Sample records for identify general features

  1. Identifying significant environmental features using feature recognition.

    DOT National Transportation Integrated Search

    2015-10-01

    The Department of Environmental Analysis at the Kentucky Transportation Cabinet has expressed an interest in feature-recognition capability because it may help analysts identify environmentally sensitive features in the landscape, : including those r...

  2. Identifying Potential Collapse Features Under Highways

    DOT National Transportation Integrated Search

    2003-01-01

    In 1994, subsidence features were identified on Interstate 70 in eastern Ohio. These : features were caused by collapse of old mine workings beneath the highway. An attempt : was made to delineate these features using geophysical methods with no avai...

  3. Identifying potential collapse features under highways.

    DOT National Transportation Integrated Search

    2003-03-01

    In 1994, subsidence features were identified on Interstate 70 in eastern Ohio. These features were caused by collapse of old mine workings beneath the highway. An attempt was made to delineate these features using geophysical methods with no avail. T...

  4. Identifying Potential Collapse Features Under Highways : Executive Summary

    DOT National Transportation Integrated Search

    2003-03-01

    In 1994, subsidence features were identified on Interstate 70 in eastern Ohio. These : features were caused by collapse of old mine workings beneath the highway. An attempt : was made to delineate these features using geophysical methods with no avai...

  5. Collective feature selection to identify crucial epistatic variants.

    PubMed

    Verma, Shefali S; Lucas, Anastasia; Zhang, Xinyuan; Veturi, Yogasudha; Dudek, Scott; Li, Binglan; Li, Ruowang; Urbanowicz, Ryan; Moore, Jason H; Kim, Dokyoon; Ritchie, Marylyn D

    2018-01-01

    Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger

  6. Identifying marker genes in transcription profiling data using a mixture of feature relevance experts.

    PubMed

    Chow, M L; Moler, E J; Mian, I S

    2001-03-08

    Transcription profiling experiments permit the expression levels of many genes to be measured simultaneously. Given profiling data from two types of samples, genes that most distinguish the samples (marker genes) are good candidates for subsequent in-depth experimental studies and developing decision support systems for diagnosis, prognosis, and monitoring. This work proposes a mixture of feature relevance experts as a method for identifying marker genes and illustrates the idea using published data from samples labeled as acute lymphoblastic and myeloid leukemia (ALL, AML). A feature relevance expert implements an algorithm that calculates how well a gene distinguishes samples, reorders genes according to this relevance measure, and uses a supervised learning method [here, support vector machines (SVMs)] to determine the generalization performances of different nested gene subsets. The mixture of three feature relevance experts examined implement two existing and one novel feature relevance measures. For each expert, a gene subset consisting of the top 50 genes distinguished ALL from AML samples as completely as all 7,070 genes. The 125 genes at the union of the top 50s are plausible markers for a prototype decision support system. Chromosomal aberration and other data support the prediction that the three genes at the intersection of the top 50s, cystatin C, azurocidin, and adipsin, are good targets for investigating the basic biology of ALL/AML. The same data were employed to identify markers that distinguish samples based on their labels of T cell/B cell, peripheral blood/bone marrow, and male/female. Selenoprotein W may discriminate T cells from B cells. Results from analysis of transcription profiling data from tumor/nontumor colon adenocarcinoma samples support the general utility of the aforementioned approach. Theoretical issues such as choosing SVM kernels and their parameters, training and evaluating feature relevance experts, and the impact of

  7. A general purpose feature extractor for light detection and ranging data.

    PubMed

    Li, Yangming; Olson, Edwin B

    2010-01-01

    Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.

  8. A General Purpose Feature Extractor for Light Detection and Ranging Data

    PubMed Central

    Li, Yangming; Olson, Edwin B.

    2010-01-01

    Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset. PMID:22163474

  9. Identifying Planar Deformation Features Using EBSD and FIB

    NASA Astrophysics Data System (ADS)

    Pickersgill, A. E.; Lee, M. R.

    2015-09-01

    Planar deformation features in quartz grains from the Gow Lake impact structure have been successfully identified and indexed using electron backscatter diffraction in combination with focused ion beam milling.

  10. An Integrated Account of Generalization across Objects and Features

    ERIC Educational Resources Information Center

    Kemp, Charles; Shafto, Patrick; Tenenbaum, Joshua B.

    2012-01-01

    Humans routinely make inductive generalizations about unobserved features of objects. Previous accounts of inductive reasoning often focus on inferences about a single object or feature: accounts of causal reasoning often focus on a single object with one or more unobserved features, and accounts of property induction often focus on a single…

  11. Feature Analysis of Generalized Data Base Management Systems.

    ERIC Educational Resources Information Center

    Conference on Data Systems Languages, Monroeville, PA. Systems Committee.

    A more complete definition of the features offered in present day generalized data base management systems is provided by this second technical report of the CODASYL Systems Committee. In a tutorial format, each feature description is followed by either narrative information covering ten systems or by a table for all systems. The ten systems…

  12. Identifying Creativity during Problem Solving Using Linguistic Features

    ERIC Educational Resources Information Center

    Skalicky, Stephen; Crossley, Scott A.; McNamara, Danielle S.; Muldner, Kasia

    2017-01-01

    Creativity is commonly assessed using divergent thinking tasks, which measure the fluency, flexibility, originality, and elaboration of participant output on a variety of different tasks. This study assesses the degree to which creativity can be identified based on linguistic features of participants' language while completing collaborative…

  13. Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

    PubMed Central

    Mooney, Catherine; Haslam, Niall J.; Pollastri, Gianluca; Shields, Denis C.

    2012-01-01

    The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides. We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4–20 amino acids) and one focused on long peptides ( amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure. We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive. PMID:23056189

  14. Longitudinal Validation of General and Specific Structural Features of Personality Pathology

    PubMed Central

    Wright, Aidan G.C.; Hopwood, Christopher J.; Skodol, Andrew E.; Morey, Leslie C.

    2016-01-01

    Theorists have long argued that personality disorder (PD) is best understood in terms of general impairments shared across the disorders as well as more specific instantiations of pathology. A model based on this theoretical structure was proposed as part of the DSM-5 revision process. However, only recently has this structure been subjected to formal quantitative evaluation, with little in the way of validation efforts via external correlates or prospective longitudinal prediction. We used the Collaborative Longitudinal Study of Personality Disorders dataset to: (1) estimate structural models that parse general from specific variance in personality disorder features, (2) examine patterns of growth in general and specific features over the course of 10 years, and (3) establish concurrent and dynamic longitudinal associations in PD features and a host of external validators including basic personality traits and psychosocial functioning scales. We found that general PD exhibited much lower absolute stability and was most strongly related to broad markers of psychosocial functioning, concurrently and longitudinally, whereas specific features had much higher mean stability and exhibited more circumscribed associations with functioning. However, both general and specific factors showed recognizable associations with normative and pathological traits. These results can inform efforts to refine the conceptualization and diagnosis of personality pathology. PMID:27819472

  15. Identifying Features of Bodily Expression As Indicators of Emotional Experience during Multimedia Learning

    PubMed Central

    Riemer, Valentin; Frommel, Julian; Layher, Georg; Neumann, Heiko; Schrader, Claudia

    2017-01-01

    The importance of emotions experienced by learners during their interaction with multimedia learning systems, such as serious games, underscores the need to identify sources of information that allow the recognition of learners’ emotional experience without interrupting the learning process. Bodily expression is gaining in attention as one of these sources of information. However, to date, the question of how bodily expression can convey different emotions has largely been addressed in research relying on acted emotion displays. Following a more contextualized approach, the present study aims to identify features of bodily expression (i.e., posture and activity of the upper body and the head) that relate to genuine emotional experience during interaction with a serious game. In a multimethod approach, 70 undergraduates played a serious game relating to financial education while their bodily expression was captured using an off-the-shelf depth-image sensor (Microsoft Kinect). In addition, self-reports of experienced enjoyment, boredom, and frustration were collected repeatedly during gameplay, to address the dynamic changes in emotions occurring in educational tasks. Results showed that, firstly, the intensities of all emotions indeed changed significantly over the course of the game. Secondly, by using generalized estimating equations, distinct features of bodily expression could be identified as significant indicators for each emotion under investigation. A participant keeping their head more turned to the right was positively related to frustration being experienced, whereas keeping their head more turned to the left was positively related to enjoyment. Furthermore, having their upper body positioned more closely to the gaming screen was also positively related to frustration. Finally, increased activity of a participant’s head emerged as a significant indicator of boredom being experienced. These results confirm the value of bodily expression as an indicator

  16. A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data

    PubMed Central

    Yang, Runtao; Zhang, Chengjin; Gao, Rui; Zhang, Lina

    2016-01-01

    The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular processes. In this paper, a new computational method is proposed for identifying cis-Golgi proteins from trans-Golgi proteins. Based on the concept of Common Spatial Patterns (CSP), a novel feature extraction technique is developed to extract evolutionary information from protein sequences. To deal with the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted. A feature selection method called Random Forest-Recursive Feature Elimination (RF-RFE) is employed to search the optimal features from the CSP based features and g-gap dipeptide composition. Based on the optimal features, a Random Forest (RF) module is used to distinguish cis-Golgi proteins from trans-Golgi proteins. Through the jackknife cross-validation, the proposed method achieves a promising performance with a sensitivity of 0.889, a specificity of 0.880, an accuracy of 0.885, and a Matthew’s Correlation Coefficient (MCC) of 0.765, which remarkably outperforms previous methods. Moreover, when tested on a common independent dataset, our method also achieves a significantly improved performance. These results highlight the promising performance of the proposed method to identify Golgi-resident protein types. Furthermore, the CSP based feature extraction method may provide guidelines for protein function predictions. PMID:26861308

  17. Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.

    PubMed

    Tuo, Youlin; An, Ning; Zhang, Ming

    2018-03-01

    The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of P<0.05. Based on the protein‑protein interactions (PPIs) in the Biological General Repository for Interaction Datasets, Human Protein Reference Database and Biomolecular Interaction Network Database, the PPI network of the feature genes was constructed. The feature genes identified by topological characteristics were then used for support vector machine (SVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an

  18. On the appropriate feature for general SAR image registration

    NASA Astrophysics Data System (ADS)

    Li, Dong; Zhang, Yunhua

    2012-09-01

    An investigation to the appropriate feature for SAR image registration is conducted. The commonly-used features such as tie points, Harris corner, the scale invariant feature transform (SIFT), and the speeded up robust feature (SURF) are comprehensively evaluated in terms of several criteria such as the geometrical invariance of feature, the extraction speed, the localization accuracy, the geometrical invariance of descriptor, the matching speed, the robustness to decorrelation, and the flexibility to image speckling. It is shown that SURF outperforms others. It is particularly indicated that SURF has good flexibility to image speckling because the Fast-Hessian detector of SURF has a potential relation with the refined Lee filter. It is recommended to perform SURF on the oversampled image with unaltered sampling step so as to improve the subpixel registration accuracy and speckle immunity. Thus SURF is more appropriate and competent for general SAR image registration.

  19. Feature extraction with deep neural networks by a generalized discriminant analysis.

    PubMed

    Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

    2012-04-01

    We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

  20. Identifying Trajectories of Borderline Personality Features in Adolescence

    PubMed Central

    Haltigan, John D.

    2016-01-01

    Objective: To examine trajectories of adolescent borderline personality (BP) features in a normative-risk cohort (n = 566) of Canadian children assessed at ages 13, 14, 15, and 16 and childhood predictors of trajectory group membership assessed at ages 8, 10, 11, and 12. Method: Data were drawn from the McMaster Teen Study, an on-going study examining relations among bullying, mental health, and academic achievement. Participants and their parents completed a battery of mental health and peer relations questionnaires at each wave of the study. Academic competence was assessed at age 8 (Grade 3). Latent class growth analysis, analysis of variance, and logistic regression were used to analyze the data. Results: Three distinct BP features trajectory groups were identified: elevated or rising, intermediate or stable, and low or stable. Parent- and child-reported mental health symptoms, peer relations risk factors, and intra-individual risk factors were significant predictors of elevated or rising and intermediate or stable trajectory groups. Child-reported attention-deficit hyperactivity disorder (ADHD) and somatization symptoms uniquely predicted elevated or rising trajectory group membership, whereas parent-reported anxiety and child-reported ADHD symptoms uniquely predicted intermediate or stable trajectory group membership. Child-reported somatization symptoms was the only predictor to differentiate the intermediate or stable and elevated or rising trajectory groups (OR 1.15, 95% CI 1.04 to 1.28). Associations between child-reported reactive temperament and elevated BP features trajectory group membership were 10.23 times higher among children who were bullied, supporting a diathesis–stress pathway in the development of BP features for these youth. Conclusions: Findings demonstrate the heterogeneous course of BP features in early adolescence and shed light on the potential prodromal course of later borderline personality disorder. PMID:27254092

  1. Identifying sports videos using replay, text, and camera motion features

    NASA Astrophysics Data System (ADS)

    Kobla, Vikrant; DeMenthon, Daniel; Doermann, David S.

    1999-12-01

    Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various classes such as sports, news, movies, or documentaries, increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we discuss the extraction of features that enable identification of sports videos directly from the compressed domain of MPEG video. These features include detecting the presence of action replays, determining the amount of scene text in vide, and calculating various statistics on camera and/or object motion. The features are derived from the macroblock, motion,and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to substantial gains in processing speeds. Full-decoding of selective frames is required only for text analysis. A decision tree classifier built using these features is able to identify sports clips with an accuracy of about 93 percent.

  2. 49 CFR 37.161 - Maintenance of accessible features: General.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 49 Transportation 1 2010-10-01 2010-10-01 false Maintenance of accessible features: General. 37.161 Section 37.161 Transportation Office of the Secretary of Transportation TRANSPORTATION SERVICES FOR INDIVIDUALS WITH DISABILITIES (ADA) Provision of Service § 37.161 Maintenance of accessible...

  3. Identifying DNA Methylation Features that Underlie Prostate Cancer Disparities

    DTIC Science & Technology

    2016-10-01

    Report We will continue to recruit African American patients and bank their prostate tissue . We will continue dissecting tumor samples into tumor...in prostate tumors and adjacent normal tissue derived from both AA and EA individuals. We will determine if DNA methylation patterns in prostate... tissue (both cancerous and normal tissue ) differ between AA and EA individuals. We will also identify methylation features that differ between tumor

  4. A Generalization Strategy for Discrete Area Feature by Using Stroke Grouping and Polarization Transportation Selection

    NASA Astrophysics Data System (ADS)

    Wang, Xiao; Burghardt, Dirk

    2018-05-01

    This paper presents a new strategy for the generalization of discrete area features by using stroke grouping method and polarization transportation selection. The mentioned stroke is constructed on derive of the refined proximity graph of area features, and the refinement is under the control of four constraints to meet different grouping requirements. The area features which belong to the same stroke are detected into the same group. The stroke-based strategy decomposes the generalization process into two sub-processes by judging whether the area features related to strokes or not. For the area features which belong to the same one stroke, they normally present a linear like pat-tern, and in order to preserve this kind of pattern, typification is chosen as the operator to implement the generalization work. For the remaining area features which are not related by strokes, they are still distributed randomly and discretely, and the selection is chosen to conduct the generalization operation. For the purpose of retaining their original distribution characteristic, a Polarization Transportation (PT) method is introduced to implement the selection operation. Buildings and lakes are selected as the representatives of artificial area feature and natural area feature respectively to take the experiments. The generalized results indicate that by adopting this proposed strategy, the original distribution characteristics of building and lake data can be preserved, and the visual perception is pre-served as before.

  5. Method of identifying features in indexed data

    DOEpatents

    Jarman, Kristin H [Richland, WA; Daly, Don Simone [Richland, WA; Anderson, Kevin K [Richland, WA; Wahl, Karen L [Richland, WA

    2001-06-26

    The present invention is a method of identifying features in indexed data, especially useful for distinguishing signal from noise in data provided as a plurality of ordered pairs. Each of the plurality of ordered pairs has an index and a response. The method has the steps of: (a) providing an index window having a first window end located on a first index and extending across a plurality of indices to a second window end; (b) selecting responses corresponding to the plurality of indices within the index window and computing a measure of dispersion of the responses; and (c) comparing the measure of dispersion to a dispersion critical value. Advantages of the present invention include minimizing signal to noise ratio, signal drift, varying baseline signal and combinations thereof.

  6. Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

    NASA Astrophysics Data System (ADS)

    Farsadnia, Farhad; Ghahreman, Bijan

    2016-04-01

    Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

  7. An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.

    PubMed

    Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui

    2017-08-17

    It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.

  8. Key clinical features to identify girls with CDKL5 mutations.

    PubMed

    Bahi-Buisson, Nadia; Nectoux, Juliette; Rosas-Vargas, Haydeé; Milh, Mathieu; Boddaert, Nathalie; Girard, Benoit; Cances, Claude; Ville, Dorothée; Afenjar, Alexandra; Rio, Marlène; Héron, Delphine; N'guyen Morel, Marie Ange; Arzimanoglou, Alexis; Philippe, Christophe; Jonveaux, Philippe; Chelly, Jamel; Bienvenu, Thierry

    2008-10-01

    Mutations in the human X-linked cyclin-dependent kinase-like 5 (CDKL5) gene have been shown to cause infantile spasms as well as Rett syndrome (RTT)-like phenotype. To date, less than 25 different mutations have been reported. So far, there are still little data on the key clinical diagnosis criteria and on the natural history of CDKL5-associated encephalopathy. We screened the entire coding region of CDKL5 for mutations in 183 females with encephalopathy with early seizures by denaturing high liquid performance chromatography and direct sequencing, and we identified in 20 unrelated girls, 18 different mutations including 7 novel mutations. These mutations were identified in eight patients with encephalopathy with RTT-like features, five with infantile spasms and seven with encephalopathy with refractory epilepsy. Early epilepsy with normal interictal EEG and severe hypotonia are the key clinical features in identifying patients likely to have CDKL5 mutations. Our study also indicates that these patients clearly exhibit some RTT features such as deceleration of head growth, stereotypies and hand apraxia and that these RTT features become more evident in older and ambulatory patients. However, some RTT signs are clearly absent such as the so called RTT disease profile (period of nearly normal development followed by regression with loss of acquired fine finger skill in early childhood and characteristic intensive eye communication) and the characteristic evolution of the RTT electroencephalogram. Interestingly, in addition to the overall stereotypical symptomatology (age of onset and evolution of the disease) resulting from CDKL5 mutations, atypical forms of CDKL5-related conditions have also been observed. Our data suggest that phenotypic heterogeneity does not correlate with the nature or the position of the mutations or with the pattern of X-chromosome inactivation, but most probably with the functional transcriptional and/or translational consequences of CDKL5

  9. Clinical features that identify children with primary immunodeficiency diseases.

    PubMed

    Subbarayan, Anbezhil; Colarusso, Gloria; Hughes, Stephen M; Gennery, Andrew R; Slatter, Mary; Cant, Andrew J; Arkwright, Peter D

    2011-05-01

    The 10 warning signs of primary immunodeficiency diseases (PID) have been promoted by various organizations in Europe and the United States to predict PID. However, the ability of these warning signs to identify children with PID has not been rigorously tested. The main goal of this study was to determine the effectiveness of these 10 warning signs in predicting defined PID among children who presented to 2 tertiary pediatric immunodeficiency centers in the north of England. A retrospective survey of 563 children who presented to 2 pediatric immunodeficiency centers was undertaken. The clinical records of 430 patients with a defined PID and 133 patients for whom detailed investigations failed to establish a specific PID were reviewed. Overall, 96% of the children with PID were referred by hospital clinicians. The strongest identifiers of PID were a family history of immunodeficiency disease in addition to use of intravenous antibiotics for sepsis in children with neutrophil PID and failure to thrive in children with T-lymphocyte PID. With these 3 signs, 96% of patients with neutrophil and complement deficiencies and 89% of children with T-lymphocyte immunodeficiencies could be identified correctly. Family history was the only warning sign that identified children with B-lymphocyte PID. PID awareness initiatives should be targeted at hospital pediatricians and families with a history of PID rather than the general public. Our results provide the general pediatrician with a simple refinement of 10 warning signs for identifying children with underlying immunodeficiency diseases.

  10. Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution

    PubMed Central

    2013-01-01

    Background Glycoproteins are involved in a diverse range of biochemical and biological processes. Changes in protein glycosylation are believed to occur in many diseases, particularly during cancer initiation and progression. The identification of biomarkers for human disease states is becoming increasingly important, as early detection is key to improving survival and recovery rates. To this end, the serum glycome has been proposed as a potential source of biomarkers for different types of cancers. High-throughput hydrophilic interaction liquid chromatography (HILIC) technology for glycan analysis allows for the detailed quantification of the glycan content in human serum. However, the experimental data from this analysis is compositional by nature. Compositional data are subject to a constant-sum constraint, which restricts the sample space to a simplex. Statistical analysis of glycan chromatography datasets should account for their unusual mathematical properties. As the volume of glycan HILIC data being produced increases, there is a considerable need for a framework to support appropriate statistical analysis. Proposed here is a methodology for feature selection in compositional data. The principal objective is to provide a template for the analysis of glycan chromatography data that may be used to identify potential glycan biomarkers. Results A greedy search algorithm, based on the generalized Dirichlet distribution, is carried out over the feature space to search for the set of “grouping variables” that best discriminate between known group structures in the data, modelling the compositional variables using beta distributions. The algorithm is applied to two glycan chromatography datasets. Statistical classification methods are used to test the ability of the selected features to differentiate between known groups in the data. Two well-known methods are used for comparison: correlation-based feature selection (CFS) and recursive partitioning (rpart). CFS

  11. Somatization symptoms and hypochondriacal features in the general population.

    PubMed

    Rief, W; Hessel, A; Braehler, E

    2001-01-01

    The principal goal of this study is to examine the base rates of somatoform symptoms and of hypochondriacal features in the general population. A representative sample of 2050 persons in Germany was examined by use of screening for somatoform symptoms and the Whiteley Index. The most frequent somatoform symptoms were back pain, joint pain, pain in extremities, and headache, as well as abdominal symptoms (bloating or intolerance of several foods) and cardiovascular symptoms (palpitation). People reported a mean of two somatization symptoms of DSM-IV somatization disorder (SD) during the prior 2 years. Strong age and medium gender effects were found for most somatoform symptoms, as well as for composite indices. However, the sex ratio suggested in DSM-IV for SD seems to be an overestimation. Hypochondriacal features showed only small sex differences but, again, pronounced age effects. In contrast to low rates for SD, the base rates for somatization and hypochondriacal features were high and represented the health care relevance of subthreshold syndromes. We present base rates of hypochondriacal and somatization features that may be important facets in the development of classification criteria and in the interpretation of health care expenditure.

  12. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting

    PubMed Central

    Caie, Peter D.; Zhou, Ying; Turnbull, Arran K.; Oniscu, Anca; Harrison, David J.

    2016-01-01

    A number of candidate histopathologic factors show promise in identifying stage II colorectal cancer (CRC) patients at a high risk of disease-specific death, however they can suffer from low reproducibility and none have replaced classical pathologic staging. We developed an image analysis algorithm which standardized the quantification of specific histopathologic features and exported a multi-parametric feature-set captured without bias. The image analysis algorithm was executed across a training set (n = 50) and the resultant big data was distilled through decision tree modelling to identify the most informative parameters to sub-categorize stage II CRC patients. The most significant, and novel, parameter identified was the ‘sum area of poorly differentiated clusters’ (AreaPDC). This feature was validated across a second cohort of stage II CRC patients (n = 134) (HR = 4; 95% CI, 1.5– 11). Finally, the AreaPDC was integrated with the significant features within the clinical pathology report, pT stage and differentiation, into a novel prognostic index (HR = 7.5; 95% CI, 3–18.5) which improved upon current clinical staging (HR = 4.26; 95% CI, 1.7– 10.3). The identification of poorly differentiated clusters as being highly significant in disease progression presents evidence to suggest that these features could be the source of novel targets to decrease the risk of disease specific death. PMID:27322148

  13. Determining local and contextual features describing appearance of difficult to identify mitotic figures

    NASA Astrophysics Data System (ADS)

    Gandomkar, Ziba; Brennan, Patrick C.; Mello-Thoms, Claudia

    2017-03-01

    Mitotic count is helpful in determining the aggressiveness of breast cancer. In previous studies, it was shown that the agreement among pathologists for grading mitotic index is fairly modest, as mitoses have a large variety of appearances and they could be mistaken for other similar objects. In this study, we determined local and contextual features that differ significantly between easily identifiable mitoses and challenging ones. The images were obtained from the Mitosis-Atypia 2014 challenge. In total, the dataset contained 453 mitotic figures. Two pathologists annotated each mitotic figure. In case of disagreement, an opinion from a third pathologist was requested. The mitoses were grouped into three categories, those recognized as "a true mitosis" by both pathologists ,those labelled as "a true mitosis" by only one of the first two readers and also the third pathologist, and those annotated as "probably a mitosis" by all readers or the majority of them. After color unmixing, the mitoses were segmented from H channel. Shape-based features along with intensity-based and textural features were extracted from H-channel, blue ratio channel and five different color spaces. Holistic features describing each image were also considered. The Kruskal-Wallis H test was used to identify significantly different features. Multiple comparisons were done using the rank-based version of Tukey-Kramer test. The results indicated that there are local and global features which differ significantly among different groups. In addition, variations between mitoses in different groups were captured in the features from HSL and LCH color space more than other ones.

  14. Generalized query-based active learning to identify differentially methylated regions in DNA.

    PubMed

    Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

    2013-01-01

    Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

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

    PubMed

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

    2015-01-01

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

  16. Identifying the features of an exercise addiction: A Delphi study

    PubMed Central

    Macfarlane, Lucy; Owens, Glynn; Cruz, Borja del Pozo

    2016-01-01

    Objectives There remains limited consensus regarding the definition and conceptual basis of exercise addiction. An understanding of the factors motivating maintenance of addictive exercise behavior is important for appropriately targeting intervention. The aims of this study were twofold: first, to establish consensus on features of an exercise addiction using Delphi methodology and second, to identify whether these features are congruous with a conceptual model of exercise addiction adapted from the Work Craving Model. Methods A three-round Delphi process explored the views of participants regarding the features of an exercise addiction. The participants were selected from sport and exercise relevant domains, including physicians, physiotherapists, coaches, trainers, and athletes. Suggestions meeting consensus were considered with regard to the proposed conceptual model. Results and discussion Sixty-three items reached consensus. There was concordance of opinion that exercising excessively is an addiction, and therefore it was appropriate to consider the suggestions in light of the addiction-based conceptual model. Statements reaching consensus were consistent with all three components of the model: learned (negative perfectionism), behavioral (obsessive–compulsive drive), and hedonic (self-worth compensation and reduction of negative affect and withdrawal). Conclusions Delphi methodology allowed consensus to be reached regarding the features of an exercise addiction, and these features were consistent with our hypothesized conceptual model of exercise addiction. This study is the first to have applied Delphi methodology to the exercise addiction field, and therefore introduces a novel approach to exercise addiction research that can be used as a template to stimulate future examination using this technique. PMID:27554504

  17. A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

    PubMed

    Wang, Nizhuan; Chang, Chunqi; Zeng, Weiming; Shi, Yuhu; Yan, Hongjie

    2017-01-01

    Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

  18. Partial polygon pruning of hydrographic features in automated generalization

    USGS Publications Warehouse

    Stum, Alexander K.; Buttenfield, Barbara P.; Stanislawski, Larry V.

    2017-01-01

    This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservoir arms, stream channels that are too narrow at the target scale, and islands that begin to coalesce—entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the U.S. Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This paper describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the United States.

  19. Trajectory analysis via a geometric feature space approach

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

    Rintoul, Mark D.; Wilson, Andrew T.

