang; Wang, James Z; Yuan, Zhen; Sobel, Eric S; Jiang, Huabei

2011-01-01

This study presents a computer-aided classification method to distinguish osteoarthritis finger joints from healthy ones based on the functional images captured by x-ray guided diffuse optical tomography. Three imaging features, joint space width, optical absorption, and scattering coefficients, are employed to train a Least Squares Support Vector Machine (LS-SVM) classifier for osteoarthritis classification. The 10-fold validation results show that all osteoarthritis joints are clearly identified and all healthy joints are ruled out by the LS-SVM classifier. The best sensitivity, specificity, and overall accuracy of the classification by experienced technicians based on manual calculation of optical properties and visual examination of optical images are only 85%, 93%, and 90%, respectively. Therefore, our LS-SVM based computer-aided classification is a considerably improved method for osteoarthritis diagnosis.

Interrelationship between Autism Diagnostic Observation Schedule-Generic (ADOS-G), Autism Diagnostic Interview-Revised (ADI-R), and the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) Classification in Children and Adolescents with Mental Retardation

ERIC Educational Resources Information Center

de Bildt, Annelies; Sytema, Sjoerd; Ketelaars, Cees; Kraijer, Dirk; Mulder, Erik; Volkmar, Fred; Minderaa, Ruud

2004-01-01

The interrelationship between the Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule-Generic (ADOS-G) and clinical classification was studied in 184 children and adolescents with Mental Retardation (MR). The agreement between the ADI-R and ADOS-G was fair, with a substantial difference between younger and older…

Reliability and validity of the Turkish version of ABILHAND-Kids' questionnaire in a group of patients with neuromuscular disorders.

PubMed

Öksüz, Çigdem; Alemdaroglu, Ipek; Kilinç, Muhammed; Abaoğlu, Hatice; Demirci, Cevher; Karahan, Sevilay; Yilmaz, Oznur; Yildirim, Sibel Aksu

2017-10-01

This study was performed to examine the reliability and validity of the Turkish version of ABILHAND-Kids questionnaire which assesses manual functions of children with neuromuscular diseases (NMDs). A cross sectional survey study design and Rasch analysis were used to assess the reliability and validity of the Turkish version of scale. Ninety-three children with different neuromuscular disorders and their parents were included in the study. The scale was applied to the parents with face-to-face interview twice; on their first visit and after an interval of 15 days. The test-retest reliability was assessed with intraclass correlation coefficient (ICC), and internal consistency of the multi-item subscales by calculating Cronbach alpha values. Brooke Upper Extremity Functional Classification (BUEFC) and Wee-Functional Independency Measurement (Wee-FIM) were correlated to determine the construct validity. The ICC value for the test/retest reliability was 0.94. The internal consistency was 0.81. Floor (1.1%) and ceiling (11.8%) effects were not significant. There were moderate correlations between the Turkish version of ABILHAND-Kids and Wee-FIM (0.67) and BUEFC (-0.37). Rasch analysis indicated good item fit, unidimensionality, and model fit. The Turkish version of ABILHAND-Kids questionnaire was found to be a reliable and valid scale for the assessment of the manual ability of children with NMDs.

Automated Assessment of Existing Patient's Revised Cardiac Risk Index Using Algorithmic Software.

PubMed

Hofer, Ira S; Cheng, Drew; Grogan, Tristan; Fujimoto, Yohei; Yamada, Takashige; Beck, Lauren; Cannesson, Maxime; Mahajan, Aman

2018-05-25

Previous work in the field of medical informatics has shown that rules-based algorithms can be created to identify patients with various medical conditions; however, these techniques have not been compared to actual clinician notes nor has the ability to predict complications been tested. We hypothesize that a rules-based algorithm can successfully identify patients with the diseases in the Revised Cardiac Risk Index (RCRI). Patients undergoing surgery at the University of California, Los Angeles Health System between April 1, 2013 and July 1, 2016 and who had at least 2 previous office visits were included. For each disease in the RCRI except renal failure-congestive heart failure, ischemic heart disease, cerebrovascular disease, and diabetes mellitus-diagnosis algorithms were created based on diagnostic and standard clinical treatment criteria. For each disease state, the prevalence of the disease as determined by the algorithm, International Classification of Disease (ICD) code, and anesthesiologist's preoperative note were determined. Additionally, 400 American Society of Anesthesiologists classes III and IV cases were randomly chosen for manual review by an anesthesiologist. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve were determined using the manual review as a gold standard. Last, the ability of the RCRI as calculated by each of the methods to predict in-hospital mortality was determined, and the time necessary to run the algorithms was calculated. A total of 64,151 patients met inclusion criteria for the study. In general, the incidence of definite or likely disease determined by the algorithms was higher than that detected by the anesthesiologist. Additionally, in all disease states, the prevalence of disease was always lowest for the ICD codes, followed by the preoperative note, followed by the algorithms. In the subset of patients for whom the records were manually reviewed, the algorithms were generally the most sensitive and the ICD codes the most specific. When computing the modified RCRI using each of the methods, the modified RCRI from the algorithms predicted in-hospital mortality with an area under the receiver operating characteristic curve of 0.70 (0.67-0.73), which compared to 0.70 (0.67-0.72) for ICD codes and 0.64 (0.61-0.67) for the preoperative note. On average, the algorithms took 12.64 ± 1.20 minutes to run on 1.4 million patients. Rules-based algorithms for disease in the RCRI can be created that perform with a similar discriminative ability as compared to physician notes and ICD codes but with significantly increased economies of scale.

Manual lymphatic drainage in chronic venous disease: a duplex ultrasound study.

PubMed

Dos Santos Crisóstomo, Rute Sofia; Candeias, Miguel Sandu; Ribeiro, Ana Margarida Martins; da Luz Belo Martins, Catarina; Armada-da-Silva, Paulo As

2014-12-01

To compare the effect of call-up and reabsorption maneuvers of manual lymphatic drainage on blood flow in femoral vein and great saphenous vein in patients with chronic venous disease and healthy controls. Forty-one subjects participated in this study (mean age: 42.68(15.23)), 23 with chronic venous disease (chronic venous disease group) with clinical classification C1-5 of clinical-etiological-anatomical-pathological (CEAP) and 18 healthy subjects (control group). Call-up and reabsorption maneuvers were randomly applied in the medial aspect of the thigh. The cross-sectional areas, as well as the peak and the mean blood flow velocity at femoral vein and great saphenous vein, were assessed by Duplex ultrasound at the baseline and during maneuvers. The venous flow volume changes were calculated. The venous flow volume in femoral vein and great saphenous vein increased during both manual lymphatic drainage maneuvers and in both groups (P < 0.05). The two maneuvers had a similar effect on femoral vein and great saphenous vein hemodynamics, and in both the chronic venous disease and control groups. As a result of the call-up maneuver, the flow volume augmentations, as a result of call-up maneuver, decreased with the severity of chronic venous disease in those patients measured by the clinical classification of CEAP (r = -0.64; P = 0.03). Manual lymphatic drainage increases the venous blood flow in the lower extremity with a magnitude that is independent from the specific maneuver employed or the presence of chronic venous disease. Therefore, manual lymphatic drainage may be an alternative strategy for the treatment and prevention of venous stasis complications in chronic venous disease. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Freight model improvement project for ECWRPC.

DOT National Transportation Integrated Search

2011-08-01

In early 2009 WisDOT, HNTB and ECWRPC completed the first phase of the Northeast Region Travel Demand Model. : While the model includes a truck trip generation based on the quick response freight manual, the model lacks enough : truck classification ...

Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification.

PubMed

Allott, Emma H; Geradts, Joseph; Sun, Xuezheng; Cohen, Stephanie M; Zirpoli, Gary R; Khoury, Thaer; Bshara, Wiam; Chen, Mengjie; Sherman, Mark E; Palmer, Julie R; Ambrosone, Christine B; Olshan, Andrew F; Troester, Melissa A

2016-06-28

Spatial heterogeneity in biomarker expression may impact breast cancer classification. The aims of this study were to estimate the frequency of spatial heterogeneity in biomarker expression within tumors, to identify technical and biological factors contributing to spatial heterogeneity, and to examine the impact of discordant biomarker status within tumors on clinical record agreement. Tissue microarrays (TMAs) were constructed using two to four cores (1.0 mm) for each of 1085 invasive breast cancers from the Carolina Breast Cancer Study, which is part of the AMBER Consortium. Immunohistochemical staining for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) was quantified using automated digital imaging analysis. The biomarker status for each core and for each case was assigned using clinical thresholds. Cases with core-to-core biomarker discordance were manually reviewed to distinguish intratumoral biomarker heterogeneity from misclassification of biomarker status by the automated algorithm. The impact of core-to-core biomarker discordance on case-level agreement between TMAs and the clinical record was evaluated. On the basis of automated analysis, discordant biomarker status between TMA cores occurred in 9 %, 16 %, and 18 % of cases for ER, PR, and HER2, respectively. Misclassification of benign epithelium and/or ductal carcinoma in situ as invasive carcinoma by the automated algorithm was implicated in discordance among cores. However, manual review of discordant cases confirmed spatial heterogeneity as a source of discordant biomarker status between cores in 2 %, 7 %, and 8 % of cases for ER, PR, and HER2, respectively. Overall, agreement between TMA and clinical record was high for ER (94 %), PR (89 %), and HER2 (88 %), but it was reduced in cases with core-to-core discordance (agreement 70 % for ER, 61 % for PR, and 57 % for HER2). Intratumoral biomarker heterogeneity may impact breast cancer classification accuracy, with implications for clinical management. Both manually confirmed biomarker heterogeneity and misclassification of biomarker status by automated image analysis contribute to discordant biomarker status between TMA cores. Given that manually confirmed heterogeneity is uncommon (<10 % of cases), large studies are needed to study the impact of heterogeneous biomarker expression on breast cancer classification and outcomes.

LTRsift: a graphical user interface for semi-automatic classification and postprocessing of de novo detected LTR retrotransposons

PubMed Central

2012-01-01

Background Long terminal repeat (LTR) retrotransposons are a class of eukaryotic mobile elements characterized by a distinctive sequence similarity-based structure. Hence they are well suited for computational identification. Current software allows for a comprehensive genome-wide de novo detection of such elements. The obvious next step is the classification of newly detected candidates resulting in (super-)families. Such a de novo classification approach based on sequence-based clustering of transposon features has been proposed before, resulting in a preliminary assignment of candidates to families as a basis for subsequent manual refinement. However, such a classification workflow is typically split across a heterogeneous set of glue scripts and generic software (for example, spreadsheets), making it tedious for a human expert to inspect, curate and export the putative families produced by the workflow. Results We have developed LTRsift, an interactive graphical software tool for semi-automatic postprocessing of de novo predicted LTR retrotransposon annotations. Its user-friendly interface offers customizable filtering and classification functionality, displaying the putative candidate groups, their members and their internal structure in a hierarchical fashion. To ease manual work, it also supports graphical user interface-driven reassignment, splitting and further annotation of candidates. Export of grouped candidate sets in standard formats is possible. In two case studies, we demonstrate how LTRsift can be employed in the context of a genome-wide LTR retrotransposon survey effort. Conclusions LTRsift is a useful and convenient tool for semi-automated classification of newly detected LTR retrotransposons based on their internal features. Its efficient implementation allows for convenient and seamless filtering and classification in an integrated environment. Developed for life scientists, it is helpful in postprocessing and refining the output of software for predicting LTR retrotransposons up to the stage of preparing full-length reference sequence libraries. The LTRsift software is freely available at http://www.zbh.uni-hamburg.de/LTRsift under an open-source license. PMID:23131050

LTRsift: a graphical user interface for semi-automatic classification and postprocessing of de novo detected LTR retrotransposons.

PubMed

Steinbiss, Sascha; Kastens, Sascha; Kurtz, Stefan

2012-11-07

Long terminal repeat (LTR) retrotransposons are a class of eukaryotic mobile elements characterized by a distinctive sequence similarity-based structure. Hence they are well suited for computational identification. Current software allows for a comprehensive genome-wide de novo detection of such elements. The obvious next step is the classification of newly detected candidates resulting in (super-)families. Such a de novo classification approach based on sequence-based clustering of transposon features has been proposed before, resulting in a preliminary assignment of candidates to families as a basis for subsequent manual refinement. However, such a classification workflow is typically split across a heterogeneous set of glue scripts and generic software (for example, spreadsheets), making it tedious for a human expert to inspect, curate and export the putative families produced by the workflow. We have developed LTRsift, an interactive graphical software tool for semi-automatic postprocessing of de novo predicted LTR retrotransposon annotations. Its user-friendly interface offers customizable filtering and classification functionality, displaying the putative candidate groups, their members and their internal structure in a hierarchical fashion. To ease manual work, it also supports graphical user interface-driven reassignment, splitting and further annotation of candidates. Export of grouped candidate sets in standard formats is possible. In two case studies, we demonstrate how LTRsift can be employed in the context of a genome-wide LTR retrotransposon survey effort. LTRsift is a useful and convenient tool for semi-automated classification of newly detected LTR retrotransposons based on their internal features. Its efficient implementation allows for convenient and seamless filtering and classification in an integrated environment. Developed for life scientists, it is helpful in postprocessing and refining the output of software for predicting LTR retrotransposons up to the stage of preparing full-length reference sequence libraries. The LTRsift software is freely available at http://www.zbh.uni-hamburg.de/LTRsift under an open-source license.

Users' manual for the Hydroecological Integrity Assessment Process software (including the New Jersey Assessment Tools)

USGS Publications Warehouse

Henriksen, James A.; Heasley, John; Kennen, Jonathan G.; Nieswand, Steven

2006-01-01

Applying the Hydroecological Integrity Assessment Process involves four steps: (1) a hydrologic classification of relatively unmodified streams in a geographic area using long-term gage records and 171 ecologically relevant indices; (2) the identification of statistically significant, nonredundant, hydroecologically relevant indices associated with the five major flow components for each stream class; and (3) the development of a stream-classification tool and a hydrologic assessment tool. Four computer software tools have been developed.

Comprehension of spoken language in non-speaking children with severe cerebral palsy: an explorative study on associations with motor type and disabilities.

PubMed

Geytenbeek, Joke J M; Vermeulen, R Jeroen; Becher, Jules G; Oostrom, Kim J

2015-03-01

To assess spoken language comprehension in non-speaking children with severe cerebral palsy (CP) and to explore possible associations with motor type and disability. Eighty-seven non-speaking children (44 males, 43 females, mean age 6y 8mo, SD 2y 1mo) with spastic (54%) or dyskinetic (46%) CP (Gross Motor Function Classification System [GMFCS] levels IV [39%] and V [61%]) underwent spoken language comprehension assessment with the computer-based instrument for low motor language testing (C-BiLLT), a new and validated diagnostic instrument. A multiple linear regression model was used to investigate which variables explained the variation in C-BiLLT scores. Associations between spoken language comprehension abilities (expressed in z-score or age-equivalent score) and motor type of CP, GMFCS and Manual Ability Classification System (MACS) levels, gestational age, and epilepsy were analysed with Fisher's exact test. A p-value <0.05 was considered statistically significant. Chronological age, motor type, and GMFCS classification explained 33% (R=0.577, R(2) =0.33) of the variance in spoken language comprehension. Of the children aged younger than 6 years 6 months, 52.4% of the children with dyskinetic CP attained comprehension scores within the average range (z-score ≥-1.6) as opposed to none of the children with spastic CP. Of the children aged older than 6 years 6 months, 32% of the children with dyskinetic CP reached the highest achievable age-equivalent score compared to 4% of the children with spastic CP. No significant difference in disability was found between CP-related variables (MACS levels, gestational age, epilepsy), with the exception of GMFCS which showed a significant difference in children aged younger than 6 years 6 months (p=0.043). Despite communication disabilities in children with severe CP, particularly in dyskinetic CP, spoken language comprehension may show no or only moderate delay. These findings emphasize the importance of introducing alternative and/or augmentative communication devices from early childhood. © 2014 Mac Keith Press.

7 CFR 2900.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AGRICULTURE ESSENTIAL AGRICULTURAL USES AND VOLUMETRIC REQUIREMENTS-NATURAL GAS POLICY ACT § 2900.2... maintenance of food quality after processing. (b) Establishment means an economic unit, generally at a single... definition used in the Standard Industrial Classification Manual, 1972 edition). (c) Essential Agricultural...

Extent and Impacts of the Virginia Department of Transportation’s Exception Process for Access Management Design Standards

DOT National Transportation Integrated Search

2018-06-01

The Virginia Department of Transportation (VDOT) Road Design Manual requires that new commercial entrances meet certain minimum spacing standards depending on a facilitys speed limit and functional classification. Landowners, however, may request ...

Application of LANDSAT data to wetland study and land use classification in west Tennessee

NASA Technical Reports Server (NTRS)

Jones, N. L.; Shahrokhi, F.

1977-01-01

The Obion-Forked Deer River Basin in northwest Tennessee is confronted with several acute land use problems which result in excessive erosion, sedimentation, pollution, and hydrologic runoff. LANDSAT data was applied to determine land use of selected watershed areas within the basin, with special emphasis on determining wetland boundaries. Densitometric analysis was performed to allow numerical classification of objects observed in the imagery on the basis of measurements of optical densities. Multispectral analysis of the LANDSAT imagery provided the capability of altering the color of the image presentation in order to enhance desired relationships. Manual mapping and classification techniques were performed in order to indicate a level of accuracy of the LANDSAT data as compared with high and low altitude photography for land use classification.

Physiologic parameters associated with sexual arousal in women with incomplete spinal cord injuries.

PubMed

Sipski, M L; Alexander, C J; Rosen, R C

1997-03-01

To compare the physiologic sexual responses of women with incomplete spinal cord injuries (SCIs) with and without preservation of the ability to perceive T11-L2 pinprick sensation. Controlled laboratory-based analysis of responses to varying combinations of audiovisual erotic stimulation, manual genital stimulation, and performance of a distracting task coupled with manual genital stimulation. The sexual physiology laboratory at our freestanding rehabilitation hospital. A volunteer sample of 17 women with incomplete SCIs. Two 78-minute protocols using 6-minute baselines alternating with 12-minute testing conditions. One protocol was designed to study the effects of psychogenic and psychogenic combined with manual sexual stimulation, while the other was designed to examine the effects of genital sexual stimulation performed in conjunction with a distracting task. Vaginal pulse amplitude, subjective arousal, heart rate, respiratory rate, and blood pressure. Subjective arousal increased in both groups of subjects with isolated audiovisual erotic stimulation; however, only those subjects with the ability to perceive T11-L2 pinprick sensation had concomitant increases in vaginal pulse amplitude. In contrast, when manual genital stimulation was added to the audiovisual erotic stimulation, both groups of subjects developed increases in vaginal pulse amplitude, whereas only those subjects with the ability to perceive T11-L2 demonstrated a further increase in their level of subjective arousal. Performance of manual genital stimulation in conjunction with a distracting task resulted in significantly increased vaginal pulse amplitude and arousal level only in those subjects with preservation of the ability to perceive T11-L2 pinprick sensation. With the changeover to masturbation, neither group of subjects developed significant increases in vaginal pulse amplitude. During masturbation, both groups of subjects had increases in their level of sexual arousal; however, only those subjects with T11-L2 pinprick preservation had a significant increase. Women with preservation of the ability to perceive T11-L2 pinprick sensation tended to maintain the ability for psychogenic genital vasocongestion. Psychogenic protocol results showed that all subjects appeared to develop reflex genital vasocongestion when manual stimulation was added to audiovisual erotic stimulation. Manual genital stimulation in conjunction with the performance of a distracting task only resulted in increased vaginal pulse amplitudes in those subjects with preservation of T11-L2 pinprick sensation. We believe that this was due to increased subjective sexual arousal and that the reason all subjects did not develop increased genital vasocongestion under these conditions was due to poor hand function. Further research examining women with lower levels of SCI is necessary to understand the neurophysiology of female sexual response after SCI.

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

PubMed Central

2018-01-01

Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy. PMID:29681996

Assessing the Agreement Between Eo-Based Semi-Automated Landslide Maps with Fuzzy Manual Landslide Delineation

NASA Astrophysics Data System (ADS)

Albrecht, F.; Hölbling, D.; Friedl, B.

2017-09-01

Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.

MEASUREMENT OF HYDRAULIC CONDUCTIVITY DISTRIBUTIONS: A MANUAL OF PRACTICE

EPA Science Inventory

The ability of hydrologists to perform field measurements of aquifer hydraulic properties must be enhanced in order to significantly improve the capacity to solve groundwater contamination problems at Superfund and other sites. The primary purpose of this manual is to provide ne...

MEASUREMENT OF HYDRAULIC CONDUCTIVITY DISTRIBUTIONS: A MANUAL OF PRACTICE

EPA Science Inventory

The ability of hydrologists to perform field measurements of aquifer hydraulic properties must be enhanced in order to significantly improve the capacity to solve groundwater contamination problems at Superfund and other sites. he primary purpose of this manual is to provide new ...

Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury.

PubMed

Yadav, Kabir; Sarioglu, Efsun; Choi, Hyeong Ah; Cartwright, Walter B; Hinds, Pamela S; Chamberlain, James M

2016-02-01

The authors have previously demonstrated highly reliable automated classification of free-text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then deidentified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The data set was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based on the National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predictive Miner 7.0. Performance of the decision tree classifier was evaluated on the test patient reports. The prevalence of TBI in the sampled population was 159 of 2,217 (7.2%). The automated classification for pediatric TBI is comparable to our prior results, with the notable exception of lower positive predictive value. Manual review of misclassified reports, 95.5% of which were false-positives, revealed that a sizable number of false-positive errors were due to differing outcome definitions between NINDS TBI findings and PECARN clinical important TBI findings and report ambiguity not meeting definition criteria. A hybrid NLP and machine learning automated classification system continues to show promise in coding free-text electronic clinical data. For complex outcomes, it can reliably identify negative reports, but manual review of positive reports may be required. As such, it can still streamline data collection for clinical research and performance improvement. © 2016 by the Society for Academic Emergency Medicine.

Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury

PubMed Central

Yadav, Kabir; Sarioglu, Efsun; Choi, Hyeong-Ah; Cartwright, Walter B.; Hinds, Pamela S.; Chamberlain, James M.

2016-01-01

Background The authors have previously demonstrated highly reliable automated classification of free text computed tomography (CT) imaging reports using a hybrid system that pairs linguistic (natural language processing) and statistical (machine learning) techniques. Previously performed for identifying the outcome of orbital fracture in unprocessed radiology reports from a clinical data repository, the performance has not been replicated for more complex outcomes. Objectives To validate automated outcome classification performance of a hybrid natural language processing (NLP) and machine learning system for brain CT imaging reports. The hypothesis was that our system has performance characteristics for identifying pediatric traumatic brain injury (TBI). Methods This was a secondary analysis of a subset of 2,121 CT reports from the Pediatric Emergency Care Applied Research Network (PECARN) TBI study. For that project, radiologists dictated CT reports as free text, which were then de-identified and scanned as PDF documents. Trained data abstractors manually coded each report for TBI outcome. Text was extracted from the PDF files using optical character recognition. The dataset was randomly split evenly for training and testing. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for negation, certainty, and temporal status. A random subset stratified by site was analyzed using descriptive quantitative content analysis to confirm identification of TBI findings based upon the National Institute of Neurological Disorders and Stroke Common Data Elements project. Findings were coded for presence or absence, weighted by frequency of mentions, and past/future/indication modifiers were filtered. After combining with the manual reference standard, a decision tree classifier was created using data mining tools WEKA 3.7.5 and Salford Predictive Miner 7.0. Performance of the decision tree classifier was evaluated on the test patient reports. Results The prevalence of TBI in the sampled population was 159 out of 2,217 (7.2%). The automated classification for pediatric TBI is comparable to our prior results, with the notable exception of lower positive predictive value (PPV). Manual review of misclassified reports, 95.5% of which were false positives, revealed that a sizable number of false-positive errors were due to differing outcome definitions between NINDS TBI findings and PECARN clinical important TBI findings, and report ambiguity not meeting definition criteria. Conclusions A hybrid NLP and machine learning automated classification system continues to show promise in coding free-text electronic clinical data. For complex outcomes, it can reliably identify negative reports, but manual review of positive reports may be required. As such, it can still streamline data collection for clinical research and performance improvement. PMID:26766600

Psychotic disorders in DSM-5 and ICD-11.