    This study aimed to organize a body of trajectories in order to identify, search for and classify both common and uncommon behaviors among objects such as aircraft and ships. Existing comparison functions such as the Fréchet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as the total distance traveled and the distance between start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally,more » these features can generally be mapped easily to behaviors of interest to humans who are searching large databases. Most of these geometric features are invariant under rigid transformation. Furthermore, we demonstrate the use of different subsets of these features to identify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories and identify outliers.« less

  20. Trajectory analysis via a geometric feature space approach

    DOE PAGES

    Rintoul, Mark D.; Wilson, Andrew T.

    2015-10-05

    This study aimed to organize a body of trajectories in order to identify, search for and classify both common and uncommon behaviors among objects such as aircraft and ships. Existing comparison functions such as the Fréchet distance are computationally expensive and yield counterintuitive results in some cases. We propose an approach using feature vectors whose components represent succinctly the salient information in trajectories. These features incorporate basic information such as the total distance traveled and the distance between start/stop points as well as geometric features related to the properties of the convex hull, trajectory curvature and general distance geometry. Additionally,more » these features can generally be mapped easily to behaviors of interest to humans who are searching large databases. Most of these geometric features are invariant under rigid transformation. Furthermore, we demonstrate the use of different subsets of these features to identify trajectories similar to an exemplar, cluster a database of several hundred thousand trajectories and identify outliers.« less

  1. Evaluation of features to support safety and quality in general practice clinical software

    PubMed Central

    2011-01-01

    Background Electronic prescribing is now the norm in many countries. We wished to find out if clinical software systems used by general practitioners in Australia include features (functional capabilities and other characteristics) that facilitate improved patient safety and care, with a focus on quality use of medicines. Methods Seven clinical software systems used in general practice were evaluated. Fifty software features that were previously rated as likely to have a high impact on safety and/or quality of care in general practice were tested and are reported here. Results The range of results for the implementation of 50 features across the 7 clinical software systems was as follows: 17-31 features (34-62%) were fully implemented, 9-13 (18-26%) partially implemented, and 9-20 (18-40%) not implemented. Key findings included: Access to evidence based drug and therapeutic information was limited. Decision support for prescribing was available but varied markedly between systems. During prescribing there was potential for medicine mis-selection in some systems, and linking a medicine with its indication was optional. The definition of 'current medicines' versus 'past medicines' was not always clear. There were limited resources for patients, and some medicines lists for patients were suboptimal. Results were provided to the software vendors, who were keen to improve their systems. Conclusions The clinical systems tested lack some of the features expected to support patient safety and quality of care. Standards and certification for clinical software would ensure that safety features are present and that there is a minimum level of clinical functionality that clinicians could expect to find in any system.

  2. An expert botanical feature extraction technique based on phenetic features for identifying plant species.

    PubMed

    Kolivand, Hoshang; Fern, Bong Mei; Rahim, Mohd Shafry Mohd; Sulong, Ghazali; Baker, Thar; Tully, David

    2018-01-01

    In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.

  3. An expert botanical feature extraction technique based on phenetic features for identifying plant species

    PubMed Central

    Fern, Bong Mei; Rahim, Mohd Shafry Mohd; Sulong, Ghazali; Baker, Thar; Tully, David

    2018-01-01

    In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost. PMID:29420568

  4. Identifying key features of early stressful experiences that produce stress vulnerability and resilience in primates

    PubMed Central

    Parker, Karen J.; Maestripieri, Dario

    2010-01-01

    This article examines the complex role of early stressful experiences in producing both vulnerability and resilience to later stress-related psychopathology in a variety of primate models of human development. Two types of models are reviewed: Parental Separation Models (e.g., isolate-rearing, peer-rearing, parental separations, and stress inoculation) and Maternal Behavior Models (e.g., foraging demands, variation in maternal style, and maternal abuse). Based on empirical evidence, it is argued that early life stress exposure does not increase adult vulnerability to stress-related psychopathology as a linear function, as is generally believed, but instead reflects a quadratic function. Features of early stress exposure including the type, duration, frequency, ecological validity, sensory modality, and developmental timing, within and between species, are identified to better understand how early stressful experiences alter neurobiological systems to produce such diverse developmental outcomes. This article concludes by identifying gaps in our current knowledge, providing directions for future research, and discussing the translational implications of these primate models for human development and psychopathology. PMID:20851145

  5. Learning from patients: Identifying design features of medicines that cause medication use problems.

    PubMed

    Notenboom, Kim; Leufkens, Hubert Gm; Vromans, Herman; Bouvy, Marcel L

    2017-01-30

    Usability is a key factor in ensuring safe and efficacious use of medicines. However, several studies showed that people experience a variety of problems using their medicines. The purpose of this study was to identify design features of oral medicines that cause use problems among older patients in daily practice. A qualitative study with semi-structured interviews on the experiences of older people with the use of their medicines was performed (n=59). Information on practical problems, strategies to overcome these problems and the medicines' design features that caused these problems were collected. The practical problems and management strategies were categorised into 'use difficulties' and 'use errors'. A total of 158 use problems were identified, of which 45 were categorized as use difficulties and 113 as use error. Design features that contributed the most to the occurrence of use difficulties were the dimensions and surface texture of the dosage form (29.6% and 18.5%, respectively). Design features that contributed the most to the occurrence of use errors were the push-through force of blisters (22.1%) and tamper evident packaging (12.1%). These findings will help developers of medicinal products to proactively address potential usability issues with their medicines. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  6. Newborn human brain identifies repeated auditory feature conjunctions of low sequential probability.

    PubMed

    Ruusuvirta, Timo; Huotilainen, Minna; Fellman, Vineta; Näätänen, Risto

    2004-11-01

    Natural environments are usually composed of multiple sources for sounds. The sounds might physically differ from one another only as feature conjunctions, and several of them might occur repeatedly in the short term. Nevertheless, the detection of rare sounds requires the identification of the repeated ones. Adults have some limited ability to effortlessly identify repeated sounds in such acoustically complex environments, but the developmental onset of this finite ability is unknown. Sleeping newborn infants were presented with a repeated tone carrying six frequent (P = 0.15 each) and six rare (P approximately 0.017 each) conjunctions of its frequency, intensity and duration. Event-related potentials recorded from the infants' scalp were found to shift in amplitude towards positive polarity selectively in response to rare conjunctions. This finding suggests that humans are relatively hard-wired to preattentively identify repeated auditory feature conjunctions even when such conjunctions occur rarely among other similar ones.

  7. Microbiota fingerprints lose individually identifying features over time.

    PubMed

    Wilkins, David; Leung, Marcus H Y; Lee, Patrick K H

    2017-01-09

    Humans host individually unique skin microbiota, suggesting that microbiota traces transferred from skin to surfaces could serve as forensic markers analogous to fingerprints. While it is known that individuals leave identifiable microbiota traces on surfaces, it is not clear for how long these traces persist. Moreover, as skin and surface microbiota change with time, even persistent traces may lose their forensic potential as they would cease to resemble the microbiota of the person who left them. We followed skin and surface microbiota within households for four seasons to determine whether accurate microbiota-based matching of individuals to their households could be achieved across long time delays. While household surface microbiota traces could be matched to the correct occupant or occupants with 67% accuracy, accuracy decreased substantially when skin and surface samples were collected in different seasons, and particularly when surface samples were collected long after skin samples. Most OTUs persisted on skin or surfaces for less than one season, indicating that OTU loss was the major cause of decreased matching accuracy. OTUs that were more useful for individual identification persisted for less time and were less likely to be deposited from skin to surface, suggesting a trade-off between the longevity and identifying value of microbiota traces. While microbiota traces have potential forensic value, unlike fingerprints they are not static and may degrade in a way that preferentially erases features useful in identifying individuals.

  8. Generalizations of the subject-independent feature set for music-induced emotion recognition.

    PubMed

    Lin, Yuan-Pin; Chen, Jyh-Horng; Duann, Jeng-Ren; Lin, Chin-Teng; Jung, Tzyy-Ping

    2011-01-01

    Electroencephalogram (EEG)-based emotion recognition has been an intensely growing field. Yet, how to achieve acceptable accuracy on a practical system with as fewer electrodes as possible is less concerned. This study evaluates a set of subject-independent features, based on differential power asymmetry of symmetric electrode pairs [1], with emphasis on its applicability to subject variability in music-induced emotion classification problem. Results of this study have evidently validated the feasibility of using subject-independent EEG features to classify four emotional states with acceptable accuracy in second-scale temporal resolution. These features could be generalized across subjects to detect emotion induced by music excerpts not limited to the music database that was used to derive the emotion-specific features.

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

    PubMed Central

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

    2015-01-01

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

  10. Generalized Feature Extraction for Wrist Pulse Analysis: From 1-D Time Series to 2-D Matrix.

    PubMed

    Dimin Wang; Zhang, David; Guangming Lu

    2017-07-01

    Traditional Chinese pulse diagnosis, known as an empirical science, depends on the subjective experience. Inconsistent diagnostic results may be obtained among different practitioners. A scientific way of studying the pulse should be to analyze the objectified wrist pulse waveforms. In recent years, many pulse acquisition platforms have been developed with the advances in sensor and computer technology. And the pulse diagnosis using pattern recognition theories is also increasingly attracting attentions. Though many literatures on pulse feature extraction have been published, they just handle the pulse signals as simple 1-D time series and ignore the information within the class. This paper presents a generalized method of pulse feature extraction, extending the feature dimension from 1-D time series to 2-D matrix. The conventional wrist pulse features correspond to a particular case of the generalized models. The proposed method is validated through pattern classification on actual pulse records. Both quantitative and qualitative results relative to the 1-D pulse features are given through diabetes diagnosis. The experimental results show that the generalized 2-D matrix feature is effective in extracting both the periodic and nonperiodic information. And it is practical for wrist pulse analysis.

  11. How Can Students Generalize Examples? Focusing on the Generalizing Geometric Properties

    ERIC Educational Resources Information Center

    Park, JinHyeong; Kim, Dong-Won

    2017-01-01

    The purpose of this study is to determine the progression of exemplifying and example generalization by students. We investigated whether example generalization occurs by analyzing collected data by identifying whether students recognize, describe, and define general features of geometric examples. We also investigate how example generalization…

  12. Identifying the nontechnical skills required of nurses in general surgical wards.

    PubMed

    Marshall, Dianne C; Finlayson, Mary P

    2018-04-01

    To identify the nontechnical skills (NTS) required of nurses in general surgical wards for safe and effective care. As the largest occupational group, nurses are in an ideal position to block the vulnerabilities of patient adverse events in a surgical ward. Previous studies in the surgical environment have identified the NTS required of nurses for safe care in operating rooms; however, these skills have not been identified for nurses in general surgical wards. A nonparticipant observational descriptive design was used. A purposive sample of 15 registered nurses was recruited from four surgical wards and observed for a full shift on a morning, afternoon or night shift. Nonparticipant observations were conducted using field notes to collect data. A coding frame was developed, and an inductive process was used to analyse the data. A taxonomy comprising seven NTS required of nurses in their roles in surgical ward teams emerged from the data analysis. They are communication, leadership and management, planning, decision-making, situation awareness, teamwork and patient advocacy. Patient care provided by general surgical nurses involved the seven identified key NTS. These particular NTS are an important component of safe nursing practice as they underpin the provision of safe and effective care for general surgical patients. Nurses block the trajectory of error by using NTS to address the vulnerabilities in the system that can lead to adverse patient events. Identifying general surgical nurses' NTS enables the development of teaching strategies that target the learning of those skills to achieve successful work outcomes and improve patient safety. © 2018 John Wiley & Sons Ltd.

  13. Feature-specific attention allocation modulates the generalization of recently acquired likes and dislikes.

    PubMed

    Spruyt, Adriaan; Klauer, Karl Christoph; Gast, Anne; De Schryver, Maarten; De Houwer, Jan

    2014-01-01

    We examined whether the generalization of recently acquired likes and dislikes depends on feature-specific attention allocation. Likes and dislikes were established by means of an evaluative-conditioning procedure in which participants were presented with several exemplars of two subordinate categories (e.g., young men vs. old women). Whereas exemplars of one category were consistently paired with negative stimuli, exemplars of the second category were consistently paired with positive stimuli. In addition, we manipulated feature-specific attention allocation for specific stimulus dimensions (e.g., gender vs. age), either during (Experiments 1 and 2) or before the acquisition phase of the experiment (Experiment 3). Both direct and indirect attitude measures revealed a clear impact of this manipulation on attitude generalization. More specifically, only generalization stimuli that were similar to the CSs in terms of the stimulus dimension that was selectively attended to were evaluated in a manner that was congruent with the acquired liking of those CSs.

  14. Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data.

    PubMed

    Liu, Zhenqiu; Sun, Fengzhu; McGovern, Dermot P

    2017-01-01

    Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L 1 , SCAD and MC+. However, none of the existing algorithms optimizes L 0 , which penalizes the number of nonzero features directly. In this paper, we develop a novel sparse generalized linear model (GLM) with L 0 approximation for feature selection and prediction with big omics data. The proposed approach approximate the L 0 optimization directly. Even though the original L 0 problem is non-convex, the problem is approximated by sequential convex optimizations with the proposed algorithm. The proposed method is easy to implement with only several lines of code. Novel adaptive ridge algorithms ( L 0 ADRIDGE) for L 0 penalized GLM with ultra high dimensional big data are developed. The proposed approach outperforms the other cutting edge regularization methods including SCAD and MC+ in simulations. When it is applied to integrated analysis of mRNA, microRNA, and methylation data from TCGA ovarian cancer, multilevel gene signatures associated with suboptimal debulking are identified simultaneously. The biological significance and potential clinical importance of those genes are further explored. The developed Software L 0 ADRIDGE in MATLAB is available at https://github.com/liuzqx/L0adridge.

  15. Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications

    NASA Astrophysics Data System (ADS)

    Fernández, Ariel; Ferrari, José A.

    2017-05-01

    Pattern recognition and feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital-only methods. We explore an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a pupil mask implemented on a high-contrast spatial light modulator for orientation/shape variation of the template. Real-time can also be achieved. In addition, by thresholding of the GHT and optically inverse transforming, the previously detected features of interest can be extracted.

  16. A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes

    PubMed Central

    Liu, Jian; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Wang, Xue-Song; Wang, Dong

    2015-01-01

    In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data. PMID:26201006

  17. A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.

    PubMed

    Liu, Jian; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Wang, Xue-Song; Wang, Dong

    2015-01-01

    In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data.

  18. The value of anthropometric indices for identifying women with features of metabolic syndrome

    USDA-ARS?s Scientific Manuscript database

    BMI is a widely used anthropometric measure for identifying CVD and metabolic syndrome (MetS) risk. Two new anthropometric indices are A Body Shape Index (ABSI) and Body Roundness Index (BRI) that may provide better correlations to features of MetS. Methods: Subject data were obtained from 91 over...

  19. System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

    DOEpatents

    Lu, Bin; Harley, Ronald G.; Du, Liang; Yang, Yi; Sharma, Santosh K.; Zambare, Prachi; Madane, Mayura A.

    2014-06-17

    A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

  20. Hydro-geomorphic connectivity and landslide features extraction to identifying potential threats and hazardous areas

    NASA Astrophysics Data System (ADS)

    Tarolli, Paolo; Fuller, Ian C.; Basso, Federica; Cavalli, Marco; Sofia, Giulia

    2017-04-01

    Hydro-geomorphic connectivity has significantly emerged as a new concept to understand the transfer of surface water and sediment through landscapes. A further scientific challenge is determining how the concept can be used to enable sustainable land and water management. This research proposes an interesting approach to integrating remote sensing techniques, connectivity theory, and geomorphometry based on high-resolution digital terrain model (HR-DTMs) to automatically extract landslides crowns and gully erosion, to determine the different rate of connectivity among the main extracted features and the river network, and thus determine a possible categorization of hazardous areas. The study takes place in two mountainous regions in the Wellington Region (New Zealand). The methodology is a three step approach. Firstly, we performed an automatic detection of the likely landslides crowns through the use of thresholds obtained by the statistical analysis of the variability of landform curvature. After that, the research considered the Connectivity Index to analyse how a complex and rugged topography induces large variations in erosion and sediment delivery in the two catchments. Lastly, the two methods have been integrated to create a unique procedure able to classify the different rate of connectivity among the main features and the river network and thus identifying potential threats and hazardous areas. The methodology is fast, and it can produce a detailed and updated inventory map that could be a key tool for erosional and sediment delivery hazard mitigation. This fast and simple method can be a useful tool to manage emergencies giving priorities to more failure-prone zones. Furthermore, it could be considered to do a preliminary interpretations of geomorphological phenomena and more in general, it could be the base to develop inventory maps. References Cavalli M, Trevisani S, Comiti F, Marchi L. 2013. Geomorphometric assessment of spatial sediment connectivity

  1. Features of hypochondriasis and illness worry in the general population in Germany.

    PubMed

    Martin, Alexandra; Jacobi, Frank

    2006-01-01

    Although hypochondriasis is considered to be of high relevance in the healthcare sector, its prevalence in the general population has been investigated in few studies. The aims of this study were to estimate prevalence rates of hypochondriasis and of subthreshold conditions and to describe their associated features such as quality of life and healthcare utilization in a representative community sample. Analyses of the present study are based on the German Health Interview and Examination Survey-Mental Health Supplement (N = 4181, representative for the German population from 18-65 years). The assessment included interviews for somatic conditions and mental disorders and self-report ratings on health-related quality of life, healthcare utilization, disability days, and physical activity. Only three cases (0.05%) were identified as meeting full criteria of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) hypochondriasis. The prevalence rate of the less restrictively defined form of hypochondriasis, ("subthreshold hypochondriasis") was 0.58% and an additional 2.12% reported having had illness worries for at least 6 months but did not meet further hypochondriasis criteria. The two subthreshold diagnostic groups provided strong evidence of difference from the nonhypochondriac controls: comorbidity with psychiatric and medical disorders and healthcare utilization were higher, and quality of life was markedly reduced. The results provide additional support to not only consider "full" DSM-IV hypochondriasis, which is a very rare disorder in the general population, but also to include less restrictive hypochondriac conditions--associated with a clinically relevant degree of psychological and physical impairment--into clinical and scientific considerations.

  2. Merging K-means with hierarchical clustering for identifying general-shaped groups.

    PubMed

    Peterson, Anna D; Ghosh, Arka P; Maitra, Ranjan

    2018-01-01

    Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K -means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K -means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

  3. Identifying predictive features in drug response using machine learning: opportunities and challenges.

    PubMed

    Vidyasagar, Mathukumalli

    2015-01-01

    This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

  4. System and method employing a minimum distance and a load feature database to identify electric load types of different electric loads

    DOEpatents

    Lu, Bin; Yang, Yi; Sharma, Santosh K; Zambare, Prachi; Madane, Mayura A

    2014-12-23

    A method identifies electric load types of a plurality of different electric loads. The method includes providing a load feature database of a plurality of different electric load types, each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal and a current signal for each of the different electric loads; determining a second load feature vector comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types of the load feature database.

  5. Exploring the limits of identifying sub-pixel thermal features using ASTER TIR data

    USGS Publications Warehouse

    Vaughan, R.G.; Keszthelyi, L.P.; Davies, A.G.; Schneider, D.J.; Jaworowski, C.; Heasler, H.

    2010-01-01

    Understanding the characteristics of volcanic thermal emissions and how they change with time is important for forecasting and monitoring volcanic activity and potential hazards. Satellite instruments view volcanic thermal features across the globe at various temporal and spatial resolutions. Thermal features that may be a precursor to a major eruption, or indicative of important changes in an on-going eruption can be subtle, making them challenging to reliably identify with satellite instruments. The goal of this study was to explore the limits of the types and magnitudes of thermal anomalies that could be detected using satellite thermal infrared (TIR) data. Specifically, the characterization of sub-pixel thermal features with a wide range of temperatures is considered using ASTER multispectral TIR data. First, theoretical calculations were made to define a "thermal mixing detection threshold" for ASTER, which quantifies the limits of ASTER's ability to resolve sub-pixel thermal mixing over a range of hot target temperatures and % pixel areas. Then, ASTER TIR data were used to model sub-pixel thermal features at the Yellowstone National Park geothermal area (hot spring pools with temperatures from 40 to 90 ??C) and at Mount Erebus Volcano, Antarctica (an active lava lake with temperatures from 200 to 800 ??C). Finally, various sources of uncertainty in sub-pixel thermal calculations were quantified for these empirical measurements, including pixel resampling, atmospheric correction, and background temperature and emissivity assumptions.

  6. Principal component analysis of three-dimensional face shape: Identifying shape features that change with age.

    PubMed

    Kurosumi, M; Mizukoshi, K

    2018-05-01

    The types of shape feature that constitutes a face have not been comprehensively established, and most previous studies of age-related changes in facial shape have focused on individual characteristics, such as wrinkle, sagging skin, etc. In this study, we quantitatively measured differences in face shape between individuals and investigated how shape features changed with age. We analyzed three-dimensionally the faces of 280 Japanese women aged 20-69 years and used principal component analysis to establish the shape features that characterized individual differences. We also evaluated the relationships between each feature and age, clarifying the shape features characteristic of different age groups. Changes in facial shape in middle age were a decreased volume of the upper face and increased volume of the whole cheeks and around the chin. Changes in older people were an increased volume of the lower cheeks and around the chin, sagging skin, and jaw distortion. Principal component analysis was effective for identifying facial shape features that represent individual and age-related differences. This method allowed straightforward measurements, such as the increase or decrease in cheeks caused by soft tissue changes or skeletal-based changes to the forehead or jaw, simply by acquiring three-dimensional facial images. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  7. [Evaluation of PAE and AE for identifying generalized tracks using snakes in Hidalgo, Mexico].

    PubMed

    Montiel-Canales, Gustavo; Mayer-Goyenechea, Irene Goyenechea; Fernández Badillo, Leonardo; Castillo Cerón, Jesús M

    2016-12-01

    One of the most important concepts in Panbiogeography is the generalized track, which represents an ancestral biota fragmented by geological events that can be recovered through several methods, including Parsimony analysis of endemicity (PAE) and endemicity analysis (EA). PAE has been frequently used to identify generalized tracks, while EA is primarily designed to find areas of endemicity, but has been recently proposed for identifying generalized tracks as well. In this study we evaluated these methods to find generalized tracks using the distribution of the 84 snake species of Hidalgo. PAE found one generalized track from three individual tracks (Agkistrodon taylori, Crotalus totonacus and Pliocercus elapoides), supported by 89 % of Bootstrap, and EA identified two generalized tracks, with endemicity index values of 2.71-2.96 and 2.84-3.09, respectively. Those areas were transformed to generalized tracks. The first generalized track was retrieved from three individual tracks (Micrurus bernadi, Rhadinaea marcellae and R. quinquelineata), and the second was recovered from two individual tracks (Geophis mutitorques and Thamnophis sumichrasti). These generalized tracks can be considered a unique distribution pattern, because they resembled each other and agreed in shape. When comparing both methods, we noted that both are useful for identifying generalized tracks, and although they can be used independently, we suggest their complementary use. Nevertheless, to obtain accurate results, it is useful to consider theoretical bases of both methods, along with an appropriate choice of the size of the area. Results using small-grid size in EA are ideal for searching biogeographical patterns within geopolitical limits. Furthermore, they can be used for conservation proposals at state level where endemic species become irreplaceable, and where losing them would imply the extinction of unique lineages.

  8. Identifying potential collapse features under highways : research implementation plan.

    DOT National Transportation Integrated Search

    2005-09-01

    There are many unmapped features under the states roadways that threaten them with major localized : collapse. The most common of these features are abandoned underground mines in the eastern part of : the state and sinkholes in portions of limest...

  9. Analysis of recently identified prostate cancer susceptibility loci in a population-based study: Associations with family history and clinical features

    PubMed Central

    FitzGerald, Liesel M.; Kwon, Erika M.; Koopmeiners, Joseph S.; Salinas, Claudia A.; Stanford, Janet L.; Ostrander, Elaine A.

    2009-01-01

    Purpose Two recent genome-wide association studies have highlighted several SNPs purported to be associated with prostate cancer risk. We investigated the significance of these SNPs in a population-based study of Caucasian men, testing the effects of each SNP in relation to family history of prostate cancer and clinicopathological features of disease. Experimental Design We genotyped 13 SNPs in 1,308 prostate cancer patients and 1,267 unaffected controls frequency matched to cases by five-year age groups. The association of each SNP with disease risk and stratified by family history of prostate cancer and clinicopathological features of disease was calculated using logistic and polytomous regression. Results These results confirm the importance of multiple previously reported SNPs in relation to prostate cancer susceptibility; 11 of the 13 SNPs were significantly associated with risk of developing prostate cancer. However, none of the SNP associations were of comparable magnitude to that associated with having a first-degree family history of the disease. Risk estimates associated with SNPs rs4242382 and rs2735839 varied by family history, while risk estimates for rs10993994 and rs5945619 varied by Gleason score. Conclusions Our results confirm that several recently identified SNPs are associated with prostate cancer risk; however the variant alleles only confer a low to moderate relative risk of disease and are generally not associated with more aggressive disease features. PMID:19366831

  10. A delphi exercise to identify characteristic features of gout - opinions from patients and physicians, the first stage in developing new classification criteria.

    PubMed

    Prowse, Rebecca L; Dalbeth, Nicola; Kavanaugh, Arthur; Adebajo, Adewale O; Gaffo, Angelo L; Terkeltaub, Robert; Mandell, Brian F; Suryana, Bagus P P; Goldenstein-Schainberg, Claudia; Diaz-Torne, Cèsar; Khanna, Dinesh; Lioté, Frederic; Mccarthy, Geraldine; Kerr, Gail S; Yamanaka, Hisashi; Janssens, Hein; Baraf, Herbert F; Chen, Jiunn-Horng; Vazquez-Mellado, Janitzia; Harrold, Leslie R; Stamp, Lisa K; Van De Laar, Mart A; Janssen, Matthijs; Doherty, Michael; Boers, Maarten; Edwards, N Lawrence; Gow, Peter; Chapman, Peter; Khanna, Puja; Helliwell, Philip S; Grainger, Rebecca; Schumacher, H Ralph; Neogi, Tuhina; Jansen, Tim L; Louthrenoo, Worawit; Sivera, Francisca; Taylor, William J; Alten, Rieke

    2013-04-01

    To identify a comprehensive list of features that might discriminate between gout and other rheumatic musculoskeletal conditions, to be used subsequently for a case-control study to develop and test new classification criteria for gout. Two Delphi exercises were conducted using Web-based questionnaires: one with physicians from several countries who had an interest in gout and one with patients from New Zealand who had gout. Physicians rated a list of potentially discriminating features that were identified by literature review and expert opinion, and patients rated a list of features that they generated themselves. Agreement was defined by the RAND/UCLA disagreement index. Forty-four experienced physicians and 9 patients responded to all iterations. For physicians, 71 items were identified by literature review and 15 more were suggested by physicians. The physician survey showed agreement for 26 discriminatory features and 15 as not discriminatory. The patients identified 46 features of gout, for which there was agreement on 25 items as being discriminatory and 7 items as not discriminatory. Patients and physicians agreed upon several key features of gout. Physicians emphasized objective findings, imaging, and patterns of symptoms, whereas patients emphasized severity, functional results, and idiographic perception of symptoms.