PubMed

Biedermann, Falko; Fleischhacker, W Wolfgang

2016-08-01

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) was published by the American Psychiatric Association (APA) in 2013, and the Work Group on the Classification of Psychotic disorders (WGPD), installed by the World Health Organization (WHO), is expected to publish the new chapter about schizophrenia and other primary psychotic disorders in 2017. We reviewed the available literature to summarize the major changes, innovations, and developments of both manuals. If available and possible, we outline the theoretical background behind these changes. Due to the fact that the development of ICD-11 has not yet been completed, the details about ICD-11 are still proposals under ongoing revision. In this ongoing process, they may be revised and therefore have to be seen as proposals. DSM-5 has eliminated schizophrenia subtypes and replaced them with a dimensional approach based on symptom assessments. ICD-11 will most likely go in a similar direction, as both manuals are planned to be more harmonized, although some differences will remain in details and the conceptual orientation. Next to these modifications, ICD-11 will provide a transsectional diagnostic criterion for schizoaffective disorders and a reorganization of acute and transient psychotic and delusional disorders. In this manuscript, we will compare the 2 classification systems.

Automatic classification of blank substrate defects

NASA Astrophysics Data System (ADS)

Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

2014-10-01

Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask Technology Center (MPMask). The Calibre ADC tool was qualified on production mask blanks against the manual classification. The classification accuracy of ADC is greater than 95% for critical defects with an overall accuracy of 90%. The sensitivity to weak defect signals and locating the defect in the images is a challenge we are resolving. The performance of the tool has been demonstrated on multiple mask types and is ready for deployment in full volume mask manufacturing production flow. Implementation of Calibre ADC is estimated to reduce the misclassification of critical defects by 60-80%.

Limitless Horizons: Careers in Aerospace.

ERIC Educational Resources Information Center

Lewis, Mary H.

This is a manual for acquainting students with pertinent information relating to career choices in aerospace science, engineering, and technology. The first chapter presents information about the aerospace industry by describing disciplines typical of this industry. The National Aeronautics and Space Administration's (NASA) classification system…

21 CFR 886.1700 - Pupillometer.

Code of Federal Regulations, 2010 CFR

2010-04-01

... eye. (b) Classification. Class I (general controls). The AC-powered device and the manual device are... Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES OPHTHALMIC DEVICES Diagnostic Devices § 886.1700 Pupillometer. (a) Identification. A pupillometer is an AC...

Improving condition severity classification with an efficient active learning based framework

PubMed Central

Nissim, Nir; Boland, Mary Regina; Tatonetti, Nicholas P.; Elovici, Yuval; Hripcsak, George; Shahar, Yuval; Moskovitch, Robert

2017-01-01

Classification of condition severity can be useful for discriminating among sets of conditions or phenotypes, for example when prioritizing patient care or for other healthcare purposes. Electronic Health Records (EHRs) represent a rich source of labeled information that can be harnessed for severity classification. The labeling of EHRs is expensive and in many cases requires employing professionals with high level of expertise. In this study, we demonstrate the use of Active Learning (AL) techniques to decrease expert labeling efforts. We employ three AL methods and demonstrate their ability to reduce labeling efforts while effectively discriminating condition severity. We incorporate three AL methods into a new framework based on the original CAESAR (Classification Approach for Extracting Severity Automatically from Electronic Health Records) framework to create the Active Learning Enhancement framework (CAESAR-ALE). We applied CAESAR-ALE to a dataset containing 516 conditions of varying severity levels that were manually labeled by seven experts. Our dataset, called the “CAESAR dataset,” was created from the medical records of 1.9 million patients treated at Columbia University Medical Center (CUMC). All three AL methods decreased labelers’ efforts compared to the learning methods applied by the original CAESER framework in which the classifier was trained on the entire set of conditions; depending on the AL strategy used in the current study, the reduction ranged from 48% to 64% that can result in significant savings, both in time and money. As for the PPV (precision) measure, CAESAR-ALE achieved more than 13% absolute improvement in the predictive capabilities of the framework when classifying conditions as severe. These results demonstrate the potential of AL methods to decrease the labeling efforts of medical experts, while increasing accuracy given the same (or even a smaller) number of acquired conditions. We also demonstrated that the methods included in the CAESAR-ALE framework (Exploitation and Combination_XA) are more robust to the use of human labelers with different levels of professional expertise. PMID:27016383

Improving condition severity classification with an efficient active learning based framework.

PubMed

Nissim, Nir; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Shahar, Yuval; Moskovitch, Robert

2016-06-01

Classification of condition severity can be useful for discriminating among sets of conditions or phenotypes, for example when prioritizing patient care or for other healthcare purposes. Electronic Health Records (EHRs) represent a rich source of labeled information that can be harnessed for severity classification. The labeling of EHRs is expensive and in many cases requires employing professionals with high level of expertise. In this study, we demonstrate the use of Active Learning (AL) techniques to decrease expert labeling efforts. We employ three AL methods and demonstrate their ability to reduce labeling efforts while effectively discriminating condition severity. We incorporate three AL methods into a new framework based on the original CAESAR (Classification Approach for Extracting Severity Automatically from Electronic Health Records) framework to create the Active Learning Enhancement framework (CAESAR-ALE). We applied CAESAR-ALE to a dataset containing 516 conditions of varying severity levels that were manually labeled by seven experts. Our dataset, called the "CAESAR dataset," was created from the medical records of 1.9 million patients treated at Columbia University Medical Center (CUMC). All three AL methods decreased labelers' efforts compared to the learning methods applied by the original CAESER framework in which the classifier was trained on the entire set of conditions; depending on the AL strategy used in the current study, the reduction ranged from 48% to 64% that can result in significant savings, both in time and money. As for the PPV (precision) measure, CAESAR-ALE achieved more than 13% absolute improvement in the predictive capabilities of the framework when classifying conditions as severe. These results demonstrate the potential of AL methods to decrease the labeling efforts of medical experts, while increasing accuracy given the same (or even a smaller) number of acquired conditions. We also demonstrated that the methods included in the CAESAR-ALE framework (Exploitation and Combination_XA) are more robust to the use of human labelers with different levels of professional expertise. Copyright © 2016 Elsevier Inc. All rights reserved.

Detection of colorectal masses in CT colonography: application of deep residual networks for differentiating masses from normal colon anatomy

NASA Astrophysics Data System (ADS)

Näppi, Janne J.; Hironaka, Toru; Yoshida, Hiroyuki

2018-02-01

Even though the clinical consequences of a missed colorectal cancer far outweigh those of a missed polyp, there has been little work on computer-aided detection (CADe) for colorectal masses in CT colonography (CTC). One of the problems is that it is not clear how to manually design mathematical image-based features that could be used to differentiate effectively between masses and certain types of normal colon anatomy such as ileocecal valves (ICVs). Deep learning has demonstrated ability to automatically determine effective discriminating features in many image-based problems. Recently, residual networks (ResNets) were developed to address the practical problems of constructing deep network architectures for optimizing the performance of deep learning. In this pilot study, we compared the classification performance of a conventional 2D-convolutional ResNet (2D-ResNet) with that of a volumetric 3D-convolutional ResNet (3D-ResNet) in differentiating masses from normal colon anatomy in CTC. For the development and evaluation of the ResNets, 695 volumetric images of biopsy-proven colorectal masses, ICVs, haustral folds, and rectal tubes were sampled from 196 clinical CTC cases and divided randomly into independent training, validation, and test datasets. The training set was expanded by use of volumetric data augmentation. Our preliminary results on the 140 test samples indicate that it is feasible to train a deep volumetric 3D-ResNet for performing effective image-based discriminations in CTC. The 3D-ResNet slightly outperformed the 2D-ResNet in the discrimination of masses and normal colon anatomy, but the statistical difference between their very high classification accuracies was not significant. The highest classification accuracy was obtained by combining the mass-likelihood estimates of the 2D- and 3D-ResNets, which enabled correct classification of all of the masses.

Automated tracking, segmentation and trajectory classification of pelvic organs on dynamic MRI.

PubMed

Nekooeimehr, Iman; Lai-Yuen, Susana; Bao, Paul; Weitzenfeld, Alfredo; Hart, Stuart

2016-08-01

Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In this paper, a two-stage method is presented to automatically track and segment pelvic organs on DMRI followed by a multiple-object trajectory classification method to improve the diagnosis of pelvic organ prolapse. Organs are first tracked using particle filters and K-means clustering with prior information. Then, they are segmented using the convex hull of the cluster of particles. Finally, the trajectories of the pelvic organs are modeled using a new Coupled Switched Hidden Markov Model (CSHMM) to classify the severity of pelvic organ prolapse. The tracking and segmentation results are validated using Dice Similarity Index (DSI) whereas the classification results are compared with two manual clinical measurements. Results demonstrate that the presented method is able to automatically track and segment pelvic organs with a DSI above 82% for 26 out of 46 cases and DSI above 75% for all 46 tested cases. The accuracy of the trajectory classification model is also better than current manual measurements.

Diagnostic criteria, severity classification and guidelines of localized scleroderma.

PubMed

Asano, Yoshihide; Fujimoto, Manabu; Ishikawa, Osamu; Sato, Shinichi; Jinnin, Masatoshi; Takehara, Kazuhiko; Hasegawa, Minoru; Yamamoto, Toshiyuki; Ihn, Hironobu

2018-04-23

We established diagnostic criteria and severity classification of localized scleroderma because there is no established diagnostic criteria or widely accepted severity classification of the disease. Also, there has been no clinical guideline for localized scleroderma, so we established its clinical guideline ahead of all over the world. In particular, the clinical guideline was established by clinical questions based on evidence-based medicine according to the New Minds Clinical Practice Guideline Creation Manual (version 1.0). We aimed to make the guideline easy to use and reliable based on the newest evidence, and to present guidance as specific as possible for various clinical problems in treatment of localized scleroderma. © 2018 Japanese Dermatological Association.

Classification of Children Intelligence with Fuzzy Logic Method

NASA Astrophysics Data System (ADS)

Syahminan; ika Hidayati, Permata

2018-04-01

Intelligence of children s An Important Thing To Know The Parents Early on. Typing Can be done With a Child’s intelligence Grouping Dominant Characteristics Of each Type of Intelligence. To Make it easier for Parents in Determining The type of Children’s intelligence And How to Overcome them, for It Created A Classification System Intelligence Grouping Children By Using Fuzzy logic method For determination Of a Child’s degree of intelligence type. From the analysis We concluded that The presence of Intelligence Classification systems Pendulum Children With Fuzzy Logic Method Of determining The type of The Child’s intelligence Can be Done in a way That is easier And The results More accurate Conclusions Than Manual tests.

The ERTS-1 investigation (ER-600). Volume 4: ERTS-1 range analysis

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The Range Analysis Team conducted an investigation to determine the utility of using LANDSAT 1 data for mapping vegetation-type information on range and related grazing lands. Two study areas within the Houston Area Test Site (HATS) were mapped to the highest classification level possible using manual image interpretation and computer aided classification techniques. Rangeland was distinguished from nonrangeland (water, urban area, and cropland) and was further classified as woodland versus nonwoodland. Finer classification of coastal features was attempted with some success in differentiating the lowland zone from the drier upland zone. Computer aided temporal analysis techniques enhanced discrimination among nearly all the vegetation types found in this investigation.

29 CFR 541.302 - Creative professionals.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., an employee's primary duty must be the performance of work requiring invention, imagination..., manual, mechanical or physical work. The exemption does not apply to work which can be produced by a person with general manual or intellectual ability and training. (b) To qualify for exemption as a...

Validation of Case Finding Algorithms for Hepatocellular Cancer from Administrative Data and Electronic Health Records using Natural Language Processing

PubMed Central

Sada, Yvonne; Hou, Jason; Richardson, Peter; El-Serag, Hashem; Davila, Jessica

2013-01-01

Background Accurate identification of hepatocellular cancer (HCC) cases from automated data is needed for efficient and valid quality improvement initiatives and research. We validated HCC ICD-9 codes, and evaluated whether natural language processing (NLP) by the Automated Retrieval Console (ARC) for document classification improves HCC identification. Methods We identified a cohort of patients with ICD-9 codes for HCC during 2005–2010 from Veterans Affairs administrative data. Pathology and radiology reports were reviewed to confirm HCC. The positive predictive value (PPV), sensitivity, and specificity of ICD-9 codes were calculated. A split validation study of pathology and radiology reports was performed to develop and validate ARC algorithms. Reports were manually classified as diagnostic of HCC or not. ARC generated document classification algorithms using the Clinical Text Analysis and Knowledge Extraction System. ARC performance was compared to manual classification. PPV, sensitivity, and specificity of ARC were calculated. Results 1138 patients with HCC were identified by ICD-9 codes. Based on manual review, 773 had HCC. The HCC ICD-9 code algorithm had a PPV of 0.67, sensitivity of 0.95, and specificity of 0.93. For a random subset of 619 patients, we identified 471 pathology reports for 323 patients and 943 radiology reports for 557 patients. The pathology ARC algorithm had PPV of 0.96, sensitivity of 0.96, and specificity of 0.97. The radiology ARC algorithm had PPV of 0.75, sensitivity of 0.94, and specificity of 0.68. Conclusion A combined approach of ICD-9 codes and NLP of pathology and radiology reports improves HCC case identification in automated data. PMID:23929403

The Standard for Clinicians’ Interview in Psychiatry (SCIP): A Clinician-administered Tool with Categorical, Dimensional, and Numeric Output—Conceptual Development, Design, and Description of the SCIP

PubMed Central

Nasrallah, Henry; Muvvala, Srinivas; El-Missiry, Ahmed; Mansour, Hader; Hill, Cheryl; Elswick, Daniel; Price, Elizabeth C.

2016-01-01

Existing standardized diagnostic interviews (SDIs) were designed for researchers and produce mainly categorical diagnoses. There is an urgent need for a clinician-administered tool that produces dimensional measures, in addition to categorical diagnoses. The Standard for Clinicians’ Interview in Psychiatry (SCIP) is a method of assessment of psychopathology for adults. It is designed to be administered by clinicians and includes the SCIP manual and the SCIP interview. Clinicians use the SCIP questions and rate the responses according to the SCIP manual rules. Clinicians use the patient’s responses to questions, observe the patient’s behaviors and make the final rating of the various signs and symptoms assessed. The SCIP method of psychiatric assessment has three components: 1) the SCIP interview (dimensional) component, 2) the etiological component, and 3) the disorder classification component. The SCIP produces three main categories of clinical data: 1) a diagnostic classification of psychiatric disorders, 2) dimensional scores, and 3) numeric data. The SCIP provides diagnoses consistent with criteria from editions of the Diagnostic and Statistical Manual (DSM) and International Classification of Disease (ICD). The SCIP produces 18 dimensional measures for key psychiatric signs or symptoms: anxiety, posttraumatic stress, obsessions, compulsions, depression, mania, suicidality, suicidal behavior, delusions, hallucinations, agitation, disorganized behavior, negativity, catatonia, alcohol addiction, drug addiction, attention, and hyperactivity. The SCIP produces numeric severity data for use in either clinical care or research. The SCIP was shown to be a valid and reliable assessment tool, and the validity and reliability results were published in 2014 and 2015. The SCIP is compatible with personalized psychiatry research and is in line with the Research Domain Criteria framework. PMID:27800284

The Standard for Clinicians' Interview in Psychiatry (SCIP): A Clinician-administered Tool with Categorical, Dimensional, and Numeric Output-Conceptual Development, Design, and Description of the SCIP.

PubMed

Aboraya, Ahmed; Nasrallah, Henry; Muvvala, Srinivas; El-Missiry, Ahmed; Mansour, Hader; Hill, Cheryl; Elswick, Daniel; Price, Elizabeth C

2016-01-01

Existing standardized diagnostic interviews (SDIs) were designed for researchers and produce mainly categorical diagnoses. There is an urgent need for a clinician-administered tool that produces dimensional measures, in addition to categorical diagnoses. The Standard for Clinicians' Interview in Psychiatry (SCIP) is a method of assessment of psychopathology for adults. It is designed to be administered by clinicians and includes the SCIP manual and the SCIP interview. Clinicians use the SCIP questions and rate the responses according to the SCIP manual rules. Clinicians use the patient's responses to questions, observe the patient's behaviors and make the final rating of the various signs and symptoms assessed. The SCIP method of psychiatric assessment has three components: 1) the SCIP interview (dimensional) component, 2) the etiological component, and 3) the disorder classification component. The SCIP produces three main categories of clinical data: 1) a diagnostic classification of psychiatric disorders, 2) dimensional scores, and 3) numeric data. The SCIP provides diagnoses consistent with criteria from editions of the Diagnostic and Statistical Manual (DSM) and International Classification of Disease (ICD). The SCIP produces 18 dimensional measures for key psychiatric signs or symptoms: anxiety, posttraumatic stress, obsessions, compulsions, depression, mania, suicidality, suicidal behavior, delusions, hallucinations, agitation, disorganized behavior, negativity, catatonia, alcohol addiction, drug addiction, attention, and hyperactivity. The SCIP produces numeric severity data for use in either clinical care or research. The SCIP was shown to be a valid and reliable assessment tool, and the validity and reliability results were published in 2014 and 2015. The SCIP is compatible with personalized psychiatry research and is in line with the Research Domain Criteria framework.

Thermal spray manual for machinery components

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

Travis, R.; Ginther, C.; Herbstritt, M.

1995-12-31

The Thermal Spray Manual For Machinery Components is a National Shipbuilding Research (SP-7) Project. This Manual is being developed by Puget Sound Naval Shipyard with the help of other government thermal spray facilities and SP-7 panel members. The purpose of the manual is to provide marine repair facilities with a ``how to do`` document that will be ``user friendly`` and known to be technically sound through production experience. The manual`s intent is to give marine repair facilities the ability to maximize the thermal spray process as a repair method for machinery components and to give these facilities guidelines on howmore » to become qualified to receive certification that they meet the requirements of Military Standard 1687A.« less

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

PubMed

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

2018-01-01

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

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

PubMed Central

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

2018-01-01

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

The influence of lower limb impairments on RaceRunning performance in athletes with hypertonia, ataxia or athetosis.

PubMed

van der Linden, Marietta L; Jahed, Sadaf; Tennant, Nicola; Verheul, Martine H G

2018-03-01

RaceRunning enables athletes with limited or no walking ability to propel themselves independently using a three-wheeled running bike that has a saddle and a chest plate for support but no pedals. For RaceRunning to be included as a Para athletics event, an evidence-based classification system is required. Therefore, the aim of this study was to assess the association between a range of impairment measures and RaceRunning performance. The following impairment measures were recorded: lower limb muscle strength assessed using Manual Muscle Testing (MMT), selective voluntary motor control assessed using the Selective Control Assessment of the Lower Extremity (SCALE), spasticity recorded using both the Australian Spasticity Assessment Score (ASAS) and Modified Ashworth Scale (MAS), passive range of motion (ROM) of the lower extremities and the maximum static step length achieved on a stationary bike (MSSL). Associations between impairment measures and 100-meter race speed were assessed using Spearman's correlation coefficients. Sixteen male and fifteen female athletes (27 with cerebral palsy), aged 23 (SD = 7) years, Gross Motor Function Classification System levels ranging from II to V, participated. The MSSL averaged over both legs and the ASAS, MAS, SCALE, and MMT summed over all joints and both legs, significantly correlated with 100 m race performance (rho: 0.40-0.54). Passive knee extension was the only ROM measure that was significantly associated with race speed (rho = 0.48). These results suggest that lower limb spasticity, isometric leg strength, selective voluntary motor control and passive knee extension impact performance in RaceRunning athletes. This supports the potential use of these measures in a future evidence-based classification system. Copyright © 2018 Elsevier B.V. All rights reserved.

Performance of automated and manual coding systems for occupational data: a case study of historical records.

PubMed

Patel, Mehul D; Rose, Kathryn M; Owens, Cindy R; Bang, Heejung; Kaufman, Jay S

2012-03-01

Occupational data are a common source of workplace exposure and socioeconomic information in epidemiologic research. We compared the performance of two occupation coding methods, an automated software and a manual coder, using occupation and industry titles from U.S. historical records. We collected parental occupational data from 1920-40s birth certificates, Census records, and city directories on 3,135 deceased individuals in the Atherosclerosis Risk in Communities (ARIC) study. Unique occupation-industry narratives were assigned codes by a manual coder and the Standardized Occupation and Industry Coding software program. We calculated agreement between coding methods of classification into major Census occupational groups. Automated coding software assigned codes to 71% of occupations and 76% of industries. Of this subset coded by software, 73% of occupation codes and 69% of industry codes matched between automated and manual coding. For major occupational groups, agreement improved to 89% (kappa = 0.86). Automated occupational coding is a cost-efficient alternative to manual coding. However, some manual coding is required to code incomplete information. We found substantial variability between coders in the assignment of occupations although not as large for major groups.

49 CFR 1245.6 - Cross reference to standard occupational classification manual.

Code of Federal Regulations, 2013 CFR

2013-10-01

...: Electrical Worker (lineman) 6433. Electrical Worker (groundsman) 6432. Communications Maintainer 6151... Maintainer Helper 8635. 320Camp Car Cooks: Camp Car Cook 5214. Camp Car Helper 5219. 400Maintenance of... Reclamations Plant 6318. Assist. General Foreman 6318. 403Equipment, Shop, Electrical Inspectors: Chief...

49 CFR 1245.6 - Cross reference to standard occupational classification manual.

Code of Federal Regulations, 2012 CFR

2012-10-01

...: Electrical Worker (lineman) 6433. Electrical Worker (groundsman) 6432. Communications Maintainer 6151... Maintainer Helper 8635. 320Camp Car Cooks: Camp Car Cook 5214. Camp Car Helper 5219. 400Maintenance of... Reclamations Plant 6318. Assist. General Foreman 6318. 403Equipment, Shop, Electrical Inspectors: Chief...

49 CFR 1245.6 - Cross reference to standard occupational classification manual.

Code of Federal Regulations, 2014 CFR

2014-10-01

...: Electrical Worker (lineman) 6433. Electrical Worker (groundsman) 6432. Communications Maintainer 6151... Maintainer Helper 8635. 320Camp Car Cooks: Camp Car Cook 5214. Camp Car Helper 5219. 400Maintenance of... Reclamations Plant 6318. Assist. General Foreman 6318. 403Equipment, Shop, Electrical Inspectors: Chief...

The utility of six over-the-counter (home) pregnancy tests.

PubMed

Cole, Laurence A

2011-08-01

The home pregnancy market is rapidly evolving. It has moved from detection of pregnancy on the day of missed menstrual bleeding, to detection claims 4 days prior. It is moving from all manual tests to digital tests, with a monitor reading the bands and informing women they are pregnant. A thorough study is needed to investigate the validity of claims and evolving usefulness of devices. Studies were proposed to examine the sensitivity and specificity of home tests and their abilities to detect pregnancy. Methods examined the abilities of tests to detect human chorionic gonadotropin (hCG), hyperglycosylated hCG, free β-subunit, a mixture of these antigens in 40 individual early pregnancy urines. Using a mixture of hCG, hyperglycosylated hCG and free β-subunit typical for early pregnancy, the sensitivity of the First Response manual and digital tests was 5.5 mIU/mL, while the sensitivities of the EPT and ClearBlue brand manual and digital tests was 22 mIU/mL. On further evaluation, the First Response manual and digital tests both detected 97% of 120 pregnancies on the day of missed menstrual bleeding. The EPT manual and digital devices detected 54% and 67% of pregnancies, respectively, and the ClearBlue manual and digital devices detected 64% and 54% of pregnancies, respectively. First Response manual and digital claim >99% detection on the day of missed menses. The results here suggest similar sensitivity for these two tests. The EPT and ClearBlue manual and digital test make similar >99% claims, the data presented here disputes their elevated claim.

Using a geographic information system and scanning technology to create high-resolution land-use data sets

USGS Publications Warehouse

Harvey, Craig A.; Kolpin, Dana W.; Battaglin, William A.

1996-01-01

A geographic information system (GIS) procedure was developed to compile low-altitude aerial photography, digitized data, and land-use data from U.S. Department of Agriculture Consolidated Farm Service Agency (CFSA) offices into a high-resolution (approximately 5 meters) land-use GIS data set. The aerial photography consisted of 35-mm slides which were scanned into tagged information file format (TIFF) images. These TIFF images were then imported into the GIS where they were registered into a geographically referenced coordinate system. Boundaries between land use were delineated from these GIS data sets using on-screen digitizing techniques. Crop types were determined using information obtained from the U.S. Department of Agriculture CFSA offices. Crop information not supplied by the CFSA was attributed by manual classification procedures. Automated methods to provide delineation of the field boundaries and land-use classification were investigated. It was determined that using these data sources, automated methods were less efficient and accurate than manual methods of delineating field boundaries and classifying land use.

Manual physical therapy: we speak gibberish.

PubMed

Flynn, Timothy W; Childs, John D; Bell, Stephania; Magel, Jake S; Rowe, Robert H; Plock, Haideh

2008-03-01

In December of 2006, the American Academy of Orthopaedic Manual Physical Therapists (AAOMPT) convened a task force to create a framework for standardizing manual physical therapy procedures. The impetus came from many years of frustration with our ability to precisely communicate to each other, as well as to stakeholders outside our profession. To this end, a contribution titled "A Model for Standardizing Manipulation Terminology In Physical Therapy Practice" is published in this issue of the Journal.

Classification of independent components of EEG into multiple artifact classes.

PubMed

Frølich, Laura; Andersen, Tobias S; Mørup, Morten

2015-01-01

In this study, we aim to automatically identify multiple artifact types in EEG. We used multinomial regression to classify independent components of EEG data, selecting from 65 spatial, spectral, and temporal features of independent components using forward selection. The classifier identified neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing them. Copyright © 2014 Society for Psychophysiological Research.

A Cognitive Computing Approach for Classification of Complaints in the Insurance Industry

NASA Astrophysics Data System (ADS)

Forster, J.; Entrup, B.

2017-10-01

In this paper we present and evaluate a cognitive computing approach for classification of dissatisfaction and four complaint specific complaint classes in correspondence documents between insurance clients and an insurance company. A cognitive computing approach includes the combination classical natural language processing methods, machine learning algorithms and the evaluation of hypothesis. The approach combines a MaxEnt machine learning algorithm with language modelling, tf-idf and sentiment analytics to create a multi-label text classification model. The result is trained and tested with a set of 2500 original insurance communication documents written in German, which have been manually annotated by the partnering insurance company. With a F1-Score of 0.9, a reliable text classification component has been implemented and evaluated. A final outlook towards a cognitive computing insurance assistant is given in the end.

Towards Automatic Classification of Wikipedia Content

NASA Astrophysics Data System (ADS)

Szymański, Julian

Wikipedia - the Free Encyclopedia encounters the problem of proper classification of new articles everyday. The process of assignment of articles to categories is performed manually and it is a time consuming task. It requires knowledge about Wikipedia structure, which is beyond typical editor competence, which leads to human-caused mistakes - omitting or wrong assignments of articles to categories. The article presents application of SVM classifier for automatic classification of documents from The Free Encyclopedia. The classifier application has been tested while using two text representations: inter-documents connections (hyperlinks) and word content. The results of the performed experiments evaluated on hand crafted data show that the Wikipedia classification process can be partially automated. The proposed approach can be used for building a decision support system which suggests editors the best categories that fit new content entered to Wikipedia.

75 FR 31702 - General Information on Postal Service

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-04

..., of the Mailing Standards of the United States Postal Service, Domestic Mail Manual (DMM), and its... Dobbins (202) 268-3789. SUPPLEMENTARY INFORMATION: The most recent Issue 300 of the Domestic Mail Manual... standards. This issue continues to (1) increase the user's ability to find information, (2) increase...

Longitudinal development of manual motor ability in autism spectrum disorder from childhood to mid-adulthood relates to adaptive daily living skills.

PubMed

Travers, Brittany G; Bigler, Erin D; Duffield, Tyler C; Prigge, Molly D B; Froehlich, Alyson L; Lange, Nicholas; Alexander, Andrew L; Lainhart, Janet E

2017-07-01

Many individuals with autism spectrum disorder (ASD) exhibit motor difficulties, but it is unknown whether manual motor skills improve, plateau, or decline in ASD in the transition from childhood into adulthood. Atypical development of manual motor skills could impact the ability to learn and perform daily activities across the life span. This study examined longitudinal grip strength and finger tapping development in individuals with ASD (n = 90) compared to individuals with typical development (n = 56), ages 5 to 40 years old. We further examined manual motor performance as a possible correlate of current and future daily living skills. The group with ASD demonstrated atypical motor development, characterized by similar performance during childhood but increasingly poorer performance from adolescence into adulthood. Grip strength was correlated with current adaptive daily living skills, and Time 1 grip strength predicted daily living skills eight years into the future. These results suggest that individuals with ASD may experience increasingly more pronounced motor difficulties from adolescence into adulthood and that manual motor performance in ASD is related to adaptive daily living skills. © 2016 John Wiley & Sons Ltd.

Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization.

PubMed

Kiranyaz, Serkan; Ince, Turker; Pulkkinen, Jenni; Gabbouj, Moncef

2010-01-01

In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.

Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI.

PubMed

Elliott, Colm; Arnold, Douglas L; Collins, D Louis; Arbel, Tal

2013-08-01

Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.

Michael's Inform Test of Student Ability (M.I.T.O.S.A.). Tester's Manual.

ERIC Educational Resources Information Center

Grafius, Thomas M.

Michael's Informal Test of Student Ability (MITOSA) is a diagnostic evaluative tool for adult students designed to test nine skills abilities in adult students functioning below a tenth grade level. The nine test sections are approximate reading level, understanding of basic math concepts and symbols, general thinking/reasoning ability, eye-hand…

Semi-automatic tracking, smoothing and segmentation of hyoid bone motion from videofluoroscopic swallowing study.

PubMed

Kim, Won-Seok; Zeng, Pengcheng; Shi, Jian Qing; Lee, Youngjo; Paik, Nam-Jong

2017-01-01

Motion analysis of the hyoid bone via videofluoroscopic study has been used in clinical research, but the classical manual tracking method is generally labor intensive and time consuming. Although some automatic tracking methods have been developed, masked points could not be tracked and smoothing and segmentation, which are necessary for functional motion analysis prior to registration, were not provided by the previous software. We developed software to track the hyoid bone motion semi-automatically. It works even in the situation where the hyoid bone is masked by the mandible and has been validated in dysphagia patients with stroke. In addition, we added the function of semi-automatic smoothing and segmentation. A total of 30 patients' data were used to develop the software, and data collected from 17 patients were used for validation, of which the trajectories of 8 patients were partly masked. Pearson correlation coefficients between the manual and automatic tracking are high and statistically significant (0.942 to 0.991, P-value<0.0001). Relative errors between automatic tracking and manual tracking in terms of the x-axis, y-axis and 2D range of hyoid bone excursion range from 3.3% to 9.2%. We also developed an automatic method to segment each hyoid bone trajectory into four phases (elevation phase, anterior movement phase, descending phase and returning phase). The semi-automatic hyoid bone tracking from VFSS data by our software is valid compared to the conventional manual tracking method. In addition, the ability of automatic indication to switch the automatic mode to manual mode in extreme cases and calibration without attaching the radiopaque object is convenient and useful for users. Semi-automatic smoothing and segmentation provide further information for functional motion analysis which is beneficial to further statistical analysis such as functional classification and prognostication for dysphagia. Therefore, this software could provide the researchers in the field of dysphagia with a convenient, useful, and all-in-one platform for analyzing the hyoid bone motion. Further development of our method to track the other swallowing related structures or objects such as epiglottis and bolus and to carry out the 2D curve registration may be needed for a more comprehensive functional data analysis for dysphagia with big data.

Change in basic motor abilities, quality of movement and everyday activities following intensive, goal-directed, activity-focused physiotherapy in a group setting for children with cerebral palsy.

PubMed

Sorsdahl, Anne Brit; Moe-Nilssen, Rolf; Kaale, Helga K; Rieber, Jannike; Strand, Liv Inger

2010-04-27

The effects of intensive training for children with cerebral palsy (CP) remain uncertain. The aim of the study was to investigate the impact on motor function, quality of movements and everyday activities of three hours of goal-directed activity-focused physiotherapy in a group setting, five days a week for a period of three weeks. A repeated measures design was applied with three baseline and two follow up assessments; immediately and three weeks after intervention. Twenty-two children with hemiplegia (n = 7), diplegia (n = 11), quadriplegia (n = 2) and ataxia (n = 2) participated, age ranging 3-9 y. All levels of Gross Motor Function Classification System (GMFCS) and Manual Ability Classification System (MACS) were represented. Parents and professionals participated in goal setting and training. ANOVA was used to analyse change over repeated measures. A main effect of time was shown in the primary outcome measure; Gross Motor Function Measure-66 (GMFM-66), mean change being 4.5 (p < 0.01) from last baseline to last follow up assessment. An interaction between time and GMFCS-levels was found, implying that children classified to GMFCS-levels I-II improved more than children classified to levels III-V. There were no main or interaction effects of age or anti-spastic medication. Change scores in the Pediatric Evaluation of Disability Inventory (PEDI) ranged 2.0-6.7, p < 0.01 in the Self-care domain of the Functional Skills dimension, and the Self-care and Mobility domains of the Caregiver Assistance dimension. The children's individual goals were on average attained, Mean Goal Attainment Scaling (GAS) T-score being 51.3. Non-significant improved scores on the Gross Motor Performance Measure (GMPM) and the Quality of Upper Extremities Skills Test (QUEST) were demonstrated. Significant improvement in GMPM scores were found in improved items of the GMFM, not in items that maintained the same score. Basic motor abilities and self-care improved in young children with CP after goal-directed activity-focused physiotherapy with involvement of their local environment, and their need for caregiver assistance in self-care and mobility decreased. The individualized training within a group context during a limited period of time was feasible and well-tolerated. The coherence between acquisition of basic motor abilities and quality of movement should be further examined.

AVNM: A Voting based Novel Mathematical Rule for Image Classification.

PubMed

Vidyarthi, Ankit; Mittal, Namita

2016-12-01

In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of a number of nearest neighbors i.e. "k" for computation. At present, it is hard to find the optimal value of "k" using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate. Motivated by the prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. The proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. The proposed classifier also reduces the effect of outliers. To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard datasets taken from UCI database and one manually created dataset. The experimental result shows that the proposed AVNM rule outperforms the KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule. The proposed AVNM rule is based on sample space reduction mechanism for identification of an optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with the state-of-art algorithm, KNN, and its variants. The proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manually created dataset. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Interobserver and intraobserver variability in the identification of the Lenke classification lumbar modifier in adolescent idiopathic scoliosis.

PubMed

Duong, Luc; Cheriet, Farida; Labelle, Hubert; Cheung, Kenneth M C; Abel, Mark F; Newton, Peter O; McCall, Richard E; Lenke, Lawrence G; Stokes, Ian A F

2009-08-01

Interobserver and intraobserver reliability study for the identification of the Lenke classification lumbar modifier by a panel of experts compared with a computer algorithm. To measure the variability of the Lenke classification lumbar modifier and determine if computer assistance using 3-dimensional spine models can improve the reliability of classification. The lumbar modifier has been proposed to subclassify Lenke scoliotic curve types into A, B, and C on the basis of the relationship between the central sacral vertical line (CSVL) and the apical lumbar vertebra. Landmarks for identification of the CSVL have not been clearly defined, and the reliability of the actual CSVL position and lumbar modifier selection have never been tested independently. Therefore, the value of the lumbar modifier for curve classification remains unknown. The preoperative radiographs of 68 patients with adolescent idiopathic scoliosis presenting a Lenke type 1 curve were measured manually twice by 6 members of the Scoliosis Research Society 3-dimensional classification committee at 6 months interval. Intraobserver and interobserver reliability was quantified using the percentage of agreement and kappa statistics. In addition, the lumbar curve of all subjects was reconstructed in 3-dimension using a stereoradiographic technique and was submitted to a computer algorithm to infer the lumbar modifier according to measurements from the pedicles. Interobserver rates for the first trial showed a mean kappa value of 0.56. Second trial rates were higher with a mean kappa value of 0.64. Intraobserver rates were evaluated at a mean kappa value of 0.69. The computer algorithm was successful in identifying the lumbar curve type and was in agreement with the observers by a proportion up to 93%. Agreement between and within observers for the Lenke lumbar modifier is only moderate to substantial with manual methods. Computer assistance with 3-dimensional models of the spine has the potential to decrease this variability.

The classification of conversion disorder (functional neurologic symptom disorder) in ICD and DSM.

PubMed

Levenson, J L; Sharpe, M

2016-01-01

The name given to functional neurologic symptoms has evolved over time in the different editions of the International Classification of Diseases (ICD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM), reflecting a gradual move away from an etiologic conception rooted in hysterical conversion to an empiric phenomenologic one, emphasizing the central role of the neurologic examination and testing in demonstrating that the symptoms are incompatible with recognized neurologic disease pathophysiology, or are internally inconsistent. © 2016 Elsevier B.V. All rights reserved.

Basic forest cover mapping using digitized remote sensor data and automated data processing techniques

NASA Technical Reports Server (NTRS)

Coggeshall, M. E.; Hoffer, R. M.

1973-01-01

Remote sensing equipment and automatic data processing techniques were employed as aids in the institution of improved forest resource management methods. On the basis of automatically calculated statistics derived from manually selected training samples, the feature selection processor of LARSYS selected, upon consideration of various groups of the four available spectral regions, a series of channel combinations whose automatic classification performances (for six cover types, including both deciduous and coniferous forest) were tested, analyzed, and further compared with automatic classification results obtained from digitized color infrared photography.

Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery.

PubMed

Chew, Robert F; Amer, Safaa; Jones, Kasey; Unangst, Jennifer; Cajka, James; Allpress, Justine; Bruhn, Mark

2018-05-09

Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative sample. Geosampling is a probability-based, gridded population sampling method that addresses some of these issues by using geographic information system (GIS) tools to create logistically manageable area units for sampling. GIS grid cells are overlaid to partition a country's existing administrative boundaries into area units that vary in size from 50 m × 50 m to 150 m × 150 m. To avoid sending interviewers to unoccupied areas, researchers manually classify grid cells as "residential" or "nonresidential" through visual inspection of aerial images. "Nonresidential" units are then excluded from sampling and data collection. This process of manually classifying sampling units has drawbacks since it is labor intensive, prone to human error, and creates the need for simplifying assumptions during calculation of design-based sampling weights. In this paper, we discuss the development of a deep learning classification model to predict whether aerial images are residential or nonresidential, thus reducing manual labor and eliminating the need for simplifying assumptions. On our test sets, the model performs comparable to a human-level baseline in both Nigeria (94.5% accuracy) and Guatemala (96.4% accuracy), and outperforms baseline machine learning models trained on crowdsourced or remote-sensed geospatial features. Additionally, our findings suggest that this approach can work well in new areas with relatively modest amounts of training data. Gridded population sampling methods like geosampling are becoming increasingly popular in countries with outdated or inaccurate census data because of their timeliness, flexibility, and cost. Using deep learning models directly on satellite images, we provide a novel method for sample frame construction that identifies residential gridded aerial units. In cases where manual classification of satellite images is used to (1) correct for errors in gridded population data sets or (2) classify grids where population estimates are unavailable, this methodology can help reduce annotation burden with comparable quality to human analysts.

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

NASA Astrophysics Data System (ADS)

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to proper wages usage. Thus a more precise and unambiguous boundaries of segments (objects) were received. As a results of the classification 5 classes of land cover (buildings, water, high and low vegetation and others) were extracted. Both pixel-based image analysis and OBIA were conducted with a minimum mapping unit of 10m2. Results were validated on the basis on manual classification and random points (80 per test area), reference data set was manually interpreted using ortophotomaps and expert knowledge of the test site areas.

49 CFR 1245.6 - Cross reference to standard occupational classification manual.

Code of Federal Regulations, 2011 CFR

2011-10-01

.... Assist. Chemist 1845. X-ray Technician 365. Supv. Estimating 149. Junior Engineer 1639. Engineer Trainee...) 8319. Grain Elevator Operator (electrical) 8319. 414Machinists: Machinist 6813. 415Sheet Metal Workers: Sheet Metal Worker 6824. 416Skilled Trades, Helpers, Maintenance of Equipment and Stores: Helper 861...

49 CFR 1245.6 - Cross reference to standard occupational classification manual.

Code of Federal Regulations, 2010 CFR

2010-10-01

...: Manager Lodging House 1351. Restaurant Manager 1351. Dining Car Supv. 5211, and 1473. Dinning car Steward 5211. 514Transportation and Dining Service Inspectors: Instructor Chef 5214. Restaurant Inspector 828. Freight Service Inspector 828. Baggage Inspector 828. 515Waiters and Kitchen Helpers (Restaurant and...

Eating Disorders in the Adolescent Population: An Overview.

ERIC Educational Resources Information Center

Reijonen, Jori H.; Pratt, Helen D.; Patel, Dilip R.; Greydanus, Donald E.

2003-01-01

Selectively reviews the literature on the diagnostic criteria for eating disorders (anorexia nervosa, bulimia nervosa, and binge-eating disorder) as described in "Diagnostic and Statistical Manual of Mental Disorders" (4th ed.) and "International Classification of Diseases" (10th ed.). Discusses the prevalence and course of…

Predicting Student Performance in Sonographic Scanning Using Spatial Ability as an Ability Determinent of Skill Acquisition

ERIC Educational Resources Information Center

Clem, Douglas Wayne

2012-01-01

Spatial ability refers to an individual's capacity to visualize and mentally manipulate three dimensional objects. Since sonographers manually manipulate 2D and 3D sonographic images to generate multi-viewed, logical, sequential renderings of an anatomical structure, it can be assumed that spatial ability is central to the perception and…

Five systems of psychiatric classification for preschool children: do differences in validity, usefulness and reliability make for competitive or complimentary constellations?

PubMed

Postert, Christian; Averbeck-Holocher, Marlies; Beyer, Thomas; Müller, Jörg; Furniss, Tilman

2009-03-01

DSM-IV and ICD-10 have limitations in the diagnostic classification of psychiatric disorders at preschool age (0-5 years). The publication of the Diagnostic Classification 0-3 (DC:0-3) in 1994, its basically revised second edition (DC:0-3R) in 2005 and the Research Diagnostic Criteria-Preschool Age (RDC-PA) in 2004 have provided several modifications of these manuals. Taking into account the growing empirical evidence highlighting the need for a diagnostic classification system for psychiatric disorders in preschool children, the main categorical classification systems in preschool psychiatry will be presented and discussed. The paper will focus on issues of validity, usefulness and reliability in DSM-IV, ICD-10, RDC-PA, DC:0-3, and DC:0-3R. The reasons for including or excluding postulated psychiatric disorder categories for preschool children with variable degrees of empirical evidence into the different diagnostic systems will be discussed.

An automatic graph-based approach for artery/vein classification in retinal images.

PubMed

Dashtbozorg, Behdad; Mendonça, Ana Maria; Campilho, Aurélio

2014-03-01

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

Patient advocacy and DSM-5.

PubMed

Stein, Dan J; Phillips, Katharine A

2013-05-17

The revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM) provides a useful opportunity to revisit debates about the nature of psychiatric classification. An important debate concerns the involvement of mental health consumers in revisions of the classification. One perspective argues that psychiatric classification is a scientific process undertaken by scientific experts and that including consumers in the revision process is merely pandering to political correctness. A contrasting perspective is that psychiatric classification is a process driven by a range of different values and that the involvement of patients and patient advocates would enhance this process. Here we draw on our experiences with input from the public during the deliberations of the Obsessive Compulsive-Spectrum Disorders subworkgroup of DSM-5, to help make the argument that psychiatric classification does require reasoned debate on a range of different facts and values, and that it is appropriate for scientist experts to review their nosological recommendations in the light of rigorous consideration of patient experience and feedback.

Back-and-Forth Methodology for Objective Voice Quality Assessment: From/to Expert Knowledge to/from Automatic Classification of Dysphonia

NASA Astrophysics Data System (ADS)

Fredouille, Corinne; Pouchoulin, Gilles; Ghio, Alain; Revis, Joana; Bonastre, Jean-François; Giovanni, Antoine

2009-12-01

This paper addresses voice disorder assessment. It proposes an original back-and-forth methodology involving an automatic classification system as well as knowledge of the human experts (machine learning experts, phoneticians, and pathologists). The goal of this methodology is to bring a better understanding of acoustic phenomena related to dysphonia. The automatic system was validated on a dysphonic corpus (80 female voices), rated according to the GRBAS perceptual scale by an expert jury. Firstly, focused on the frequency domain, the classification system showed the interest of 0-3000 Hz frequency band for the classification task based on the GRBAS scale. Later, an automatic phonemic analysis underlined the significance of consonants and more surprisingly of unvoiced consonants for the same classification task. Submitted to the human experts, these observations led to a manual analysis of unvoiced plosives, which highlighted a lengthening of VOT according to the dysphonia severity validated by a preliminary statistical analysis.

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

PubMed

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

2017-01-01

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

Multilingual Twitter Sentiment Classification: The Role of Human Annotators

PubMed Central

Mozetič, Igor; Grčar, Miha; Smailović, Jasmina

2016-01-01

What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered. PMID:27149621

Convolutional neural network with transfer learning for rice type classification

NASA Astrophysics Data System (ADS)

Patel, Vaibhav Amit; Joshi, Manjunath V.

2018-04-01

Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

Automated compound classification using a chemical ontology.

PubMed

Bobach, Claudia; Böhme, Timo; Laube, Ulf; Püschel, Anett; Weber, Lutz

2012-12-29

Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated.

Automated compound classification using a chemical ontology

PubMed Central

2012-01-01

Background Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. Results In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. Conclusions A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated. PMID:23273256

Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

PubMed

Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

2017-01-01

Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NB SW =NB BI-GRAM =SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we have done here, utilizing readily-available off-the-shelf machine learning techniques and resulting in only a fraction of narratives that require manual review. Human-machine ensemble methods are likely to improve performance over total manual coding. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

ABILHAND-Kids: a measure of manual ability in children with cerebral palsy.