  11. Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features

    PubMed Central

    Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B.; Hofmann-Apitius, Martin

    2017-01-01

    Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes. PMID:28731430

  12. Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

    PubMed

    Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

    2017-01-01

    Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

  13. Identifying Medication Management Smartphone App Features Suitable for Young Adults With Developmental Disabilities: Delphi Consensus Study

    PubMed Central

    Salgado, Teresa M; Fedrigon, Alexa; Riccio Omichinski, Donna; Meade, Michelle A

    2018-01-01

    Background Smartphone apps can be a tool to facilitate independent medication management among persons with developmental disabilities. At present, multiple medication management apps exist in the market, but only 1 has been specifically designed for persons with developmental disabilities. Before initiating further app development targeting this population, input from stakeholders including persons with developmental disabilities, caregivers, and professionals regarding the most preferred features should be obtained. Objective The aim of this study was to identify medication management app features that are suitable to promote independence in the medication management process by young adults with developmental disabilities using a Delphi consensus method. Methods A compilation of medication management app features was performed by searching the iTunes App Store, United States, in February 2016, using the following terms: adherence, medication, medication management, medication list, and medication reminder. After identifying features within the retrieved apps, a final list of 42 features grouped into 4 modules (medication list, medication reminder, medication administration record, and additional features) was included in a questionnaire for expert consensus rating. A total of 52 experts in developmental disabilities, including persons with developmental disabilities, caregivers, and professionals, were invited to participate in a 3-round Delphi technique. The purpose was to obtain consensus on features that are preferred and suitable to promote independence in the medication management process among persons with developmental disabilities. Consensus for the first, second, and third rounds was defined as ≥90%, ≥80%, and ≥75% agreement, respectively. Results A total of 75 responses were received over the 3 Delphi rounds—30 in the first round, 24 in the second round, and 21 in the third round. At the end of the third round, cumulative consensus was achieved

  14. Features of Representations in General Chemistry Textbooks: A Peek through the Lens of the Cognitive Load Theory

    ERIC Educational Resources Information Center

    Nyachwaya, James M.; Gillaspie, Merry

    2016-01-01

    The goals of this study were (1) determine the prevalence of various features of representations in five general chemistry textbooks used in the United States, and (2) use cognitive load theory to draw implications of the various features of analyzed representations. We adapted the Graphical Analysis Protocol (GAP) (Slough et al., 2010) to look at…

  15. Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.

    PubMed

    Sabooh, M Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H F

    2018-05-01

    This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (m 5 C) plays a vital role in a number of biological processes. For better comprehension of the biological functions and mechanism it is necessary to recognize m 5 C sites in RNA precisely. The laboratory techniques and procedures are available to identify m 5 C sites in RNA, but these procedures require a lot of time and resources. This study develops a new computational method for extracting the features of RNA sequence. In this method, first the RNA sequence is encoded via composite feature vector, then, for the selection of discriminate features, the minimum-redundancy-maximum-relevance algorithm was used. Secondly, the classification method used has been based on a support vector machine by using jackknife cross validation test. The suggested method efficiently identifies m 5 C sites from non- m 5 C sites and the outcome of the suggested algorithm is 93.33% with sensitivity of 90.0 and specificity of 96.66 on bench mark datasets. The result exhibits that proposed algorithm shown significant identification performance compared to the existing computational techniques. This study extends the knowledge about the occurrence sites of RNA modification which paves the way for better comprehension of the biological uses and mechanism. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Identifying Trajectories of Borderline Personality Features in Adolescence: Antecedent and Interactive Risk Factors.

    PubMed

    Haltigan, John D; Vaillancourt, Tracy

    2016-03-01

    To examine trajectories of adolescent borderline personality (BP) features in a normative-risk cohort (n = 566) of Canadian children assessed at ages 13, 14, 15, and 16 and childhood predictors of trajectory group membership assessed at ages 8, 10, 11, and 12. Data were drawn from the McMaster Teen Study, an on-going study examining relations among bullying, mental health, and academic achievement. Participants and their parents completed a battery of mental health and peer relations questionnaires at each wave of the study. Academic competence was assessed at age 8 (Grade 3). Latent class growth analysis, analysis of variance, and logistic regression were used to analyze the data. Three distinct BP features trajectory groups were identified: elevated or rising, intermediate or stable, and low or stable. Parent- and child-reported mental health symptoms, peer relations risk factors, and intra-individual risk factors were significant predictors of elevated or rising and intermediate or stable trajectory groups. Child-reported attention-deficit hyperactivity disorder (ADHD) and somatization symptoms uniquely predicted elevated or rising trajectory group membership, whereas parent-reported anxiety and child-reported ADHD symptoms uniquely predicted intermediate or stable trajectory group membership. Child-reported somatization symptoms was the only predictor to differentiate the intermediate or stable and elevated or rising trajectory groups (OR 1.15, 95% CI 1.04 to 1.28). Associations between child-reported reactive temperament and elevated BP features trajectory group membership were 10.23 times higher among children who were bullied, supporting a diathesis-stress pathway in the development of BP features for these youth. Findings demonstrate the heterogeneous course of BP features in early adolescence and shed light on the potential prodromal course of later borderline personality disorder. © The Author(s) 2015.

  17. Martian aeolian features and deposits - Comparisons with general circulation model results

    NASA Astrophysics Data System (ADS)

    Greeley, R.; Skypeck, A.; Pollack, J. B.

    1993-02-01

    The relationships between near-surface winds and the distribution of wind-related features are investigated by means of a general circulation model of Mars' atmosphere. Predictions of wind surface stress as a function of season and dust optical depth are used to investigate the distribution and orientation of wind streaks, yardangs, and rock abundance on the surface. The global distribution of rocks on the surface correlates well with predicted wind stress, particularly during the dust storm season. The rocky areas are sites of strong winds, suggesting that fine material is swept away by the wind, leaving rocks and coarser material behind.

  18. Dependent Measure and Time Constraints Modulate the Competition between Conflicting Feature-Based and Rule-Based Generalization Processes

    ERIC Educational Resources Information Center

    Cobos, Pedro L.; Gutiérrez-Cobo, María J.; Morís, Joaquín; Luque, David

    2017-01-01

    In our study, we tested the hypothesis that feature-based and rule-based generalization involve different types of processes that may affect each other producing different results depending on time constraints and on how generalization is measured. For this purpose, participants in our experiments learned cue-outcome relationships that followed…

  19. Identifying Novel Transcriptional and Epigenetic Features of Nuclear Lamina-associated Genes.

    PubMed

    Wu, Feinan; Yao, Jie

    2017-03-07

    Because a large portion of the mammalian genome is associated with the nuclear lamina (NL), it is interesting to study how native genes resided there are transcribed and regulated. In this study, we report unique transcriptional and epigenetic features of nearly 3,500 NL-associated genes (NL genes). Promoter regions of active NL genes are often excluded from NL-association, suggesting that NL-promoter interactions may repress transcription. Active NL genes with higher RNA polymerase II (Pol II) recruitment levels tend to display Pol II promoter-proximal pausing, while Pol II recruitment and Pol II pausing are not correlated among non-NL genes. At the genome-wide scale, NL-association and H3K27me3 distinguishes two large gene classes with low transcriptional activities. Notably, NL-association is anti-correlated with both transcription and active histone mark levels among genes not significantly enriched with H3K9me3 or H3K27me3, suggesting that NL-association may represent a novel gene repression pathway. Interestingly, an NL gene subgroup is not significantly enriched with H3K9me3 or H3K27me3 and is transcribed at higher levels than the rest of NL genes. Furthermore, we identified distal enhancers associated with active NL genes and reported their epigenetic features.

  20. Identifying unmet needs in older patients--nurse-GP collaboration in general practice.

    PubMed

    Williams, Ian D; O'Doherty, Lorna J; Mitchell, Geoffrey K; Williams, Karen E

    2007-09-01

    Australia's rapidly aging population has a high prevalence of chronic disease and disability, leading to an increased social and economic burden. The Enhanced Primary Care program seeks to reduce this burden by promoting preventive and coordinated care. This study aimed to identify unmet needs in community dwelling general practice patients aged 75 years and over through annual health assessments performed by a general practitioner-nurse team. Community dwelling patients of a large suburban general practice aged 75 years and over were invited to participate. Five hundred and forty-six consecutive, eligible patients were recruited. Data were collected by GP-nurse teams on physical and psychosocial variables using a combination of physical examination, self reporting, and rating scales. Fifty percent of the women and 25% of the men lived alone. Over 90% of participants reported one or more health problems, with musculoskeletal issues being most common. Men rated their health more poorly than women. Incontinence affected one-third of patients, mainly women. Women reported more psychological distress. There were age and gender differences in activities of daily living (ADL). Mobility, ADL, visual impairment, bowel problems, use of sleep medications and psychological wellbeing were strongly associated to self reported health. Health assessments were effective in identifying significant physical and psychosocial problems in older adults. The importance of such assessments is underscored by strong associations between various domains and perceived general health. Collaboration between a GP and a practice based community nurse represents a potential solution to identifying (and responding to) unmet physical and psychosocial needs to improve quality of life in community dwelling older adults.

  1. Identifying multiple influential spreaders based on generalized closeness centrality

    NASA Astrophysics Data System (ADS)

    Liu, Huan-Li; Ma, Chuang; Xiang, Bing-Bing; Tang, Ming; Zhang, Hai-Feng

    2018-02-01

    To maximize the spreading influence of multiple spreaders in complex networks, one important fact cannot be ignored: the multiple spreaders should be dispersively distributed in networks, which can effectively reduce the redundance of information spreading. For this purpose, we define a generalized closeness centrality (GCC) index by generalizing the closeness centrality index to a set of nodes. The problem converts to how to identify multiple spreaders such that an objective function has the minimal value. By comparing with the K-means clustering algorithm, we find that the optimization problem is very similar to the problem of minimizing the objective function in the K-means method. Therefore, how to find multiple nodes with the highest GCC value can be approximately solved by the K-means method. Two typical transmission dynamics-epidemic spreading process and rumor spreading process are implemented in real networks to verify the good performance of our proposed method.

  2. A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data.

    PubMed

    Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu

    2018-01-01

    Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density ( d ), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction.

  3. A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data

    PubMed Central

    Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu

    2018-01-01

    Abstract Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density (d), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction. PMID:29707064

  4. kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets.

    PubMed

    Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S; Beer, Michael A

    2013-07-01

    Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167-80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org.

  5. kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

    PubMed Central

    Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

    2013-01-01

    Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

  6. The general/specific breakdown of semantic memory and the nature of superordinate knowledge: insights from superordinate and basic-level feature norms.

    PubMed

    Marques, J Frederico

    2007-12-01

    The deterioration of semantic memory usually proceeds from more specific to more general superordinate categories, although rarer cases of superordinate knowledge impairment have also been reported. The nature of superordinate knowledge and the explanation of these two semantic impairments were evaluated from the analysis of superordinate and basic-level feature norms. The results show that, in comparison to basic-level concepts, superordinate concepts are not generally less informative and have similar feature distinctiveness and proportion of individual sensory features, but their features are less shared by their members. Results are in accord with explanations based on feature connection weights and/or concept confusability for the superordinate advantage cases. Results especially support an explanation for superordinate impairments in terms of higher semantic control requirements as related to features being less shared between concept members. Implications for patients with semantic impairments are also discussed.

  7. Identifying At-Risk Students in General Chemistry via Cluster Analysis of Affective Characteristics

    ERIC Educational Resources Information Center

    Chan, Julia Y. K.; Bauer, Christopher F.

    2014-01-01

    The purpose of this study is to identify academically at-risk students in first-semester general chemistry using affective characteristics via cluster analysis. Through the clustering of six preselected affective variables, three distinct affective groups were identified: low (at-risk), medium, and high. Students in the low affective group…

  8. Memory instability as a gateway to generalization

    PubMed Central

    2018-01-01

    Our present frequently resembles our past. Patterns of actions and events repeat throughout our lives like a motif. Identifying and exploiting these patterns are fundamental to many behaviours, from creating grammar to the application of skill across diverse situations. Such generalization may be dependent upon memory instability. Following their formation, memories are unstable and able to interact with one another, allowing, at least in principle, common features to be extracted. Exploiting these common features creates generalized knowledge that can be applied across varied circumstances. Memory instability explains many of the biological and behavioural conditions necessary for generalization and offers predictions for how generalization is produced. PMID:29554094

  9. Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data

    PubMed Central

    Kate, Rohit J.; Swartz, Ann M.; Welch, Whitney A.; Strath, Scott J.

    2016-01-01

    Wearable accelerometers can be used to objectively assess physical activity. However, the accuracy of this assessment depends on the underlying method used to process the time series data obtained from accelerometers. Several methods have been proposed that use this data to identify the type of physical activity and estimate its energy cost. Most of the newer methods employ some machine learning technique along with suitable features to represent the time series data. This paper experimentally compares several of these techniques and features on a large dataset of 146 subjects doing eight different physical activities wearing an accelerometer on the hip. Besides features based on statistics, distance based features and simple discrete features straight from the time series were also evaluated. On the physical activity type identification task, the results show that using more features significantly improve results. Choice of machine learning technique was also found to be important. However, on the energy cost estimation task, choice of features and machine learning technique were found to be less influential. On that task, separate energy cost estimation models trained specifically for each type of physical activity were found to be more accurate than a single model trained for all types of physical activities. PMID:26862679

  10. Nightmares in the general population: identifying potential causal factors.

    PubMed

    Rek, Stephanie; Sheaves, Bryony; Freeman, Daniel

    2017-09-01

    Nightmares are inherently distressing, prevent restorative sleep, and are associated with a number of psychiatric problems, but have rarely been the subject of empirical study. Negative affect, linked to stressful events, is generally considered the key trigger of nightmares; hence nightmares have most often been considered in the context of post-traumatic stress disorder (PTSD). However, many individuals with heightened negative affect do not have nightmares. The objective of this study was to identify mechanistically plausible factors, beyond negative affect, that may explain why individuals experience nightmares. 846 participants from the UK general population completed an online survey about nightmare occurrence and severity (pre-occupation, distress, and impairment), negative affect, worry, depersonalisation, hallucinatory experiences, paranoia, alcohol use, sleep duration, physical activity levels, PTSD symptoms, and stressful life events. Associations of nightmares with the putative predictive factors were tested controlling for levels of negative affect. Analyses were also repeated controlling for levels of PTSD and the recent occurrence of stressful life events. Nightmare occurrence, adjusting for negative affect, was associated with higher levels of worry, depersonalisation, hallucinatory experiences, paranoia, and sleep duration (odds ratios 1.25-1.45). Nightmare severity, controlling for negative affect, was associated with higher levels of worry, depersonalisation, hallucinatory experiences, and paranoia (R 2 s: 0.33-0.39). Alcohol use and physical activity levels were not associated with nightmares. The study identifies a number of potential predictors of the occurrence and severity of nightmares. Causal roles require testing in future longitudinal, experimental, and treatment studies.

  11. Featured Image: Identifying Weird Galaxies

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2017-08-01

    Hoags Object, an example of a ring galaxy. [NASA/Hubble Heritage Team/Ray A. Lucas (STScI/AURA)]The above image (click for the full view) shows PanSTARRSobservationsof some of the 185 galaxies identified in a recent study as ring galaxies bizarre and rare irregular galaxies that exhibit stars and gas in a ring around a central nucleus. Ring galaxies could be formed in a number of ways; one theory is that some might form in a galaxy collision when a smaller galaxy punches through the center of a larger one, triggering star formation around the center. In a recent study, Ian Timmis and Lior Shamir of Lawrence Technological University in Michigan explore ways that we may be able to identify ring galaxies in the overwhelming number of images expected from large upcoming surveys. They develop a computer analysis method that automatically finds ring galaxy candidates based on their visual appearance, and they test their approach on the 3 million galaxy images from the first PanSTARRS data release. To see more of the remarkable galaxies the authors found and to learn more about their identification method, check out the paper below.CitationIan Timmis and Lior Shamir 2017 ApJS 231 2. doi:10.3847/1538-4365/aa78a3

  12. Identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis by using the Delphi Technique

    NASA Astrophysics Data System (ADS)

    Halim, N. Z. A.; Sulaiman, S. A.; Talib, K.; Ng, E. G.

    2018-02-01

    This paper explains the process carried out in identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis. The research was initially a part of a larger research exercise to identify the significance of NDCDB from the legal, technical, role and land-based analysis perspectives. The research methodology of applying the Delphi technique is substantially discussed in this paper. A heterogeneous panel of 14 experts was created to determine the importance of NDCDB from the technical relevance standpoint. Three statements describing the relevant features of NDCDB for spatial analysis were established after three rounds of consensus building. It highlighted the NDCDB’s characteristics such as its spatial accuracy, functions, and criteria as a facilitating tool for spatial analysis. By recognising the relevant features of NDCDB for spatial analysis in this study, practical application of NDCDB for various analysis and purpose can be widely implemented.

  13. New features of the Moon revealed and identified by CLTM-s01

    NASA Astrophysics Data System (ADS)

    Huang, Qian; Ping, Jinsong; Su, Xiaoli; Shu, Rong; Tang, Geshi

    2009-12-01

    Previous analyses showed a clear asymmetry in the topography, geological material distribution, and crustal thickness between the nearside and farside of the Moon. Lunar detecting data, such as topography and gravity, have made it possible to interpret this hemisphere dichotomy. The high-resolution lunar topographic model CLTM-s01 has revealed that there still exist four unknown features, namely, quasi-impact basin Sternfeld-Lewis (20°S, 232°E), confirmed impact basin Fitzgerald-Jackson (25°N, 191°E), crater Wugang (13°N, 189°E) and volcanic deposited highland Yutu (14°N, 308°E). Furthermore, we analyzed and identified about eleven large-scale impact basins that have been proposed since 1994, and classified them according to their circular characteristics.

  14. Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection.

    PubMed

    Pan, Xiaoyong; Hu, Xiaohua; Zhang, Yu Hang; Feng, Kaiyan; Wang, Shao Peng; Chen, Lei; Huang, Tao; Cai, Yu Dong

    2018-04-12

    Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew's correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles.

  15. General practitioners' perceptions of their ability to identify and refer patients with suspected axial spondyloarthritis: a qualitative study.

    PubMed

    van Onna, Marloes; Gorter, Simone; van Meerendonk, Aniek; van Tubergen, Astrid

    2014-05-01

    To explore the knowledge, beliefs, and experiences of general practitioners (GP) about inflammatory back pain (IBP) and axial spondyloarthritis (axSpA) and potential barriers for referral of patients suspected of having axSpA. A qualitative study involving semistructured interviews with GP was conducted. Transcripts of the interviews were independently read and annotated by 2 readers. Illustrative themes were identified and a coding system to categorize the data was developed. Ten GP (all men; mean age 49 yrs) were interviewed. All could adequately describe "classic" ankylosing spondylitis (AS) and mentioned chronic back pain and/or stiffness as key features. All GP thought that AS is almost exclusively diagnosed in men. Six GP knew that there is a difference between mechanical back pain and IBP, but could recall only a limited number of variables indicative of IBP, such as awakening night pain (4 GP), insidious onset of back pain (1 GP), improvement with movement (1 GP), and (morning) stiffness (2 GP). Two GP mentioned peripheral arthritis as other SpA features, none mentioned dactylitis or enthesitis. GP awareness of associated extraarticular manifestations was low. Most GP expressed that (practical) referral measures would be useful. GP are aware of "classic", but longterm features of axSpA. Knowledge about the disease spectrum and early detection is, however, limited. Addressing these issues in training programs may improve recognition of axSpA in primary care. This may ultimately contribute to earlier referral, diagnosis, and initiation of effective treatment in patients with axSpA.

  16. Experiments to Distribute Map Generalization Processes

    NASA Astrophysics Data System (ADS)

    Berli, Justin; Touya, Guillaume; Lokhat, Imran; Regnauld, Nicolas

    2018-05-01

    Automatic map generalization requires the use of computationally intensive processes often unable to deal with large datasets. Distributing the generalization process is the only way to make them scalable and usable in practice. But map generalization is a highly contextual process, and the surroundings of a generalized map feature needs to be known to generalize the feature, which is a problem as distribution might partition the dataset and parallelize the processing of each part. This paper proposes experiments to evaluate the past propositions to distribute map generalization, and to identify the main remaining issues. The past propositions to distribute map generalization are first discussed, and then the experiment hypotheses and apparatus are described. The experiments confirmed that regular partitioning was the quickest strategy, but also the less effective in taking context into account. The geographical partitioning, though less effective for now, is quite promising regarding the quality of the results as it better integrates the geographical context.

  17. Critical differences between elective and emergency surgery: identifying domains for quality improvement in emergency general surgery.

    PubMed

    Columbus, Alexandra B; Morris, Megan A; Lilley, Elizabeth J; Harlow, Alyssa F; Haider, Adil H; Salim, Ali; Havens, Joaquim M

    2018-04-01

    The objective of our study was to characterize providers' impressions of factors contributing to disproportionate rates of morbidity and mortality in emergency general surgery to identify targets for care quality improvement. Emergency general surgery is characterized by a high-cost burden and disproportionate morbidity and mortality. Factors contributing to these observed disparities are not comprehensively understood and targets for quality improvement have not been formally developed. Using a grounded theory approach, emergency general surgery providers were recruited through purposive-criterion-based sampling to participate in semi-structured interviews and focus groups. Participants were asked to identify contributors to emergency general surgery outcomes, to define effective care for EGS patients, and to describe operating room team structure. Interviews were performed to thematic saturation. Transcripts were iteratively coded and analyzed within and across cases to identify emergent themes. Member checking was performed to establish credibility of the findings. A total of 40 participants from 5 academic hospitals participated in either individual interviews (n = 25 [9 anesthesia, 12 surgery, 4 nursing]) or focus groups (n = 2 [15 nursing]). Emergency general surgery was characterized by an exceptionally high level of variability, which can be subcategorized as patient-variability (acute physiology and comorbidities) and system-variability (operating room resources and workforce). Multidisciplinary communication is identified as a modifier to variability in emergency general surgery; however, nursing is often left out of early communication exchanges. Critical variability in emergency general surgery may impact outcomes. Patient-variability and system-variability, with focus on multidisciplinary communication, represent potential domains for quality improvement in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Human body as a set of biometric features identified by means of optoelectronics

    NASA Astrophysics Data System (ADS)

    Podbielska, Halina; Bauer, Joanna

    2005-09-01

    Human body posses many unique, singular features that are impossible to copy or forge. Nowadays, to establish and to ensure the public security requires specially designed devices and systems. Biometrics is a field of science and technology, exploiting human body characteristics for people recognition. It identifies the most characteristic and unique ones in order to design and construct systems capable to recognize people. In this paper some overview is given, presenting the achievements in biometrics. The verification and identification process is explained, along with the way of evaluation of biometric recognition systems. The most frequently human biometrics used in practice are shortly presented, including fingerprints, facial imaging (including thermal characteristic), hand geometry and iris patterns.

  19. Learning discriminative functional network features of schizophrenia

    NASA Astrophysics Data System (ADS)

    Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

    2017-03-01

    Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

  20. Diagnostic accuracy of clinical examination features for identifying large rotator cuff tears in primary health care

    PubMed Central

    Cadogan, Angela; McNair, Peter; Laslett, Mark; Hing, Wayne; Taylor, Stephen

    2013-01-01

    Objectives: Rotator cuff tears are a common and disabling complaint. The early diagnosis of medium and large size rotator cuff tears can enhance the prognosis of the patient. The aim of this study was to identify clinical features with the strongest ability to accurately predict the presence of a medium, large or multitendon (MLM) rotator cuff tear in a primary care cohort. Methods: Participants were consecutively recruited from primary health care practices (n = 203). All participants underwent a standardized history and physical examination, followed by a standardized X-ray series and diagnostic ultrasound scan. Clinical features associated with the presence of a MLM rotator cuff tear were identified (P<0.200), a logistic multiple regression model was derived for identifying a MLM rotator cuff tear and thereafter diagnostic accuracy was calculated. Results: A MLM rotator cuff tear was identified in 24 participants (11.8%). Constant pain and a painful arc in abduction were the strongest predictors of a MLM tear (adjusted odds ratio 3.04 and 13.97 respectively). Combinations of ten history and physical examination variables demonstrated highest levels of sensitivity when five or fewer were positive [100%, 95% confidence interval (CI): 0.86–1.00; negative likelihood ratio: 0.00, 95% CI: 0.00–0.28], and highest specificity when eight or more were positive (0.91, 95% CI: 0.86–0.95; positive likelihood ratio 4.66, 95% CI: 2.34–8.74). Discussion: Combinations of patient history and physical examination findings were able to accurately detect the presence of a MLM rotator cuff tear. These findings may aid the primary care clinician in more efficient and accurate identification of rotator cuff tears that may require further investigation or orthopedic consultation. PMID:24421626

  1. ToNER: A tool for identifying nucleotide enrichment signals in feature-enriched RNA-seq data.

    PubMed

    Promworn, Yuttachon; Kaewprommal, Pavita; Shaw, Philip J; Intarapanich, Apichart; Tongsima, Sissades; Piriyapongsa, Jittima

    2017-01-01

    Biochemical methods are available for enriching 5' ends of RNAs in prokaryotes, which are employed in the differential RNA-seq (dRNA-seq) and the more recent Cappable-seq protocols. Computational methods are needed to locate RNA 5' ends from these data by statistical analysis of the enrichment. Although statistical-based analysis methods have been developed for dRNA-seq, they may not be suitable for Cappable-seq data. The more efficient enrichment method employed in Cappable-seq compared with dRNA-seq could affect data distribution and thus algorithm performance. We present Transformation of Nucleotide Enrichment Ratios (ToNER), a tool for statistical modeling of enrichment from RNA-seq data obtained from enriched and unenriched libraries. The tool calculates nucleotide enrichment scores and determines the global transformation for fitting to the normal distribution using the Box-Cox procedure. From the transformed distribution, sites of significant enrichment are identified. To increase power of detection, meta-analysis across experimental replicates is offered. We tested the tool on Cappable-seq and dRNA-seq data for identifying Escherichia coli transcript 5' ends and compared the results with those from the TSSAR tool, which is designed for analyzing dRNA-seq data. When combining results across Cappable-seq replicates, ToNER detects more known transcript 5' ends than TSSAR. In general, the transcript 5' ends detected by ToNER but not TSSAR occur in regions which cannot be locally modeled by TSSAR. ToNER uses a simple yet robust statistical modeling approach, which can be used for detecting RNA 5'ends from Cappable-seq data, in particular when combining information from experimental replicates. The ToNER tool could potentially be applied for analyzing other RNA-seq datasets in which enrichment for other structural features of RNA is employed. The program is freely available for download at ToNER webpage (http://www4a.biotec.or.th/GI/tools/toner) and Git

  2. ToNER: A tool for identifying nucleotide enrichment signals in feature-enriched RNA-seq data

    PubMed Central

    Promworn, Yuttachon; Kaewprommal, Pavita; Shaw, Philip J.; Intarapanich, Apichart; Tongsima, Sissades

    2017-01-01

    Background Biochemical methods are available for enriching 5′ ends of RNAs in prokaryotes, which are employed in the differential RNA-seq (dRNA-seq) and the more recent Cappable-seq protocols. Computational methods are needed to locate RNA 5′ ends from these data by statistical analysis of the enrichment. Although statistical-based analysis methods have been developed for dRNA-seq, they may not be suitable for Cappable-seq data. The more efficient enrichment method employed in Cappable-seq compared with dRNA-seq could affect data distribution and thus algorithm performance. Results We present Transformation of Nucleotide Enrichment Ratios (ToNER), a tool for statistical modeling of enrichment from RNA-seq data obtained from enriched and unenriched libraries. The tool calculates nucleotide enrichment scores and determines the global transformation for fitting to the normal distribution using the Box-Cox procedure. From the transformed distribution, sites of significant enrichment are identified. To increase power of detection, meta-analysis across experimental replicates is offered. We tested the tool on Cappable-seq and dRNA-seq data for identifying Escherichia coli transcript 5′ ends and compared the results with those from the TSSAR tool, which is designed for analyzing dRNA-seq data. When combining results across Cappable-seq replicates, ToNER detects more known transcript 5′ ends than TSSAR. In general, the transcript 5′ ends detected by ToNER but not TSSAR occur in regions which cannot be locally modeled by TSSAR. Conclusion ToNER uses a simple yet robust statistical modeling approach, which can be used for detecting RNA 5′ends from Cappable-seq data, in particular when combining information from experimental replicates. The ToNER tool could potentially be applied for analyzing other RNA-seq datasets in which enrichment for other structural features of RNA is employed. The program is freely available for download at ToNER webpage (http://www4a

  3. Identifying persistent and characteristic features in firearm tool marks on cartridge cases

    NASA Astrophysics Data System (ADS)

    Ott, Daniel; Soons, Johannes; Thompson, Robert; Song, John

    2017-12-01

    Recent concerns about subjectivity in forensic firearm identification have motivated the development of algorithms to compare firearm tool marks that are imparted on ammunition and to generate quantitative measures of similarity. In this paper, we describe an algorithm that identifies impressed tool marks on a cartridge case that are both consistent between firings and contribute strongly to a surface similarity metric. The result is a representation of the tool mark topography that emphasizes both significant and persistent features across firings. This characteristic surface map is useful for understanding the variability and persistence of the tool marks created by a firearm and can provide improved discrimination between the comparison scores of samples fired from the same firearm and the scores of samples fired from different firearms. The algorithm also provides a convenient method for visualizing areas of similarity that may be useful in providing quantitative support for visual comparisons by trained examiners.

  4. Artificial Language Learning and Feature-Based Generalization

    ERIC Educational Resources Information Center

    Finley, Sara; Badecker, William

    2009-01-01

    Abstract representations such as subsegmental phonological features play such a vital role in explanations of phonological processes that many assume that these representations play an equally prominent role in the learning process. This assumption is tested in three artificial grammar experiments involving a mini language with morpho-phonological…

  5. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.

    PubMed

    Davies, Gail; Lam, Max; Harris, Sarah E; Trampush, Joey W; Luciano, Michelle; Hill, W David; Hagenaars, Saskia P; Ritchie, Stuart J; Marioni, Riccardo E; Fawns-Ritchie, Chloe; Liewald, David C M; Okely, Judith A; Ahola-Olli, Ari V; Barnes, Catriona L K; Bertram, Lars; Bis, Joshua C; Burdick, Katherine E; Christoforou, Andrea; DeRosse, Pamela; Djurovic, Srdjan; Espeseth, Thomas; Giakoumaki, Stella; Giddaluru, Sudheer; Gustavson, Daniel E; Hayward, Caroline; Hofer, Edith; Ikram, M Arfan; Karlsson, Robert; Knowles, Emma; Lahti, Jari; Leber, Markus; Li, Shuo; Mather, Karen A; Melle, Ingrid; Morris, Derek; Oldmeadow, Christopher; Palviainen, Teemu; Payton, Antony; Pazoki, Raha; Petrovic, Katja; Reynolds, Chandra A; Sargurupremraj, Muralidharan; Scholz, Markus; Smith, Jennifer A; Smith, Albert V; Terzikhan, Natalie; Thalamuthu, Anbupalam; Trompet, Stella; van der Lee, Sven J; Ware, Erin B; Windham, B Gwen; Wright, Margaret J; Yang, Jingyun; Yu, Jin; Ames, David; Amin, Najaf; Amouyel, Philippe; Andreassen, Ole A; Armstrong, Nicola J; Assareh, Amelia A; Attia, John R; Attix, Deborah; Avramopoulos, Dimitrios; Bennett, David A; Böhmer, Anne C; Boyle, Patricia A; Brodaty, Henry; Campbell, Harry; Cannon, Tyrone D; Cirulli, Elizabeth T; Congdon, Eliza; Conley, Emily Drabant; Corley, Janie; Cox, Simon R; Dale, Anders M; Dehghan, Abbas; Dick, Danielle; Dickinson, Dwight; Eriksson, Johan G; Evangelou, Evangelos; Faul, Jessica D; Ford, Ian; Freimer, Nelson A; Gao, He; Giegling, Ina; Gillespie, Nathan A; Gordon, Scott D; Gottesman, Rebecca F; Griswold, Michael E; Gudnason, Vilmundur; Harris, Tamara B; Hartmann, Annette M; Hatzimanolis, Alex; Heiss, Gerardo; Holliday, Elizabeth G; Joshi, Peter K; Kähönen, Mika; Kardia, Sharon L R; Karlsson, Ida; Kleineidam, Luca; Knopman, David S; Kochan, Nicole A; Konte, Bettina; Kwok, John B; Le Hellard, Stephanie; Lee, Teresa; Lehtimäki, Terho; Li, Shu-Chen; Liu, Tian; Koini, Marisa; London, Edythe; Longstreth, Will T; Lopez, Oscar L; Loukola, Anu; Luck, Tobias; Lundervold, Astri J; Lundquist, Anders; Lyytikäinen, Leo-Pekka; Martin, Nicholas G; Montgomery, Grant W; Murray, Alison D; Need, Anna C; Noordam, Raymond; Nyberg, Lars; Ollier, William; Papenberg, Goran; Pattie, Alison; Polasek, Ozren; Poldrack, Russell A; Psaty, Bruce M; Reppermund, Simone; Riedel-Heller, Steffi G; Rose, Richard J; Rotter, Jerome I; Roussos, Panos; Rovio, Suvi P; Saba, Yasaman; Sabb, Fred W; Sachdev, Perminder S; Satizabal, Claudia L; Schmid, Matthias; Scott, Rodney J; Scult, Matthew A; Simino, Jeannette; Slagboom, P Eline; Smyrnis, Nikolaos; Soumaré, Aïcha; Stefanis, Nikos C; Stott, David J; Straub, Richard E; Sundet, Kjetil; Taylor, Adele M; Taylor, Kent D; Tzoulaki, Ioanna; Tzourio, Christophe; Uitterlinden, André; Vitart, Veronique; Voineskos, Aristotle N; Kaprio, Jaakko; Wagner, Michael; Wagner, Holger; Weinhold, Leonie; Wen, K Hoyan; Widen, Elisabeth; Yang, Qiong; Zhao, Wei; Adams, Hieab H H; Arking, Dan E; Bilder, Robert M; Bitsios, Panos; Boerwinkle, Eric; Chiba-Falek, Ornit; Corvin, Aiden; De Jager, Philip L; Debette, Stéphanie; Donohoe, Gary; Elliott, Paul; Fitzpatrick, Annette L; Gill, Michael; Glahn, David C; Hägg, Sara; Hansell, Narelle K; Hariri, Ahmad R; Ikram, M Kamran; Jukema, J Wouter; Vuoksimaa, Eero; Keller, Matthew C; Kremen, William S; Launer, Lenore; Lindenberger, Ulman; Palotie, Aarno; Pedersen, Nancy L; Pendleton, Neil; Porteous, David J; Räikkönen, Katri; Raitakari, Olli T; Ramirez, Alfredo; Reinvang, Ivar; Rudan, Igor; Dan Rujescu; Schmidt, Reinhold; Schmidt, Helena; Schofield, Peter W; Schofield, Peter R; Starr, John M; Steen, Vidar M; Trollor, Julian N; Turner, Steven T; Van Duijn, Cornelia M; Villringer, Arno; Weinberger, Daniel R; Weir, David R; Wilson, James F; Malhotra, Anil; McIntosh, Andrew M; Gale, Catharine R; Seshadri, Sudha; Mosley, Thomas H; Bressler, Jan; Lencz, Todd; Deary, Ian J

    2018-05-29

    General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10 -8 ) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

  6. Multiplicative Multitask Feature Learning

    PubMed Central

    Wang, Xin; Bi, Jinbo; Yu, Shipeng; Sun, Jiangwen; Song, Minghu

    2016-01-01

    We investigate a general framework of multiplicative multitask feature learning which decomposes individual task’s model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effects of different regularizers. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. An efficient blockwise coordinate descent algorithm is developed suitable for solving the entire family of formulations with rigorous convergence analysis. Simulation studies have identified the statistical properties of data that would be in favor of the new formulations. Extensive empirical studies on various classification and regression benchmark data sets have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks. PMID:28428735

  7. Feature-level sentiment analysis by using comparative domain corpora

    NASA Astrophysics Data System (ADS)

    Quan, Changqin; Ren, Fuji

    2016-06-01

    Feature-level sentiment analysis (SA) is able to provide more fine-grained SA on certain opinion targets and has a wider range of applications on E-business. This study proposes an approach based on comparative domain corpora for feature-level SA. The proposed approach makes use of word associations for domain-specific feature extraction. First, we assign a similarity score for each candidate feature to denote its similarity extent to a domain. Then we identify domain features based on their similarity scores on different comparative domain corpora. After that, dependency grammar and a general sentiment lexicon are applied to extract and expand feature-oriented opinion words. Lastly, the semantic orientation of a domain-specific feature is determined based on the feature-oriented opinion lexicons. In evaluation, we compare the proposed method with several state-of-the-art methods (including unsupervised and semi-supervised) using a standard product review test collection. The experimental results demonstrate the effectiveness of using comparative domain corpora.

  8. A novel feature extraction approach for microarray data based on multi-algorithm fusion

    PubMed Central

    Jiang, Zhu; Xu, Rong

    2015-01-01

    Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

  9. A novel feature extraction approach for microarray data based on multi-algorithm fusion.

    PubMed

    Jiang, Zhu; Xu, Rong

    2015-01-01

    Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

  10. Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

    PubMed Central

    Shen, Li; Qi, Yuan; Kim, Sungeun; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Saykin, Andrew J.

    2010-01-01

    We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer’s disease (AD) classification to make accurate prediction and identify critical imaging markers relevant to AD at the same time. ARD is one of the most successful Bayesian feature selection methods. PARD is a powerful Bayesian feature selection method, and provides sparse models that is easy to interpret. PARD selects the model with the best estimate of the predictive performance instead of choosing the one with the largest marginal model likelihood. Comparative study with support vector machine (SVM) shows that ARD/PARD in general outperform SVM in terms of prediction accuracy. Additional comparison with surface-based general linear model (GLM) analysis shows that regions with strongest signals are identified by both GLM and ARD/PARD. While GLM P-map returns significant regions all over the cortex, ARD/PARD provide a small number of relevant and meaningful imaging markers with predictive power, including both cortical and subcortical measures. PMID:20879451

  11. [Importance of the hyperuricaemia, gout and gender nosological features in the activity of general practitioner - family doctor].

    PubMed

    Rudichenko, V M

    2012-01-01

    In this article there were analyzed gender data about features of hyperuricaemia and gout: women are much older at the onset of gout arthritis (one of main reasons, probably, makes menopause by itself), have more associated comorbid deseases as hypertension and kidney failure and drinks less alcoholic beverages. It was noticed, that typical localisation of the lesion on the first toe is less often in women, and women are more inclined to use diuretics among medical drugs. Abovementioned clinical features are of some importance for the broad activity of general practitioners - family doctors. Gender features of polyarthicular gout are not uniformed. Scientific researches confirmed possibility of the genetic basis of the uric acid metabolism, which influences some fenotypical features of the organism. Several genes are known for their influence on serum uric acid: PDZK1, GCKR, SLC2A9, ABCG2, LRRC16A, SLC17A3, SLC16A9 and SLC22A12. However, conclusions of the research works confirm the necessity of scientific clarification of the importance of different factors of gender differences.

  12. Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset

    USGS Publications Warehouse

    Stanislawski, L.V.

    2009-01-01

    The United States Geological Survey has been researching generalization approaches to enable multiple-scale display and delivery of geographic data. This paper presents automated methods to prune network and polygon features of the United States high-resolution National Hydrography Dataset (NHD) to lower resolutions. Feature-pruning rules, data enrichment, and partitioning are derived from knowledge of surface water, the NHD model, and associated feature specification standards. Relative prominence of network features is estimated from upstream drainage area (UDA). Network and polygon features are pruned by UDA and NHD reach code to achieve a drainage density appropriate for any less detailed map scale. Data partitioning maintains local drainage density variations that characterize the terrain. For demonstration, a 48 subbasin area of 1:24 000-scale NHD was pruned to 1:100 000-scale (100 K) and compared to a benchmark, the 100 K NHD. The coefficient of line correspondence (CLC) is used to evaluate how well pruned network features match the benchmark network. CLC values of 0.82 and 0.77 result from pruning with and without partitioning, respectively. The number of polygons that remain after pruning is about seven times that of the benchmark, but the area covered by the polygons that remain after pruning is only about 10% greater than the area covered by benchmark polygons. ?? 2009.

  13. Imaging Characteristics of Pathologically Proven Thymic Hyperplasia: Identifying Features That Can Differentiate True From Lymphoid Hyperplasia

    PubMed Central

    Araki, Tetsuro; Sholl, Lynette M.; Gerbaudo, Victor H.; Hatabu, Hiroto; Nishino, Mizuki

    2014-01-01

    OBJECTIVE The purpose of this article is to investigate the imaging characteristics of pathologically proven thymic hyperplasia and to identify features that can differentiate true hyperplasia from lymphoid hyperplasia. MATERIALS AND METHODS Thirty-one patients (nine men and 22 women; age range, 20–68 years) with pathologically confirmed thymic hyperplasia (18 true and 13 lymphoid) who underwent preoperative CT (n = 27), PET/CT (n = 5), or MRI (n = 6) were studied. The length and thickness of each thymic lobe and the transverse and anterior-posterior diameters and attenuation of the thymus were measured on CT. Thymic morphologic features and heterogeneity on CT and chemical shift on MRI were evaluated. Maximum standardized uptake values were measured on PET. Imaging features between true and lymphoid hyperplasia were compared. RESULTS No significant differences were observed between true and lymphoid hyperplasia in terms of thymic length, thickness, diameters, morphologic features, and other qualitative features (p > 0.16). The length, thickness, and diameters of thymic hyperplasia were significantly larger than the mean values of normal glands in the corresponding age group (p < 0.001). CT attenuation of lymphoid hyperplasia was significantly higher than that of true hyperplasia among 15 patients with contrast-enhanced CT (median, 47.9 vs 31.4 HU; Wilcoxon p = 0.03). The receiver operating characteristic analysis yielded greater than 41.2 HU as the optimal threshold for differentiating lymphoid hyperplasia from true hyperplasia, with 83% sensitivity and 89% specificity. A decrease of signal intensity on opposed-phase images was present in all four cases with in- and opposed-phase imaging. The mean maximum standardized uptake value was 2.66. CONCLUSION CT attenuation of the thymus was significantly higher in lymphoid hyperplasia than in true hyperplasia, with an optimal threshold of greater than 41.2 HU in this cohort of patients with pathologically confirmed

  14. Distinguishing obsessive features and worries: the role of thought-action fusion.

    PubMed

    Coles, M E; Mennin, D S; Heimberg, R G

    2001-08-01

    Obsessions are a key feature of obsessive-compulsive disorder (OCD), and chronic worry is the cardinal feature of generalized anxiety disorder (GAD). However, these two cognitive processes are conceptually very similar, and there is a need to determine how they differ. Recent studies have attempted to identify cognitive processes that may be differentially related to obsessive features and worry. In the current study we proposed that (1) obsessive features and worry could be differentiated and that (2) a measure of the cognitive process thought-action fusion would distinguish between obsessive features and worry, being strongly related to obsessive features after controlling for the effects of worry. These hypotheses were supported in a sample of 173 undergraduate students. Thought-action fusion may be a valuable construct in differentiating between obsessive features and worry.

  15. Comparative analyses of Legionella species identifies genetic features of strains causing Legionnaires' disease.

    PubMed

    Gomez-Valero, Laura; Rusniok, Christophe; Rolando, Monica; Neou, Mario; Dervins-Ravault, Delphine; Demirtas, Jasmin; Rouy, Zoe; Moore, Robert J; Chen, Honglei; Petty, Nicola K; Jarraud, Sophie; Etienne, Jerome; Steinert, Michael; Heuner, Klaus; Gribaldo, Simonetta; Médigue, Claudine; Glöckner, Gernot; Hartland, Elizabeth L; Buchrieser, Carmen

    2014-01-01

    The genus Legionella comprises over 60 species. However, L. pneumophila and L. longbeachae alone cause over 95% of Legionnaires’ disease. To identify the genetic bases underlying the different capacities to cause disease we sequenced and compared the genomes of L. micdadei, L. hackeliae and L. fallonii (LLAP10), which are all rarely isolated from humans. We show that these Legionella species possess different virulence capacities in amoeba and macrophages, correlating with their occurrence in humans. Our comparative analysis of 11 Legionella genomes belonging to five species reveals highly heterogeneous genome content with over 60% representing species-specific genes; these comprise a complete prophage in L. micdadei, the first ever identified in a Legionella genome. Mobile elements are abundant in Legionella genomes; many encode type IV secretion systems for conjugative transfer, pointing to their importance for adaptation of the genus. The Dot/Icm secretion system is conserved, although the core set of substrates is small, as only 24 out of over 300 described Dot/Icm effector genes are present in all Legionella species. We also identified new eukaryotic motifs including thaumatin, synaptobrevin or clathrin/coatomer adaptine like domains. Legionella genomes are highly dynamic due to a large mobilome mainly comprising type IV secretion systems, while a minority of core substrates is shared among the diverse species. Eukaryotic like proteins and motifs remain a hallmark of the genus Legionella. Key factors such as proteins involved in oxygen binding, iron storage, host membrane transport and certain Dot/Icm substrates are specific features of disease-related strains.

  16. A feature-based inference model of numerical estimation: the split-seed effect.

    PubMed

    Murray, Kyle B; Brown, Norman R

    2009-07-01

    Prior research has identified two modes of quantitative estimation: numerical retrieval and ordinal conversion. In this paper we introduce a third mode, which operates by a feature-based inference process. In contrast to prior research, the results of three experiments demonstrate that people estimate automobile prices by combining metric information associated with two critical features: product class and brand status. In addition, Experiments 2 and 3 demonstrated that when participants are seeded with the actual current base price of one of the to-be-estimated vehicles, they respond by revising the general metric and splitting the information carried by the seed between the two critical features. As a result, the degree of post-seeding revision is directly related to the number of these features that the seed and the transfer items have in common. The paper concludes with a general discussion of the practical and theoretical implications of our findings.

  17. Predicting protein-protein interactions by combing various sequence- derived features into the general form of Chou's Pseudo amino acid composition.

    PubMed

    Zhao, Xiao-Wei; Ma, Zhi-Qiang; Yin, Ming-Hao

    2012-05-01

    Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

  18. Features of primary health care teams associated with successful quality improvement of diabetes care: a qualitative study.

    PubMed

    Stevenson, K; Baker, R; Farooqi, A; Sorrie, R; Khunti, K

    2001-02-01

    In quality improvement activities such as audit, some general practices succeed in improving care and some do not. With audit of care likely to be one of the major tools in clinical governance, it would be helpful to establish what features of primary health care teams are associated with successful audit in general practice. The aim of the present study was to identify those features of primary health care teams that were associated with successful quality improvement during systematic audit of diabetes care. Semi-structured tape-recorded interviews were carried out with lead GPs and practice nurses in 18 general practices in Leicestershire that had the opportunity to improve their care and had completed two data collections in a multipractice audit of diabetes care. The interviewees were asked to describe their practice's approach to audit and the transcripts were coded for common features and judged for strength of feeling by blinded independent raters. Features common to practices that had, and those that had not, managed to improve diabetes care were identified. Six features were identified reliably in the transcripts by blinded independent raters. Four were significantly associated with the successful improvement of care. Success was more likely in teams in which: the GP or nurse felt personally involved in the audit; they perceived their teamwork as good; they had recognized the need for systematic plans to address obstacles to quality improvement; and their teams had a positive attitude to continued monitoring of care. A positive attitude to audit and a personal interest in the disease were not associated with improvement in care. Success in improving diabetes care is associated with certain organizational features of primary health care teams. Experimental studies are required to determine whether the development of teamwork enables practice teams to identify and overcome systematically the obstacles to improved quality of patient care that face them.

  19. Object-based selection from spatially-invariant representations: evidence from a feature-report task.

    PubMed

    Matsukura, Michi; Vecera, Shaun P

    2011-02-01

    Attention selects objects as well as locations. When attention selects an object's features, observers identify two features from a single object more accurately than two features from two different objects (object-based effect of attention; e.g., Duncan, Journal of Experimental Psychology: General, 113, 501-517, 1984). Several studies have demonstrated that object-based attention can operate at a late visual processing stage that is independent of objects' spatial information (Awh, Dhaliwal, Christensen, & Matsukura, Psychological Science, 12, 329-334, 2001; Matsukura & Vecera, Psychonomic Bulletin & Review, 16, 529-536, 2009; Vecera, Journal of Experimental Psychology: General, 126, 14-18, 1997; Vecera & Farah, Journal of Experimental Psychology: General, 123, 146-160, 1994). In the present study, we asked two questions regarding this late object-based selection mechanism. In Part I, we investigated how observers' foreknowledge of to-be-reported features allows attention to select objects, as opposed to individual features. Using a feature-report task, a significant object-based effect was observed when to-be-reported features were known in advance but not when this advance knowledge was absent. In Part II, we examined what drives attention to select objects rather than individual features in the absence of observers' foreknowledge of to-be-reported features. Results suggested that, when there was no opportunity for observers to direct their attention to objects that possess to-be-reported features at the time of stimulus presentation, these stimuli must retain strong perceptual cues to establish themselves as separate objects.

  20. Melancholic depression prediction by identifying representative features in metabolic and microarray profiles with missing values.

    PubMed

    Nie, Zhi; Yang, Tao; Liu, Yashu; Li, Qingyang; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

    2015-01-01

    Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed

  1. Epidemiologic features of metabolic syndrome in a general Mongolian population.

    PubMed

    Enkh-Oyun, Tsogzolbaatar; Kotani, Kazuhiko; Davaalkham, Dambadarjaa; Davaa, Gombojav; Ganchimeg, Ulziibayar; Angarmurun, Dayan; Khuderchuluun, Nanjid; Batzorig, Bayartsogt; Tsuboi, Satoshi; Ae, Ryusuke; Aoyama, Yasuko; Nakamura, Yosikazu

    2015-05-01

    Although cardiovascular health is a crucial problem for Mongolian people, little information about metabolic syndrome, which is well known to be associated with the development of cardiovascular disease, is available in Mongolia. The aim of this study was to observe the epidemiological features of metabolic syndrome in a general Mongolian population. This cross-sectional study was performed in 1911 general Mongolian subjects (717 men, 1194 women), who were ≥40 years old and free of ischemic heart disease, by using a dataset from a nationwide population-based cohort study in Mongolia. The prevalence of metabolic syndrome, as defined by International Diabetes Federation criteria, was determined. Alcohol consumption, smoking habits, and physical activity were evaluated. Education, marital status, income, and occupation were also examined as factors of socioeconomic status (SES). Their association with metabolic syndrome was determined by logistic regression models. The prevalence of metabolic syndrome was significantly higher in women (n=488, 40.6%) than in men (n=138, 19.4%). The prevalence of metabolic syndrome was high, especially in the Khangai region, in women. Moderate-to-high alcohol consumption was a significantly positively associated factor of metabolic syndrome in men [odds ratio (OR)=2.01; 95% confidence interval (CI) 1.15-3.51; adjusted odds ratio (AOR)=2.41; 95% CI 1.31-4.44] and widowed status was a significantly positively associated factor of metabolic syndrome in women (OR=1.61, 95% CI 1.18-2.18; AOR=1.49, 95% CI 1.07-2.08). Metabolic syndrome was prevalent in women compared with men among Mongolian adults. Preventive strategies aimed at men with a higher alcohol consumption and women with widowed status may help reduce metabolic syndrome, thereby improving cardiovascular health conditions in Mongolia.

  2. Feature-based Morphometry

    PubMed Central

    Toews, Matthew; Wells, William M.; Collins, Louis; Arbel, Tal

    2013-01-01

    This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for identifying group-related differences in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between all subjects, FBM models images as a collage of distinct, localized image features which may not be present in all subjects. FBM thus explicitly accounts for the case where the same anatomical tissue cannot be reliably identified in all subjects due to disease or anatomical variability. A probabilistic model describes features in terms of their appearance, geometry, and relationship to sub-groups of a population, and is automatically learned from a set of subject images and group labels. Features identified indicate group-related anatomical structure that can potentially be used as disease biomarkers or as a basis for computer-aided diagnosis. Scale-invariant image features are used, which reflect generic, salient patterns in the image. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer’s (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and obtains an equal error classification rate of 0.78 on new subjects. PMID:20426102

  3. General tensor discriminant analysis and gabor features for gait recognition.

    PubMed

    Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

    2007-10-01

    The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine

  4. Two case reports of orofacial paraesthesia demonstrating the role of the general dental practitioner in identifying patients with intracranial tumours.

    PubMed

    Barber, Andrew J; Lawson, David D A; Field, E Anne

    2009-04-01

    The following case reports describe the clinical features, diagnosis and management of two patients who presented to their general dental practitioner with a complaint of orofacial paraesthesia. After appropriate investigations, both patients were diagnosed as having benign intracranial tumours and were managed by a neurosurgeon. These cases illustrate the important role the general dental practitioner has in the early recognition of potentially life-threatening conditions.

  5. Cemento-osseous dysplasia of the jaw bones: key radiographic features

    PubMed Central

    Alsufyani, NA; Lam, EWN

    2011-01-01

    Objective The purpose of this study is to assess possible diagnostic differences between general dentists (GPs) and oral and maxillofacial radiologists (RGs) in the identification of pathognomonic radiographic features of cemento-osseous dysplasia (COD) and its interpretation. Methods Using a systematic objective survey instrument, 3 RGs and 3 GPs reviewed 50 image sets of COD and similarly appearing entities (dense bone island, cementoblastoma, cemento-ossifying fibroma, fibrous dysplasia, complex odontoma and sclerosing osteitis). Participants were asked to identify the presence or absence of radiographic features and then to make an interpretation of the images. Results RGs identified a well-defined border (odds ratio (OR) 6.67, P < 0.05); radiolucent periphery (OR 8.28, P < 0.005); bilateral occurrence (OR 10.23, P < 0.01); mixed radiolucent/radiopaque internal structure (OR 10.53, P < 0.01); the absence of non-concentric bony expansion (OR 7.63, P < 0.05); and the association with anterior and posterior teeth (OR 4.43, P < 0.05) as key features of COD. Consequently, RGs were able to correctly interpret 79.3% of COD cases. In contrast, GPs identified the absence of root resorption (OR 4.52, P < 0.05) and the association with anterior and posterior teeth (OR 3.22, P = 0.005) as the only key features of COD and were able to correctly interpret 38.7% of COD cases. Conclusions There are statistically significant differences between RGs and GPs in the identification and interpretation of the radiographic features associated with COD (P < 0.001). We conclude that COD is radiographically discernable from other similarly appearing entities only if the characteristic radiographic features are correctly identified and then correctly interpreted. PMID:21346079

  6. Cemento-osseous dysplasia of the jaw bones: key radiographic features.

    PubMed

    Alsufyani, N A; Lam, E W N

    2011-03-01

    The purpose of this study is to assess possible diagnostic differences between general dentists (GPs) and oral and maxillofacial radiologists (RGs) in the identification of pathognomonic radiographic features of cemento-osseous dysplasia (COD) and its interpretation. Using a systematic objective survey instrument, 3 RGs and 3 GPs reviewed 50 image sets of COD and similarly appearing entities (dense bone island, cementoblastoma, cemento-ossifying fibroma, fibrous dysplasia, complex odontoma and sclerosing osteitis). Participants were asked to identify the presence or absence of radiographic features and then to make an interpretation of the images. RGs identified a well-defined border (odds ratio (OR) 6.67, P < 0.05); radiolucent periphery (OR 8.28, P < 0.005); bilateral occurrence (OR 10.23, P < 0.01); mixed radiolucent/radiopaque internal structure (OR 10.53, P < 0.01); the absence of non-concentric bony expansion (OR 7.63, P < 0.05); and the association with anterior and posterior teeth (OR 4.43, P < 0.05) as key features of COD. Consequently, RGs were able to correctly interpret 79.3% of COD cases. In contrast, GPs identified the absence of root resorption (OR 4.52, P < 0.05) and the association with anterior and posterior teeth (OR 3.22, P = 0.005) as the only key features of COD and were able to correctly interpret 38.7% of COD cases. There are statistically significant differences between RGs and GPs in the identification and interpretation of the radiographic features associated with COD (P < 0.001). We conclude that COD is radiographically discernable from other similarly appearing entities only if the characteristic radiographic features are correctly identified and then correctly interpreted.