PubMed

Arnould, Carlyne; Penta, Massimo; Renders, Anne; Thonnard, Jean-Louis

2004-09-28

To develop a clinical tool for measuring manual ability (ABILHAND-Kids) in children with cerebral palsy (CP) using the Rasch measurement model. The authors developed a 74-item questionnaire based on existing scales and experts' advice. The questionnaire was submitted to 113 children with CP (59% boys; mean age, 10 years) without major intellectual deficits (IQ > 60) and to their parents, and resubmitted to both groups after 1 month. The children's and parents' responses were analyzed separately with the WINSTEPS Rasch software to select items presenting an ordered rating scale, sharing the same discrimination, and fitting a unidimensional scale. The final ABILHAND-Kids scale consisted of 21 mostly bimanual items rated by the parents. The parents reported a finer perception of their children's ability than the children themselves, leading to a wider range of measurement, a higher reliability (R = 0.94), and a good reproducibility over time (R = 0.91). The item difficulty hierarchy was consistent between the parents and the experts. The ABILHAND-kids measures are significantly related to school education, type of CP, and gross motor function. ABILHAND-Kids is a functional scale specifically developed to measure manual ability in children with CP providing guidelines for goal setting in treatment planning. Its range and measurement precision are appropriate for clinical practice.

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...

Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia.

PubMed

Hripcsak, George; Knirsch, Charles; Zhou, Li; Wilcox, Adam; Melton, Genevieve B

2007-03-01

Data mining in electronic medical records may facilitate clinical research, but much of the structured data may be miscoded, incomplete, or non-specific. The exploitation of narrative data using natural language processing may help, although nesting, varying granularity, and repetition remain challenges. In a study of community-acquired pneumonia using electronic records, these issues led to poor classification. Limiting queries to accurate, complete records led to vastly reduced, possibly biased samples. We exploited knowledge latent in the electronic records to improve classification. A similarity metric was used to cluster cases. We defined discordance as the degree to which cases within a cluster give different answers for some query that addresses a classification task of interest. Cases with higher discordance are more likely to be incorrectly classified, and can be reviewed manually to adjust the classification, improve the query, or estimate the likely accuracy of the query. In a study of pneumonia--in which the ICD9-CM coding was found to be very poor--the discordance measure was statistically significantly correlated with classification correctness (.45; 95% CI .15-.62).

Comorbidity in "DSM" Childhood Mental Disorders: A Functional Perspective

ERIC Educational Resources Information Center

Cipani, Ennio

2014-01-01

In this article, I address the issue of comorbidity and its prevalence in the prior "Diagnostic and Statistical Manual of Mental Disorders" ("DSM") classification systems. The focus on the topography or form of presenting problems as the venue for determining mental disorders is scrutinized as the possible cause. Addressing the…

Biology. Student Investigations and Readings. Investigations in Natural Science.

ERIC Educational Resources Information Center

Renner, John W.; And Others

Investigations in Natural Science is a program in secondary school biology, chemistry, and physics based upon the description of science as a quest for knowledge, not the knowledge itself. This student manual contains the 18 biology investigations. These investigations focus on concepts related to: organisms; classification; populations;…

A Transdiagnostic Perspective on Cognitive, Affective, and Neurobiological Processes Underlying Human Suffering

ERIC Educational Resources Information Center

Garland, Eric L.; Howard, Matthew O.

2014-01-01

The Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases classify mental health disorders on the basis of their putatively distinct symptom profiles. Although these nosologies are highly influential, they also have been derided as mere "field guides" because they focus solely on the…

77 FR 44721 - Medicare Program; Revisions to Payment Policies Under the Physician Fee Schedule, DME Face to...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-30

... Classification of Diseases IMRT Intensity Modulated Radiation Therapy IOM Internet-only Manual IPCI Indirect... RIA Regulatory impact analysis RVU Relative value unit SBRT Stereotactic body radiation therapy SGR... adjust the payment rates for two common radiation oncology treatment delivery methods, intensity...

A Study of Rootkit Stealth Techniques and Associated Detection Methods

DTIC Science & Technology

2007-03-01

Manual . . . . . . . 20 3.1. Rootkit Technology Picture . . . . . . . . . . . . . . . . . . . . 23 3.2. Temporal ordering of a detoured function ...3.12. Detection Classification 3 . . . . . . . . . . . . . . . . . . . . . 43 3.13. Trampoline Function with Modification . . . . . . . . . . . . . 46...63 x List of Tables Table Page 2.1. Incident Categories (AFI 33-138) . . . . . . . . . . . . . . . . . 12 3.1. Inline Function Hook

Procedures of Operation at Cranbrook Central Library.

ERIC Educational Resources Information Center

Cranbrook Institutions, Bloomfield Hills, MI. Central Library.

This manual outlines the ordering, cataloging and classification, and processing procedures for books, periodicals, government documents, and non-book materials for a central library serving three schools--a co-ed elementary school and separate junior-senior high schools for boys and girls--and four special libraries--a fine and rare books…

Need Assessment: Winnowing Expressed Concerns for Critical Needs. A Training Manual.

ERIC Educational Resources Information Center

Eastmond, Jefferson N.

A procedure is given for determining various concerns for the identification of priority needs or important problems. Chapters deal with procedures for harvesting educational concerns, illustrations of the systematic harvesting of concerns, concerns classification and analysis, and conducting the need assessment. A diagram of the procedure for…

Iowa Community Colleges Accounting Manual.

ERIC Educational Resources Information Center

Iowa State Dept. of Education, Des Moines. Div. of Community Colleges and Workforce Preparation.

This document describes account classifications and definitions for the accounting system of the Iowa community colleges. In view of the objectives of the accounting system, it is necessary to segregate the assets of the community college according to its source and intended use. Additionally, the accounting system should provide for accounting by…

Positive Psychology Progress: Empirical Validation of Interventions

ERIC Educational Resources Information Center

Seligman, Martin E. P.; Steen, Tracy A.; Park, Nansook; Peterson, Christopher

2005-01-01

Positive psychology has flourished in the last 5 years. The authors review recent developments in the field, including books, meetings, courses, and conferences. They also discuss the newly created classification of character strengths and virtues, a positive complement to the various editions of the Diagnostic and Statistical Manual of Mental…

StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2

PubMed Central

Eftimov, Tome; Korošec, Peter; Koroušić Seljak, Barbara

2017-01-01

The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed—a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc.). In this paper, we introduce a semi-automatic system for classifying and describing foods according to FoodEx2, which consists of three parts. The first involves a machine learning approach and classifies foods into four FoodEx2 categories, with two for single foods: raw (r) and derivatives (d), and two for composite foods: simple (s) and aggregated (c). The second uses a natural language processing approach and probability theory to describe foods. The third combines the result from the first and the second part by defining post-processing rules in order to improve the result for the classification part. We tested the system using a set of food items (from Slovenia) manually-coded according to FoodEx2. The new semi-automatic system obtained an accuracy of 89% for the classification part and 79% for the description part, or an overall result of 79% for the whole system. PMID:28587103

StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2.

PubMed

Eftimov, Tome; Korošec, Peter; Koroušić Seljak, Barbara

2017-05-26

The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed-a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc.). In this paper, we introduce a semi-automatic system for classifying and describing foods according to FoodEx2, which consists of three parts. The first involves a machine learning approach and classifies foods into four FoodEx2 categories, with two for single foods: raw (r) and derivatives (d), and two for composite foods: simple (s) and aggregated (c). The second uses a natural language processing approach and probability theory to describe foods. The third combines the result from the first and the second part by defining post-processing rules in order to improve the result for the classification part. We tested the system using a set of food items (from Slovenia) manually-coded according to FoodEx2. The new semi-automatic system obtained an accuracy of 89% for the classification part and 79% for the description part, or an overall result of 79% for the whole system.

A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.

PubMed

Dimitriadis, Stavros I; Salis, Christos; Linden, David

2018-04-01

Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resource intensive and time consuming, and thus considerable effort must be spent to ensure inter-rater reliability. As a result, there is a great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC). In this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. We validate our methodology in a second sleep dataset. We achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and also a high mean F1 score (92%, range 90-94%) when a multi-class Naive Bayes classifier was applied. High classification performance has been achieved also in the second sleep dataset. Our method outperformed the accuracy of previous studies not only on different datasets but also on the same database. Single-sensor ASSC makes the entire methodology appropriate for longitudinal monitoring using wearable EEG in real-world and laboratory-oriented environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Obsessive compulsive and related disorders: comparing DSM-5 and ICD-11.

PubMed

Marras, Anna; Fineberg, Naomi; Pallanti, Stefano

2016-08-01

Obsessive-compulsive disorder (OCD) has been recognized as mainly characterized by compulsivity rather than anxiety and, therefore, was removed from the anxiety disorders chapter and given its own in both the American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the Beta Draft Version of the 11th revision of the World Health Organization (WHO) International Classification of Diseases (ICD-11). This revised clustering is based on increasing evidence of common affected neurocircuits between disorders, differently from previous classification systems based on interrater agreement. In this article, we focus on the classification of obsessive-compulsive and related disorders (OCRDs), examining the differences in approach adopted by these 2 nosological systems, with particular attention to the proposed changes in the forthcoming ICD-11. At this stage, notable differences in the ICD classification are emerging from the previous revision, apparently converging toward a reformulation of OCRDs that is closer to the DSM-5.

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

PubMed

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

2013-02-01

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

Comparison of manual sleep staging with automated neural network-based analysis in clinical practice.

PubMed

Caffarel, Jennifer; Gibson, G John; Harrison, J Phil; Griffiths, Clive J; Drinnan, Michael J

2006-03-01

We have compared sleep staging by an automated neural network (ANN) system, BioSleep (Oxford BioSignals) and a human scorer using the Rechtschaffen and Kales scoring system. Sleep study recordings from 114 patients with suspected obstructed sleep apnoea syndrome (OSA) were analysed by ANN and by a blinded human scorer. We also examined human scorer reliability by calculating the agreement between the index scorer and a second independent blinded scorer for 28 of the 114 studies. For each study, we built contingency tables on an epoch-by-epoch (30 s epochs) comparison basis. From these, we derived kappa (kappa) coefficients for different combinations of sleep stages. The overall agreement of automatic and manual scoring for the 114 studies for the classification {wake / light-sleep / deep-sleep / REM} was poor (median kappa = 0.305) and only a little better (kappa = 0.449) for the crude {wake / sleep} distinction. For the subgroup of 28 randomly selected studies, the overall agreement of automatic and manual scoring was again relatively low (kappa = 0.331 for {wake light-sleep / deep-sleep REM} and kappa = 0.505 for {wake / sleep}), whereas inter-scorer reliability was higher (kappa = -0.641 for {wake / light-sleep / deep-sleep / REM} and kappa = 0.737 for {wake / sleep}). We conclude that such an ANN-based analysis system is not sufficiently accurate for sleep study analyses using the R&K classification system.

Construction Electrician 3 & 2. Rate Training Manual and Nonresident Career Course.

ERIC Educational Resources Information Center

Naval Education and Training Command, Pensacola, FL.

One of a series of training manuals prepared for enlisted personnel in the Navy and Naval Reserve, this self-study package provides subject matter that relates directly to the tasks required of the Construction Electrician, which include abilities to install, operate, service, and overhaul electric generating and distribution systems and wire…

Programs Under Bilingual Education Act (Title VII, ESEA): Manual for Project Applicants and Grantees.

ERIC Educational Resources Information Center

Bureau of Elementary and Secondary Education (DHEW/OE), Washington, DC.

The Bilingual Education Program, designed to help children having limited English-speaking ability develop greater competence in English, become more proficient in their dominant language, and profit from increased educational opportunity, is described in this manual for project applicants and grantees. Eight chapters include a review of: (1)…

The Working Experience. Teacher's Manual.

ERIC Educational Resources Information Center

Smith, Jeanne H.; Ringel, Harry

A teacher's manual is presented for "The Working Experience," a series of three texts for English-as-a-Second-Language (ESL) students. The series builds on oral skills to develop reading and writing ability while still expanding oral English-language proficiency. Because one of the basic principles underlying the series is the idea that students…

Electrician's Mate 1 & C. Rate Training Manual and Nonresident Career Course.

ERIC Educational Resources Information Center

Naval Education and Training Command, Pensacola, FL.

One of a series of training manuals prepared for enlisted personnel in the Navy and Naval Reserve, this self-study program is designed to enable the electrician's mate to prepare himself for the increased responsibilities of a senior petty officer with ability to operate, maintain, and repair voltage and frequency regulating equipment…

Operation and Maintenance of Wastewater Collection Systems: A Field Study Training Program.

ERIC Educational Resources Information Center

California State Univ., Sacramento. Dept. of Civil Engineering.

This manual was prepared by experienced wastewater collection system workers to provide a home study course to develop new qualified workers and expand the abilities of existing workers. The objective of this manual is to provide the knowledge and skills necessary for certification. Participants learn to effectively operate and maintain wastewater…

Operation of Wastewater Treatment Plants: A Home Study Training Program.

ERIC Educational Resources Information Center

California State Univ., Sacramento. Dept. of Civil Engineering.

This manual was prepared by experienced wastewater treatment plant operators to provide a home study course to develop new qualified workers and expand the abilities of existing workers. The objective of this manual is to provide the knowledge and skills necessary for certification. Participants learn the basic operational aspects of treatment…

Infant Manual Exploration of Composite Substrates

ERIC Educational Resources Information Center

Fontenelle, Sarah A.; Kahrs, Bjorn Alexander; Neal, S. Ashley; Newton, A. Taylor; Lockman, Jeffrey J.

2007-01-01

Everyday environments, even small regions within reach, vary dramatically in terms of material composition. Adapting one's manual behavior to such transitions can be considered to be an important element of skilled action. To investigate the origins of this ability, we presented 8-month-olds (n=24) and 10-month-olds (n=24) hard or soft objects on…

The feasibility of using manual segmentation in a multifeature computer-aided diagnosis system for classification of skin lesions: a retrospective comparative study.

PubMed

Chang, Wen-Yu; Huang, Adam; Chen, Yin-Chun; Lin, Chi-Wei; Tsai, John; Yang, Chung-Kai; Huang, Yin-Tseng; Wu, Yi-Fan; Chen, Gwo-Shing

2015-05-03

To investigate the feasibility of manual segmentation by users of different backgrounds in a previously developed multifeature computer-aided diagnosis (CADx) system to classify melanocytic and non-melanocytic skin lesions based on conventional digital photographic images. In total, 347 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed, and manually segmented by two groups of physicians, dermatologists and general practitioners, as well as by an automated segmentation software program, JSEG. The performance of CADx based on inputs from these two groups of physicians and that of the JSEG program was compared using feature agreement analysis. The estimated area under the receiver operating characteristic curve for classification of benign or malignant skin lesions based were comparable on individual segmentation by the gold standard (0.893, 95% CI 0.856 to 0.930), dermatologists (0.886, 95% CI 0.863 to 0.908), general practitioners (0.883, 95% CI 0.864 to 0.903) and JSEG (0.856, 95% CI 0.812 to 0.899). The agreement in the malignancy probability scores among the physicians was excellent (intraclass correlation coefficient: 0.91). By selecting an optimal cut-off value of malignancy probability score, the sensitivity and specificity were 80.07% and 81.47% for dermatologists and 79.90% and 80.20% for general practitioners. This study suggests that manual segmentation by general practitioners is feasible in the described CADx system for classifying benign and malignant skin lesions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Manual Control Age and Sex Differences in 4 to 11 Year Old Children

PubMed Central

Flatters, Ian; Hill, Liam J. B.; Williams, Justin H. G.; Barber, Sally E.; Mon-Williams, Mark

2014-01-01

To what degree does being male or female influence the development of manual skills in pre-pubescent children? This question is important because of the emphasis placed on developing important new manual skills during this period of a child's education (e.g. writing, drawing, using computers). We investigated age and sex-differences in the ability of 422 children to control a handheld stylus. A task battery deployed using tablet PC technology presented interactive visual targets on a computer screen whilst simultaneously recording participant's objective kinematic responses, via their interactions with the on-screen stimuli using the handheld stylus. The battery required children use the stylus to: (i) make a series of aiming movements, (ii) trace a series of abstract shapes and (iii) track a moving object. The tasks were not familiar to the children, allowing measurement of a general ability that might be meaningfully labelled ‘manual control’, whilst minimising culturally determined differences in experience (as much as possible). A reliable interaction between sex and age was found on the aiming task, with girls' movement times being faster than boys in younger age groups (e.g. 4–5 years) but with this pattern reversing in older children (10–11 years). The improved performance in older boys on the aiming task is consistent with prior evidence of a male advantage for gross-motor aiming tasks, which begins to emerge during adolescence. A small but reliable sex difference was found in tracing skill, with girls showing a slightly higher level of performance than boys irrespective of age. There were no reliable sex differences between boys and girls on the tracking task. Overall, the findings suggest that prepubescent girls are more likely to have superior manual control abilities for performing novel tasks. However, these small population differences do not suggest that the sexes require different educational support whilst developing their manual skills. PMID:24523931

Manual control age and sex differences in 4 to 11 year old children.

PubMed

Flatters, Ian; Hill, Liam J B; Williams, Justin H G; Barber, Sally E; Mon-Williams, Mark

2014-01-01

To what degree does being male or female influence the development of manual skills in pre-pubescent children? This question is important because of the emphasis placed on developing important new manual skills during this period of a child's education (e.g. writing, drawing, using computers). We investigated age and sex-differences in the ability of 422 children to control a handheld stylus. A task battery deployed using tablet PC technology presented interactive visual targets on a computer screen whilst simultaneously recording participant's objective kinematic responses, via their interactions with the on-screen stimuli using the handheld stylus. The battery required children use the stylus to: (i) make a series of aiming movements, (ii) trace a series of abstract shapes and (iii) track a moving object. The tasks were not familiar to the children, allowing measurement of a general ability that might be meaningfully labelled 'manual control', whilst minimising culturally determined differences in experience (as much as possible). A reliable interaction between sex and age was found on the aiming task, with girls' movement times being faster than boys in younger age groups (e.g. 4-5 years) but with this pattern reversing in older children (10-11 years). The improved performance in older boys on the aiming task is consistent with prior evidence of a male advantage for gross-motor aiming tasks, which begins to emerge during adolescence. A small but reliable sex difference was found in tracing skill, with girls showing a slightly higher level of performance than boys irrespective of age. There were no reliable sex differences between boys and girls on the tracking task. Overall, the findings suggest that prepubescent girls are more likely to have superior manual control abilities for performing novel tasks. However, these small population differences do not suggest that the sexes require different educational support whilst developing their manual skills.

Mining hidden data to predict patient prognosis: texture feature extraction and machine learning in mammography

NASA Astrophysics Data System (ADS)

Leighs, J. A.; Halling-Brown, M. D.; Patel, M. N.

2018-03-01

The UK currently has a national breast cancer-screening program and images are routinely collected from a number of screening sites, representing a wealth of invaluable data that is currently under-used. Radiologists evaluate screening images manually and recall suspicious cases for further analysis such as biopsy. Histological testing of biopsy samples confirms the malignancy of the tumour, along with other diagnostic and prognostic characteristics such as disease grade. Machine learning is becoming increasingly popular for clinical image classification problems, as it is capable of discovering patterns in data otherwise invisible. This is particularly true when applied to medical imaging features; however clinical datasets are often relatively small. A texture feature extraction toolkit has been developed to mine a wide range of features from medical images such as mammograms. This study analysed a dataset of 1,366 radiologist-marked, biopsy-proven malignant lesions obtained from the OPTIMAM Medical Image Database (OMI-DB). Exploratory data analysis methods were employed to better understand extracted features. Machine learning techniques including Classification and Regression Trees (CART), ensemble methods (e.g. random forests), and logistic regression were applied to the data to predict the disease grade of the analysed lesions. Prediction scores of up to 83% were achieved; sensitivity and specificity of the models trained have been discussed to put the results into a clinical context. The results show promise in the ability to predict prognostic indicators from the texture features extracted and thus enable prioritisation of care for patients at greatest risk.

Motor function domains in alternating hemiplegia of childhood.

PubMed

Masoud, Melanie; Gordon, Kelly; Hall, Amanda; Jasien, Joan; Lardinois, Kara; Uchitel, Julie; Mclean, Melissa; Prange, Lyndsey; Wuchich, Jeffrey; Mikati, Mohamad A

2017-08-01

To characterize motor function profiles in alternating hemiplegia of childhood, and to investigate interrelationships between these domains and with age. We studied a cohort of 23 patients (9 males, 14 females; mean age 9y 4mo, range 4mo-43y) who underwent standardized tests to assess gross motor, upper extremity motor control, motor speech, and dysphagia functions. Gross Motor Function Classification System (GMFCS), Gross Motor Function Measure-88 (GMFM-88), Manual Ability Classification System (MACS), and Revised Melbourne Assessment (MA2) scales manifested predominantly mild impairments; motor speech, moderate to severe; Modified Dysphagia Outcome and Severity Scale (M-DOSS), mild-to moderate deficits. GMFCS correlated with GMFM-88 scores (Pearson's correlation, p=0.002), MACS (p=0.038), and MA2 fluency (p=0.005) and accuracy (p=0.038) scores. GMFCS did not correlate with motor speech (p=0.399), MA2 dexterity (p=0.247), range of motion (p=0.063), or M-DOSS (p=0.856). Motor speech was more severely impaired than the GMFCS (p<0.013). There was no correlation between any of the assessment tools and age (p=0.210-0.798). Our data establish a detailed profile of motor function in alternating hemiplegia of childhood, argue against the presence of worse motor function in older patients, identify tools helpful in evaluating this population, and identify oropharyngeal function as the more severely affected domain, suggesting that brain areas controlling this function are more affected than others. © 2017 Mac Keith Press.

Oropharyngeal dysphagia and gross motor skills in children with cerebral palsy.

PubMed

Benfer, Katherine A; Weir, Kelly A; Bell, Kristie L; Ware, Robert S; Davies, Peter S W; Boyd, Roslyn N

2013-05-01

To determine the prevalence of oropharyngeal dysphagia (OPD) and its subtypes (oral phase, pharyngeal phase, saliva control), and their relationship to gross motor functional skills in preschool children with cerebral palsy (CP). It was hypothesized that OPD would be present across all gross motor severity levels, and children with more severe gross motor function would have increased prevalence and severity of OPD. Children with a confirmed diagnosis of CP, 18 to 36 months corrected age, born in Queensland between 2006 and 2009, participated. Children with neurodegenerative conditions were excluded. This was a cross-sectional population-based study. Children were assessed by using 2 direct OPD measures (Schedule for Oral Motor Assessment; Dysphagia Disorders Survey), and observations of signs suggestive of pharyngeal phase impairment and impaired saliva control. Gross motor skills were described by using the Gross Motor Function Measure, Gross Motor Function Classification System (GMFCS), Manual Ability Classification System, and motor type/ distribution. OPD was prevalent in 85% of children with CP, and there was a stepwise relationship between OPD and GMFCS level. There was a significant increase in odds of having OPD, or a subtype, for children who were nonambulant (GMFCS V) compared with those who were ambulant (GMFCS I) (odds ratio = 17.9, P = .036). OPD was present across all levels of gross motor severity using direct assessments. This highlights the need for proactive screening of all young children with CP, even those with mild impairments, to improve growth and nutritional outcomes and respiratory health.

Observer reliability of the Gross Motor Performance Measure and the Quality of Upper Extremity Skills Test, based on video recordings.

PubMed

Sorsdahl, Anne Brit; Moe-Nilssen, Rolf; Strand, Liv Inger

2008-02-01

The aim of this study was to examine observer reliability of the Gross Motor Performance Measure (GMPM) and the Quality of Upper Extremity Skills Test (QUEST) based on video clips. The tests were administered to 26 children with cerebral palsy (CP; 14 males, 12 females; range 2-13y, mean 7y 6mo), 24 with spastic CP, and two with dyskinesia. Respectively, five, six, five, four, and six children were classified in Gross Motor Function Classification System Levels I to V; and four, nine, five, five, and three children were classified in Manual Ability Classification System levels I to V. The children's performances were recorded and edited. Two experienced paediatric physical therapists assessed the children from watching the video clips. Intraobserver and interobserver reliability values of the total scores were mostly high, intraclass correlation coefficient (ICC)(1,1) varying from 0.69 to 0.97 with only one coefficient below 0.89. The ICCs of subscores varied from 0.36 to 0.95, finding'Alignment'and'Weight shift'in GMPM and'Protective extension'in QUEST highly reliable. The subscores'Dissociated movements'in GMPM and QUEST, and'Grasp'in QUEST were the least reliable, and recommendations are made to increase reliability of these subscores. Video scoring was time consuming, but was found to offer many advantages; the possibility to review performance, to use special trained observers for scoring and less demanding assessment for the children.

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

PubMed Central

Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

2017-01-01

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results. PMID:28467468

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

PubMed

Rogiers, Bart; Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

2017-01-01

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

PubMed

Durant, Thomas J S; Olson, Eben M; Schulz, Wade L; Torres, Richard

2017-12-01

Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool. © 2017 American Association for Clinical Chemistry.

An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

Objectively classifying Southern Hemisphere extratropical cyclones

NASA Astrophysics Data System (ADS)

Catto, Jennifer

2016-04-01

There has been a long tradition in attempting to separate extratropical cyclones into different classes depending on their cloud signatures, airflows, synoptic precursors, or upper-level flow features. Depending on these features, the cyclones may have different impacts, for example in their precipitation intensity. It is important, therefore, to understand how the distribution of different cyclone classes may change in the future. Many of the previous classifications have been performed manually. In order to be able to evaluate climate models and understand how extratropical cyclones might change in the future, we need to be able to use an automated method to classify cyclones. Extratropical cyclones have been identified in the Southern Hemisphere from the ERA-Interim reanalysis dataset with a commonly used identification and tracking algorithm that employs 850 hPa relative vorticity. A clustering method applied to large-scale fields from ERA-Interim at the time of cyclone genesis (when the cyclone is first detected), has been used to objectively classify identified cyclones. The results are compared to the manual classification of Sinclair and Revell (2000) and the four objectively identified classes shown in this presentation are found to match well. The relative importance of diabatic heating in the clusters is investigated, as well as the differing precipitation characteristics. The success of the objective classification shows its utility in climate model evaluation and climate change studies.

Do you see what I see? Mobile eye-tracker contextual analysis and inter-rater reliability.

PubMed

Stuart, S; Hunt, D; Nell, J; Godfrey, A; Hausdorff, J M; Rochester, L; Alcock, L

2018-02-01

Mobile eye-trackers are currently used during real-world tasks (e.g. gait) to monitor visual and cognitive processes, particularly in ageing and Parkinson's disease (PD). However, contextual analysis involving fixation locations during such tasks is rarely performed due to its complexity. This study adapted a validated algorithm and developed a classification method to semi-automate contextual analysis of mobile eye-tracking data. We further assessed inter-rater reliability of the proposed classification method. A mobile eye-tracker recorded eye-movements during walking in five healthy older adult controls (HC) and five people with PD. Fixations were identified using a previously validated algorithm, which was adapted to provide still images of fixation locations (n = 116). The fixation location was manually identified by two raters (DH, JN), who classified the locations. Cohen's kappa correlation coefficients determined the inter-rater reliability. The algorithm successfully provided still images for each fixation, allowing manual contextual analysis to be performed. The inter-rater reliability for classifying the fixation location was high for both PD (kappa = 0.80, 95% agreement) and HC groups (kappa = 0.80, 91% agreement), which indicated a reliable classification method. This study developed a reliable semi-automated contextual analysis method for gait studies in HC and PD. Future studies could adapt this methodology for various gait-related eye-tracking studies.

Automatic Fault Characterization via Abnormality-Enhanced Classification

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

Bronevetsky, G; Laguna, I; de Supinski, B R

Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help tomore » identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.« less

Two techniques for mapping and area estimation of small grains in California using Landsat digital data

NASA Technical Reports Server (NTRS)

Sheffner, E. J.; Hlavka, C. A.; Bauer, E. M.

1984-01-01

Two techniques have been developed for the mapping and area estimation of small grains in California from Landsat digital data. The two techniques are Band Ratio Thresholding, a semi-automated version of a manual procedure, and LCLS, a layered classification technique which can be fully automated and is based on established clustering and classification technology. Preliminary evaluation results indicate that the two techniques have potential for providing map products which can be incorporated into existing inventory procedures and automated alternatives to traditional inventory techniques and those which currently employ Landsat imagery.

Supervised pixel classification using a feature space derived from an artificial visual system

NASA Technical Reports Server (NTRS)

Baxter, Lisa C.; Coggins, James M.

1991-01-01

Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

Alignment of classification paradigms for communication abilities in children with cerebral palsy.

PubMed

Hustad, Katherine C; Oakes, Ashley; McFadd, Emily; Allison, Kristen M

2016-06-01

We examined three communication ability classification paradigms for children with cerebral palsy (CP): the Communication Function Classification System (CFCS), the Viking Speech Scale (VSS), and the Speech Language Profile Groups (SLPG). Questions addressed interjudge reliability, whether the VSS and the CFCS captured impairments in speech and language, and whether there were differences in speech intelligibility among levels within each classification paradigm. Eighty children (42 males, 38 females) with a range of types and severity levels of CP participated (mean age 60mo, range 50-72mo [SD 5mo]). Two speech-language pathologists classified each child via parent-child interaction samples and previous experience with the children for the CFCS and VSS, and using quantitative speech and language assessment data for the SLPG. Intelligibility scores were obtained using standard clinical intelligibility measurement. Kappa values were 0.67 (95% confidence interval [CI] 0.55-0.79) for the CFCS, 0.82 (95% CI 0.72-0.92) for the VSS, and 0.95 (95% CI 0.72-0.92) for the SLPG. Descriptively, reliability within levels of each paradigm varied, with the lowest agreement occurring within the CFCS at levels II (42%), III (40%), and IV (61%). Neither the CFCS nor the VSS were sensitive to language impairments captured by the SLPG. Significant differences in speech intelligibility were found among levels for all classification paradigms. Multiple tools are necessary to understand speech, language, and communication profiles in children with CP. Characterization of abilities at all levels of the International Classification of Functioning, Disability and Health will advance our understanding of the ways that speech, language, and communication abilities present in children with CP. © 2015 Mac Keith Press.

Endpoint Accuracy in Manual Control of a Steerable Needle.

PubMed

van de Berg, Nick J; Dankelman, Jenny; van den Dobbelsteen, John J

2017-02-01

To study the ability of a human operator to manually correct for errors in the needle insertion path without partial withdrawal of the needle by means of an active, tip-articulated steerable needle. The needle is composed of a 1.32-mm outer-diameter cannula, with a flexure joint near the tip, and a retractable stylet. The bending stiffness of the needle resembles that of a 20-gauge hypodermic needle. The needle functionality was evaluated in manual insertions by steering to predefined targets and a lateral displacement of 20 mm from the straight insertion line. Steering tasks were conducted in 5 directions and 2 tissue simulants under image guidance from a camera. The repeatability in instrument actuations was assessed during 100 mm deep automated insertions with a linear motor. In addition to tip position, tip angles were tracked during the insertions. The targeting error (mean absolute error ± standard deviation) during manual steering to 5 different targets in stiff tissue was 0.5 mm ± 1.1. This variability in manual tip placement (1.1 mm) was less than the variability among automated insertions (1.4 mm) in the same tissue type. An increased tissue stiffness resulted in an increased lateral tip displacement. The tip angle was directly controlled by the user interface, and remained unaffected by the tissue stiffness. This study demonstrates the ability to manually steer needles to predefined target locations under image guidance. Copyright © 2016 SIR. Published by Elsevier Inc. All rights reserved.

Measuring Metasyntactic Abilities: On a Classification of Metasyntactic Tasks.

PubMed

Simard, Daphnée; Labelle, Marie; Bergeron, Annie

2017-04-01

Researchers working on metasyntactic abilities (i.e., the metalinguistic ability associated with syntax) face the problem of defining and measuring them. Metasyntactic abilities is a multifaceted concept, which encompasses various types of behaviours, from being able to intentionally manipulate syntactic structures to being able to state syntactic rules, and the way in which it is defined and measured varies greatly from one study to another. The present paper proposes a theoretically informed classification of syntax related tasks. The first part presents previous research defining and distinguishing various types of syntactic and metasyntactic abilities and their interrelations. In the second part, commonly used tasks are described and analyzed in terms of the framework presented, with the aim of better pinpointing the type of ability measured by each task. Ultimately, with this analysis of commonly used tasks, we hope to offer criteria for discriminating between the various measures of metasyntactic abilities.

Higher-Order Factor Structure of the Differential Ability Scales-II: Consistency across Ages 4 to 17

ERIC Educational Resources Information Center

Keith, Timothy Z.; Low, Justin A.; Reynolds, Matthew R.; Patel, Puja G.; Ridley, Kristen P.

2010-01-01

The recently published second edition of the Differential Abilities Scale (DAS-II) is designed to measure multiple broad and general abilities from Cattell-Horn-Carroll (CHC) theory. Although the technical manual presents information supporting the test's structure, additional research is needed to determine the constructs measured by the test and…

Degree Classification and Recent Graduates' Ability: Is There Any Signalling Effect?

ERIC Educational Resources Information Center

Di Pietro, Giorgio

2017-01-01

Research across several countries has shown that degree classification (i.e. the final grade awarded to students successfully completing university) is an important determinant of graduates' first destination outcome. Graduates leaving university with higher degree classifications have better employment opportunities and a higher likelihood of…

A Part, Not Apart: A Systematic Approach to Integrated Recreation and Leisure for Developmentally Disabled Adults.

ERIC Educational Resources Information Center

Congdon, David M.; And Others

This program manual was developed by the Grant-Blackford Development Center in order to help other professionals and organizations working with developmentally disabled persons to improve their recreation and leisure programs. The manual's philosophy is to structure recreation programs for fun, not just for skill teaching: stressing abilities, not…

Hospital Corpsman 3 & 2. Rate Training Manual and Nonresident Career Course.

ERIC Educational Resources Information Center

Smith, Frederick R.

This Rate Training Manual and Nonresident Career Course (RTM/NRCC) form a self-study package that will enable Hospital Corps personnel to help themselves fulfill the requirements of their rating. Among these requirements are the abilities to perform duties as assistants in the prevention, recognition, and treatment of disease and injuries, and in…

Generic ABILHAND Questionnaire Can Measure Manual Ability across a Variety of Motor Impairments

ERIC Educational Resources Information Center

Simone, Anna; Rota, Viviana; Tesio, Luigi; Perucca, Laura

2011-01-01

ABILHAND is, in its original version, a 46-item, 4-level questionnaire. It measures the difficulty perceived by patients with rheumatoid arthritis as they do various daily manual tasks. ABILHAND was originally built through Rasch analysis. In a later study, it was simplified to a generic 23-item, three-level questionnaire, showing both…

Predictive Validity of DSM-IV and ICD-10 Criteria for ADHD and Hyperkinetic Disorder

ERIC Educational Resources Information Center

Lee, Soyoung I.; Schachar, Russell J.; Chen, Shirley X.; Ornstein, Tisha J.; Charach, Alice; Barr, Cathy; Ickowicz, Abel

2008-01-01

Background: The goal of this study was to compare the predictive validity of the two main diagnostic schemata for childhood hyperactivity--attention-deficit hyperactivity disorder (ADHD; "Diagnostic and Statistical Manual"-IV) and hyperkinetic disorder (HKD; "International Classification of Diseases"-10th Edition). Methods: Diagnostic criteria for…

To ID or Not to ID? Changes in Classification Rates of Intellectual Disability Using "DSM-5"

ERIC Educational Resources Information Center

Papazoglou, Aimilia; Jacobson, Lisa A.; McCabe, Marie; Kaufmann, Walter; Zabel, T. Andrew

2014-01-01

The "Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition" ("DSM-5") diagnostic criteria for intellectual disability (ID) include a change to the definition of adaptive impairment. New criteria require impairment in one adaptive domain rather than two or more skill areas. The authors examined the diagnostic…

Special Rights for Special Children: A Manual for Parents of Handicapped Children in New Jersey.

ERIC Educational Resources Information Center

Education Law Center, Inc., Newark, NJ.

The booklet is intended to acquaint parents of handicapped children in New Jersey with their rights. Information is provided on types of handicaps and the rights of a handicapped child (free appropriate public education, evaluation, classification, Individualized Education Program, and placement). Parental rights are explained, with special…

Re-Conceptualizing ASD within a Dimensional Framework: Positive, Negative, and Cognitive Feature Clusters

ERIC Educational Resources Information Center

Foss-Feig, Jennifer H.; McPartland, James C.; Anticevic, Alan; Wolf, Julie

2016-01-01

Introduction of the National Institute of Mental Health's Research Domain Criteria and revision of diagnostic classification for Autism Spectrum Disorder in the latest diagnostic manual call for a new way of conceptualizing heterogeneous ASD features. We propose a novel conceptualization of ASD, borrowing from the schizophrenia literature in…

Understanding the Latent Structure of the Emotional Disorders in Children and Adolescents

ERIC Educational Resources Information Center

Trosper, Sarah E.; Whitton, Sarah W.; Brown, Timothy A.; Pincus, Donna B.

2012-01-01

Investigators are persistently aiming to clarify structural relationships among the emotional disorders in efforts to improve diagnostic classification. The high co-occurrence of anxiety and mood disorders, however, has led investigators to portray the current structure of anxiety and depression in the "Diagnostic and Statistical Manual of Mental…

Observing and Producing Sounds, Elementary School Science, Level Four, Teaching Manual.

ERIC Educational Resources Information Center

Hale, Helen E.

This pilot teaching unit is one of a series developed for use in elementary school science programs. This unit is designed to help children discover specific concepts which relate to sound, such as volume, pitch, and echo. The student activities employ important scientific processes, such as observation, communication, inference, classification,…

PUNCHED CARD SYSTEM NEEDN'T BE COMPLEX TO GIVE COMPLETE CONTROL.

ERIC Educational Resources Information Center

BEMIS, HAZEL T.

AT WORCESTER JUNIOR COLLEGE, MASSACHUSETTS, USE OF A MANUALLY OPERATED PUNCHED CARD SYSTEM HAS RESULTED IN (1) SIMPLIFIED REGISTRATION PROCEDURES, (2) QUICK ANALYSIS OF CONFLICTS AND PROBLEMS IN CLASS SCHEDULING, (3) READY ACCESS TO STATISTICAL INFORMATION, (4) DIRECTORY INFORMATION IN A WIDE RANGE OF CLASSIFICATIONS, (5) EASY VERIFICATION OF…

The Alternative Lenses of Assessment: Educating Social Workers about Psychopathology

ERIC Educational Resources Information Center

Satterly, Brent A.

2007-01-01

The use of the "Diagnostic and Statistical Manual of Mental Disorders" (DSM IV) as a teaching tool for social workers to understand mental illness has been debated for many years. The general consensus is that social workers need to be "familiar" with this classification system. Social Work's person in environment perspective, however, requires…

Plate Tectonics in the Classification of Personality Disorder: Shifting to a Dimensional Model

ERIC Educational Resources Information Center

Widiger, Thomas A.; Trull, Timothy J.

2007-01-01

The diagnostic categories of the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders were developed in the spirit of a traditional medical model that considers mental disorders to be qualitatively distinct conditions (see, e.g., American Psychiatric Association, 2000). Work is now beginning on the fifth edition…

Three Diagnostic Systems for Autism: DSM-III, DSM-III-R, and ICD-10.

ERIC Educational Resources Information Center

Volkmar, Fred R.; And Others

1992-01-01

This paper compared clinicians' diagnosis and DSM-III (Diagnostic and Statistical Manual), DSM-III-R (Revised), and ICD-10 (International Classification of Diseases) diagnoses of 52 individuals with autism and 62 nonautistic, developmentally disordered individuals. The DSM-III-R system overdiagnosed the presence of autism, and ICD-10 closely…

Postsecondary Education Facilities Inventory and Classification Manual (FICM): 2006 Edition. NCES 2006-160

ERIC Educational Resources Information Center

Cyros, Kreon L.; Korb, Roslyn

2006-01-01

Along with its human resources, financial assets, and intellectual cache, space is a primary resource of an educational institution. Indeed, the dollar value (initial cost, replacement cost, or market value) of a postsecondary education institution's buildings sometimes exceeds its annual operating budget and endowment. Without information on how…

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using self-organizing maps to classify humpback whale song units and quantify their similarity.

PubMed

Allen, Jenny A; Murray, Anita; Noad, Michael J; Dunlop, Rebecca A; Garland, Ellen C

2017-10-01

Classification of vocal signals can be undertaken using a wide variety of qualitative and quantitative techniques. Using east Australian humpback whale song from 2002 to 2014, a subset of vocal signals was acoustically measured and then classified using a Self-Organizing Map (SOM). The SOM created (1) an acoustic dictionary of units representing the song's repertoire, and (2) Cartesian distance measurements among all unit types (SOM nodes). Utilizing the SOM dictionary as a guide, additional song recordings from east Australia were rapidly (manually) transcribed. To assess the similarity in song sequences, the Cartesian distance output from the SOM was applied in Levenshtein distance similarity analyses as a weighting factor to better incorporate unit similarity in the calculation (previously a qualitative process). SOMs provide a more robust and repeatable means of categorizing acoustic signals along with a clear quantitative measurement of sound type similarity based on acoustic features. This method can be utilized for a wide variety of acoustic databases especially those containing very large datasets and can be applied across the vocalization research community to help address concerns surrounding inconsistency in manual classification.

Scientific progress and the prospects for culture-bound syndromes.

PubMed

Blease, Charlotte

2010-12-01

This paper aims to show that the classification by the American Psychiatric Association (APA) in the Diagnostic and Statistical Manual of Mental Disorders (DSM) of a distinct listing of disorders known as Culture-Bound Syndromes (CBS) is misguided. I argue that the list of CBS (in Appendix I of the manual) comprises either (a) genuine disorders that should be included within the main body of the DSM; or (b) ersatz-disorders that serve a practical role for psychiatrists dealing with patients from certain cultures but will one day be eliminated or assimilated by bona fide DSM classifications. In support of these views I draw on claims from two key themes in the philosophy of science: (1) the claim that all folk (that is, non-scientific) explanations for phenomena are thoroughly theoretical and therefore fallible; and (2) the occurrence of theoretical elimination in the history of science. I contend that any ersatz-disorders located in the DSM that are judged to be radically false do not differ in kind from eliminated theories in the history of pre-science. Copyright © 2010 Elsevier Ltd. All rights reserved.

Crack detection in oak flooring lamellae using ultrasound-excited thermography

NASA Astrophysics Data System (ADS)

Pahlberg, Tobias; Thurley, Matthew; Popovic, Djordje; Hagman, Olle

2018-01-01

Today, a large number of people are manually grading and detecting defects in wooden lamellae in the parquet flooring industry. This paper investigates the possibility of using the ensemble methods random forests and boosting to automatically detect cracks using ultrasound-excited thermography and a variety of predictor variables. When friction occurs in thin cracks, they become warm and thus visible to a thermographic camera. Several image processing techniques have been used to suppress the noise and enhance probable cracks in the images. The most successful predictor variables captured the upper part of the heat distribution, such as the maximum temperature, kurtosis and percentile values 92-100 of the edge pixels. The texture in the images was captured by Completed Local Binary Pattern histograms and cracks were also segmented by background suppression and thresholding. The classification accuracy was significantly improved from previous research through added image processing, introduction of more predictors, and by using automated machine learning. The best ensemble methods reach an average classification accuracy of 0.8, which is very close to the authors' own manual attempt at separating the images (0.83).

DSM-5 and neurodevelopmental and other disorders of childhood and adolescence.

PubMed

Wills, Cheryl D

2014-01-01

In the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), the classification of mental disorders for children and adolescents has been revised. Although some changes are welcome and needed, others have been controversial. In this article, I examine the diagnostic changes along with some of the associated controversies and resolutions. The implications for the practice of child forensic psychiatry, including problems that may be encountered by forensic psychiatrists who evaluate adults with childhood-onset mental disorders, are examined. The pitfalls associated with improper use of The Manual by legal professionals are also reviewed. © 2014 American Academy of Psychiatry and the Law.

Classifications of Acute Scaphoid Fractures: A Systematic Literature Review.

PubMed

Ten Berg, Paul W; Drijkoningen, Tessa; Strackee, Simon D; Buijze, Geert A

2016-05-01

Background In the lack of consensus, surgeon-based preference determines how acute scaphoid fractures are classified. There is a great variety of classification systems with considerable controversies. Purposes The purpose of this study was to provide an overview of the different classification systems, clarifying their subgroups and analyzing their popularity by comparing citation indexes. The intention was to improve data comparison between studies using heterogeneous fracture descriptions. Methods We performed a systematic review of the literature based on a search of medical literature from 1950 to 2015, and a manual search using the reference lists in relevant book chapters. Only original descriptions of classifications of acute scaphoid fractures in adults were included. Popularity was based on citation index as reported in the databases of Web of Science (WoS) and Google Scholar. Articles that were cited <10 times in WoS were excluded. Results Our literature search resulted in 308 potentially eligible descriptive reports of which 12 reports met the inclusion criteria. We distinguished 13 different (sub) classification systems based on (1) fracture location, (2) fracture plane orientation, and (3) fracture stability/displacement. Based on citations numbers, the Herbert classification was most popular, followed by the Russe and Mayo classifications. All classification systems were based on plain radiography. Conclusions Most classification systems were based on fracture location, displacement, or stability. Based on the controversy and limited reliability of current classification systems, suggested research areas for an updated classification include three-dimensional fracture pattern etiology and fracture fragment mobility assessed by dynamic imaging.

Sequential Markov chain Monte Carlo filter with simultaneous model selection for electrocardiogram signal modeling.

PubMed

Edla, Shwetha; Kovvali, Narayan; Papandreou-Suppappola, Antonia

2012-01-01

Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.

A Single Session of Mirror-based Tactile and Motor Training Improves Tactile Dysfunction in Children with Unilateral Cerebral Palsy: A Replicated Randomized Controlled Case Series.

PubMed

Auld, Megan L; Johnston, Leanne M; Russo, Remo N; Moseley, G Lorimer

2017-10-01

This replicated randomized controlled crossover case series investigated the effect of mirror-based tactile and motor training on tactile registration and perception in children with unilateral cerebral palsy (UCP). Six children with UCP (6-18 years; median 10 years, five male, three-left hemiplegia, four-manual ability classification system (MACS) I, one MACS II and one MACS III) participated. They attended two 90-minute sessions - one of mirror-based training and one of standard practice, bimanual therapy - in alternated order. Tactile registration (Semmes Weinstein Monofilaments) and perception (double simultaneous or single-point localization) were assessed before and after each session. Change was estimated using reliable change index (RCI). Tactile perception improved in four participants (RCI > 1.75), with mirror-based training, but was unchanged with bimanual therapy (RCI < 1.0 for all participants). Neither intervention affected tactile registration. Mirror-based training demonstrates potential to improve tactile perception in children with UCP. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Perceived ability to perform daily hand activities after stroke and associated factors: a cross-sectional study.