  7. Psychometric Features of the General Aptitude Test-Verbal Part (GAT-V): A Large-Scale Assessment of High School Graduates in Saudi Arabia

    ERIC Educational Resources Information Center

    Dimitrov, Dimiter M.; Shamrani, Abdul Rahman

    2015-01-01

    This study examines the psychometric features of a General Aptitude Test-Verbal Part, which is used with assessments of high school graduates in Saudi Arabia. The data supported a bifactor model, with one general factor and three content domains (Analogy, Sentence Completion, and Reading Comprehension) as latent aspects of verbal aptitude.

  8. On general features of warm dark matter with reduced relativistic gas

    NASA Astrophysics Data System (ADS)

    Hipólito-Ricaldi, W. S.; vom Marttens, R. F.; Fabris, J. C.; Shapiro, I. L.; Casarini, L.

    2018-05-01

    Reduced relativistic gas (RRG) is a useful approach to describe the warm dark matter (WDM) or the warmness of baryonic matter in the approximation when the interaction between the particles is irrelevant. The use of Maxwell distribution leads to the complicated equation of state of the Jüttner model of relativistic ideal gas. The RRG enables one to reproduce the same physical situation but in a much simpler form. For this reason RRG can be a useful tool for the theories with some sort of a "new Physics". On the other hand, even without the qualitatively new physical implementations, the RRG can be useful to describe the general features of WDM in a model-independent way. In this sense one can see, in particular, to which extent the cosmological manifestations of WDM may be dependent on its Particle Physics background. In the present work RRG is used as a complementary approach to derive the main observational features for the WDM in a model-independent way. The only assumption concerns a non-negligible velocity v for dark matter particles which is parameterized by the warmness parameter b. The relatively high values of b ( b^2˜ 10^{-6}) erase the radiation (photons and neutrinos) dominated epoch and cause an early warm matter domination after inflation. Furthermore, RRG approach enables one to quantify the lack of power in linear matter spectrum at small scales and in particular, reproduces the relative transfer function commonly used in context of WDM with accuracy of ≲ 1%. A warmness with b^2≲ 10^{-6} (equivalent to v≲ 300 km/s) does not alter significantly the CMB power spectrum and is in agreement with the background observational tests.

  9. Integrated feature extraction and selection for neuroimage classification

    NASA Astrophysics Data System (ADS)

    Fan, Yong; Shen, Dinggang

    2009-02-01

    Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

  10. A General Purpose Feature Extractor for Light Detection and Ranging Data

    DTIC Science & Technology

    2010-11-17

    datasets, and the 3D MIT DARPA Urban Challenge dataset. Keywords: SLAM ; LIDARs ; feature detection; uncertainty estimates; descriptors 1. Introduction The...November 2010 Abstract: Feature extraction is a central step of processing Light Detection and Ranging ( LIDAR ) data. Existing detectors tend to exploit...detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image

  11. A genome-wide association scan in admixed Latin Americans identifies loci influencing facial and scalp hair features

    PubMed Central

    Adhikari, Kaustubh; Fontanil, Tania; Cal, Santiago; Mendoza-Revilla, Javier; Fuentes-Guajardo, Macarena; Chacón-Duque, Juan-Camilo; Al-Saadi, Farah; Johansson, Jeanette A.; Quinto-Sanchez, Mirsha; Acuña-Alonzo, Victor; Jaramillo, Claudia; Arias, William; Barquera Lozano, Rodrigo; Macín Pérez, Gastón; Gómez-Valdés, Jorge; Villamil-Ramírez, Hugo; Hunemeier, Tábita; Ramallo, Virginia; Silva de Cerqueira, Caio C.; Hurtado, Malena; Villegas, Valeria; Granja, Vanessa; Gallo, Carla; Poletti, Giovanni; Schuler-Faccini, Lavinia; Salzano, Francisco M.; Bortolini, Maria-Cátira; Canizales-Quinteros, Samuel; Rothhammer, Francisco; Bedoya, Gabriel; Gonzalez-José, Rolando; Headon, Denis; López-Otín, Carlos; Tobin, Desmond J.; Balding, David; Ruiz-Linares, Andrés

    2016-01-01

    We report a genome-wide association scan in over 6,000 Latin Americans for features of scalp hair (shape, colour, greying, balding) and facial hair (beard thickness, monobrow, eyebrow thickness). We found 18 signals of association reaching genome-wide significance (P values 5 × 10−8 to 3 × 10−119), including 10 novel associations. These include novel loci for scalp hair shape and balding, and the first reported loci for hair greying, monobrow, eyebrow and beard thickness. A newly identified locus influencing hair shape includes a Q30R substitution in the Protease Serine S1 family member 53 (PRSS53). We demonstrate that this enzyme is highly expressed in the hair follicle, especially the inner root sheath, and that the Q30R substitution affects enzyme processing and secretion. The genome regions associated with hair features are enriched for signals of selection, consistent with proposals regarding the evolution of human hair. PMID:26926045

  12. A general model-based design of experiments approach to achieve practical identifiability of pharmacokinetic and pharmacodynamic models.

    PubMed

    Galvanin, Federico; Ballan, Carlo C; Barolo, Massimiliano; Bezzo, Fabrizio

    2013-08-01

    The use of pharmacokinetic (PK) and pharmacodynamic (PD) models is a common and widespread practice in the preliminary stages of drug development. However, PK-PD models may be affected by structural identifiability issues intrinsically related to their mathematical formulation. A preliminary structural identifiability analysis is usually carried out to check if the set of model parameters can be uniquely determined from experimental observations under the ideal assumptions of noise-free data and no model uncertainty. However, even for structurally identifiable models, real-life experimental conditions and model uncertainty may strongly affect the practical possibility to estimate the model parameters in a statistically sound way. A systematic procedure coupling the numerical assessment of structural identifiability with advanced model-based design of experiments formulations is presented in this paper. The objective is to propose a general approach to design experiments in an optimal way, detecting a proper set of experimental settings that ensure the practical identifiability of PK-PD models. Two simulated case studies based on in vitro bacterial growth and killing models are presented to demonstrate the applicability and generality of the methodology to tackle model identifiability issues effectively, through the design of feasible and highly informative experiments.

  13. General features of the retinal connectome determine the computation of motion anticipation

    PubMed Central

    Johnston, Jamie; Lagnado, Leon

    2015-01-01

    Motion anticipation allows the visual system to compensate for the slow speed of phototransduction so that a moving object can be accurately located. This correction is already present in the signal that ganglion cells send from the retina but the biophysical mechanisms underlying this computation are not known. Here we demonstrate that motion anticipation is computed autonomously within the dendritic tree of each ganglion cell and relies on feedforward inhibition. The passive and non-linear interaction of excitatory and inhibitory synapses enables the somatic voltage to encode the actual position of a moving object instead of its delayed representation. General rather than specific features of the retinal connectome govern this computation: an excess of inhibitory inputs over excitatory, with both being randomly distributed, allows tracking of all directions of motion, while the average distance between inputs determines the object velocities that can be compensated for. DOI: http://dx.doi.org/10.7554/eLife.06250.001 PMID:25786068

  14. Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions.

    PubMed

    Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X

    2010-05-01

    Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.

  15. Patient-centred care in general dental practice - a systematic review of the literature

    PubMed Central

    2014-01-01

    Background Delivering improvements in quality is a key objective within most healthcare systems, and a view which has been widely embraced within the NHS in the United Kingdom. Within the NHS, quality is evaluated across three key dimensions: clinical effectiveness, safety and patient experience, with the latter modelled on the Picker Principles of Patient-Centred Care (PCC). Quality improvement is an important feature of the current dental contract reforms in England, with “patient experience” likely to have a central role in the evaluation of quality. An understanding and appreciation of the evidence underpinning PCC within dentistry is highly relevant if we are to use this as a measure of quality in general dental practice. Methods A systematic review of the literature was undertaken to identify the features of PCC relevant to dentistry and ascertain the current research evidence base underpinning its use as a measure of quality within general dental practice. Results Three papers were identified which met the inclusion criteria and demonstrated the use of primary research to provide an understanding of the key features of PCC within dentistry. None of the papers identified were based in general dental practice and none of the three studies sought the views of patients. Some distinct differences were noted between the key features of PCC reported within the dental literature and those developed within the NHS Patient Experience Framework. Conclusions This systematic review reveals a lack of understanding of PCC within dentistry, and in particular general dental practice. There is currently a poor evidence base to support the use of the current patient reported outcome measures as indicators of patient-centredness. Further research is necessary to understand the important features of PCC in dentistry and patients’ views should be central to this research. PMID:24902842

  16. Identifying the optimal segmentors for mass classification in mammograms

    NASA Astrophysics Data System (ADS)

    Zhang, Yu; Tomuro, Noriko; Furst, Jacob; Raicu, Daniela S.

    2015-03-01

    In this paper, we present the results of our investigation on identifying the optimal segmentor(s) from an ensemble of weak segmentors, used in a Computer-Aided Diagnosis (CADx) system which classifies suspicious masses in mammograms as benign or malignant. This is an extension of our previous work, where we used various parameter settings of image enhancement techniques to each suspicious mass (region of interest (ROI)) to obtain several enhanced images, then applied segmentation to each image to obtain several contours of a given mass. Each segmentation in this ensemble is essentially a "weak segmentor" because no single segmentation can produce the optimal result for all images. Then after shape features are computed from the segmented contours, the final classification model was built using logistic regression. The work in this paper focuses on identifying the optimal segmentor(s) from an ensemble mix of weak segmentors. For our purpose, optimal segmentors are those in the ensemble mix which contribute the most to the overall classification rather than the ones that produced high precision segmentation. To measure the segmentors' contribution, we examined weights on the features in the derived logistic regression model and computed the average feature weight for each segmentor. The result showed that, while in general the segmentors with higher segmentation success rates had higher feature weights, some segmentors with lower segmentation rates had high classification feature weights as well.

  17. Identifying clinical features in primary care electronic health record studies: methods for codelist development.

    PubMed

    Watson, Jessica; Nicholson, Brian D; Hamilton, Willie; Price, Sarah

    2017-11-22

    Analysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development. We describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an 'uncertainty' variable to allow sensitivity analysis. These methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software. The codelist was used to estimate the frequency of shortness of breath in a cohort of 28 216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD). Of 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13 091 episodes of shortness of breath were identified in the cohort of 28 216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice. Although initially time consuming, using a rigorous and reproducible method for codelist generation 'future-proofs' findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including

  18. Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.

    PubMed

    Mathieson, Luke; Mendes, Alexandre; Marsden, John; Pond, Jeffrey; Moscato, Pablo

    2017-01-01

    This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

  19. Comparisons of observed seasonal climate features with a winter and summer numerical simulation produced with the GLAS general circulation model

    NASA Technical Reports Server (NTRS)

    Halem, M.; Shukla, J.; Mintz, Y.; Wu, M. L.; Godbole, R.; Herman, G.; Sud, Y.

    1979-01-01

    Results are presented from numerical simulations performed with the general circulation model (GCM) for winter and summer. The monthly mean simulated fields for each integration are compared with observed geographical distributions and zonal averages. In general, the simulated sea level pressure and upper level geopotential height field agree well with the observations. Well simulated features are the winter Aleutian and Icelandic lows, the summer southwestern U.S. low, the summer and winter oceanic subtropical highs in both hemispheres, and the summer upper level Tibetan high and Atlantic ridge. The surface and upper air wind fields in the low latitudes are in good agreement with the observations. The geographical distirbutions of the Earth-atmosphere radiation balance and of the precipitation rates over the oceans are well simulated, but not all of the intensities of these features are correct. Other comparisons are shown for precipitation along the ITCZ, rediation balance, zonally averaged temperatures and zonal winds, and poleward transports of momentum and sensible heat.

  20. [Identifying barriers to screening for abdominal aortic aneurysm in general practice: Qualitative study of 14 general practitioners in Paris].

    PubMed

    Niclot, J; Stansal, A; Saint-Lary, O; Lazareth, I; Priollet, P

    2018-05-01

    Abdominal aortic aneurysm (AAA) is a silent pathology with often fatal consequences in case of rupture. AAA screening, recommended in France and many other countries, has shown its effectiveness in reducing specific mortality. However, AAA screening rate remains insufficient. To identify barriers to AAA screening in general practice. Qualitative study carried out during 2016 among general practitioners based in Paris. Fourteen physicians were included. Most of the barriers were related to the physician: unawareness about AAA and screening recommendations, considering AAA as a secondary question not discussed with the patient, abdominal aorta not included in cardiovascular assessment, no search for a familial history of AAA, AAA considered a question for the specialist, lack of time, lack of training, numerous screenings to propose, oversight. Some barriers are related to the patient: unawareness of the pathology and family history of AAA, refusal, questioning the pertinence of the doctor's comments, failure to respect the care pathway. Others are related to AAA: source of anxiety, low prevalence, rarity of complications. The remaining barriers are related to screening: cost-benefit and risk-benefit ratios, sonographer unavailability, constraint for the patient, overmedicalization. Information and training of general practitioners about AAA must be strengthened in order to optimize AAA screening and reduce specific mortality. Copyright © 2018. Published by Elsevier Masson SAS.

  1. The ZJU index is a powerful index for identifying NAFLD in the general Chinese population.

    PubMed

    Li, Linman; You, Wenyi; Ren, Wei

    2017-10-01

    The ZJU index is a novel model for detecting non-alcoholic fatty liver disease (NAFLD) that it is calculated based on combination of the body mass index, fasting plasma glucose, triglycerides, and the serum alanine aminotransferase-to-aspartate transaminase ratio. We aimed to evaluate the diagnostic accuracy of the ZJU index in detecting NAFLD in the Chinese population. This was a cross-sectional study. Anthropometric measurements, laboratory data, and ultrasonography features were collected through a standard protocol. The ZJU index, fatty liver index, hepatic steatosis index, lipid accumulation product, and visceral adiposity index were calculated. Then the predictive values of the five indices were compared according to the area under receiver-operating characteristic curve (AUROC) values. A total of 19,804 participants were recruited, of whom 7324 participants were diagnosed with NFALD and 12,480 subjects were regarded as controls. The AUROC value for NAFLD identification by the ZJU index was 0.925 (95% confidence interval [CI]: 0.919-0.931), which was significantly higher than the values for the other four models (P < 0.001). Furthermore, from age 31 years to >60 years, the AUROC for the ZJU increased from 87.1 to 95.4%, values which were also greater than those for the other four indices. Analysis by sex also showed that the performance of the ZJU index in males and females was better than that of the other four indices. The ZJU index is an accurate and easy to employ tool for identifying NAFLD in the general Chinese population.

  2. Critical Thinking Skills among Elementary School Students: Comparing Identified Gifted and General Education Student Performance

    ERIC Educational Resources Information Center

    Kettler, Todd

    2014-01-01

    Education reform efforts, including the current adoption of Common Core State Standards, have increased attention to teaching critical thinking skills to all students. This study investigated the critical thinking skills of fourth-grade students from a school district in Texas, including 45 identified gifted students and 163 general education…

  3. Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

    NASA Astrophysics Data System (ADS)

    Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

    2018-03-01

    Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

  4. The existential dimension in general practice: identifying understandings and experiences of general practitioners in Denmark

    PubMed Central

    Assing Hvidt, Elisabeth; Søndergaard, Jens; Ammentorp, Jette; Bjerrum, Lars; Gilså Hansen, Dorte; Olesen, Frede; Pedersen, Susanne S.; Timm, Helle; Timmermann, Connie; Hvidt, Niels Christian

    2016-01-01

    Objective The objective of this study is to identify points of agreement and disagreements among general practitioners (GPs) in Denmark concerning how the existential dimension is understood, and when and how it is integrated in the GP–patient encounter. Design A qualitative methodology with semi-structured focus group interviews was employed. Setting General practice setting in Denmark. Subjects Thirty-one GPs from two Danish regions between 38 and 68 years of age participated in seven focus group interviews. Results Although understood to involve broad life conditions such as present and future being and identity, connectedness to a society and to other people, the existential dimension was primarily reported integrated in connection with life-threatening diseases and death. Furthermore, integration of the existential dimension was characterized as unsystematic and intuitive. Communication about religious or spiritual questions was mostly avoided by GPs due to shyness and perceived lack of expertise. GPs also reported infrequent referrals of patients to chaplains. Conclusion GPs integrate issues related to the existential dimension in implicit and non-standardized ways and are hindered by cultural barriers. As a way to enhance a practice culture in which GPs pay more explicit attention to the patients’ multidimensional concerns, opportunities for professional development could be offered (courses or seminars) that focus on mutual sharing of existential reflections, ideas and communication competencies. Key pointsAlthough integration of the existential dimension is recommended for patient care in general practice, little is known about GPs’ understanding and integration of this dimension in the GP–patient encounter.The existential dimension is understood to involve broad and universal life conditions having no explicit reference to spiritual or religious aspects.The integration of the existential dimension is delimited to patient cases where life

  5. A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer’s Disease

    PubMed Central

    Wang, Xulong; Philip, Vivek M.; Ananda, Guruprasad; White, Charles C.; Malhotra, Ankit; Michalski, Paul J.; Karuturi, Krishna R. Murthy; Chintalapudi, Sumana R.; Acklin, Casey; Sasner, Michael; Bennett, David A.; De Jager, Philip L.; Howell, Gareth R.; Carter, Gregory W.

    2018-01-01

    Recent technical and methodological advances have greatly enhanced genome-wide association studies (GWAS). The advent of low-cost, whole-genome sequencing facilitates high-resolution variant identification, and the development of linear mixed models (LMM) allows improved identification of putatively causal variants. While essential for correcting false positive associations due to sample relatedness and population stratification, LMMs have commonly been restricted to quantitative variables. However, phenotypic traits in association studies are often categorical, coded as binary case-control or ordered variables describing disease stages. To address these issues, we have devised a method for genomic association studies that implements a generalized LMM (GLMM) in a Bayesian framework, called Bayes-GLMM. Bayes-GLMM has four major features: (1) support of categorical, binary, and quantitative variables; (2) cohesive integration of previous GWAS results for related traits; (3) correction for sample relatedness by mixed modeling; and (4) model estimation by both Markov chain Monte Carlo sampling and maximal likelihood estimation. We applied Bayes-GLMM to the whole-genome sequencing cohort of the Alzheimer’s Disease Sequencing Project. This study contains 570 individuals from 111 families, each with Alzheimer’s disease diagnosed at one of four confidence levels. Using Bayes-GLMM we identified four variants in three loci significantly associated with Alzheimer’s disease. Two variants, rs140233081 and rs149372995, lie between PRKAR1B and PDGFA. The coded proteins are localized to the glial-vascular unit, and PDGFA transcript levels are associated with Alzheimer’s disease-related neuropathology. In summary, this work provides implementation of a flexible, generalized mixed-model approach in a Bayesian framework for association studies. PMID:29507048

  6. New candidates for carbon stars with silicate features

    NASA Technical Reports Server (NTRS)

    Chan, S. J.; Kwok, Sun

    1991-01-01

    All stars in the General Catalog of Cool Galactic Carbon Stars with IRAS 12-micron fluxes greater than 10 Jy were searched for Low-Resolution-Spectrometer (LRS) spectra in the IRAS LRS data base. Out of the 532 spectra examined, 11 were found to show the 9.7-micron silicate emission feature. Four of these are identified for the first time. This group of carbon stars may represent transition objects between oxygen-rich and carbon-rich stars on the asymptotic giant branch.

  7. A General Symbolic Method with Physical Applications

    NASA Astrophysics Data System (ADS)

    Smith, Gregory M.

    2000-06-01

    A solution to the problem of unifying the General Relativistic and Quantum Theoretical formalisms is given which introduces a new non-axiomatic symbolic method and an algebraic generalization of the Calculus to non-finite symbolisms without reference to the concept of a limit. An essential feature of the non-axiomatic method is the inadequacy of any (finite) statements: Identifying this aspect of the theory with the "existence of an external physical reality" both allows for the consistency of the method with the results of experiments and avoids the so-called "measurement problem" of quantum theory.

  8. TCGA study identifies genomic features of cervical cancer

    Cancer.gov

    Investigators with The Cancer Genome Atlas (TCGA) Research Network have identified novel genomic and molecular characteristics of cervical cancer that will aid in subclassification of the disease and may help target therapies that are most appropriate for each patient.

  9. An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors

    PubMed Central

    Vassallo, Michael

    2018-01-01

    This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment. PMID:29495299

  10. Enhancing facial features by using clear facial features

    NASA Astrophysics Data System (ADS)

    Rofoo, Fanar Fareed Hanna

    2017-09-01

    The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.

  11. Provision of mental health care in general practice in Italy.

    PubMed Central

    Tansella, M; Bellantuono, C

    1991-01-01

    The main features of the psychiatric system and of the general practice system in Italy since the psychiatric reform and the introduction of a national health service are briefly described. Research conducted in Italy confirms that a large proportion of patients seen by general practitioners have psychological disorders and that only some of those patients whose psychological problems are identified by general practitioners are referred to specialist psychiatric care. Thus, the need to identify the best model of collaboration between psychiatric services and general practice services is becoming increasingly urgent. The chances of improving links between the two services and of developing a satisfactory liaison model are probably greater in countries such as Italy where psychiatric services are highly decentralized and community-based, than in countries where the psychiatric services are hospital-based. PMID:1807308

  12. Pathologic features of metastatic lymph nodes identified from prophylactic central neck dissection in patients with papillary thyroid carcinoma.

    PubMed

    Lee, Hyoung Shin; Park, Chanwoo; Kim, Sung Won; Noh, Woong Jae; Lim, Soo Jin; Chun, Bong Kwon; Kim, Beom Su; Hong, Jong Chul; Lee, Kang Dae

    2016-10-01

    The importance of pathologic features of metastatic lymph nodes (LNs), such as size, number, and extranodal extension, has been recently emphasized in patients with papillary thyroid carcinoma (PTC). We evaluated the characteristics of metastatic LNs identified after prophylactic central neck dissection (CND) in patients with PTC. We performed a retrospective review of 1,046 patients who underwent unilateral or bilateral thyroidectomy with ipsilateral prophylactic CND. We reviewed the characteristics of the metastatic LNs and analyzed their correlation to the clinicopathologic characteristics of the primary tumor. Cervical LN metastasis after prophylactic CND was identified in 280 out of 1046 patients (26.8 %). The size of metastatic foci (≥2 mm) was independently correlated with primary tumor size (≥1 cm) (p = 0.016, OR = 1.88). Primary tumor size (≥1 cm) was also correlated to the number of metastatic LNs (≥5) (p = 0.004, OR = 3.14) and extranodal extension (p = 0.021, OR = 2.41) in univariate analysis. The size of the primary tumor affects pathologic features of subclinical LN metastasis in patients with PTC. Patients with primary tumors ≥1 cm have an increased risk of larger LN metastases (≥2 mm), an increased number of LN metastases (≥5), and a higher incidence of ENE, which should be considered in decision for prophylactic CND.

  13. Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data.

    PubMed

    Mallik, Saurav; Bhadra, Tapas; Maulik, Ujjwal

    2017-01-01

    Epigenetic Biomarker discovery is an important task in bioinformatics. In this article, we develop a new framework of identifying statistically significant epigenetic biomarkers using maximal-relevance and minimal-redundancy criterion based feature (gene) selection for multi-omics dataset. Firstly, we determine the genes that have both expression as well as methylation values, and follow normal distribution. Similarly, we identify the genes which consist of both expression and methylation values, but do not follow normal distribution. For each case, we utilize a gene-selection method that provides maximal-relevant, but variable-weighted minimum-redundant genes as top ranked genes. For statistical validation, we apply t-test on both the expression and methylation data consisting of only the normally distributed top ranked genes to determine how many of them are both differentially expressed andmethylated. Similarly, we utilize Limma package for performing non-parametric Empirical Bayes test on both expression and methylation data comprising only the non-normally distributed top ranked genes to identify how many of them are both differentially expressed and methylated. We finally report the top-ranking significant gene-markerswith biological validation. Moreover, our framework improves positive predictive rate and reduces false positive rate in marker identification. In addition, we provide a comparative analysis of our gene-selection method as well as othermethods based on classificationperformances obtained using several well-known classifiers.

  14. Varying irrelevant phonetic features hinders learning of the feature being trained.

    PubMed

    Antoniou, Mark; Wong, Patrick C M

    2016-01-01

    Learning to distinguish nonnative words that differ in a critical phonetic feature can be difficult. Speech training studies typically employ methods that explicitly direct the learner's attention to the relevant nonnative feature to be learned. However, studies on vision have demonstrated that perceptual learning may occur implicitly, by exposing learners to stimulus features, even if they are irrelevant to the task, and it has recently been suggested that this task-irrelevant perceptual learning framework also applies to speech. In this study, subjects took part in a seven-day training regimen to learn to distinguish one of two nonnative features, namely, voice onset time or lexical tone, using explicit training methods consistent with most speech training studies. Critically, half of the subjects were exposed to stimuli that varied not only in the relevant feature, but in the irrelevant feature as well. The results showed that subjects who were trained with stimuli that varied in the relevant feature and held the irrelevant feature constant achieved the best learning outcomes. Varying both features hindered learning and generalization to new stimuli.

  15. [Identifying ways to address the crisis facing a medical specialty: a case study of general surgery].

    PubMed

    Nirel, Nurit; Hendin, Ayala; Rabau, Micha

    2012-03-01

    In a previous study we defined criteria for a medical specialty in crisis' and measures to assess the scale of the problem, and possible resolutions suggested based on experience abroad. This study seeks to gain further knowledge by exploring how front-line Israeli surgeons envisage the problems and possible solutions. To identify ways to address the workforce crisis in general surgery (GS) white focusing on issues that can be dealt with at the department and the hospital levels. An action study of GS conducted in two stages: (1) Semi-structured interviews with 180 GS residents. (2) The use of the retrospective method of "Learning from success" in five general surgical departments recognized as "successful" in attracting residents and integrating them into the departments while providing high-level training. The factors attracting medical students to specialize in GS are presented along with the problems perceived by residents during their residency. AdditionaLLy, 12 general principles identified in the study are presented, which can be transmitted to and implemented by other GS departments. They are related to three key topics: the mode and quality of residency training; work schedules, departmental organization of work and departmental atmosphere; and the comportment of senior physicians. The value of implementing these principles should be weighed in terms of being identified as constituting "leverage for change". Study findings will facilitate recommendations on internal organizational/professional factors of attracting and integrating residents to the specialty and the department. The study can serve as a basis for similar action research in other medical specialties.