PubMed

Ekstrand, Elisabeth; Rylander, Lars; Lexell, Jan; Brogårdh, Christina

2016-11-02

Despite that disability of the upper extremity is common after stroke, there is limited knowledge how it influences self-perceived ability to perform daily hand activities. The aim of this study was to describe which daily hand activities that persons with mild to moderate impairments of the upper extremity after stroke perceive difficult to perform and to evaluate how several potential factors are associated with the self-perceived performance. Seventy-five persons (72 % male) with mild to moderate impairments of the upper extremity after stroke (4 to 116 months) participated. Self-perceived ability to perform daily hand activities was rated with the ABILHAND Questionnaire. The perceived ability to perform daily hand activities and the potentially associated factors (age, gender, social and vocational situation, affected hand, upper extremity pain, spasticity, grip strength, somatosensation of the hand, manual dexterity, perceived participation and life satisfaction) were evaluated by linear regression models. The activities that were perceived difficult or impossible for a majority of the participants were bimanual tasks that required fine manual dexterity of the more affected hand. The factor that had the strongest association with perceived ability to perform daily hand activities was dexterity (p < 0.001), which together with perceived participation (p = 0.002) explained 48 % of the variance in the final multivariate model. Persons with mild to moderate impairments of the upper extremity after stroke perceive that bimanual activities requiring fine manual dexterity are the most difficult to perform. Dexterity and perceived participation are factors specifically important to consider in the rehabilitation of the upper extremity after stroke in order to improve the ability to use the hands in daily life.

Automatic classification of seismic events within a regional seismograph network

NASA Astrophysics Data System (ADS)

Tiira, Timo; Kortström, Jari; Uski, Marja

2015-04-01

A fully automatic method for seismic event classification within a sparse regional seismograph network is presented. The tool is based on a supervised pattern recognition technique, Support Vector Machine (SVM), trained here to distinguish weak local earthquakes from a bulk of human-made or spurious seismic events. The classification rules rely on differences in signal energy distribution between natural and artificial seismic sources. Seismic records are divided into four windows, P, P coda, S, and S coda. For each signal window STA is computed in 20 narrow frequency bands between 1 and 41 Hz. The 80 discrimination parameters are used as a training data for the SVM. The SVM models are calculated for 19 on-line seismic stations in Finland. The event data are compiled mainly from fully automatic event solutions that are manually classified after automatic location process. The station-specific SVM training events include 11-302 positive (earthquake) and 227-1048 negative (non-earthquake) examples. The best voting rules for combining results from different stations are determined during an independent testing period. Finally, the network processing rules are applied to an independent evaluation period comprising 4681 fully automatic event determinations, of which 98 % have been manually identified as explosions or noise and 2 % as earthquakes. The SVM method correctly identifies 94 % of the non-earthquakes and all the earthquakes. The results imply that the SVM tool can identify and filter out blasts and spurious events from fully automatic event solutions with a high level of confidence. The tool helps to reduce work-load in manual seismic analysis by leaving only ~5 % of the automatic event determinations, i.e. the probable earthquakes for more detailed seismological analysis. The approach presented is easy to adjust to requirements of a denser or wider high-frequency network, once enough training examples for building a station-specific data set are available.

Intensive care unit depth of sleep: proof of concept of a simple electroencephalography index in the non-sedated

PubMed Central

2014-01-01

Introduction Intensive care unit (ICU) patients are known to experience severely disturbed sleep, with possible detrimental effects on short- and long- term outcomes. Investigation into the exact causes and effects of disturbed sleep has been hampered by cumbersome and time consuming methods of measuring and staging sleep. We introduce a novel method for ICU depth of sleep analysis, the ICU depth of sleep index (IDOS index), using single channel electroencephalography (EEG) and apply it to outpatient recordings. A proof of concept is shown in non-sedated ICU patients. Methods Polysomnographic (PSG) recordings of five ICU patients and 15 healthy outpatients were analyzed using the IDOS index, based on the ratio between gamma and delta band power. Manual selection of thresholds was used to classify data as either wake, sleep or slow wave sleep (SWS). This classification was compared to visual sleep scoring by Rechtschaffen & Kales criteria in normal outpatient recordings and ICU recordings to illustrate face validity of the IDOS index. Results When reduced to two or three classes, the scoring of sleep by IDOS index and manual scoring show high agreement for normal sleep recordings. The obtained overall agreements, as quantified by the kappa coefficient, were 0.84 for sleep/wake classification and 0.82 for classification into three classes (wake, non-SWS and SWS). Sensitivity and specificity were highest for the wake state (93% and 93%, respectively) and lowest for SWS (82% and 76%, respectively). For ICU recordings, agreement was similar to agreement between visual scorers previously reported in literature. Conclusions Besides the most satisfying visual resemblance with manually scored normal PSG recordings, the established face-validity of the IDOS index as an estimator of depth of sleep was excellent. This technique enables real-time, automated, single channel visualization of depth of sleep, facilitating the monitoring of sleep in the ICU. PMID:24716479

Cancer of the esophagus and esophagogastric junction: data-driven staging for the seventh edition of the American Joint Committee on Cancer/International Union Against Cancer Cancer Staging Manuals.

PubMed

Rice, Thomas W; Rusch, Valerie W; Ishwaran, Hemant; Blackstone, Eugene H

2010-08-15

Previous American Joint Committee on Cancer/International Union Against Cancer (AJCC/UICC) stage groupings for esophageal cancer have not been data driven or harmonized with stomach cancer. At the request of the AJCC, worldwide data from 3 continents were assembled to develop data-driven, harmonized esophageal staging for the seventh edition of the AJCC/UICC cancer staging manuals. All-cause mortality among 4627 patients with esophageal and esophagogastric junction cancer who underwent surgery alone (no preoperative or postoperative adjuvant therapy) was analyzed by using novel random forest methodology to produce stage groups for which survival was monotonically decreasing, distinctive, and homogeneous. For lymph node-negative pN0M0 cancers, risk-adjusted 5-year survival was dominated by pathologic tumor classification (pT) but was modulated by histopathologic cell type, histologic grade, and location. For lymph node-positive, pN+M0 cancers, the number of cancer-positive lymph nodes (a new pN classification) dominated survival. Resulting stage groupings departed from a simple, logical arrangement of TNM. Stage groupings for stage I and II adenocarcinoma were based on pT, pN, and histologic grade; and groupings for squamous cell carcinoma were based on pT, pN, histologic grade, and location. Stage III was similar for histopathologic cell types and was based only on pT and pN. Stage 0 and stage IV, by definition, were categorized as tumor in situ (Tis) (high-grade dysplasia) and pM1, respectively. The prognosis for patients with esophageal and esophagogastric junction cancer depends on the complex interplay of TNM classifications as well as nonanatomic factors, including histopathologic cell type, histologic grade, and cancer location. These features were incorporated into a data-driven staging of these cancers for the seventh edition of the AJCC/UICC cancer staging manuals. Copyright (c) 2010 American Cancer Society.

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

NASA Technical Reports Server (NTRS)

Instrella, Ron; Chirayath, Ved

2016-01-01

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

Neurodevelopmental Disorders (ASD and ADHD): DSM-5, ICD-10, and ICD-11.

PubMed

Doernberg, Ellen; Hollander, Eric

2016-08-01

Neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have undergone considerable diagnostic evolution in the past decade. In the United States, the current system in place is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), whereas worldwide, the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) serves as a general medical system. This review will examine the differences in neurodevelopmental disorders between these two systems. First, we will review the important revisions made from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) to the DSM-5, with respect to ASD and ADHD. Next, we will cover the similarities and differences between ASD and ADHD classification in the DSM-5 and the ICD-10, and how these differences may have an effect on neurodevelopmental disorder diagnostics and classification. By examining the changes made for the DSM-5 in 2013, and critiquing the current ICD-10 system, we can help to anticipate and advise on the upcoming ICD-11, due to come online in 2017. Overall, this review serves to highlight the importance of progress towards complementary diagnostic classification systems, keeping in mind the difference in tradition and purpose of the DSM and the ICD, and that these systems are dynamic and changing as more is learned about neurodevelopmental disorders and their underlying etiology. Finally this review will discuss alternative diagnostic approaches, such as the Research Domain Criteria (RDoC) initiative, which links symptom domains to underlying biological and neurological mechanisms. The incorporation of new diagnostic directions could have a great effect on treatment development and insurance coverage for neurodevelopmental disorders worldwide.

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

NASA Astrophysics Data System (ADS)

Instrella, R.; Chirayath, V.

2015-12-01

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches

NASA Astrophysics Data System (ADS)

Diesing, Markus; Green, Sophie L.; Stephens, David; Lark, R. Murray; Stewart, Heather A.; Dove, Dayton

2014-08-01

Marine spatial planning and conservation need underpinning with sufficiently detailed and accurate seabed substrate and habitat maps. Although multibeam echosounders enable us to map the seabed with high resolution and spatial accuracy, there is still a lack of fit-for-purpose seabed maps. This is due to the high costs involved in carrying out systematic seabed mapping programmes and the fact that the development of validated, repeatable, quantitative and objective methods of swath acoustic data interpretation is still in its infancy. We compared a wide spectrum of approaches including manual interpretation, geostatistics, object-based image analysis and machine-learning to gain further insights into the accuracy and comparability of acoustic data interpretation approaches based on multibeam echosounder data (bathymetry, backscatter and derivatives) and seabed samples with the aim to derive seabed substrate maps. Sample data were split into a training and validation data set to allow us to carry out an accuracy assessment. Overall thematic classification accuracy ranged from 67% to 76% and Cohen's kappa varied between 0.34 and 0.52. However, these differences were not statistically significant at the 5% level. Misclassifications were mainly associated with uncommon classes, which were rarely sampled. Map outputs were between 68% and 87% identical. To improve classification accuracy in seabed mapping, we suggest that more studies on the effects of factors affecting the classification performance as well as comparative studies testing the performance of different approaches need to be carried out with a view to developing guidelines for selecting an appropriate method for a given dataset. In the meantime, classification accuracy might be improved by combining different techniques to hybrid approaches and multi-method ensembles.

Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure

ERIC Educational Resources Information Center

Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.

2006-01-01

The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…

The Costs of Supervised Classification: The Effect of Learning Task on Conceptual Flexibility

ERIC Educational Resources Information Center

Hoffman, Aaron B.; Rehder, Bob

2010-01-01

Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of within-category information. Accordingly, we predicted that classification learning would produce a deficit in people's ability to draw "novel…

Leucocyte classification for leukaemia detection using image processing techniques.

PubMed

Putzu, Lorenzo; Caocci, Giovanni; Di Ruberto, Cecilia

2014-11-01

The counting and classification of blood cells allow for the evaluation and diagnosis of a vast number of diseases. The analysis of white blood cells (WBCs) allows for the detection of acute lymphoblastic leukaemia (ALL), a blood cancer that can be fatal if left untreated. Currently, the morphological analysis of blood cells is performed manually by skilled operators. However, this method has numerous drawbacks, such as slow analysis, non-standard accuracy, and dependences on the operator's skill. Few examples of automated systems that can analyse and classify blood cells have been reported in the literature, and most of these systems are only partially developed. This paper presents a complete and fully automated method for WBC identification and classification using microscopic images. In contrast to other approaches that identify the nuclei first, which are more prominent than other components, the proposed approach isolates the whole leucocyte and then separates the nucleus and cytoplasm. This approach is necessary to analyse each cell component in detail. From each cell component, different features, such as shape, colour and texture, are extracted using a new approach for background pixel removal. This feature set was used to train different classification models in order to determine which one is most suitable for the detection of leukaemia. Using our method, 245 of 267 total leucocytes were properly identified (92% accuracy) from 33 images taken with the same camera and under the same lighting conditions. Performing this evaluation using different classification models allowed us to establish that the support vector machine with a Gaussian radial basis kernel is the most suitable model for the identification of ALL, with an accuracy of 93% and a sensitivity of 98%. Furthermore, we evaluated the goodness of our new feature set, which displayed better performance with each evaluated classification model. The proposed method permits the analysis of blood cells automatically via image processing techniques, and it represents a medical tool to avoid the numerous drawbacks associated with manual observation. This process could also be used for counting, as it provides excellent performance and allows for early diagnostic suspicion, which can then be confirmed by a haematologist through specialised techniques. Copyright © 2014 Elsevier B.V. All rights reserved.

29 CFR 541.302 - Creative professionals.

Code of Federal Regulations, 2012 CFR

2012-07-01

... person with general manual or intellectual ability and training. (b) To qualify for exemption as a... ability to express the concept; essayists, novelists, short-story writers and screen-play writers who... contribute a unique interpretation or analysis to a news product. Thus, for example, newspaper reporters who...

29 CFR 541.302 - Creative professionals.

Code of Federal Regulations, 2013 CFR

2013-07-01

... person with general manual or intellectual ability and training. (b) To qualify for exemption as a... ability to express the concept; essayists, novelists, short-story writers and screen-play writers who... contribute a unique interpretation or analysis to a news product. Thus, for example, newspaper reporters who...

Maintenance Manual for the Automated Airdrop Information Retrieval System; Human Factors Database

DTIC Science & Technology

1994-09-01

Sensorimotor Abilities Loss of Cognitive/Perceptual Abilities Treatment drug therapy physical therapy cognitive therapy biofeedback therapy 63 9...Device (AOD) Oxygen System oxygen mask oxygen hose oxygen cylinders on/off valve prebreather Floatation Devices life preserver Scuba Gear Ankle Braces

SU-C-BRA-05: Delineating High-Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms

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

Cardenas, C; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Wong, A

Purpose: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. Methods: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results inmore » non-uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high-dose CTV given a manually-contoured GTV: (1) regression-support vector machines(SVM) and (2) classification-SVM. These models were trained and tested on each patient group using leave-one-out cross-validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto-contoured CTV were calculated to evaluate the results. Distances from GTV-to-CTV were computed about each patient’s GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression-SVM method. The classification-SVM method used categorical voxel-information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV. Results: Volumes for the auto-contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto-contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression-SVM and classification-SVM methods, respectively. Conclusion: We developed two novel machine learning methods to delineate high-dose CTV for H&N patients. Both methods showed promising results that hint to a solution to the standardization of the contouring process of clinical target volumes. Varian Medical Systems grant.« less

Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches.

PubMed

Solti, Imre; Cooke, Colin R; Xia, Fei; Wurfel, Mark M

2009-11-01

This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to recognize ALI and lead to reductions in mortality. For our study, 96 and 857 chest x-ray reports in two corpora were labeled by domain experts for ALI. We developed a keyword and a Maximum Entropy-based classification system. Word unigram and character n-grams provided the features for the machine learning system. The Maximum Entropy algorithm with character 6-gram achieved the highest performance (Recall=0.91, Precision=0.90 and F-measure=0.91) on the 857-report corpus. This study has shown that for the classification of ALI chest x-ray reports, the machine learning approach is superior to the keyword based system and achieves comparable results to highest performing physician annotators.

Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches

PubMed Central

Solti, Imre; Cooke, Colin R.; Xia, Fei; Wurfel, Mark M.

2010-01-01

This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to recognize ALI and lead to reductions in mortality. For our study, 96 and 857 chest x-ray reports in two corpora were labeled by domain experts for ALI. We developed a keyword and a Maximum Entropy-based classification system. Word unigram and character n-grams provided the features for the machine learning system. The Maximum Entropy algorithm with character 6-gram achieved the highest performance (Recall=0.91, Precision=0.90 and F-measure=0.91) on the 857-report corpus. This study has shown that for the classification of ALI chest x-ray reports, the machine learning approach is superior to the keyword based system and achieves comparable results to highest performing physician annotators. PMID:21152268

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.

Social Work Program. Field Placement Manual for Social Work Field Placement I, Social Work Field Placement II.

ERIC Educational Resources Information Center

Miller, Howard J.; And Others

This document is a manual for a social work field placement program. The social work field placement is described as a learning experience designed to translate the students' interests, interpersonal abilities, and academic knowledge and theory into the capability of enabling others to solve problems. Expectations of skills to be learned in the…

Is there a link between writing ability, drawing aptitude and manual skills of dental students?

PubMed

Gillet, Dominique; Quinton, André; Jeannel, Alain

2002-05-01

In France, students have to choose between medical or dental courses, according to their rank, after a competitive examination at the end of their first year of study. Intellectual ability is evaluated, while manual competence is not, and this is a paradox. The purpose of the present study is to investigate whether it is possible to predict the manual aptitude of a dental student through tests that allow the qualities of reflection and organization to be judged. We administered writing tests and drawing tests to 45 students of the Bordeaux dental school to ascertain whether there was a correlation between the competitive examination, the criterion examined (reflection, organization, aesthetics em leader) and the results of the dental practical assessments during the first year of dental study. The results showed that although manual competence in dental practical work, graphic qualities and writing skills are connected, it is difficult to correlate them directly with competitive examination performance. In view of the number of uncontrolled variables influencing the students' outcome, is it useful to be able to predict who will become a good practitioner? One indication may be found in the moral reasoning of candidates.

Automatic stent strut detection in intravascular OCT images using image processing and classification technique

NASA Astrophysics Data System (ADS)

Lu, Hong; Gargesha, Madhusudhana; Wang, Zhao; Chamie, Daniel; Attizani, Guilherme F.; Kanaya, Tomoaki; Ray, Soumya; Costa, Marco A.; Rollins, Andrew M.; Bezerra, Hiram G.; Wilson, David L.

2013-02-01

Intravascular OCT (iOCT) is an imaging modality with ideal resolution and contrast to provide accurate in vivo assessments of tissue healing following stent implantation. Our Cardiovascular Imaging Core Laboratory has served >20 international stent clinical trials with >2000 stents analyzed. Each stent requires 6-16hrs of manual analysis time and we are developing highly automated software to reduce this extreme effort. Using classification technique, physically meaningful image features, forward feature selection to limit overtraining, and leave-one-stent-out cross validation, we detected stent struts. To determine tissue coverage areas, we estimated stent "contours" by fitting detected struts and interpolation points from linearly interpolated tissue depths to a periodic cubic spline. Tissue coverage area was obtained by subtracting lumen area from the stent area. Detection was compared against manual analysis of 40 pullbacks. We obtained recall = 90+/-3% and precision = 89+/-6%. When taking struts deemed not bright enough for manual analysis into consideration, precision improved to 94+/-6%. This approached inter-observer variability (recall = 93%, precision = 96%). Differences in stent and tissue coverage areas are 0.12 +/- 0.41 mm2 and 0.09 +/- 0.42 mm2, respectively. We are developing software which will enable visualization, review, and editing of automated results, so as to provide a comprehensive stent analysis package. This should enable better and cheaper stent clinical trials, so that manufacturers can optimize the myriad of parameters (drug, coverage, bioresorbable versus metal, etc.) for stent design.

A new computer-based Farnsworth Munsell 100-hue test for evaluation of color vision.

PubMed

Ghose, Supriyo; Parmar, Twinkle; Dada, Tanuj; Vanathi, Murugesan; Sharma, Sourabh

2014-08-01

To evaluate a computer-based Farnsworth-Munsell (FM) 100-hue test and compare it with a manual FM 100-hue test in normal and congenital color-deficient individuals. Fifty color defective subjects and 200 normal subjects with a best-corrected visual acuity ≥ 6/12 were compared using a standard manual FM 100-hue test and a computer-based FM 100-hue test under standard operating conditions as recommended by the manufacturer after initial trial testing. Parameters evaluated were total error scores (TES), type of defect and testing time. Pearson's correlation coefficient was used to determine the relationship between the test scores. Cohen's kappa was used to assess agreement of color defect classification between the two tests. A receiver operating characteristic curve was used to determine the optimal cut-off score for the computer-based FM 100-hue test. The mean time was 16 ± 1.5 (range 6-20) min for the manual FM 100-hue test and 7.4 ± 1.4 (range 5-13) min for the computer-based FM 100-hue test, thus reducing testing time to <50 % (p < 0.05). For grading color discrimination, Pearson's correlation coefficient for TES between the two tests was 0.91 (p < 0.001). For color defect classification, Cohen's agreement coefficient was 0.98 (p < 0.01). The computer-based FM 100-hue is an effective and rapid method for detecting, classifying and grading color vision anomalies.

Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.

PubMed

Griffis, Joseph C; Allendorfer, Jane B; Szaflarski, Jerzy P

2016-01-15

Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. Our method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations. Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts. Copyright © 2015 Elsevier B.V. All rights reserved.

Using machine learning techniques to automate sky survey catalog generation

NASA Technical Reports Server (NTRS)

Fayyad, Usama M.; Roden, J. C.; Doyle, R. J.; Weir, Nicholas; Djorgovski, S. G.

1993-01-01

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

Alignment of classification paradigms for communication abilities in children with cerebral palsy

PubMed Central

Hustad, Katherine C.; Oakes, Ashley; McFadd, Emily; Allison, Kristen M.

2015-01-01

Aim We examined three communication ability classification paradigms for children with cerebral palsy (CP): the Communication Function Classification System (CFCS), the Viking Speech Scale (VSS), and the Speech Language Profile Groups (SLPG). Questions addressed inter-judge reliability, whether the VSS and the CFCS captured impairments in speech and language, and whether there were differences in speech intelligibility among levels within each classification paradigm. Method 80 children (42 males) with a range of types and severity levels of CP participated (mean age, 60 months; SD 4.8 months). Two speech-language pathologists classified each child via parent-child interaction samples and previous experience with the children for the CFCS and VSS, and uisng quantitative speech and language assessment data for the SLPG. Intelligibility scores were obtained using standard clinical intelligibility measurement. Results Kappa values were .67 (95% CI [.55, .79]) for the CFCS, .82 (95% CI [.72, .92]), for the VSS, .95 (95% CI [.72, .92]) for the SLPG. Descriptively, reliability within levels of each paradigm varied, with the lowest agreement occurring within the CFCS at levels II (42%), III (40%), and IV (61%). Neither the CFCS nor the VSS were sensitive to language impairments captured by the SLPG. Significant differences in speech intelligibility were found among levels for all classification paradigms. Interpretation Multiple tools are necessary to understand speech, language, and communication profiles in children with CP. Characterization of abilities at all levels of the ICF will advance our understanding of the ways that speech, language, and communication abilities present in children with CP. PMID:26521844

Measuring Metasyntactic Abilities: On a Classification of Metasyntactic Tasks

ERIC Educational Resources Information Center

Simard, Daphnée; Labelle, Marie; Bergeron, Annie

2017-01-01

Researchers working on "metasyntactic abilities" (i.e., the metalinguistic ability associated with syntax) face the problem of defining and measuring them. Metasyntactic abilities is a multifaceted concept, which encompasses various types of behaviours, from being able to intentionally manipulate syntactic structures to being able to…

Classification and Short-Term Course of DSM-IV Cannabis, Hallucinogen, Cocaine, and Opioid Disorders in Treated Adolescents

ERIC Educational Resources Information Center

Chung, Tammy; Martin, Christoper S.