  16. Gene expression profiles of breast biopsies from healthy women identify a group with claudin-low features

    PubMed Central

    2011-01-01

    Background Increased understanding of the variability in normal breast biology will enable us to identify mechanisms of breast cancer initiation and the origin of different subtypes, and to better predict breast cancer risk. Methods Gene expression patterns in breast biopsies from 79 healthy women referred to breast diagnostic centers in Norway were explored by unsupervised hierarchical clustering and supervised analyses, such as gene set enrichment analysis and gene ontology analysis and comparison with previously published genelists and independent datasets. Results Unsupervised hierarchical clustering identified two separate clusters of normal breast tissue based on gene-expression profiling, regardless of clustering algorithm and gene filtering used. Comparison of the expression profile of the two clusters with several published gene lists describing breast cells revealed that the samples in cluster 1 share characteristics with stromal cells and stem cells, and to a certain degree with mesenchymal cells and myoepithelial cells. The samples in cluster 1 also share many features with the newly identified claudin-low breast cancer intrinsic subtype, which also shows characteristics of stromal and stem cells. More women belonging to cluster 1 have a family history of breast cancer and there is a slight overrepresentation of nulliparous women in cluster 1. Similar findings were seen in a separate dataset consisting of histologically normal tissue from both breasts harboring breast cancer and from mammoplasty reductions. Conclusion This is the first study to explore the variability of gene expression patterns in whole biopsies from normal breasts and identified distinct subtypes of normal breast tissue. Further studies are needed to determine the specific cell contribution to the variation in the biology of normal breasts, how the clusters identified relate to breast cancer risk and their possible link to the origin of the different molecular subtypes of breast

  17. Gene expression profiles of breast biopsies from healthy women identify a group with claudin-low features.

    PubMed

    Haakensen, Vilde D; Lingjaerde, Ole Christian; Lüders, Torben; Riis, Margit; Prat, Aleix; Troester, Melissa A; Holmen, Marit M; Frantzen, Jan Ole; Romundstad, Linda; Navjord, Dina; Bukholm, Ida K; Johannesen, Tom B; Perou, Charles M; Ursin, Giske; Kristensen, Vessela N; Børresen-Dale, Anne-Lise; Helland, Aslaug

    2011-11-01

    Increased understanding of the variability in normal breast biology will enable us to identify mechanisms of breast cancer initiation and the origin of different subtypes, and to better predict breast cancer risk. Gene expression patterns in breast biopsies from 79 healthy women referred to breast diagnostic centers in Norway were explored by unsupervised hierarchical clustering and supervised analyses, such as gene set enrichment analysis and gene ontology analysis and comparison with previously published genelists and independent datasets. Unsupervised hierarchical clustering identified two separate clusters of normal breast tissue based on gene-expression profiling, regardless of clustering algorithm and gene filtering used. Comparison of the expression profile of the two clusters with several published gene lists describing breast cells revealed that the samples in cluster 1 share characteristics with stromal cells and stem cells, and to a certain degree with mesenchymal cells and myoepithelial cells. The samples in cluster 1 also share many features with the newly identified claudin-low breast cancer intrinsic subtype, which also shows characteristics of stromal and stem cells. More women belonging to cluster 1 have a family history of breast cancer and there is a slight overrepresentation of nulliparous women in cluster 1. Similar findings were seen in a separate dataset consisting of histologically normal tissue from both breasts harboring breast cancer and from mammoplasty reductions. This is the first study to explore the variability of gene expression patterns in whole biopsies from normal breasts and identified distinct subtypes of normal breast tissue. Further studies are needed to determine the specific cell contribution to the variation in the biology of normal breasts, how the clusters identified relate to breast cancer risk and their possible link to the origin of the different molecular subtypes of breast cancer.

  18. General morphological and biological features of neoplasms: integration of molecular findings.

    PubMed

    Diaz-Cano, S J

    2008-07-01

    This review highlights the importance of morphology-molecular correlations for a proper implementation of new markers. It covers both general aspects of tumorigenesis (which are normally omitted in papers analysing molecular pathways) and the general mechanisms for the acquired capabilities of neoplasms. The mechanisms are also supported by appropriate diagrams for each acquired capability that include overlooked features such as mobilization of cellular resources and changes in chromatin, transcription and epigenetics; fully accepted oncogenes and tumour suppressor genes are highlighted, while the pathways are also presented as activating or inactivating with appropriate colour coding. Finally, the concepts and mechanisms presented enable us to understand the basic requirements for the appropriate implementation of molecular tests in clinical practice. In summary, the basic findings are presented to serve as a bridge to clinical applications. The current definition of neoplasm is descriptive and difficult to apply routinely. Biologically, neoplasms develop through acquisition of capabilities that involve tumour cell aspects and modified microenvironment interactions, resulting in unrestricted growth due to a stepwise accumulation of cooperative genetic alterations that affect key molecular pathways. The correlation of these molecular aspects with morphological changes is essential for better understanding of essential concepts as early neoplasms/precancerous lesions, progression/dedifferentiation, and intratumour heterogeneity. The acquired capabilities include self-maintained replication (cell cycle dysregulation), extended cell survival (cell cycle arrest, apoptosis dysregulation, and replicative lifespan), genetic instability (chromosomal and microsatellite), changes of chromatin, transcription and epigenetics, mobilization of cellular resources, and modified microenvironment interactions (tumour cells, stromal cells, extracellular, endothelium). The acquired

  19. Adult preferences for infantile facial features: an ethological approach.

    PubMed

    Sternglanz, S H; Gray, J L; Murakami, M

    1977-02-01

    In 1943 Konrad Lorenz postulated that certain infantile cues served as releasers for caretaking behaviour in human adults. This study is an attempt to confirm this hypothesis and to identify relevant cues. The stimuli studied were variations in facial features, and the responses were ratings of the attractiveness of the resultant infant faces. Parametric variations of eye height, eye width, eye height and width, iris size, and vertical variations in feature position (all presented in full-face drawings) were tested for their effect on the ratings, and highly significant preferences for particular stimuli were found. In general these preferences are consistent across a wide variety of environmental factors such as social class and experience with children. These findings are consistent with an ethological interpretation of the data.

  20. Alarming Prevalence of Emergency Hypertension Levels in the General Public Identified by a Hypertension Awareness Campaign.

    PubMed

    Caligiuri, Stephanie P B; Austria, Jose Alejandro; Pierce, Grant N

    2017-03-01

    Hypertension is a major cause of mortality and morbidity today. The "silent" nature of hypertension makes it critical to determine its prevalence and its severity in the general public and to identify strategies to identify people unaware of its presence. A mobile hypertension awareness campaign was created to: (i) determine the prevalence and types of hypertension in an urban North American center, (ii) increase hypertension awareness, and (iii) identify reasons for lack of therapy adherence. Mobile clinics were provided at shopping malls, workplaces, hospitals, and community centres to measure blood pressure in the public. Blood pressure recordings were done on a voluntary basis. Of 1097 participants, 50% presented with high blood pressure which was higher than expected. Of particular clinical significance, an unexpectedly large number of participants (2%) exhibited a hypertensive urgency/emergency. Most of these people were not adherent to medications (if their hypertension was detected previously), were unaware of their hypertensive state, and/or unwilling to acknowledge or ignored the clinical significance of the extremely high blood pressure readings. Reasons for lack of adherence included: denial, being unaware of health consequences, and proper management of hypertension. A relatively large segment of an urban population lives unaware of severe emergency levels of hypertension. A public mobile hypertension clinic provides a valuable strategy for identifying hypertension in the general public and for knowledge translation of hypertension management. © American Journal of Hypertension, Ltd 2017. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  1. Hypothesis testing for differentially correlated features.

    PubMed

    Sheng, Elisa; Witten, Daniela; Zhou, Xiao-Hua

    2016-10-01

    In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  2. Featured Image: Identifying a Glowing Shell

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2018-05-01

    New nebulae are being discovered and classified every day and this false-color image reveals one of the more recent objects of interest. This nebula, IPHASX J210204.7+471015, was recently imaged by the Andalucia Faint Object Spectrograph and Camera mounted on the 2.5-m Nordic Optical Telescope in La Palma, Spain. J210204 was initially identified as a possible planetary nebula a remnant left behind at the end of a red giants lifetime. Based on the above imaging, however, a team of authors led by Martn Guerrero (Institute of Astrophysics of Andalusia, Spain) is arguing that this shell of glowing gas was instead expelled around a classical nova. In a classical nova eruption, a white dwarf and its binary companion come very close together, and mass transfers to form a thin atmosphere of hydrogen around the white dwarf. When this hydrogen suddenly ignites in runaway fusion, this outer atmosphere can be expelled, forming a short-lived nova remnant which is what Guerrero and collaborators think were seeing with J210204. If so, this nebula can reveal information about the novathat caused it. To find out more about what the authors learned from this nebula, check out the paper below.CitationMartn A. Guerrero et al 2018 ApJ 857 80. doi:10.3847/1538-4357/aab669

  3. A Feature-based Approach to Big Data Analysis of Medical Images

    PubMed Central

    Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M.

    2015-01-01

    This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

  4. A Feature-Based Approach to Big Data Analysis of Medical Images.

    PubMed

    Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M

    2015-01-01

    This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

  5. Microdeletions are a general feature of adult and adolescent acute lymphoblastic leukemia: Unexpected similarities with pediatric disease

    PubMed Central

    Paulsson, Kajsa; Cazier, Jean-Baptiste; MacDougall, Finlay; Stevens, Jane; Stasevich, Irina; Vrcelj, Nikoletta; Chaplin, Tracy; Lillington, Debra M.; Lister, T. Andrew; Young, Bryan D.

    2008-01-01

    We present here a genome-wide map of abnormalities found in diagnostic samples from 45 adults and adolescents with acute lymphoblastic leukemia (ALL). A 500K SNP array analysis uncovered frequent genetic abnormalities, with cryptic deletions constituting half of the detected changes, implying that microdeletions are a characteristic feature of this malignancy. Importantly, the pattern of deletions resembled that recently reported in pediatric ALL, suggesting that adult, adolescent, and childhood cases may be more similar on the genetic level than previously thought. Thus, 70% of the cases displayed deletion of one or more of the CDKN2A, PAX5, IKZF1, ETV6, RB1, and EBF1 genes. Furthermore, several genes not previously implicated in the pathogenesis of ALL were identified as possible recurrent targets of deletion. In total, the SNP array analysis identified 367 genetic abnormalities not corresponding to known copy number polymorphisms, with all but two cases (96%) displaying at least one cryptic change. The resolution level of this SNP array study is the highest used to date to investigate a malignant hematologic disorder. Our findings provide insights into the leukemogenic process and may be clinically important in adult and adolescent ALL. Most importantly, we report that microdeletions of key genes appear to be a common, characteristic feature of ALL that is shared among different clinical, morphological, and cytogenetic subgroups. PMID:18458336

  6. Memory Loss, Alzheimer's Disease and General Anesthesia: A Preoperative Concern.

    PubMed

    Thaler, Adam; Siry, Read; Cai, Lufan; García, Paul S; Chen, Linda; Liu, Renyu

    2012-02-20

    The long-term cognitive effects of general anesthesia are under intense scrutiny. Here we present 5 cases from 2 academic institutions to analyze some common features where the patient's or the patient family member has made a request to address their concern on memory loss, Alzheimer's disease and general anesthesia before surgery. Records of anesthesia consultation separate from standard preoperative evaluation were retrieved to identify consultations related to memory loss and Alzheimer's disease from the patient and/or patient family members. The identified cases were extensively reviewed for features in common. We used Google® (http://www. google.com/) to identify available online information using "anesthesia memory loss" as a search phrase. Five cases were collected as a specific preoperative consultation related to memory loss, Alzheimer's disease and general anesthesia from two institutions. All of the individuals either had perceived memory impairment after a prior surgical procedure with general anesthesia or had a family member with Alzheimer's disease. They all accessed public media sources to find articles related to anesthesia and memory loss. On May 2 nd , 2011, searching "anesthesia memory loss" in Google yielded 764,000 hits. Only 3 of the 50 Google top hits were from peer-reviewed journals. Some of the lay media postings made a causal association between general anesthesia and memory loss and/or Alzheimer's disease without conclusive scientific literature support. The potential link between memory loss and Alzheimer's disease with general anesthesia is an important preoperative concern from patients and their family members. This concern arises from individuals who have had history of cognitive impairment or have had a family member with Alzheimer disease and have tried to obtain information from public media. Proper preoperative consultation with the awareness of the lay literature can be useful in reducing patient and patient family member

  7. Identifying environmental features for land management decisions

    NASA Technical Reports Server (NTRS)

    1983-01-01

    Pairs of HCMM day-night thermal infrared (IR) data were selected to examine patterns of surface temperature and thermal inertia (TI) of peninsular Florida. GOES and NOAA-6 thermal IR, as well as National Climatic Center temperatures and rainfall, were also used. The HCMM apparent thermal inertia (ATI) images closely correspond to the General Soil Map of Florida, based on soil drainage classes. Areas with low ATI overlay well-drained soils, such as deep sands and drained organic soils. Areas with high ATI overlay areas with wetlands and bodies of water. The HCMM ATI images also correspond well with GOES-detected winter nocturnal cold-prone areas. Use of HCMM data with Carlson's energy balance model shows both high moisture availability (MA) and high thermal inertia (TI) of wetland-type surfaces and low MA and low TI of upland, well-drained soils. Since soil areas with low TI develop higher temperatures during the day, then antecedent patterns of highest maximum daytime surface temperature can also be used to predict nocturnal cold-prone areas in Florida.

  8. Electroencephalography in the Diagnosis of Genetic Generalized Epilepsy Syndromes

    PubMed Central

    Seneviratne, Udaya; Cook, Mark J.; D’Souza, Wendyl Jude

    2017-01-01

    Genetic generalized epilepsy (GGE) consists of several syndromes diagnosed and classified on the basis of clinical features and electroencephalographic (EEG) abnormalities. The main EEG feature of GGE is bilateral, synchronous, symmetric, and generalized spike-wave complex. Other classic EEG abnormalities are polyspikes, epileptiform K-complexes and sleep spindles, polyspike-wave discharges, occipital intermittent rhythmic delta activity, eye-closure sensitivity, fixation-off sensitivity, and photoparoxysmal response. However, admixed with typical changes, atypical epileptiform discharges are also commonly seen in GGE. There are circadian variations of generalized epileptiform discharges. Sleep, sleep deprivation, hyperventilation, intermittent photic stimulation, eye closure, and fixation-off are often used as activation techniques to increase the diagnostic yield of EEG recordings. Reflex seizure-related EEG abnormalities can be elicited by the use of triggers such as cognitive tasks and pattern stimulation during the EEG recording in selected patients. Distinct electrographic abnormalities to help classification can be identified among different electroclinical syndromes. PMID:28993753

  9. Teachers' Understanding of Algebraic Generalization

    NASA Astrophysics Data System (ADS)

    Hawthorne, Casey Wayne

    Generalization has been identified as a cornerstone of algebraic thinking (e.g., Lee, 1996; Sfard, 1995) and is at the center of a rich conceptualization of K-8 algebra (Kaput, 2008; Smith, 2003). Moreover, mathematics teachers are being encouraged to use figural-pattern generalizing tasks as a basis of student-centered instruction, whereby teachers respond to and build upon the ideas that arise from students' explorations of these activities. Although more and more teachers are engaging their students in such generalizing tasks, little is known about teachers' understanding of generalization and their understanding of students' mathematical thinking in this domain. In this work, I addressed this gap, exploring the understanding of algebraic generalization of 4 exemplary 8th-grade teachers from multiple perspectives. A significant feature of this investigation is an examination of teachers' understanding of the generalization process, including the use of algebraic symbols. The research consisted of two phases. Phase I was an examination of the teachers' understandings of the underlying quantities and quantitative relationships represented by algebraic notation. In Phase II, I observed the instruction of 2 of these teachers. Using the lens of professional noticing of students' mathematical thinking, I explored the teachers' enacted knowledge of algebraic generalization, characterizing how it supported them to effectively respond to the needs and queries of their students. Results indicated that teachers predominantly see these figural patterns as enrichment activities, disconnected from course content. Furthermore, in my analysis, I identified conceptual difficulties teachers experienced when solving generalization tasks, in particular, connecting multiple symbolic representations with the quantities in the figures. Moreover, while the teachers strived to overcome the challenges of connecting different representations, they invoked both productive and unproductive

  10. Study on identifying deciduous forest by the method of feature space transformation

    NASA Astrophysics Data System (ADS)

    Zhang, Xuexia; Wu, Pengfei

    2009-10-01

    The thematic remotely sensed information extraction is always one of puzzling nuts which the remote sensing science faces, so many remote sensing scientists devotes diligently to this domain research. The methods of thematic information extraction include two kinds of the visual interpretation and the computer interpretation, the developing direction of which is intellectualization and comprehensive modularization. The paper tries to develop the intelligent extraction method of feature space transformation for the deciduous forest thematic information extraction in Changping district of Beijing city. The whole Chinese-Brazil resources satellite images received in 2005 are used to extract the deciduous forest coverage area by feature space transformation method and linear spectral decomposing method, and the result from remote sensing is similar to woodland resource census data by Chinese forestry bureau in 2004.

  11. The importance of internal facial features in learning new faces.

    PubMed

    Longmore, Christopher A; Liu, Chang Hong; Young, Andrew W

    2015-01-01

    For familiar faces, the internal features (eyes, nose, and mouth) are known to be differentially salient for recognition compared to external features such as hairstyle. Two experiments are reported that investigate how this internal feature advantage accrues as a face becomes familiar. In Experiment 1, we tested the contribution of internal and external features to the ability to generalize from a single studied photograph to different views of the same face. A recognition advantage for the internal features over the external features was found after a change of viewpoint, whereas there was no internal feature advantage when the same image was used at study and test. In Experiment 2, we removed the most salient external feature (hairstyle) from studied photographs and looked at how this affected generalization to a novel viewpoint. Removing the hair from images of the face assisted generalization to novel viewpoints, and this was especially the case when photographs showing more than one viewpoint were studied. The results suggest that the internal features play an important role in the generalization between different images of an individual's face by enabling the viewer to detect the common identity-diagnostic elements across non-identical instances of the face.

  12. Identifying Key Features of Effective Active Learning: The Effects of Writing and Peer Discussion

    PubMed Central

    Pangle, Wiline M.; Wyatt, Kevin H.; Powell, Karli N.; Sherwood, Rachel E.

    2014-01-01

    We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. PMID:25185230

  13. Features of resilience

    DOE PAGES

    Connelly, Elizabeth B.; Allen, Craig R.; Hatfield, Kirk; ...

    2017-02-20

    The National Academy of Sciences (NAS) definition of resilience is used here to organize common concepts and synthesize a set of key features of resilience that can be used across diverse application domains. The features in common include critical functions (services), thresholds, cross-scale (both space and time) interactions, and memory and adaptive management. We propose a framework for linking these features to the planning, absorbing, recovering, and adapting phases identified in the NAS definition. As a result, the proposed delineation of resilience can be important in understanding and communicating resilience concepts.

  14. Features of resilience

    USGS Publications Warehouse

    Connelly, Elizabeth B.; Allen, Craig R.; Hatfield, Kirk; Palma-Oliveira, José M.; Woods, David D.; Linkov, Igor

    2017-01-01

    The National Academy of Sciences (NAS) definition of resilience is used here to organize common concepts and synthesize a set of key features of resilience that can be used across diverse application domains. The features in common include critical functions (services), thresholds, cross-scale (both space and time) interactions, and memory and adaptive management. We propose a framework for linking these features to the planning, absorbing, recovering, and adapting phases identified in the NAS definition. The proposed delineation of resilience can be important in understanding and communicating resilience concepts.

  15. Access to general practice for Pacific peoples: a place for cultural competency.

    PubMed

    Ludeke, Melissa; Puni, Ronald; Cook, Lynley; Pasene, Maria; Abel, Gillian; Sopoaga, Faafetai

    2012-06-01

    Access to primary health care services has been identified as a problem for Pacific peoples. Although cost is the most frequently cited barrier to Pacific service utilisation, some research has indicated that access may also be influenced by features of mainstream primary care services. This study aimed to identify features of mainstream general practice services that act as barriers to accessing these services for Pacific peoples in order to explore strategies that providers could adopt to enable their practices to be more welcoming, accessible and appropriate for Pacific peoples. Pacific participants were recruited through Pacific networks known to Pegasus Health and via 'snowball' sampling. In total, 20 participants participated in one of three focus groups. A semi-structured interview explored the participants' views and experiences of mainstream general practice care. Thematic analysis was utilised to interpret the data. The analysis revealed five themes highlighting non-financial features of mainstream general practice services that may influence the availability and acceptability of these services to Pacific peoples: language and communication; rushed consultations; appointment availability; reception; and Pacific presence. The findings indicate that all personnel within the primary care setting have the ability to directly engage in the improvement of the health status of Pacific peoples in New Zealand by developing cultural competency and incorporating flexibility and diversity into the care and service they provide.

  16. 34 CFR 222.39 - How does a State educational agency identify generally comparable local educational agencies for...

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ..., low-income families, children with disabilities, neglected or delinquent children, low-achieving..., DEPARTMENT OF EDUCATION IMPACT AID PROGRAMS Payments for Federally Connected Children Under Section 8003(b) and (e) of the Act § 222.39 How does a State educational agency identify generally comparable local...

  17. 34 CFR 222.39 - How does a State educational agency identify generally comparable local educational agencies for...

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ..., low-income families, children with disabilities, neglected or delinquent children, low-achieving..., DEPARTMENT OF EDUCATION IMPACT AID PROGRAMS Payments for Federally Connected Children Under Section 8003(b) and (e) of the Act § 222.39 How does a State educational agency identify generally comparable local...

  18. Special object extraction from medieval books using superpixels and bag-of-features

    NASA Astrophysics Data System (ADS)

    Yang, Ying; Rushmeier, Holly

    2017-01-01

    We propose a method to extract special objects in images of medieval books, which generally represent, for example, figures and capital letters. Instead of working on the single-pixel level, we consider superpixels as the basic classification units for improved time efficiency. More specifically, we classify superpixels into different categories/objects by using a bag-of-features approach, where a superpixel category classifier is trained with the local features of the superpixels of the training images. With the trained classifier, we are able to assign the category labels to the superpixels of a historical document image under test. Finally, special objects can easily be identified and extracted after analyzing the categorization results. Experimental results demonstrate that, as compared to the state-of-the-art algorithms, our method provides comparable performance for some historical books but greatly outperforms them in terms of generality and computational time.

  19. Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

    PubMed Central

    Toews, Matthew; Wells, William M.

    2013-01-01

    This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. PMID:23265799

  20. Harvesting geographic features from heterogeneous raster maps

    NASA Astrophysics Data System (ADS)

    Chiang, Yao-Yi

    2010-11-01

    Raster maps offer a great deal of geospatial information and are easily accessible compared to other geospatial data. However, harvesting geographic features locked in heterogeneous raster maps to obtain the geospatial information is challenging. This is because of the varying image quality of raster maps (e.g., scanned maps with poor image quality and computer-generated maps with good image quality), the overlapping geographic features in maps, and the typical lack of metadata (e.g., map geocoordinates, map source, and original vector data). Previous work on map processing is typically limited to a specific type of map and often relies on intensive manual work. In contrast, this thesis investigates a general approach that does not rely on any prior knowledge and requires minimal user effort to process heterogeneous raster maps. This approach includes automatic and supervised techniques to process raster maps for separating individual layers of geographic features from the maps and recognizing geographic features in the separated layers (i.e., detecting road intersections, generating and vectorizing road geometry, and recognizing text labels). The automatic technique eliminates user intervention by exploiting common map properties of how road lines and text labels are drawn in raster maps. For example, the road lines are elongated linear objects and the characters are small connected-objects. The supervised technique utilizes labels of road and text areas to handle complex raster maps, or maps with poor image quality, and can process a variety of raster maps with minimal user input. The results show that the general approach can handle raster maps with varying map complexity, color usage, and image quality. By matching extracted road intersections to another geospatial dataset, we can identify the geocoordinates of a raster map and further align the raster map, separated feature layers from the map, and recognized features from the layers with the geospatial

  1. Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

    NASA Astrophysics Data System (ADS)

    Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

    2017-10-01

    This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

  2. Feature selection with harmony search.

    PubMed

    Diao, Ren; Shen, Qiang

    2012-12-01

    Many search strategies have been exploited for the task of feature selection (FS), in an effort to identify more compact and better quality subsets. Such work typically involves the use of greedy hill climbing (HC), or nature-inspired heuristics, in order to discover the optimal solution without going through exhaustive search. In this paper, a novel FS approach based on harmony search (HS) is presented. It is a general approach that can be used in conjunction with many subset evaluation techniques. The simplicity of HS is exploited to reduce the overall complexity of the search process. The proposed approach is able to escape from local solutions and identify multiple solutions owing to the stochastic nature of HS. Additional parameter control schemes are introduced to reduce the effort and impact of parameter configuration. These can be further combined with the iterative refinement strategy, tailored to enforce the discovery of quality subsets. The resulting approach is compared with those that rely on HC, genetic algorithms, and particle swarm optimization, accompanied by in-depth studies of the suggested improvements.

  3. Systematic Review and Meta-Analysis of CT Features for Differentiating Complicated and Uncomplicated Appendicitis.

    PubMed

    Kim, Hae Young; Park, Ji Hoon; Lee, Yoon Jin; Lee, Sung Soo; Jeon, Jong-June; Lee, Kyoung Ho

    2018-04-01

    Purpose To perform a systematic review and meta-analysis to identify computed tomographic (CT) features for differentiating complicated appendicitis in patients suspected of having appendicitis and to summarize their diagnostic accuracy. Materials and Methods Studies on diagnostic accuracy of CT features for differentiating complicated appendicitis (perforated or gangrenous appendicitis) in patients suspected of having appendicitis were searched in Ovid-MEDLINE, EMBASE, and the Cochrane Library. Overlapping descriptors used in different studies to denote the same image finding were subsumed under a single CT feature. Pooled diagnostic accuracy of the CT features was calculated by using a bivariate random effects model. CT features with pooled diagnostic odds ratios with 95% confidence intervals not including 1 were considered as informative. Results Twenty-three studies were included, and 184 overlapping descriptors for various CT findings were subsumed under 14 features. Of these, 10 features were informative for complicated appendicitis. There was a general tendency for these features to show relatively high specificity but low sensitivity. Extraluminal appendicolith, abscess, appendiceal wall enhancement defect, extraluminal air, ileus, periappendiceal fluid collection, ascites, intraluminal air, and intraluminal appendicolith showed pooled specificity greater than 70% (range, 74%-100%), but sensitivity was limited (range, 14%-59%). Periappendiceal fat stranding was the only feature that showed high sensitivity (94%; 95% confidence interval: 86%, 98%) but low specificity (40%; 95% confidence interval, 23%, 60%). Conclusion Ten informative CT features for differentiating complicated appendicitis were identified in this study, nine of which showed high specificity, but low sensitivity. © RSNA, 2017 Online supplemental material is available for this article.

  4. A general method for identifying major hybrid male sterility genes in Drosophila.

    PubMed

    Zeng, L W; Singh, R S

    1995-10-01

    The genes responsible for hybrid male sterility in species crosses are usually identified by introgressing chromosome segments, monitored by visible markers, between closely related species by continuous backcrosses. This commonly used method, however, suffers from two problems. First, it relies on the availability of markers to monitor the introgressed regions and so the portion of the genome examined is limited to the marked regions. Secondly, the introgressed regions are usually large and it is impossible to tell if the effects of the introgressed regions are the result of single (or few) major genes or many minor genes (polygenes). Here we introduce a simple and general method for identifying putative major hybrid male sterility genes which is free of these problems. In this method, the actual hybrid male sterility genes (rather than markers), or tightly linked gene complexes with large effects, are selectively introgressed from one species into the background of another species by repeated backcrosses. This is performed by selectively backcrossing heterozygous (for hybrid male sterility gene or genes) females producing fertile and sterile sons in roughly equal proportions to males of either parental species. As no marker gene is required for this procedure, this method can be used with any species pairs that produce unisexual sterility. With the application of this method, a small X chromosome region of Drosophila mauritiana which produces complete hybrid male sterility (aspermic testes) in the background of D. simulans was identified. Recombination analysis reveals that this region contains a second major hybrid male sterility gene linked to the forked locus located at either 62.7 +/- 0.66 map units or at the centromere region of the X chromosome of D. mauritiana.

  5. Evaluating public awareness of new currency design features

    NASA Astrophysics Data System (ADS)

    DiNunzio, Lisa; Church, Sara E.

    2002-04-01

    One of the goals of the 1996 series design was to integrate highly recognizable features that enable the general public to more easily distinguish counterfeit from genuine notes, thereby reducing the chance of counterfeit notes being passed. The purpose of this study is to evaluate how knowledgeable the public is concerning the new currency, to identify the channels through which the public learns about new currency design, and to assess the usefulness of the new currency's authentication features. Also, the study will serve as a baseline measurement for future design studies and in comparative analysis with other countries. The results of the qualitative research will be described in the following sections of this paper. The quantitative research is scheduled to begin in February 2002, at the same time as the Netherlands' opinion poll of the Euro and NLG-notes in an effort to compare results.