2005-01-01

This study examined the latent class structure of Diagnostic and Statistical Manual of Mental Disorders (text rev.; DSM-IV; American Psychiatric Association, 2000) symptoms used to diagnose cannabis, hallucinogen, cocaine, and opiate disorders among 501 adolescents recruited from addictions treatment. Latent class results were compared with the…

Space Propulsion Hazards Analysis Manual (SPHAM). Volume 2. Appendices

DTIC Science & Technology

1988-10-01

lb. RESTRICTIVE MARKINGS UNCLASSIFIED 2a. SECURITY CLASSIFICATION AUTHORITY 3 . DISTRIBUTION/AVAILABILITY OF REPORT Approved for public release...Volume I Chapter 2 - Requirementb and the Hazards Analysis Process .... Volume I Chapter 3 - Accident Scenarios...list of the hazardous materials that are discussed; 3 ) description of the failure scenarios; 4) type of post-accident environment that is discussed

Disposal of Chemotherapeutic Agent -- Contaminated Waste

DTIC Science & Technology

1989-03-01

RESTRICTIVE MARKINGS 2a SECURITY CLASSIFICATION AUTHORITY 3 . DISTRIBUTION/AVAILABILITY OF REPORT 2b. DECLASSIFICATION/DOWNGRADING SCHEDULE Approved for Public...AIR .............. 22 INCINERATION SYSTEM 2 CHEMOTHERAPEUTIC WASTE THERMAL ...... 32 DESTRUCTION DISPOSAL SYSTEM 3 FRONT VIEW OF INCINERATION...The Environmental Protection Agency has published a manual (Reference 1) which provides guidelines on handling and 3 disposal of infectious waste from

Drug Scene Syllabus, A Manual on Drugs and Volatile Chemical of Potential Abuse.

ERIC Educational Resources Information Center

Johnson, Robert B.; And Others

A brief historical review of attempts to control the abuse of drugs introduces a series of tables listing pertinent information about drugs of potential abuse. Each table provides the common commercial and slang names for the drugs, their medical and legal classification, their potential for emotional and physical dependence, whether the user…

Panic Disorder and Agoraphobia: Considerations for DSM-V

ERIC Educational Resources Information Center

Schmidt, Norman B.; Norr, Aaron M.; Korte, Kristina J.

2014-01-01

With the upcoming release of the fifth edition of the "Diagnostic and Statistical Manual of Mental Disorders" (DSM-V) there has been a necessary critique of the DSM-IV including questions regarding how to best improve the next iteration of the DSM classification system. The aim of this article is to provide commentary on the probable…

Dyslexia and Developmental Co-Ordination Disorder in Further and Higher Education--Similarities and Differences. Does the "Label" Influence the Support Given?

ERIC Educational Resources Information Center

Kirby, Amanda; Sugden, David; Beveridge, Sally; Edwards, Lisa; Edwards, Rachel

2008-01-01

Developmental co-ordination disorder (DCD) is a developmental disorder affecting motor co-ordination. The "Diagnostics Statistics Manual"--IV classification for DCD describes difficulties across a range of activities of daily living, impacting on everyday skills and academic performance in school. Recent evidence has shown that…

An Evaluation Method of Words Tendency Depending on Time-Series Variation and Its Improvements.

ERIC Educational Resources Information Center

Atlam, El-Sayed; Okada, Makoto; Shishibori, Masami; Aoe, Jun-ichi

2002-01-01

Discussion of word frequency and keywords in text focuses on a method to estimate automatically the stability classes that indicate a word's popularity with time-series variations based on the frequency change in past electronic text data. Compares the evaluation of decision tree stability class results with manual classification results.…

Plant Science. Instructor Guide [and] Student Reference. Volume 24, Numbers 3 and 4.

ERIC Educational Resources Information Center

Humphrey, John Kevin

This document consists of two separately published guides for a course on plant science: an instructor's guide and a student's reference manual. Each part consists of eight lessons and cover the following topics: (1) importance of plants; (2) classification of plants; (3) plant growth factors; (4) weeds, diseases, insects; (5) germination; (6)…

Innovative Instruction in Higher Education: Thirty Exemplary Projects Conducted in Selected Institutions of Post-Secondary Education--State of Oregon.

ERIC Educational Resources Information Center

Harper, Ronald; And Others

This manual reviews thirty projects selected by the Oregon Educational Coordinating Council (ECC) as exemplary in method, operation, and development. The projects are organized into 9 broad classifications: large group-small group alternatives, autotutorial programmed instruction, process centered, computer and simulation, on-site/field study,…

What Is Intellectual Disability? How Is It Assessed and Classified?

ERIC Educational Resources Information Center

Parmenter, Trevor R.

2011-01-01

People with an intellectual disability have existed across human history, making up a part of all cultures. They represent a small part of the extremely wide variety of people in the human population at any one time. This review essay examines the 11th and latest Definition and Classification Manual published by the American Association on…

Drug Dependence in Pregnancy: Clinical Management of Mother and Child. Services Research Reports and Monograph Series.

ERIC Educational Resources Information Center

Finnegan, Loretta P., Ed.

This resouce manual compiles research findings concerning treatment of pregnant addicts. Major topics covered are: (1) prevalence and classification of psychotropic drug use; (2) pharmacologic effects on mother and infant; (3) clinical management during pregnancy; (4) management of labor, delivery, and the immediate post-partum period; (5)…

Interoperability of Medication Classification Systems: Lessons Learned Mapping Established Pharmacologic Classes (EPCs) to SNOMED CT

PubMed Central

Nelson, Scott D; Parker, Jaqui; Lario, Robert; Winnenburg, Rainer; Erlbaum, Mark S.; Lincoln, Michael J.; Bodenreider, Olivier

2018-01-01

Interoperability among medication classification systems is known to be limited. We investigated the mapping of the Established Pharmacologic Classes (EPCs) to SNOMED CT. We compared lexical and instance-based methods to an expert-reviewed reference standard to evaluate contributions of these methods. Of the 543 EPCs, 284 had an equivalent SNOMED CT class, 205 were more specific, and 54 could not be mapped. Precision, recall, and F1 score were 0.416, 0.620, and 0.498 for lexical mapping and 0.616, 0.504, and 0.554 for instance-based mapping. Each automatic method has strengths, weaknesses, and unique contributions in mapping between medication classification systems. In our experience, it was beneficial to consider the mapping provided by both automated methods for identifying potential matches, gaps, inconsistencies, and opportunities for quality improvement between classifications. However, manual review by subject matter experts is still needed to select the most relevant mappings. PMID:29295234

Voxel classification based airway tree segmentation

NASA Astrophysics Data System (ADS)

Lo, Pechin; de Bruijne, Marleen

2008-03-01

This paper presents a voxel classification based method for segmenting the human airway tree in volumetric computed tomography (CT) images. In contrast to standard methods that use only voxel intensities, our method uses a more complex appearance model based on a set of local image appearance features and Kth nearest neighbor (KNN) classification. The optimal set of features for classification is selected automatically from a large set of features describing the local image structure at several scales. The use of multiple features enables the appearance model to differentiate between airway tree voxels and other voxels of similar intensities in the lung, thus making the segmentation robust to pathologies such as emphysema. The classifier is trained on imperfect segmentations that can easily be obtained using region growing with a manual threshold selection. Experiments show that the proposed method results in a more robust segmentation that can grow into the smaller airway branches without leaking into emphysematous areas, and is able to segment many branches that are not present in the training set.

Virus Database and Online Inquiry System Based on Natural Vectors.

PubMed

Dong, Rui; Zheng, Hui; Tian, Kun; Yau, Shek-Chung; Mao, Weiguang; Yu, Wenping; Yin, Changchuan; Yu, Chenglong; He, Rong Lucy; Yang, Jie; Yau, Stephen St

2017-01-01

We construct a virus database called VirusDB (http://yaulab.math.tsinghua.edu.cn/VirusDB/) and an online inquiry system to serve people who are interested in viral classification and prediction. The database stores all viral genomes, their corresponding natural vectors, and the classification information of the single/multiple-segmented viral reference sequences downloaded from National Center for Biotechnology Information. The online inquiry system serves the purpose of computing natural vectors and their distances based on submitted genomes, providing an online interface for accessing and using the database for viral classification and prediction, and back-end processes for automatic and manual updating of database content to synchronize with GenBank. Submitted genomes data in FASTA format will be carried out and the prediction results with 5 closest neighbors and their classifications will be returned by email. Considering the one-to-one correspondence between sequence and natural vector, time efficiency, and high accuracy, natural vector is a significant advance compared with alignment methods, which makes VirusDB a useful database in further research.

A Study of Light Level Effect on the Accuracy of Image Processing-based Tomato Grading

NASA Astrophysics Data System (ADS)

Prijatna, D.; Muhaemin, M.; Wulandari, R. P.; Herwanto, T.; Saukat, M.; Sugandi, W. K.

2018-05-01

Image processing method has been used in non-destructive tests of agricultural products. Compared to manual method, image processing method may produce more objective and consistent results. Image capturing box installed in currently used tomato grading machine (TEP-4) is equipped with four fluorescence lamps to illuminate the processed tomatoes. Since the performance of any lamp will decrease if its service time has exceeded its lifetime, it is predicted that this will affect tomato classification. The objective of this study was to determine the minimum light levels which affect classification accuracy. This study was conducted by varying light level from minimum and maximum on tomatoes in image capturing boxes and then investigates its effects on image characteristics. Research results showed that light intensity affects two variables which are important for classification, for example, area and color of captured image. Image processing program was able to determine correctly the weight and classification of tomatoes when light level was 30 lx to 140 lx.

Performance analysis of distributed applications using automatic classification of communication inefficiencies

DOEpatents

Vetter, Jeffrey S.

2005-02-01

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

Classification of multiple sclerosis lesions using adaptive dictionary learning.

PubMed

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

X-ray agricultural product inspection: segmentation and classification

NASA Astrophysics Data System (ADS)

Casasent, David P.; Talukder, Ashit; Lee, Ha-Woon

1997-09-01

Processing of real-time x-ray images of randomly oriented and touching pistachio nuts for product inspection is considered. We describe the image processing used to isolate individual nuts (segmentation). This involves a new watershed transform algorithm. Segmentation results on approximately 3000 x-ray (film) and real time x-ray (linescan) nut images were excellent (greater than 99.9% correct). Initial classification results on film images are presented that indicate that the percentage of infested nuts can be reduced to 1.6% of the crop with only 2% of the good nuts rejected; this performance is much better than present manual methods and other automated classifiers have achieved.

A Comparison of Computer-Based Classification Testing Approaches Using Mixed-Format Tests with the Generalized Partial Credit Model

ERIC Educational Resources Information Center

Kim, Jiseon

2010-01-01

Classification testing has been widely used to make categorical decisions by determining whether an examinee has a certain degree of ability required by established standards. As computer technologies have developed, classification testing has become more computerized. Several approaches have been proposed and investigated in the context of…

Concepts Within Reach: Action Performance Predicts Action Language Processing in Stroke

PubMed Central

Desai, Rutvik H.; Herter, Troy; Riccardi, Nicholas; Rorden, Chris; Fridriksson, Julius

2015-01-01

The relationship between the brain’s conceptual or semantic and sensory-motor systems remains controversial. Here, we tested manual and conceptual abilities of 41 chronic stroke patients in order to examine their relationship. Manual abilities were assed through a reaching task using an exoskeleton robot. Semantic abilities were assessed with implicit as well as explicit semantic tasks, for both verbs and nouns. The results show that that the degree of selective impairment for action word processing was predicted by the degree of impairment in reaching performance. Moreover, the implicit semantic measures showed a correlation with a global reaching parameter, while the explicit semantic similarity judgment task predicted performance in action initiation. These results indicate that action concepts are dynamically grounded through motoric simulations, and that more details are simulated for more explicit semantic tasks. This is evidence for a close and causal relationship between sensory-motor and conceptual systems of the brain. PMID:25858602

GISentinel: a software platform for automatic ulcer detection on capsule endoscopy videos

NASA Astrophysics Data System (ADS)

Yi, Steven; Jiao, Heng; Meng, Fan; Leighton, Jonathon A.; Shabana, Pasha; Rentz, Lauri

2014-03-01

In this paper, we present a novel and clinically valuable software platform for automatic ulcer detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos take about 8 hours. They have to be reviewed manually by physicians to detect and locate diseases such as ulcers and bleedings. The process is time consuming. Moreover, because of the long-time manual review, it is easy to lead to miss-finding. Working with our collaborators, we were focusing on developing a software platform called GISentinel, which can fully automated GI tract ulcer detection and classification. This software includes 3 parts: the frequency based Log-Gabor filter regions of interest (ROI) extraction, the unique feature selection and validation method (e.g. illumination invariant feature, color independent features, and symmetrical texture features), and the cascade SVM classification for handling "ulcer vs. non-ulcer" cases. After the experiments, this SW gave descent results. In frame-wise, the ulcer detection rate is 69.65% (319/458). In instance-wise, the ulcer detection rate is 82.35%(28/34).The false alarm rate is 16.43% (34/207). This work is a part of our innovative 2D/3D based GI tract disease detection software platform. The final goal of this SW is to find and classification of major GI tract diseases intelligently, such as bleeding, ulcer, and polyp from the CE videos. This paper will mainly describe the automatic ulcer detection functional module.

Career Oriented Mathematics, Student's Manual. [Includes Owning an Automobile and Driving as a Career; Retail Sales; Measurement; and Area-Perimeter.

ERIC Educational Resources Information Center

Mahaffey, Michael L.; McKillip, William D.

This volume includes student manuals for four units in the Career Oriented Mathematics Program, which was developed to improve computational abilities and attitudes of secondary students by presenting the material in a job-relevant context. The units are titled: (1) Owning an Automobile and Driving as a Career, (2) Retail Sales, (3) Measurement,…

A Manual for the Identification of Invasive Plants in Southern Forests

Treesearch

Lewis Zimmerman

2012-01-01

This manual was created specifically for use by the U.S. Forest Service Southern Research Station (SRS), Forest Inventory and Analysis (FIA) field survey crews. The SRS FIA unit currently collects data on 33 invasive plants or groups across 13 States. The ability to accurately identify plant species in the field is a crucial component of monitoring a species’ presence...

A Model for Developing a Continuous Progress Program, April 1977. A Manual for Teachers and Administrators Concerned with Improving Reading.

ERIC Educational Resources Information Center

Goldman, Rosalie; And Others

The focus of this manual is on the step-by-step development and implementation of a continuous-progress reading program--a system that permits instruction at each student's diagnosed level of ability. Analysis of program development includes advice on choosing a committee, writing the program, and presenting the program to others. The implications…

[Comparative study of device labeling regulation in U.S.A. and China].

PubMed

Li, Fei; Wei, Jing; Ma, Yanbin; Li, Zhu

2010-09-01

To provide references for the evolvement of medical devices labeling and manual administration in China, By content analysis, 10 juristic documents relevant to device labeling and manual were collected from FDA website, compared to which, the federal regulation was mainly analyzed. There are five main differences of device labeling regulation between U.S.A. and China: juristic system, administrative scope, administrative target, characteristics and practice, A set of comprehensive juristic system for device labeling has been established by FDA. from which China should draw experience, to administrate the prescription devices and the over-the-counter devices in classification, and set up device labeling guidance, thus guarantee the safety and efficacy of device.

Automated surface quality inspection with ARGOS: a case study

NASA Astrophysics Data System (ADS)

Kiefhaber, Daniel; Etzold, Fabian; Warken, Arno F.; Asfour, Jean-Michel

2017-06-01

The commercial availability of automated inspection systems for optical surfaces specified according to ISO 10110-7 promises unsupervised and automated quality control with reproducible results. In this study, the classification results of the ARGOS inspection system are compared to the decisions by well-trained inspectors based on manual-visual inspection. Both are found to agree in 93.6% of the studied cases. Exemplary cases with differing results are studied, and shown to be partly caused by shortcomings of the ISO 10110-7 standard, which was written for the industry standard manual-visual inspection. Applying it to high resolution images of the whole surface of objective machine vision systems brings with it a few challenges which are discussed.

DEVELOPMENTS IN TECHNICAL AND VOCATIONAL EDUCATION.

ERIC Educational Resources Information Center

HARRIS, NORMAN C.

THE JUNIOR COLLEGE HAS EMERGED AS THE APPROPRIATE TRAINING AGENCY FOR STUDENTS ENTERING MIDDLE MANPOWER JOBS REQUIRING A BALANCE OF COGNITIVE AND MANUAL ABILITY. THESE ARE THE STUDENTS WITH MIDDLE LEVEL ABILITIES AND ACCOMPLISHMENTS, FOR WHOM HIGH SCHOOL EDUCATION IS NOT ENOUGH AND A 4-YEAR DEGREE NOT NECESSARY. PROBLEMS ENCOUNTERED IN TRAINING…

THE INFLUENCE OF MATCHING AND MOTOR-IMITATION ABILITIES ON RAPID ACQUISITION OF MANUAL SIGNS AND EXCHANGE-BASED COMMUNICATIVE RESPONSES

PubMed Central

Gregory, Meagan K; DeLeon, Iser G; Richman, David M

2009-01-01

Establishing a relation between existing skills and acquisition of communicative responses may be useful in guiding selection of alternative communication systems. Matching and motor-imitation skills were assessed for 6 children with developmental disabilities, followed by training to request the same set of preferred items using exchange-based communication and manual signs. Three participants displayed both skills and rapidly acquired both communicative response forms. Three others displayed neither skill; 1 mastered exchange-based responses but not manual signs, and neither of the other 2 easily acquired either response form. PMID:19949531

The Influence of Endmember Selection Method in Extracting Impervious Surface from Airborne Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Wang, J.; Feng, B.

2016-12-01

Impervious surface area (ISA) has long been studied as an important input into moisture flux models. In general, ISA impedes groundwater recharge, increases stormflow/flood frequency, and alters in-stream and riparian habitats. Urban area is recognized as one of the richest ISA environment. Urban ISA mapping assists flood prevention and urban planning. Hyperspectral imagery (HI), for its ability to detect subtle spectral signature, becomes an ideal candidate in urban ISA mapping. To map ISA from HI involves endmember (EM) selection. The high degree of spatial and spectral heterogeneity of urban environment puts great difficulty in this task: a compromise point is needed between the automatic degree and the good representativeness of the method. The study tested one manual and two semi-automatic EM selection strategies. The manual and the first semi-automatic methods have been widely used in EM selection. The second semi-automatic EM selection method is rather new and has been only proposed for moderate spatial resolution satellite. The manual method visually selected the EM candidates from eight landcover types in the original image. The first semi-automatic method chose the EM candidates using a threshold over the pixel purity index (PPI) map. The second semi-automatic method used the triangle shape of the HI scatter plot in the n-Dimension visualizer to identify the V-I-S (vegetation-impervious surface-soil) EM candidates: the pixels locate at the triangle points. The initial EM candidates from the three methods were further refined by three indexes (EM average RMSE, minimum average spectral angle, and count based EM selection) and generated three spectral libraries, which were used to classify the test image. Spectral angle mapper was applied. The accuracy reports for the classification results were generated. The overall accuracy are 85% for the manual method, 81% for the PPI method, and 87% for the V-I-S method. The V-I-S EM selection method performs best in this study. This fact proves the value of V-I-S EM selection method in not only moderate spatial resolution satellite image but also the more and more accessible high spatial resolution airborne image. This semi-automatic EM selection method can be adopted into a wide range of remote sensing images and provide ISA map for hydrology analysis.

Investigation of complexity of the instruction manuals for electrical coffeepots.

PubMed

Fernandes, C A; Teixeira, J M; Merino, E A D

2014-01-01

Electrical coffeepots are commonly used in professional and residential environments. Their instruction manuals are related to issues that involve the user's safety and ability to operate the machine correctly. To provide the best product performance to the user, one must indicate or inform the correct usability, to turn the interaction easier. This research proposes to investigate the instruction manuals of the electrical coffeepots. Four coffee pot instruction manuals were analyzed in relation to the complexity through the heuristic evaluation. For that, eight experts of the Graphic Design were chosen to answer twenty four questions with the aim of analyzing: images; texts; layout development; information and warnings. This study shows the results of the 04 (four) items analyzed: a) images; b) texts; c) layout development; d) information and warnings, together with the suggestions of improvements for each manual. It is believed that the methodological procedures for the application of the heuristic evaluation have facilitated the diagnosis of fragilities and barriers that the users find during the interaction with electrical coffeepot manuals.

The Cognitive Orientation to daily Occupational Performance (CO-OP) Approach: Best responders in children with cerebral palsy and brain injury.

PubMed

Jackman, Michelle; Novak, Iona; Lannin, Natasha A; Galea, Claire; Froude, Elspeth

2018-07-01

Identifying the characteristics of individuals who are most likely to respond to a certain rehabilitation intervention is advantageous for the child, family, clinicians and the healthcare system. To investigate the individual characteristics of children with cerebral palsy or brain injury who responded best to the Cognitive Orientation to daily Occupational Performance (CO-OP) Approach. Post hoc analyses were conducted on 30 participants who participated in CO-OP within a larger randomized controlled trial. Inclusion: cerebral palsy or brain injury; age 4-15 years; Manual Abilities Classification System (MACS) I-IV; goals related to hand function; sufficient cognitive, language and behavioral ability to undertake CO-OP. Outcome measures were the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scale (GAS) collected immediately following the two week intervention period. Following CO-OP, 67% (n = 20) of participants showed a statistically significant response on the COPM, and 73%(n = 22) on the GAS. Nine participants were classified as best responders. When compared to non-responders, best responders were more likely to be female (p = .025) and to have received a higher dose of CO-OP (p = .028). Neither age nor MACS were predictors of response. To be successful in CO-OP, children should meet the prerequisites of CO-OP, particularly the language and cognitive ability to set goals and communicate effectively with the therapist. In this small sample, children with comorbidities were less likely to achieve goals, females were more likely to respond and dose of therapy was important to success. Copyright © 2018 Elsevier Ltd. All rights reserved.

Object exploration in extremely preterm infants between 6 and 9 months and relation to cognitive and language development at 24 months.

PubMed

Zuccarini, Mariagrazia; Guarini, Annalisa; Savini, Silvia; Iverson, Jana M; Aureli, Tiziana; Alessandroni, Rosina; Faldella, Giacomo; Sansavini, Alessandra

2017-09-01

Although early object exploration is considered a key ability for subsequent achievements, very few studies have analyzed its development in extremely low gestational age infants (ELGA- GA <28 weeks), whose early motor skills are delayed. Moreover, no studies have examined its developmental relationship with cognitive and language skills. The present study examined developmental change in Motor Object Exploration (MOE) and different types of MOE (Holding, Oral, Manual and Manual Rhythmic Exploration) in 20 ELGA and 20 full term (FT) infants observed during mother-infant play interaction at 6 and 9 months. It also explored whether specific types of MOE were longitudinally related to 24-month language and cognitive abilities (GMDS-R scores). ELGA infants increased MOE duration from 6 to 9 months, eliminating the initial difference with FT infants. In addition, ELGA infants showed a different pattern of Oral Exploration, that did not increase at 6 months and decrease at 9 months. Oral and Manual Exploration durations at 6 months were longitudinally related to 24-month GMDS-R language and cognitive performance scores respectively. We discuss the relevance of assessing early exploratory abilities in ELGA infants in order to implement customized intervention programs for supporting the development of these skills. Copyright © 2017 Elsevier Ltd. All rights reserved.

Results from an experiment that collected visible-light polarization data using unresolved imagery for classification of geosynchronous satellites

NASA Astrophysics Data System (ADS)

Speicher, Andy; Matin, Mohammad; Tippets, Roger; Chun, Francis; Strong, David

2015-05-01

In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. The objective of this study was to collect and analyze visible-spectrum polarization data from unresolved images of geosynchronous satellites taken over various solar phase angles. Different collection geometries were used to evaluate the polarization contribution of solar arrays, thermal control materials, antennas, and the satellite bus as the solar phase angle changed. Since materials on space objects age due to the space environment, their polarization signature may change enough to allow discrimination of identical satellites launched at different times. Preliminary data suggests this optical signature may lead to positive identification or classification of each satellite by an automated process on a shorter timeline. The instrumentation used in this experiment was a United States Air Force Academy (USAFA) Department of Physics system that consists of a 20-inch Ritchey-Chrétien telescope and a dual focal plane optical train fed with a polarizing beam splitter. Following a rigorous calibration, polarization data was collected during two nights on eight geosynchronous satellites built by various manufacturers and launched several years apart. When Stokes parameters were plotted against time and solar phase angle, the data indicates that a polarization signature from unresolved images may have promise in classifying specific satellites.

A Cross-sectional Survey of Growth and Nutritional Status in Children With Cerebral Palsy in West China.

PubMed

Wang, Fangfang; Cai, Qianyun; Shi, Wei; Jiang, Huayin; Li, Na; Ma, Dan; Wang, Qiu; Luo, Rong; Mu, Dezhi

2016-05-01

We describe the growth and nutritional status of children with cerebral palsy (2 to 18 years old) in West China and to explore the correlation between the nutritional status and age, gender, and gross and fine motor function. We performed a cross-sectional survey of children registered as having cerebral palsy in the China Disabled Persons' Federation branch in Chengdu. Growth (height and weight) and nutritional (body mass index) status were recorded. Gross Motor Function Classification System (GMFCS) and Manual Ability Classification System (MACS) were used to determine gross and fine motor function, respectively. The association between nutritional status and age, GMFCS and MACS levels was evaluated. We enrolled 377 children (53.6% male), among whom 160 (42.4%) were stunting, 48 (12.7%) underweight, 81 (21.5%) thin, and 70 (18.5%) overweight and obese. Thinness was the main nutritional problem in older patients (12 to 18 years), whereas overweight and obesity were the major issues in younger patients (2 to 12 years). Growth deviation and malnutrition were significantly more prevalent in patients with severe motor impairments. A significant negative correlation was found between nutritional status and age, GMFCS and MACS levels, and between growth and GMFCS and MACS levels. Growth abnormality is common in children with cerebral palsy. Malnutrition and overnutrition both exist in children with cerebral palsy. Characteristics at different age stages and motor functional levels should be taken into consideration in the management of growth and nutrition in this population. Copyright © 2016 Elsevier Inc. All rights reserved.

Classification, Seriation, and Counting in Grades 1, 2, and 3 as Two-Year Longitudinal Predictors for Low Achieving in Numerical Facility and Arithmetical Achievement?

ERIC Educational Resources Information Center

Desoete, Annemie; Stock, Pieter; Schepens, Annemie; Baeyens, Dieter; Roeyers, Herbert

2009-01-01

Previous research stresses the importance of seriation, classification, and counting abilities that should be assessed in kindergarten, when looking for crucial predictors of mathematical learning disabilities in Grade 1. This study examines (n = 158) two-year-long predictive relationships between children's seriation, classification, procedural…

Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data

PubMed Central

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

2011-01-01

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

Tree classification with fused mobile laser scanning and hyperspectral data.

PubMed

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

2011-01-01

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

Widening the lens: what the manual modality reveals about language, learning and cognition.

PubMed

Goldin-Meadow, Susan

2014-09-19

The goal of this paper is to widen the lens on language to include the manual modality. We look first at hearing children who are acquiring language from a spoken language model and find that even before they use speech to communicate, they use gesture. Moreover, those gestures precede, and predict, the acquisition of structures in speech. We look next at deaf children whose hearing losses prevent them from using the oral modality, and whose hearing parents have not presented them with a language model in the manual modality. These children fall back on the manual modality to communicate and use gestures, which take on many of the forms and functions of natural language. These homemade gesture systems constitute the first step in the emergence of manual sign systems that are shared within deaf communities and are full-fledged languages. We end by widening the lens on sign language to include gesture and find that signers not only gesture, but they also use gesture in learning contexts just as speakers do. These findings suggest that what is key in gesture's ability to predict learning is its ability to add a second representational format to communication, rather than a second modality. Gesture can thus be language, assuming linguistic forms and functions, when other vehicles are not available; but when speech or sign is possible, gesture works along with language, providing an additional representational format that can promote learning. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Tactile agnosia. Underlying impairment and implications for normal tactile object recognition.

PubMed

Reed, C L; Caselli, R J; Farah, M J

1996-06-01

In a series of experimental investigations of a subject with a unilateral impairment of tactile object recognition without impaired tactile sensation, several issues were addressed. First, is tactile agnosia secondary to a general impairment of spatial cognition? On tests of spatial ability, including those directed at the same spatial integration process assumed to be taxed by tactile object recognition, the subject performed well, implying a more specific impairment of high level, modality specific tactile perception. Secondly, within the realm of high level tactile perception, is there a distinction between the ability to derive shape ('what') and spatial ('where') information? Our testing showed an impairment confined to shape perception. Thirdly, what aspects of shape perception are impaired in tactile agnosia? Our results indicate that despite accurate encoding of metric length and normal manual exploration strategies, the ability tactually to perceive objects with the impaired hand, deteriorated as the complexity of shape increased. In addition, asymmetrical performance was not found for other body surfaces (e.g. her feet). Our results suggest that tactile shape perception can be disrupted independent of general spatial ability, tactile spatial ability, manual shape exploration, or even the precise perception of metric length in the tactile modality.

Final Tactical Decision Aid (FTDA) for Infrared (8-12 Micrometers) Systems - Manual Version. Volume II. Appendix A. Atmospheric Transmission Tables,

DTIC Science & Technology

1982-09-15

OBSOLETE UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (When Date Efntee -- - -- I I New SSD-T-8-6061-007 (1I)-82 TABLE OF CONTENTS 1. A Brief...possible transformation of airmasses with a continntal origin so that their aerosol asames the extinction properties of a maritime aerosol. Figure A-IB

Hand Function in Relation to Brain Lesions and Corticomotor-Projection Pattern in Children with Unilateral Cerebral Palsy

ERIC Educational Resources Information Center

Holmstrom, Linda; Vollmer, Brigitte; Tedroff, Kristina; Islam, Mominul; Persson, Jonas Ke; Kits, Annika; Forssberg, Hans; Eliasson, Ann-Christin

2010-01-01

Aim: To investigate relationships between hand function, brain lesions, and corticomotor projections in children with unilateral cerebral palsy (CP). Method: The study included 17 children (nine males, eight females; mean age 11.4 [SD 2.4] range 7-16y), with unilateral CP at Gross Motor Function Classification System level I and Manual Ability…

Implications of "DSM"-IV to "DSM"-5 Substance Use Disorder Diagnostic Changes in Adolescents Enrolled in a School-Based Intervention

ERIC Educational Resources Information Center

Stewart, David G.; Arlt, Virginia K.; Siebert, Erin C.; Chapman, Meredith K.; Hu, Emily M.

2016-01-01

This study aimed to examine (a) the impact of the change in the "Diagnostic and Statistical Manual of Mental Disorders" ("DSM") from a categorical to dimensional classification of substance use diagnoses, (b) the elimination of the legal criterion, and (c) the inclusion of a craving criterion in the "DSM"-5.…

Nursing Care Hour Standards Study. Part 1. Section A. Patient Classification System Model Development

DTIC Science & Technology

1981-09-01

Change = 2311 (257) Teaching - Diabetic = 2313 (258) Labor Room Examination and Preparation, Routine = 2434 (259) Fetal Heart Tones, Manual = 2412 (260... Fetal Heart Tones, Doppler = 2413 (261) Dilatation and Effecement Assessment = 2403 (262) Dilatation and Effacement Assessment, Assisting Physician...Ultrasonic Transducer/Tocotransducer = 2435 (270) Monitoring Fetal Heart Tones, Ultrasonic Transducer = 2436 (271) Monitoring Fetal Heart Tones, Ultrasonic

Auditory Processing Disorder in Relation to Developmental Disorders of Language, Communication and Attention: A Review and Critique

ERIC Educational Resources Information Center

Dawes, Piers; Bishop, Dorothy

2009-01-01

Background: Auditory Processing Disorder (APD) does not feature in mainstream diagnostic classifications such as the "Diagnostic and Statistical Manual of Mental Disorders, 4th Edition" (DSM-IV), but is frequently diagnosed in the United States, Australia and New Zealand, and is becoming more frequently diagnosed in the United Kingdom. Aims: To…

Demographic Variables as Factors Influencing Accessibility and Utilisation of Library Software by Undergraduates in Two Private Universities in Nigeria

ERIC Educational Resources Information Center

Tolulope, Akano

2017-01-01

Libraries before the 21st century carried out daily routine library task such as cataloguing and classification, acquisition, reference services etc using manual procedures only but the advent of Information Technology as transformed these routine task that libraries can now automate their activities by deploying the use of library software in…

Digital Troposcatter Performance Model. Users Manual.

DTIC Science & Technology

1983-11-01

and Information Systems - .,- - - UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE (When Data Entered) S REPORT DOCUIAENTATION PAGE READ...Diffraction Multipath Prediction MD-918 Modem Error Rate Prediction AN/TRC-170 Link Analysis 20. ABSTRACT (Continue en reverse esie If neceseay end...configurations used in the Defense Communications System (DCS), and prediction of the performance of both the MD-918 and AN/TRC-170 digital troposcatter modems

Federal Standardization Manual

DTIC Science & Technology

1994-01-01

susceptible to categorizing in the Federal Supply Classification system . Examples are PACK (packaging, packing, preservation and transportability) and... system . This involves a technical review of supply items to identify duplicating or overlapping items. It leads to a reduction in a number of similar...firms engaged in producing, distrib- uting and supporting such products. Metrication. Any act tending to increase the use of the metric system (SI

New approaches for measuring changes in the cortical surface using an automatic reconstruction algorithm

NASA Astrophysics Data System (ADS)

Pham, Dzung L.; Han, Xiao; Rettmann, Maryam E.; Xu, Chenyang; Tosun, Duygu; Resnick, Susan; Prince, Jerry L.

2002-05-01

In previous work, the authors presented a multi-stage procedure for the semi-automatic reconstruction of the cerebral cortex from magnetic resonance images. This method suffered from several disadvantages. First, the tissue classification algorithm used can be sensitive to noise within the image. Second, manual interaction was required for masking out undesired regions of the brain image, such as the ventricles and putamen. Third, iterated median filters were used to perform a topology correction on the initial cortical surface, resulting in an overly smoothed initial surface. Finally, the deformable surface used to converge to the cortex had difficulty capturing narrow gyri. In this work, all four disadvantages of the procedure have been addressed. A more robust tissue classification algorithm is employed and the manual masking step is replaced by an automatic method involving level set deformable models. Instead of iterated median filters, an algorithm developed specifically for topology correction is used. The last disadvantage is addressed using an algorithm that artificially separates adjacent sulcal banks. The new procedure is more automated but also more accurate than the previous one. Its utility is demonstrated by performing a preliminary study on data from the Baltimore Longitudinal Study of Aging.

Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology.

PubMed

Cruzan, Mitchell B; Weinstein, Ben G; Grasty, Monica R; Kohrn, Brendan F; Hendrickson, Elizabeth C; Arredondo, Tina M; Thompson, Pamela G

2016-09-01

Low-elevation surveys with small aerial drones (micro-unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology.

Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification.

PubMed

Soares, João V B; Leandro, Jorge J G; Cesar Júnior, Roberto M; Jelinek, Herbert F; Cree, Michael J

2006-09-01

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et al., 2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

Posture recognition associated with lifting of heavy objects using Kinect and Adaboost

NASA Astrophysics Data System (ADS)

Raut, Sayli; Navaneethakrishna, M.; Ramakrishnan, S.

2017-12-01

Lifting of heavy objects is the common task in the industries. Recent statistics from the Bureau of Labour indicate, back injuries account for one of every five injuries in the workplace. Eighty per cent of these injuries occur to the lower back and are associated with manual materials handling tasks. According to the Industrial ergonomic safety manual, Squatting is the correct posture for lifting a heavy object. In this work, an attempt has been made to monitor posture of the workers during squat and stoop using 3D motion capture and machine learning techniques. For this, Microsoft Kinect V2 is used for capturing the depth data. Further, Dynamic Time Warping and Euclidian distance algorithms are used for extraction of features. Ada-boost algorithm is used for classification of stoop and squat. The results show that the 3D image data is large and complex to analyze. The application of nonlinear and linear metrics captures the variation in the lifting pattern. Additionally, the features extracted from this metric resulted in a classification accuracy of 85% and 81% respectively. This framework may be put-upon to alert the workers in the industrial ergonomic environments.

Artificial neuron-glia networks learning approach based on cooperative coevolution.

PubMed

Mesejo, Pablo; Ibáñez, Oscar; Fernández-Blanco, Enrique; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana B

2015-06-01

Artificial Neuron-Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

The 1984 ARI Survey of Army Recruits: User’s Manual

DTIC Science & Technology

1986-05-01

KCEY WOROS (Caiu.a on toaa.. aide linaloiiaial ai .,, Ida &r bloog naea Army Recruiting, New Recruit Survey Enlistment Motivations . -’Recruit...designed in 1982 to answer questions concerning the demo- graphics and enlistment motivations of new Army recruits. In addition to the ability to track...SURVIY OF ARMY RECRUITS: USER’S MANUAL EXECUTIVE SUMMArY ~Reguirement: To obtain information on the characteristics, enlistment motivations , attitudes

Advanced Steel Microstructural Classification by Deep Learning Methods.

PubMed

Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

2018-02-01

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

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

PubMed Central

Zhang, Fan; Zhang, Xinhong

2011-01-01

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

Lidar-based individual tree species classification using convolutional neural network

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

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

PubMed

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

2016-08-05

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

The road to 11th edition of the International Classification of Diseases: trajectories of scientific consensus and contested science in the classification of intellectual disability/intellectual developmental disorders.

PubMed

Salvador-Carulla, Luis; Bertelli, Marco; Martinez-Leal, Rafael

2018-03-01

To increase the expert knowledge-base on intellectual developmental disorders (IDDs) by investigating the typology trajectories of consensus formation in the classification systems up to the 11th edition of the International Classification of Diseases (ICD-11). This expert review combines an analysis of key recent literature and the revision of the consensus formation and contestation in the expert committees contributing to the classification systems since the 1950s. Historically two main approaches have contributed to the development of this knowledge-base: a neurodevelopmental-clinical approach and a psychoeducational-social approach. These approaches show a complex interaction throughout the history of IDD and have had a diverse influence on its classification. Although in theory Diagnostic and Statistical Manual (DSM)-5 and ICD adhere to the neurodevelopmental-clinical model, the new definition in the ICD-11 follows a restrictive normality approach to intellectual quotient and to the measurement of adaptive behaviour. On the contrary DSM-5 is closer to the recommendations made by the WHO 'Working Group on Mental Retardation' for ICD-11 for an integrative approach. A cyclical pattern of consensus formation has been identified in IDD. The revision of the three major classification systems in the last decade has increased the terminological and conceptual variability and the overall scientific contestation on IDD.

Evolving land cover classification algorithms for multispectral and multitemporal imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Theiler, James P.; Bloch, Jeffrey J.; Harvey, Neal R.; Perkins, Simon J.; Szymanski, John J.; Young, Aaron C.

2002-01-01

The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.

[Clinical Implications of Changes in Child Psychiatry in the DSM-5. Strengths and Weaknesses of the Changes].

PubMed

Botero-Franco, Diana; Palacio-Ortíz, Juan David; Arroyave-Sierra, Pilar; Piñeros-Ortíz, Sandra

2016-01-01

The Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Statistical Classification of Diseases and related health problems (ICD) integrate the diagnostic criteria commonly used in psychiatric practice, but the DSM-IV-TR was insufficient for current clinical work. The DSM-5 was first made public in May at the Congress of the American Psychiatric Association, and it includes changes to some aspects of Child Psychiatry, as many of the conditions that were at the beginning in chapter of infancy, childhood and adolescence disorders have been transferred to other chapters and there are new diagnostic criteria or new terms are added. It is therefore important to provide it to Psychiatrists who attend children in order to assess the changes they will be facing in the nomenclature and classification in pursuit of a better classification of the childhood psychopathology. Copyright © 2016. Publicado por Elsevier España.

Between DSM and ICD: Paraphilias and the Transformation of Sexual Norms.

PubMed

Giami, Alain

2015-07-01

The simultaneous revision of the two major international classifications of disease, the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases, serves as an opportunity to observe the dynamic processes through which social norms of sexuality are constructed and are subject to change in relation to social, political, and historical context. This article argues that the classifications of sexual disorders, which define pathological aspects of "sexually arousing fantasies, sexual urges or behaviors" are representations of contemporary sexual norms, gender identifications, and gender relations. It aims to demonstrate how changes in the medical treatment of sexual perversions/paraphilias passed, over the course of the 20th century, from a model of pathologization (and sometimes criminalization) of non-reproductive sexual behaviors to a model that reflects and privileges sexual well-being and responsibility, and pathologizes the absence or the limitation of consent in sexual relations.

Detection and classification of subject-generated artifacts in EEG signals using autoregressive models.

PubMed

Lawhern, Vernon; Hairston, W David; McDowell, Kaleb; Westerfield, Marissa; Robbins, Kay

2012-07-15

We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals. Copyright © 2012 Elsevier B.V. All rights reserved.

A Web-Based Framework For a Time-Domain Warehouse

NASA Astrophysics Data System (ADS)

Brewer, J. M.; Bloom, J. S.; Kennedy, R.; Starr, D. L.

2009-09-01

The Berkeley Transients Classification Pipeline (TCP) uses a machine-learning classifier to automatically categorize transients from large data torrents and provide automated notification of astronomical events of scientific interest. As part of the training process, we created a large warehouse of light-curve sources with well-labelled classes that serve as priors to the classification engine. This web-based interactive framework, which we are now making public via DotAstro.org (http://dotastro.org/), allows us to ingest time-variable source data in a wide variety of formats and store it in a common internal data model. Data is passed between pipeline modules in a prototype XML representation of time-series format (VOTimeseries), which can also be emitted to collaborators through dotastro.org. After import, the sources can be visualized using Google Sky, light curves can be inspected interactively, and classifications can be manually adjusted.

Towards a controlled vocabulary on software engineering education

NASA Astrophysics Data System (ADS)

Pizard, Sebastián; Vallespir, Diego

2017-11-01

Software engineering is the discipline that develops all the aspects of the production of software. Although there are guidelines about what topics to include in a software engineering curricula, it is usually unclear which are the best methods to teach them. In any science discipline the construction of a classification schema is a common approach to understand a thematic area. This study examines previous publications in software engineering education to obtain a first controlled vocabulary (a more formal definition of a classification schema) in the field. Publications from 1988 to 2014 were collected and processed using automatic clustering techniques and the outcomes were analysed manually. The result is an initial controlled vocabulary with a taxonomy form with 43 concepts that were identified as the most used in the research publications. We present the classification of the concepts in three facets: 'what to teach', 'how to teach' and 'where to teach' and the evolution of concepts over time.

Tree Classification Software

NASA Technical Reports Server (NTRS)

Buntine, Wray

1993-01-01

This paper introduces the IND Tree Package to prospective users. IND does supervised learning using classification trees. This learning task is a basic tool used in the development of diagnosis, monitoring and expert systems. The IND Tree Package was developed as part of a NASA project to semi-automate the development of data analysis and modelling algorithms using artificial intelligence techniques. The IND Tree Package integrates features from CART and C4 with newer Bayesian and minimum encoding methods for growing classification trees and graphs. The IND Tree Package also provides an experimental control suite on top. The newer features give improved probability estimates often required in diagnostic and screening tasks. The package comes with a manual, Unix 'man' entries, and a guide to tree methods and research. The IND Tree Package is implemented in C under Unix and was beta-tested at university and commercial research laboratories in the United States.

Segmentation and classification of cell cycle phases in fluorescence imaging.

PubMed

Ersoy, Ilker; Bunyak, Filiz; Chagin, Vadim; Cardoso, M Christina; Palaniappan, Kannappan

2009-01-01

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

Automated retinal vessel type classification in color fundus images

NASA Astrophysics Data System (ADS)

Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

2013-02-01

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

Object-based classification of earthquake damage from high-resolution optical imagery using machine learning

NASA Astrophysics Data System (ADS)

Bialas, James; Oommen, Thomas; Rebbapragada, Umaa; Levin, Eugene

2016-07-01

Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

Multi-label literature classification based on the Gene Ontology graph.

PubMed

Jin, Bo; Muller, Brian; Zhai, Chengxiang; Lu, Xinghua

2008-12-08

The Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification. In this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community. Through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.

Manual dexterity aptitude testing: a soap carving study.

PubMed

Tang, Christopher G; Hilsinger, Raymond L; Cruz, Raul M; Schloegel, Luke J; Byl, Fred M; Rasgon, Barry M

2014-03-01

Currently there are few validated metrics for predicting surgical skill among otolaryngology residency applicants. To determine whether manual dexterity aptitude testing in the form of soap carving during otolaryngology residency interviews at Kaiser Permanente Medical Center Oakland predicts surgical skill at the time of graduation from otolaryngology residency programs. This study was conducted to determine how applicants with the best and worst soap carvings compared at the time of graduation with respect to various metrics including visuospatial ability and manual dexterity. Over the last 25 years, applicants to the residency program at Kaiser Permanente Oakland were required to carve soap during their residency interview. The 3 best and 3 worst soap carvings from 1990 through 2006 were determined. Of the individuals who carved those soaps, 62 qualified for the study and matriculated into otolaryngology residency programs. Surveys were sent to the 62 individuals' residency programs to evaluate those individuals on a 5-point Likert scale in various categories as well as to rank those individuals as being in the top 50% or bottom 50% of their graduating class. All else being equal, we hypothesized that applicants who had the manual dexterity and visuospatial skills to accurately carve a bar of soap would more likely possess the skills necessary to become a good surgeon. There was no difference between individuals with the best soap carvings and those with the worst soap carvings in all categories: cognitive knowledge, visuospatial ability, manual dexterity, decision making, and overall score (P > .10 for all categories). There was a 95% response rate, with 35 of 37 residency programs responding and 59 of 62 surveys returned. Manual dexterity aptitude testing in the form of soap carving does not appear to correlate with surgical skill at the time of graduation. Further studies need to be conducted to determine the role of manual dexterity and visuospatial aptitude testing in the otolaryngology application process.

Comparing drug classification systems.

PubMed

Mahoney, Anne; Evans, Jonathan

2008-11-06

An essential quality of drug classification systems is the ability to assign medications to a structured hierarchy for categories such as mechanism of action, physiological effects, and therapeutic indications. No single classification system can meet all of these needs; however, there should be consistency among those that group by the same underlying principals. We discovered discrepancies in how drugs with multiple therapeutic indications are classified among four widely used schemas.

Differences on Six Horn Abilities for 14 Age Groups between 15-16 and 75-94 Years.

ERIC Educational Resources Information Center

Kaufman, Alan S.; And Others

1996-01-01

Six abilities derived from the fluid and crystallized intelligence theory of J. L. Horn were studied with 1,193 individuals in age groups between 15 and 94 years. Results supported Horn's classification of crystallized and quantitative as maintained abilities and of fluid and broad visualization as vulnerable abilities. (SLD)

Speech variability effects on recognition accuracy associated with concurrent task performance by pilots

NASA Technical Reports Server (NTRS)

Simpson, C. A.

1985-01-01

In the present study of the responses of pairs of pilots to aircraft warning classification tasks using an isolated word, speaker-dependent speech recognition system, the induced stress was manipulated by means of different scoring procedures for the classification task and by the inclusion of a competitive manual control task. Both speech patterns and recognition accuracy were analyzed, and recognition errors were recorded by type for an isolated word speaker-dependent system and by an offline technique for a connected word speaker-dependent system. While errors increased with task loading for the isolated word system, there was no such effect for task loading in the case of the connected word system.