  6. Automatic Extraction of Planetary Image Features

    NASA Technical Reports Server (NTRS)

    Troglio, G.; LeMoigne, J.; Moser, G.; Serpico, S. B.; Benediktsson, J. A.

    2009-01-01

    With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration.

  7. 34 CFR 222.39 - How does a State educational agency identify generally comparable local educational agencies for...

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ..., DEPARTMENT OF EDUCATION IMPACT AID PROGRAMS Payments for Federally Connected Children Under Section 8003(b... 34 Education 1 2010-07-01 2010-07-01 false How does a State educational agency identify generally comparable local educational agencies for local contribution rate purposes? 222.39 Section 222.39 Education...

  8. [General features of the patient-physician relationship].

    PubMed

    Baeza, H; Bueno, G

    1997-03-01

    The communication between physicians and patients is often deficient. Little time is devoted to it and the patient receives scanty information with a low emotional content. Some features of our medicine can explain this situation. The rationalist and mechanistic biological model, allows to study only those things that can be undertaken with the scientific method. Psychological, social and spiritual aspects are surpassed. It only looks at material aspects of people, limiting the communication. Patients express their symptoms in an emotional way, with multiple beliefs and fears. The physician converts them to a precise, scientific, measurable and rational medical logical type. This language is not understood by patients, generating hesitancy in the communication. The paternalism is based in the power that physicians have over patients. We give knowledge and ask the patient to subordinate and accept our power. The patient loses his moral right to be informed, to ask, to have doubts or to disagree. Our personal communication is almost always formal, unemotional and with no explanations, further limiting communication.

  9. Automated Recognition of 3D Features in GPIR Images

    NASA Technical Reports Server (NTRS)

    Park, Han; Stough, Timothy; Fijany, Amir

    2007-01-01

    A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a

  10. Cue combination in a combined feature contrast detection and figure identification task.

    PubMed

    Meinhardt, Günter; Persike, Malte; Mesenholl, Björn; Hagemann, Cordula

    2006-11-01

    Target figures defined by feature contrast in spatial frequency, orientation or both cues had to be detected in Gabor random fields and their shape had to be identified in a dual task paradigm. Performance improved with increasing feature contrast and was strongly correlated among both tasks. Subjects performed significantly better with combined cues than with single cues. The improvement due to cue summation was stronger than predicted by the assumption of independent feature specific mechanisms, and increased with the performance level achieved with single cues until it was limited by ceiling effects. Further, cue summation was also strongly correlated among tasks: when there was benefit due to the additional cue in feature contrast detection, there was also benefit in figure identification. For the same performance level achieved with single cues, cue summation was generally larger in figure identification than in feature contrast detection, indicating more benefit when processes of shape and surface formation are involved. Our results suggest that cue combination improves spatial form completion and figure-ground segregation in noisy environments, and therefore leads to more stable object vision.

  11. Phylogeny of isolates of Prunus necrotic ringspot virus from the Ilarvirus Ringtest and identification of group-specific features.

    PubMed

    Hammond, R W

    2003-06-01

    Isolates of Prunus necrotic ringspot virus (PNRSV) were examined to establish the level of naturally occurring sequence variation in the coat protein (CP) gene and to identify group-specific genome features that may prove valuable for the generation of diagnostic reagents. Phylogenetic analysis of a 452 bp sequence of 68 virus isolates, 20 obtained from the European Union Ilarvirus Ringtest held in October 1998, confirmed the clustering of the isolates into three distinct groups. Although no correlation was found between the sequence and host or geographic origin, there was a general trend for severe isolates to cluster into one group. Group-specific features have been identified for discrimination between virus strains.

  12. Inhibitory dendrite dynamics as a general feature of the adult cortical microcircuit.

    PubMed

    Chen, Jerry L; Flanders, Genevieve H; Lee, Wei-Chung Allen; Lin, Walter C; Nedivi, Elly

    2011-08-31

    The mammalian neocortex is functionally subdivided into architectonically distinct regions that process various types of information based on their source of afferent input. Yet, the modularity of neocortical organization in terms of cell type and intrinsic circuitry allows afferent drive to continuously reassign cortical map space. New aspects of cortical map plasticity include dynamic turnover of dendritic spines on pyramidal neurons and remodeling of interneuron dendritic arbors. While spine remodeling occurs in multiple cortical regions, it is not yet known whether interneuron dendrite remodeling is common across primary sensory and higher-level cortices. It is also unknown whether, like pyramidal dendrites, inhibitory dendrites respect functional domain boundaries. Given the importance of the inhibitory circuitry to adult cortical plasticity and the reorganization of cortical maps, we sought to address these questions by using two-photon microscopy to monitor interneuron dendritic arbors of thy1-GFP-S transgenic mice expressing GFP in neurons sparsely distributed across the superficial layers of the neocortex. We find that interneuron dendritic branch tip remodeling is a general feature of the adult cortical microcircuit, and that remodeling rates are similar across primary sensory regions of different modalities, but may differ in magnitude between primary sensory versus higher cortical areas. We also show that branch tip remodeling occurs in bursts and respects functional domain boundaries.

  13. Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry

    USGS Publications Warehouse

    Stanislawski, Larry V.; Buttenfield, Barbara P.; Raposo, Paulo; Cameron, Madeline; Falgout, Jeff T.

    2015-01-01

    Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of the features through cartographic generalization (Regnauld and McMaster 2008, Stanislawski et al. 2014). A variety of algorithms exist to simplify linear cartographic features, and all of the methods affect the positional accuracy of the features (Shahriari and Tao 2002, Regnauld and McMaster 2008, Stanislawski et al. 2012). In general, simplification operations are controlled by one or more tolerance parameters that limit the amount of positional change the operation can make to features. Using a single tolerance value can have varying levels of positional change on features; depending on local shape, texture, or geometric characteristics of the original features (McMaster and Shea 1992, Shahriari and Tao 2002, Buttenfield et al. 2010). Consequently, numerous researchers have advocated calibration of simplification parameters to control quantifiable properties of resulting changes to the features (Li and Openshaw 1990, Raposo 2013, Tobler 1988, Veregin 2000, and Buttenfield, 1986, 1989).This research identifies relations between local topographic conditions and geometric characteristics of linear features that are available in the National Hydrography Dataset (NHD). The NHD is a comprehensive vector dataset of surface 18 th ICA Workshop on Generalisation and Multiple Representation, Rio de Janiero, Brazil 2015 2 water features within the United States that is maintained by the U.S. Geological Survey (USGS). In this paper, geometric characteristics of cartographic representations for natural stream and river features are summarized for subbasin watersheds within entire regions of the

  14. The extraction and use of facial features in low bit-rate visual communication.

    PubMed

    Pearson, D

    1992-01-29

    A review is given of experimental investigations by the author and his collaborators into methods of extracting binary features from images of the face and hands. The aim of the research has been to enable deaf people to communicate by sign language over the telephone network. Other applications include model-based image coding and facial-recognition systems. The paper deals with the theoretical postulates underlying the successful experimental extraction of facial features. The basic philosophy has been to treat the face as an illuminated three-dimensional object and to identify features from characteristics of their Gaussian maps. It can be shown that in general a composite image operator linked to a directional-illumination estimator is required to accomplish this, although the latter can often be omitted in practice.

  15. Paper-based and web-based intervention modeling experiments identified the same predictors of general practitioners' antibiotic-prescribing behavior.

    PubMed

    Treweek, Shaun; Bonetti, Debbie; Maclennan, Graeme; Barnett, Karen; Eccles, Martin P; Jones, Claire; Pitts, Nigel B; Ricketts, Ian W; Sullivan, Frank; Weal, Mark; Francis, Jill J

    2014-03-01

    To evaluate the robustness of the intervention modeling experiment (IME) methodology as a way of developing and testing behavioral change interventions before a full-scale trial by replicating an earlier paper-based IME. Web-based questionnaire and clinical scenario study. General practitioners across Scotland were invited to complete the questionnaire and scenarios, which were then used to identify predictors of antibiotic-prescribing behavior. These predictors were compared with the predictors identified in an earlier paper-based IME and used to develop a new intervention. Two hundred seventy general practitioners completed the questionnaires and scenarios. The constructs that predicted simulated behavior and intention were attitude, perceived behavioral control, risk perception/anticipated consequences, and self-efficacy, which match the targets identified in the earlier paper-based IME. The choice of persuasive communication as an intervention in the earlier IME was also confirmed. Additionally, a new intervention, an action plan, was developed. A web-based IME replicated the findings of an earlier paper-based IME, which provides confidence in the IME methodology. The interventions will now be evaluated in the next stage of the IME, a web-based randomized controlled trial. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Feature Interactions Enable Decoding of Sensorimotor Transformations for Goal-Directed Movement

    PubMed Central

    Barany, Deborah A.; Della-Maggiore, Valeria; Viswanathan, Shivakumar; Cieslak, Matthew

    2014-01-01

    Neurophysiology and neuroimaging evidence shows that the brain represents multiple environmental and body-related features to compute transformations from sensory input to motor output. However, it is unclear how these features interact during goal-directed movement. To investigate this issue, we examined the representations of sensory and motor features of human hand movements within the left-hemisphere motor network. In a rapid event-related fMRI design, we measured cortical activity as participants performed right-handed movements at the wrist, with either of two postures and two amplitudes, to move a cursor to targets at different locations. Using a multivoxel analysis technique with rigorous generalization tests, we reliably distinguished representations of task-related features (primarily target location, movement direction, and posture) in multiple regions. In particular, we identified an interaction between target location and movement direction in the superior parietal lobule, which may underlie a transformation from the location of the target in space to a movement vector. In addition, we found an influence of posture on primary motor, premotor, and parietal regions. Together, these results reveal the complex interactions between different sensory and motor features that drive the computation of sensorimotor transformations. PMID:24828640

  17. Identification of features of electronic prescribing systems to support quality and safety in primary care using a modified Delphi process.

    PubMed

    Sweidan, Michelle; Williamson, Margaret; Reeve, James F; Harvey, Ken; O'Neill, Jennifer A; Schattner, Peter; Snowdon, Teri

    2010-04-15

    Electronic prescribing is increasingly being used in primary care and in hospitals. Studies on the effects of e-prescribing systems have found evidence for both benefit and harm. The aim of this study was to identify features of e-prescribing software systems that support patient safety and quality of care and that are useful to the clinician and the patient, with a focus on improving the quality use of medicines. Software features were identified by a literature review, key informants and an expert group. A modified Delphi process was used with a 12-member multidisciplinary expert group to reach consensus on the expected impact of the features in four domains: patient safety, quality of care, usefulness to the clinician and usefulness to the patient. The setting was electronic prescribing in general practice in Australia. A list of 114 software features was developed. Most of the features relate to the recording and use of patient data, the medication selection process, prescribing decision support, monitoring drug therapy and clinical reports. The expert group rated 78 of the features (68%) as likely to have a high positive impact in at least one domain, 36 features (32%) as medium impact, and none as low or negative impact. Twenty seven features were rated as high positive impact across 3 or 4 domains including patient safety and quality of care. Ten features were considered "aspirational" because of a lack of agreed standards and/or suitable knowledge bases. This study defines features of e-prescribing software systems that are expected to support safety and quality, especially in relation to prescribing and use of medicines in general practice. The features could be used to develop software standards, and could be adapted if necessary for use in other settings and countries.

  18. Identification of features of electronic prescribing systems to support quality and safety in primary care using a modified Delphi process

    PubMed Central

    2010-01-01

    Background Electronic prescribing is increasingly being used in primary care and in hospitals. Studies on the effects of e-prescribing systems have found evidence for both benefit and harm. The aim of this study was to identify features of e-prescribing software systems that support patient safety and quality of care and that are useful to the clinician and the patient, with a focus on improving the quality use of medicines. Methods Software features were identified by a literature review, key informants and an expert group. A modified Delphi process was used with a 12-member multidisciplinary expert group to reach consensus on the expected impact of the features in four domains: patient safety, quality of care, usefulness to the clinician and usefulness to the patient. The setting was electronic prescribing in general practice in Australia. Results A list of 114 software features was developed. Most of the features relate to the recording and use of patient data, the medication selection process, prescribing decision support, monitoring drug therapy and clinical reports. The expert group rated 78 of the features (68%) as likely to have a high positive impact in at least one domain, 36 features (32%) as medium impact, and none as low or negative impact. Twenty seven features were rated as high positive impact across 3 or 4 domains including patient safety and quality of care. Ten features were considered "aspirational" because of a lack of agreed standards and/or suitable knowledge bases. Conclusions This study defines features of e-prescribing software systems that are expected to support safety and quality, especially in relation to prescribing and use of medicines in general practice. The features could be used to develop software standards, and could be adapted if necessary for use in other settings and countries. PMID:20398294

  19. Somatostatinoma: collision with neurofibroma and ultrastructural features.

    PubMed

    Varikatt, W; Yong, J L C; Killingsworth, M C

    2006-11-01

    The clinical presentation, histopathology and immunoelectron microscopic features of two cases of duodenal somatostatinoma are described, one of which is a hitherto unreported example of a collision tumour with a neurofibroma. Ultrastructural morphometric immunoelectron microscopy studies revealed the presence of four types of cells in both tumours, but there was no difference in the proportions of these cells between the collision tumour and the non-collision tumour. Neurosecretory granules ranging in size from 255-815 nm were generally larger than those previously reported for somatostatinomas and somatostatin was identified in granules of all sizes across this range. Neither tumour was associated with the somatostatinoma syndrome comprising associated diabetes mellitis, steatorrhoea and cholelithiasis.

  20. Novel and general approach to linear filter design for contrast-to-noise ratio enhancement of magnetic resonance images with multiple interfering features in the scene

    NASA Astrophysics Data System (ADS)

    Soltanian-Zadeh, Hamid; Windham, Joe P.

    1992-04-01

    Maximizing the minimum absolute contrast-to-noise ratios (CNRs) between a desired feature and multiple interfering processes, by linear combination of images in a magnetic resonance imaging (MRI) scene sequence, is attractive for MRI analysis and interpretation. A general formulation of the problem is presented, along with a novel solution utilizing the simple and numerically stable method of Gram-Schmidt orthogonalization. We derive explicit solutions for the case of two interfering features first, then for three interfering features, and, finally, using a typical example, for an arbitrary number of interfering feature. For the case of two interfering features, we also provide simplified analytical expressions for the signal-to-noise ratios (SNRs) and CNRs of the filtered images. The technique is demonstrated through its applications to simulated and acquired MRI scene sequences of a human brain with a cerebral infarction. For these applications, a 50 to 100% improvement for the smallest absolute CNR is obtained.

  1. An investigation of the psychological characteristics of stalkers: empathy, problem-solving, attachment and borderline personality features.

    PubMed

    Lewis, S F; Fremouw, W J; Del Ben, K; Farr, C

    2001-01-01

    This study examined the psychological characteristics of a sample of self-reported stalkers in comparison with a control group, on measures of empathy, problem-solving skills, attachment, and borderline personality features. Stalkers were identified by their endorsement of specific behavioral items, consistent with a widely adopted definition of stalking, denoting behaviors that: (a) are repeatedly directed toward an identified target; (b) are intrusive and unwanted; and (c) evoke fear in the victim. Stalkers scored significantly higher than controls on measures of insecure attachment and borderline personality features, suggesting that the stalking group demonstrates a general pattern of inadequate interpersonal attachment, has limited abilities to form and maintain appropriate relationships, is emotionally labile and unstable, and experiences ambivalence regarding their interpersonal relationships. Treatment implications are discussed herein.

  2. Efficient and robust model-to-image alignment using 3D scale-invariant features.

    PubMed

    Toews, Matthew; Wells, William M

    2013-04-01

    This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

  3. Permafrost features on Earth and Mars: Similarities, differences

    NASA Technical Reports Server (NTRS)

    Joens, H. P.

    1985-01-01

    Typical permafrost features on Earth are polygonal structures, pingos and soli-/gelifluxion features. In areas around the poles and in mountain ranges the precipitation accumulates to inland ice or ice streams. On Mars the same features were identified: polygonal features cover the larger part of the northern lowlands indicating probably an ice wedge-/sand wedge system or desiccation cracks. These features indicate the extend of large mud accumulations which seem to be related to large outflow events of the chaotic terrains. The shore line of this mud accumulation is indicated by a special set of relief types. In some areas large pingo-like hills were identified. In the vicinity of the largest martian volcano, Olympus Mons, the melting of underlying permafrost and/or ground ice led to the downslope sliding of large parts of the primary shield which formed the aureole around Olympus Mons. Glacier-like features are identified along the escarpment which separates the Southern Uplands from the Northern Lowlands.

  4. Pulmonary nodule characterization, including computer analysis and quantitative features.

    PubMed

    Bartholmai, Brian J; Koo, Chi Wan; Johnson, Geoffrey B; White, Darin B; Raghunath, Sushravya M; Rajagopalan, Srinivasan; Moynagh, Michael R; Lindell, Rebecca M; Hartman, Thomas E

    2015-03-01

    Pulmonary nodules are commonly detected in computed tomography (CT) chest screening of a high-risk population. The specific visual or quantitative features on CT or other modalities can be used to characterize the likelihood that a nodule is benign or malignant. Visual features on CT such as size, attenuation, location, morphology, edge characteristics, and other distinctive "signs" can be highly suggestive of a specific diagnosis and, in general, be used to determine the probability that a specific nodule is benign or malignant. Change in size, attenuation, and morphology on serial follow-up CT, or features on other modalities such as nuclear medicine studies or MRI, can also contribute to the characterization of lung nodules. Imaging analytics can objectively and reproducibly quantify nodule features on CT, nuclear medicine, and magnetic resonance imaging. Some quantitative techniques show great promise in helping to differentiate benign from malignant lesions or to stratify the risk of aggressive versus indolent neoplasm. In this article, we (1) summarize the visual characteristics, descriptors, and signs that may be helpful in management of nodules identified on screening CT, (2) discuss current quantitative and multimodality techniques that aid in the differentiation of nodules, and (3) highlight the power, pitfalls, and limitations of these various techniques.

  5. Identifying critical success factors for designing selection processes into postgraduate specialty training: the case of UK general practice.

    PubMed

    Plint, Simon; Patterson, Fiona

    2010-06-01

    The UK national recruitment process into general practice training has been developed over several years, with incremental introduction of stages which have been piloted and validated. Previously independent processes, which encouraged multiple applications and produced inconsistent outcomes, have been replaced by a robust national process which has high reliability and predictive validity, and is perceived to be fair by candidates and allocates applicants equitably across the country. Best selection practice involves a job analysis which identifies required competencies, then designs reliable assessment methods to measure them, and over the long term ensures that the process has predictive validity against future performance. The general practitioner recruitment process introduced machine markable short listing assessments for the first time in the UK postgraduate recruitment context, and also adopted selection centre workplace simulations. The key success factors have been identified as corporate commitment to the goal of a national process, with gradual convergence maintaining locus of control rather than the imposition of change without perceived legitimate authority.

  6. Detection and clustering of features in aerial images by neuron network-based algorithm

    NASA Astrophysics Data System (ADS)

    Vozenilek, Vit

    2015-12-01

    The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

  7. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques

    PubMed Central

    Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Calhoun, Vince D.

    2013-01-01

    Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398

  8. Anterior cruciate ligament injury: Identifying information sources and risk factor awareness among the general population.

    PubMed

    Nagano, Yasuharu; Yako-Suketomo, Hiroko; Natsui, Hiroaki

    2018-01-01

    Raising awareness on a disorder is important for its prevention and for promoting public health. However, for sports injuries like the anterior cruciate ligament (ACL) injury no studies have investigated the awareness on risk factors for injury and possible preventative measures in the general population. The sources of information among the population are also unclear. The purpose of the present study was to identify these aspects of public awareness about the ACL injury. A questionnaire was randomly distributed among the general population registered with a web based questionnaire supplier, to recruit 900 participants who were aware about the ACL injury. The questionnaire consisted of two parts: Question 1 asked them about their sources of information regarding the ACL injury; Question 2 asked them about the risk factors for ACL injury. Multivariate logistic regression was used to determine the information sources that provide a good understanding of the risk factors. The leading source of information for ACL injury was television (57.0%). However, the results of logistic regression analysis revealed that television was not an effective medium to create awareness about the risk factors, among the general population. Instead "Lecture by a coach", "Classroom session on Health", and "Newspaper" were significantly more effective in creating a good awareness of the risk factors (p < 0.001).

  9. Dynamic feature analysis for Voyager at the Image Processing Laboratory

    NASA Technical Reports Server (NTRS)

    Yagi, G. M.; Lorre, J. J.; Jepsen, P. L.

    1978-01-01

    Voyager 1 and 2 were launched from Cape Kennedy to Jupiter, Saturn, and beyond on September 5, 1977 and August 20, 1977. The role of the Image Processing Laboratory is to provide the Voyager Imaging Team with the necessary support to identify atmospheric features (tiepoints) for Jupiter and Saturn data, and to analyze and display them in a suitable form. This support includes the software needed to acquire and store tiepoints, the hardware needed to interactively display images and tiepoints, and the general image processing environment necessary for decalibration and enhancement of the input images. The objective is an understanding of global circulation in the atmospheres of Jupiter and Saturn. Attention is given to the Voyager imaging subsystem, the Voyager imaging science objectives, hardware, software, display monitors, a dynamic feature study, decalibration, navigation, and data base.

  10. TU-CD-BRB-10: 18F-FDG PET Image-Derived Tumor Features Highlight Altered Pathways Identified by Trancriptomic Analysis in Head and Neck Cancer

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

    Tixier, F; INSERM UMR1101 LaTIM, Brest; Cheze-Le-Rest, C

    2015-06-15

    Purpose: Several quantitative features can be extracted from 18F-FDG PET images, such as standardized uptake values (SUVs), metabolic tumor volume (MTV), shape characterization (SC) or intra-tumor radiotracer heterogeneity quantification (HQ). Some of these features calculated from baseline 18F-FDG PET images have shown a prognostic and predictive clinical value. It has been hypothesized that these features highlight underlying tumor patho-physiological processes at smaller scales. The objective of this study was to investigate the ability of recovering alterations of signaling pathways from FDG PET image-derived features. Methods: 52 patients were prospectively recruited from two medical centers (Brest and Poitiers). All patients underwentmore » an FDG PET scan for staging and biopsies of both healthy and primary tumor tissues. Biopsies went through a transcriptomic analysis performed in four spates on 4×44k chips (Agilent™). Primary tumors were delineated in the PET images using the Fuzzy Locally Adaptive Bayesian algorithm and characterized using 10 features including SUVs, SC and HQ. A module network algorithm followed by functional annotation was exploited in order to link PET features with signaling pathways alterations. Results: Several PET-derived features were found to discriminate differentially expressed genes between tumor and healthy tissue (fold-change >2, p<0.01) into 30 co-regulated groups (p<0.05). Functional annotations applied to these groups of genes highlighted associations with well-known pathways involved in cancer processes, such as cell proliferation and apoptosis, as well as with more specific ones such as unsaturated fatty acids. Conclusion: Quantitative features extracted from baseline 18F-FDG PET images usually exploited only for diagnosis and staging, were identified in this work as being related to specific altered pathways and may show promise as tools for personalizing treatment decisions.« less

  11. Three featured plenary sessions

    NASA Astrophysics Data System (ADS)

    2012-07-01

    The conference included three plenary sessions. The plenary on Governance, Security, Economy, and the Ecosystem of the Changing Arctic featured Vera Alexander, president, Arctic Research Consortium of the U.S.; Alan Thornhill, chief environmental officer, U.S. Department of the Interior's Bureau of Ocean Energy Management; and Fran Ulmer, chair, U.S. Arctic Research Commission. A plenary on the U.N. Convention on the Law of the Sea featured Ambassador David Balton, deputy assistant secretary for oceans and fisheries, U.S. Department of State; and Rear Admiral Frederick Kenney Jr., judge advocate general and chief counsel, U.S. Coast Guard. The plenary on Science and the 21st Century featured Phil Keslin, chief technology officer, small lab within Google.

  12. 15q13.3 microdeletions increase risk of idiopathic generalized epilepsy.

    PubMed

    Helbig, Ingo; Mefford, Heather C; Sharp, Andrew J; Guipponi, Michel; Fichera, Marco; Franke, Andre; Muhle, Hiltrud; de Kovel, Carolien; Baker, Carl; von Spiczak, Sarah; Kron, Katherine L; Steinich, Ines; Kleefuss-Lie, Ailing A; Leu, Costin; Gaus, Verena; Schmitz, Bettina; Klein, Karl M; Reif, Philipp S; Rosenow, Felix; Weber, Yvonne; Lerche, Holger; Zimprich, Fritz; Urak, Lydia; Fuchs, Karoline; Feucht, Martha; Genton, Pierre; Thomas, Pierre; Visscher, Frank; de Haan, Gerrit-Jan; Møller, Rikke S; Hjalgrim, Helle; Luciano, Daniela; Wittig, Michael; Nothnagel, Michael; Elger, Christian E; Nürnberg, Peter; Romano, Corrado; Malafosse, Alain; Koeleman, Bobby P C; Lindhout, Dick; Stephani, Ulrich; Schreiber, Stefan; Eichler, Evan E; Sander, Thomas

    2009-02-01

    We identified 15q13.3 microdeletions encompassing the CHRNA7 gene in 12 of 1,223 individuals with idiopathic generalized epilepsy (IGE), which were not detected in 3,699 controls (joint P = 5.32 x 10(-8)). Most deletion carriers showed common IGE syndromes without other features previously associated with 15q13.3 microdeletions, such as intellectual disability, autism or schizophrenia. Our results indicate that 15q13.3 microdeletions constitute the most prevalent risk factor for common epilepsies identified to date.

  13. Features and characterization needs of rubber composite structures

    NASA Technical Reports Server (NTRS)

    Tabaddor, Farhad

    1989-01-01

    Some of the major unique features of rubber composite structures are outlined. The features covered are those related to the material properties, but the analytical features are also briefly discussed. It is essential to recognize these features at the planning stage of any long-range analytical, experimental, or application program. The development of a general and comprehensive program which fully accounts for all the important characteristics of tires, under all the relevant modes of operation, may present a prohibitively expensive and impractical task at the near future. There is therefore a need to develop application methodologies which can utilize the less general models, beyond their theoretical limitations and yet with reasonable reliability, by proper mix of analytical, experimental, and testing activities.

  14. Memory Loss, Alzheimer’s Disease and General Anesthesia: A Preoperative Concern

    PubMed Central

    Thaler, Adam; Siry, Read; Cai, Lufan; García, Paul S.; Chen, Linda; Liu, RenYu

    2012-01-01

    Background The long-term cognitive effects of general anesthesia are under intense scrutiny. Here we present 5 cases from 2 academic institutions to analyze some common features where the patient’s or the patient family member has made a request to address their concern on memory loss, Alzheimer’s disease and general anesthesia before surgery. Methods Records of anesthesia consultation separate from standard preoperative evaluation were retrieved to identify consultations related to memory loss and Alzheimer’s disease from the patient and/or patient family members. The identified cases were extensively reviewed for features in common. We used Google® (http://www. google.com/) to identify available online information using “anesthesia memory loss” as a search phrase. Results Five cases were collected as a specific preoperative consultation related to memory loss, Alzheimer’s disease and general anesthesia from two institutions. All of the individuals either had perceived memory impairment after a prior surgical procedure with general anesthesia or had a family member with Alzheimer’s disease. They all accessed public media sources to find articles related to anesthesia and memory loss. On May 2nd, 2011, searching “anesthesia memory loss” in Google yielded 764,000 hits. Only 3 of the 50 Google top hits were from peer-reviewed journals. Some of the lay media postings made a causal association between general anesthesia and memory loss and/or Alzheimer’s disease without conclusive scientific literature support. Conclusion The potential link between memory loss and Alzheimer’s disease with general anesthesia is an important preoperative concern from patients and their family members. This concern arises from individuals who have had history of cognitive impairment or have had a family member with Alzheimer disease and have tried to obtain information from public media. Proper preoperative consultation with the awareness of the lay literature can

  15. Algebraic features of some generalizations of the Lotka-Volterra system

    NASA Astrophysics Data System (ADS)

    Bibik, Yu. V.; Sarancha, D. A.

    2010-10-01

    For generalizations of the Lotka-Volterra system, an integration method is proposed based on the nontrivial algebraic structure of these generalizations. The method makes use of an auxiliary first-order differential equation derived from the phase curve equation with the help of this algebraic structure. Based on this equation, a Hamiltonian approach can be developed and canonical variables (moreover, action-angle variables) can be constructed.

  16. DISTINCTIVE FEATURE THEORY AND NASAL ASSIMILATION IN SPANISH.

    ERIC Educational Resources Information Center

    HARRIS, JAMES W.

    CERTAIN FEATURES IN THE MEXICAN PRONUNCIATION OF NASAL CONSONANTS ARE PRESENTED HERE AND LINGUISTIC GENERALIZATIONS ARE FORMULATED--FIRST IN TERMS OF A CURRENT THEORY OF UNIVERSAL PHONOLOGICAL DISTINCTIVE FEATURES, AND SECOND IN TERMS OF A REVISED DISTINCTIVE FEATURE FRAMEWORK INCORPORATING THE CHANGES PROPOSED BY CHOMSKY AND HALLE IN "THE SOUND…

  17. Automatic extraction of planetary image features

    NASA Technical Reports Server (NTRS)

    LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

    2013-01-01

    A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

  18. Identifying model error in metabolic flux analysis - a generalized least squares approach.

    PubMed

    Sokolenko, Stanislav; Quattrociocchi, Marco; Aucoin, Marc G

    2016-09-13

    The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit. In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range). The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.

  19. Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.

    PubMed

    Ghorbanian, Parham; Devilbiss, David M; Hess, Terry; Bernstein, Allan; Simon, Adam J; Ashrafiuon, Hashem

    2015-09-01

    We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (β) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.

  20. A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

    PubMed Central

    Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

    2017-01-01

    The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

  1. The cytologic features of NUT midline carcinoma.

    PubMed

    Bellizzi, Andrew M; Bruzzi, Cynthia; French, Christopher A; Stelow, Edward B

    2009-12-25

    Nuclear protein in testis (NUT) midline carcinoma (NMC) represents an aggressive, high-grade carcinoma typically involving the upper aerodigestive tract or mediastinum. Although the tumor was originally noted in young persons, we have subsequently identified 5 adult cases. To our knowledge, the cytology of NMC has not been systematically described. We recently published a series of NMCs identified by fluorescent in situ hybridization for characteristic NUT rearrangement. Three of these patients had undergone fine-needle aspiration. Patient age, sex, primary tumor location, and aspiration site were noted. Cases were assessed for the following: cellularity, architecture, cytoplasm, cell size, nuclear contours, nucleoli, chromatin, anisonucleosis/cytosis, mitotic activity, background, and nuclear crush. The 3 cases occurred in 2 women and 1 man, ages 31-79 years. Primaries involved the sinonasal tract (2) and larynx. Aspirates were of right neck masses (2) and supraclavicular lymph node. Smears were highly cellular and generally noncohesive. Cytoplasm was scant/delicate, although occasional cells with denser cytoplasm were noted in 1 case. Cells were 2-3 times the diameter of a small lymphocyte with irregular nuclear contours, discrete nucleoli, and fine/granular to vesicular chromatin. Anisonucleosis/cytosis was slight to moderate. Mitotic figures were noted in each case. The background contained naked nuclei and karyorrhectic debris; nuclear crush was noted. NMCs exhibit cytologic features of a poorly differentiated or undifferentiated carcinoma. Although reports mention squamous differentiation as a histologic feature, it is typically focal, and overt squamous differentiation was not identified in our cases. Given morphologic overlap with other high-grade carcinomas, diagnosis requires a high index of suspicion. (c) 2009 American Cancer Society.

  2. Identifying key features of effective active learning: the effects of writing and peer discussion.

    PubMed

    Linton, Debra L; Pangle, Wiline M; Wyatt, Kevin H; Powell, Karli N; Sherwood, Rachel E

    2014-01-01

    We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. © 2014 D. L. Linton et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  3. Edge enhancement and noise suppression for infrared image based on feature analysis

    NASA Astrophysics Data System (ADS)

    Jiang, Meng

    2018-06-01

    Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

  4. The future of primordial features with 21 cm tomography

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

    Chen, Xingang; Meerburg, P. Daniel; Münchmeyer, Moritz, E-mail: xingang.chen@cfa.harvard.edu, E-mail: meerburg@cita.utoronto.ca, E-mail: munchmey@iap.fr

    2016-09-01

    Detecting a deviation from a featureless primordial power spectrum of fluctuations would give profound insight into the physics of the primordial Universe. Depending on their nature, primordial features can either provide direct evidence for the inflation scenario or pin down details of the inflation model. Thus far, using the cosmic microwave background (CMB) we have only been able to put stringent constraints on the amplitude of features, but no significant evidence has been found for such signals. Here we explore the limit of the experimental reach in constraining such features using 21 cm tomography at high redshift. A measurement ofmore » the 21 cm power spectrum from the Dark Ages is generally considered as the ideal experiment for early Universe physics, with potentially access to a large number of modes. We consider three different categories of theoretically motivated models: the sharp feature models, resonance models, and standard clock models. We study the improvements on bounds on features as a function of the total number of observed modes and identify parameter degeneracies. The detectability depends critically on the amplitude, frequency and scale-location of the features, as well as the angular and redshift resolution of the experiment. We quantify these effects by considering different fiducial models. Our forecast shows that a cosmic variance limited 21 cm experiment measuring fluctuations in the redshift range 30 ≤ z ≤ 100 with a 0.01-MHz bandwidth and sub-arcminute angular resolution could potentially improve bounds by several orders of magnitude for most features compared to current Planck bounds. At the same time, 21 cm tomography also opens up a unique window into features that are located on very small scales.« less

  5. Textural features for radar image analysis

    NASA Technical Reports Server (NTRS)

    Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

    1981-01-01

    Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

  6. Designing attractive gamification features for collaborative storytelling websites.

    PubMed

    Hsu, Shang Hwa; Chang, Jen-Wei; Lee, Chun-Chia

    2013-06-01

    Gamification design is considered as the predictor of collaborative storytelling websites' success. Although aforementioned studies have mentioned a broad range of factors that may influence gamification, they neither depicted the actual design features nor relative attractiveness among them. This study aims to identify attractive gamification features for collaborative storytelling websites. We first constructed a hierarchical system structure of gamification design of collaborative storytelling websites and conducted a focus group interview with eighteen frequent users to identify 35gamification features. After that, this study determined the relative attractiveness of these gamification features by administrating an online survey to 6333 collaborative storytelling websites users. The results indicated that the top 10 most attractive gamification features could account for more than 50% of attractiveness among these 35 gamification features. The feature of unpredictable time pressure is important to website users, yet not revealed in previous relevant studies. Implications of the findings were discussed.

  7. Generalized onset seizures with focal evolution (GOFE) - A unique seizure type in the setting of generalized epilepsy.

    PubMed

    Linane, Avriel; Lagrange, Andre H; Fu, Cary; Abou-Khalil, Bassel

    2016-01-01

    We report clinical and electrographic features of generalized onset seizures with focal evolution (GOFE) and present arguments for the inclusion of this seizure type in the seizure classification. The adult and pediatric Epilepsy Monitoring Unit databases at Vanderbilt Medical Center and Children's Hospital were screened to identify generalized onset seizures with focal evolution. We reviewed medical records for epilepsy characteristics, epilepsy risk factors, MRI abnormalities, neurologic examination, antiepileptic medications before and after diagnosis, and response to medications. We also reviewed ictal and interictal EEG tracings, as well as video-recorded semiology. Ten patients were identified, 7 males and 3 females. All of the patients developed generalized epilepsy in childhood or adolescence (ages 3-15years). Generalized onset seizures with focal evolution developed years after onset in 9 patients, with a semiology concerning for focal seizures or nonepileptic events. Ictal discharges had a generalized onset on EEG, described as either generalized spike-and-wave and/or polyspike-and-wave discharges, or generalized fast activity. This electrographic activity then evolved to focal rhythmic activity most commonly localized to one temporal or frontal region; five patients had multiple seizures evolving to focal activity in different regions of both hemispheres. The predominant interictal epileptiform activity included generalized spike-and-wave and/or polyspike-and-wave discharges in all patients. Taking into consideration all clinical and EEG data, six patients were classified with genetic (idiopathic) generalized epilepsy, and four were classified with structural/metabolic (symptomatic) generalized epilepsy. All of the patients had modifications to their medications following discharge, with three becoming seizure-free and five responding with >50% reduction in seizure frequency. Generalized onset seizures may occasionally have focal evolution with semiology

  8. Use of a Machine-learning Method for Predicting Highly Cited Articles Within General Radiology Journals.

    PubMed

    Rosenkrantz, Andrew B; Doshi, Ankur M; Ginocchio, Luke A; Aphinyanaphongs, Yindalon

    2016-12-01

    This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features. We downloaded from PubMed the title, abstract, and medical subject heading terms for 10,065 articles published in 25 general radiology journals in 2012 and 2013. Three machine-learning models were applied to predict the top 10% of included articles in terms of the number of citations to the article in 2014 (reflecting the 2-year time window in conventional impact factor calculations). The model having the highest area under the curve was selected to derive a list of article features (words) predicting high citation volume, which was iteratively reduced to identify the smallest possible core feature list maintaining predictive power. Overall themes were qualitatively assigned to the core features. The regularized logistic regression (Bayesian binary regression) model had highest performance, achieving an area under the curve of 0.814 in predicting articles in the top 10% of citation volume. We reduced the initial 14,083 features to 210 features that maintain predictivity. These features corresponded with topics relating to various imaging techniques (eg, diffusion-weighted magnetic resonance imaging, hyperpolarized magnetic resonance imaging, dual-energy computed tomography, computed tomography reconstruction algorithms, tomosynthesis, elastography, and computer-aided diagnosis), particular pathologies (prostate cancer; thyroid nodules; hepatic adenoma, hepatocellular carcinoma, non-alcoholic fatty liver disease), and other topics (radiation dose, electroporation, education, general oncology, gadolinium, statistics). Machine learning can be successfully applied to create specific feature-based models for predicting articles likely to achieve high influence within the radiological literature. Copyright © 2016 The Association of University

  9. Feature Extraction Assessment Study.

    DTIC Science & Technology

    1984-11-01

    base in the form of orthophotos , control manuscripts, . or maps or charts; aids to feature identification such as im- agery (rectified and unrectified...manually delineated (i.e. , drawn by * hand) on a feature manuscript which may be a mylar overlay on an orthophoto or other control base. Once delineated...partition of tiled constant gray level regions, with addi- tive noise in each, it is not clear that any segmentation tech- nique would identify each

  10. Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

    NASA Astrophysics Data System (ADS)

    Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

    2017-11-01

    Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

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

    PubMed

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

    2018-05-21

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

  12. Methodology for classification of geographical features with remote sensing images: Application to tidal flats

    NASA Astrophysics Data System (ADS)

    Revollo Sarmiento, G. N.; Cipolletti, M. P.; Perillo, M. M.; Delrieux, C. A.; Perillo, Gerardo M. E.

    2016-03-01

    Tidal flats generally exhibit ponds of diverse size, shape, orientation and origin. Studying the genesis, evolution, stability and erosive mechanisms of these geographic features is critical to understand the dynamics of coastal wetlands. However, monitoring these locations through direct access is hard and expensive, not always feasible, and environmentally damaging. Processing remote sensing images is a natural alternative for the extraction of qualitative and quantitative data due to their non-invasive nature. In this work, a robust methodology for automatic classification of ponds and tidal creeks in tidal flats using Google Earth images is proposed. The applicability of our method is tested in nine zones with different morphological settings. Each zone is processed by a segmentation stage, where ponds and tidal creeks are identified. Next, each geographical feature is measured and a set of shape descriptors is calculated. This dataset, together with a-priori classification of each geographical feature, is used to define a regression model, which allows an extensive automatic classification of large volumes of data discriminating ponds and tidal creeks against other various geographical features. In all cases, we identified and automatically classified different geographic features with an average accuracy over 90% (89.7% in the worst case, and 99.4% in the best case). These results show the feasibility of using freely available Google Earth imagery for the automatic identification and classification of complex geographical features. Also, the presented methodology may be easily applied in other wetlands of the world and perhaps employing other remote sensing imagery.

  13. The use of mobile applications to support self-management for people with asthma: a systematic review of controlled studies to identify features associated with clinical effectiveness and adherence.

    PubMed

    Hui, Chi Yan; Walton, Robert; McKinstry, Brian; Jackson, Tracy; Parker, Richard; Pinnock, Hilary

    2017-05-01

    Telehealth is promoted as a strategy to support self-management of long-term conditions. The aim of this systematic review is to identify which information and communication technology features implemented in mobile apps to support asthma self-management are associated with adoption, adherence to usage, and clinical effectiveness. We systematically searched 9 databases, scanned reference lists, and undertook manual searches (January 2000 to April 2016). We include randomized controlled trials (RCTs) and quasiexperimental studies with adults. All eligible papers were assessed for quality, and we extracted data on the features included, health-related outcomes (asthma control, exacerbation rate), process/intermediate outcomes (adherence to monitoring or treatment, self-efficacy), and level of adoption of and adherence to use of technology. Meta-analysis and narrative synthesis were used. We included 12 RCTs employing a range of technologies. A meta-analysis (n = 3) showed improved asthma control (mean difference -0.25 [95% CI, -0.37 to -0.12]). Included studies incorporated 10 features grouped into 7 categories (education, monitoring/electronic diary, action plans, medication reminders/prompts, facilitating professional support, raising patient awareness of asthma control, and decision support for professionals). The most successful interventions included multiple features, but effects on health-related outcomes were inconsistent. No studies explicitly reported adoption of and adherence to the technology system. Meta-analysis of data from 3 trials showed improved asthma control, though overall the clinical effectiveness of apps, typically incorporating multiple features, varied. Further studies are needed to identify the features that are associated with adoption of and adherence to use of the mobile app and those that improve health outcomes. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All

  14. An evaluation of applicability of seismic refraction method in identifying shallow archaeological features A case study at archaeological site

    NASA Astrophysics Data System (ADS)

    Jahangardi, Morteza; Hafezi Moghaddas, Naser; Keivan Hosseini, Sayyed; Garazhian, Omran

    2015-04-01

    We applied the seismic refraction method at archaeological site, Tepe Damghani located in Sabzevar, NE of Iran, in order to determine the structures of archaeological interests. This pre-historical site has special conditions with respect to geographical location and geomorphological setting, so it is an urban archaeological site, and in recent years it has been used as an agricultural field. In spring and summer of 2012, the third season of archaeological excavation was carried out. Test trenches of excavations in this site revealed that cultural layers were often disturbed adversely due to human activities such as farming and road construction in recent years. Conditions of archaeological cultural layers in southern and eastern parts of Tepe are slightly better, for instance, in test trench 3×3 m²1S03, third test trench excavated in the southern part of Tepe, an adobe in situ architectural structure was discovered that likely belongs to cultural features of a complex with 5 graves. After conclusion of the third season of archaeological excavation, all of the test trenches were filled with the same soil of excavated test trenches. Seismic refraction method was applied with12 channels of P geophones in three lines with a geophone interval of 0.5 meter and a 1.5 meter distance between profiles on test trench 1S03. The goal of this operation was evaluation of applicability of seismic method in identification of archaeological features, especially adobe wall structures. Processing of seismic data was done with the seismic software, SiesImager. Results were presented in the form of seismic section for every profile, so that identification of adobe wall structures was achieved hardly. This could be due to that adobe wall had been built with the same materials of the natural surrounding earth. Thus, there is a low contrast and it has an inappropriate effect on seismic processing and identifying of archaeological features. Hence the result could be that application of

  15. Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

    PubMed Central

    Moomen, Abdelniser; Ali, Abdulbaset; Ramahi, Omar M.

    2016-01-01

    Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms. PMID:27104533

  16. Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma

    PubMed Central

    Kebir, Sied; Khurshid, Zain; Gaertner, Florian C.; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A.; Glas, Martin

    2017-01-01

    Rationale Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Methods Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Results Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Principal Conclusions Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression. PMID:28030820

  17. Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

    PubMed

    Kebir, Sied; Khurshid, Zain; Gaertner, Florian C; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A; Glas, Martin

    2017-01-31

    Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression.

  18. Effect of Proximity of Features on the Damage Threshold During Submicron Additive Manufacturing Via Two-Photon Polymerization

    DOE PAGES

    Saha, Sourabh K.; Divin, Chuck; Cuadra, Jefferson A.; ...

    2017-05-12

    Two-photon polymerization (TPP) is a laser writing process that enables fabrication of millimeter scale three-dimensional (3D) structures with submicron features. In TPP, writing is achieved via nonlinear two-photon absorption that occurs at high laser intensities. Thus, it is essential to carefully select the incident power to prevent laser damage during polymerization. Currently, the feasible range of laser power is identified by writing small test patterns at varying power levels. Here in this paper, we demonstrate that the results of these tests cannot be generalized, because the damage threshold power depends on the proximity of features and reduces by as muchmore » as 47% for overlapping features. We have identified that this reduction occurs primarily due to an increase in the single-photon absorptivity of the resin after curing. We have captured the damage from proximity effects via X-ray 3D computed tomography (CT) images of a non-homogenous part that has varying feature density. Part damage manifests as internal spherical voids that arise due to boiling of the resist. We have empirically quantified this proximity effect by identifying the damage threshold power at different writing speeds and feature overlap spacings. In addition, we present a first-order analytical model that captures the scaling of this proximity effect. Based on this model and the experiments, we have identified that the proximity effect is more significant at high writing speeds; therefore, it adversely affects the scalability of manufacturing. The scaling laws and the empirical data generated here can be used to select the appropriate TPP writing parameters.« less

  19. Effect of Proximity of Features on the Damage Threshold During Submicron Additive Manufacturing Via Two-Photon Polymerization

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

    Saha, Sourabh K.; Divin, Chuck; Cuadra, Jefferson A.

    Two-photon polymerization (TPP) is a laser writing process that enables fabrication of millimeter scale three-dimensional (3D) structures with submicron features. In TPP, writing is achieved via nonlinear two-photon absorption that occurs at high laser intensities. Thus, it is essential to carefully select the incident power to prevent laser damage during polymerization. Currently, the feasible range of laser power is identified by writing small test patterns at varying power levels. Here in this paper, we demonstrate that the results of these tests cannot be generalized, because the damage threshold power depends on the proximity of features and reduces by as muchmore » as 47% for overlapping features. We have identified that this reduction occurs primarily due to an increase in the single-photon absorptivity of the resin after curing. We have captured the damage from proximity effects via X-ray 3D computed tomography (CT) images of a non-homogenous part that has varying feature density. Part damage manifests as internal spherical voids that arise due to boiling of the resist. We have empirically quantified this proximity effect by identifying the damage threshold power at different writing speeds and feature overlap spacings. In addition, we present a first-order analytical model that captures the scaling of this proximity effect. Based on this model and the experiments, we have identified that the proximity effect is more significant at high writing speeds; therefore, it adversely affects the scalability of manufacturing. The scaling laws and the empirical data generated here can be used to select the appropriate TPP writing parameters.« less

  20. Histopathological features of Proteus syndrome.

    PubMed

    Hoey, S E H; Eastwood, D; Monsell, F; Kangesu, L; Harper, J I; Sebire, N J

    2008-05-01

    Proteus syndrome is a rare, sporadic overgrowth disorder for which the underlying genetic defect remains unknown. Although the clinical course is well-described there is no systematic histopathological description of the lesional pathology. To describe the histopathological features encountered in a series of patients with Proteus syndrome from a single centre. Patients with Proteus syndrome who had undergone therapeutic surgical resection or biopsy were identified from a database and the histopathological findings were reviewed, with particular regard to descriptive features of the underlying tissue abnormality. There were 18 surgical specimens from nine patients, median age 4 years (range 1-9), classified into four main categories: soft-tissue swellings (lipomatous lesions), vascular anomalies (vascular malformation and haemangioma), macrodactyly (hamartomatous overgrowth) and others (sebaceous naevus and nonspecific features). In all cases, the clinical features of overgrowth were due to increased amounts of disorganized tissue, indicating a hamartomatous-type defect in which normal tissue constituents were present, but with an abnormal distribution and architecture. Vascular malformations represented a prominent category of lesions, accounting for 50% of the specimens, predominantly comprising lymphatic and lymphovascular malformations. No malignancy or cytological atypia was identified in any case. The histopathological features of lesions resected from children with Proteus syndrome predominantly include hamartomatous mixed connective tissue lesions, benign neoplasms such as lipomata, and lymphatic-rich vascular malformations.

  1. Elastic properties of uniaxial-fiber reinforced composites - General features

    NASA Astrophysics Data System (ADS)

    Datta, Subhendu; Ledbetter, Hassel; Lei, Ming

    The salient features of the elastic properties of uniaxial-fiber-reinforced composites are examined by considering the complete set of elastic constants of composites comprising isotropic uniaxial fibers in an isotropic matrix. Such materials exhibit transverse-isotropic symmetry and five independent elastic constants in Voigt notation: C(11), C(33), C(44), C(66), and C(13). These C(ij) constants are calculated over the entire fiber-volume-fraction range 0.0-1.0, using a scattered-plane-wave ensemple-average model. Some practical elastic constants such as the principal Young moduli and the principal Poisson ratios are considered, and the behavior of these constants is discussed. Also presented are the results for the four principal sound velocities used to study uniaxial-fiber-reinforced composites: v(11), v(33), v(12), and v(13).

  2. Crowding with detection and coarse discrimination of simple visual features.

    PubMed

    Põder, Endel

    2008-04-24

    Some recent studies have suggested that there are actually no crowding effects with detection and coarse discrimination of simple visual features. The present study tests the generality of this idea. A target Gabor patch, surrounded by either 2 or 6 flanker Gabors, was presented briefly at 4 deg eccentricity of the visual field. Each Gabor patch was oriented either vertically or horizontally (selected randomly). Observers' task was either to detect the presence of the target (presented with probability 0.5) or to identify the orientation of the target. The target-flanker distance was varied. Results were similar for the two tasks but different for 2 and 6 flankers. The idea that feature detection and coarse discrimination are immune to crowding may be valid for the two-flanker condition only. With six flankers, a normal crowding effect was observed. It is suggested that the complexity of the full pattern (target plus flankers) could explain the difference.

  3. Pediatric Eosinophilic Esophagitis Symptom Scores (PEESS® v2.0) identify histologic and molecular correlates of the key clinical features of disease

    PubMed Central

    Martin, Lisa J.; Franciosi, James P.; Collins, Margaret H.; Abonia, J. Pablo; Lee, James J.; Hommel, Kevin A.; Varni, James W.; Grotjan, J. Tommie; Eby, Michael; He, Hua; Marsolo, Keith; Putnam, Philip E.; Garza, Jose M.; Kaul, Ajay; Wen, Ting; Rothenberg, Marc E.

    2015-01-01

    Background The Pediatric Eosinophilic Esophagitis Symptom Score (PEESS® v2.0) measures patient-relevant outcomes. However, whether patient-identified domains (dysphagia, gastrointestinal reflux disease (GERD), nausea/vomiting, and pain) align with clinical symptomology and histopathologic and molecular features of eosinophilic esophagitis (EoE) is unclear. Objective The purpose of this study was to determine if clinical features of EoE, measured through the PEESS® v2.0, associate with histopathologic and molecular features of EoE. This represents a novel approach for analysis of allergic diseases, given the availability of allergic tissue biopsy specimens. Methods We systematically recruited treated and untreated, pediatric patients with EoE (aged 2–18 years) and examined parent proxy–reported symptoms using the PEESS® v2.0. Clinical symptomology was collected by questionnaire. Esophageal biopsy samples were quantified for levels of eosinophils, eosinophil peroxidase (EPX) immunohistochemical staining, and mast cells. Molecular features were assessed by the EoE Diagnostic Panel (94 EoE-related gene transcripts). Associations between domain scores and clinical symptoms and biologic features were analyzed using Wilcoxon Rank Sum and Spearman correlation. Results The PEESS® v2.0 domains correlated to specific parent-reported symptoms: dysphagia (p = 0.0012), GERD (p = 0.0001), and nausea/vomiting (p < 0.0001). Pain correlated with multiple symptoms (p < 0.0005). Dysphagia correlated most strongly with overall histopathology, particularly in the proximal esophagus (p ≤ 0.0049). Markers of esophageal activity (EPX) were significantly associated with dysphagia (strongest r = .37; p = 0.02). Eosinophil levels were more associated with pain (r = 0.27; p=0.06) than for dysphagia (r = 0.24; p = 0.13). The dysphagia domain correlated the most with esophageal gene transcript levels, predominantly with mast cell–specific genes. Conclusion We have 1) established a

  4. GATOR: Requirements capturing of telephony features

    NASA Technical Reports Server (NTRS)

    Dankel, Douglas D., II; Walker, Wayne; Schmalz, Mark

    1992-01-01

    We are developing a natural language-based, requirements gathering system called GATOR (for the GATherer Of Requirements). GATOR assists in the development of more accurate and complete specifications of new telephony features. GATOR interacts with a feature designer who describes a new feature, set of features, or capability to be implemented. The system aids this individual in the specification process by asking for clarifications when potential ambiguities are present, by identifying potential conflicts with other existing features, and by presenting its understanding of the feature to the designer. Through user interaction with a model of the existing telephony feature set, GATOR constructs a formal representation of the new, 'to be implemented' feature. Ultimately GATOR will produce a requirements document and will maintain an internal representation of this feature to aid in future design and specification. This paper consists of three sections that describe (1) the structure of GATOR, (2) POND, GATOR's internal knowledge representation language, and (3) current research issues.

  5. Feature-opinion pair identification of product reviews in Chinese: a domain ontology modeling method

    NASA Astrophysics Data System (ADS)

    Yin, Pei; Wang, Hongwei; Guo, Kaiqiang

    2013-03-01

    With the emergence of the new economy based on social media, a great amount of consumer feedback on particular products are conveyed through wide-spreading product online reviews, making opinion mining a growing interest for both academia and industry. According to the characteristic mode of expression in Chinese, this research proposes an ontology-based linguistic model to identify the basic appraisal expression in Chinese product reviews-"feature-opinion pair (FOP)." The product-oriented domain ontology is constructed automatically at first, then algorithms to identify FOP are designed by mapping product features and opinions to the conceptual space of the domain ontology, and finally comparative experiments are conducted to evaluate the model. Experimental results indicate that the performance of the proposed approach in this paper is efficient in obtaining a more accurate result compared to the state-of-art algorithms. Furthermore, through identifying and analyzing FOPs, the unstructured product reviews are converted into structured and machine-sensible expression, which provides valuable information for business application. This paper contributes to the related research in opinion mining by developing a solid foundation for further sentiment analysis at a fine-grained level and proposing a general way for automatic ontology construction.

  6. Adapting Local Features for Face Detection in Thermal Image.

    PubMed

    Ma, Chao; Trung, Ngo Thanh; Uchiyama, Hideaki; Nagahara, Hajime; Shimada, Atsushi; Taniguchi, Rin-Ichiro

    2017-11-27

    A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

  7. A systematic review of the emotional, behavioural and cognitive features exhibited by school-aged children experiencing neglect or emotional abuse.

    PubMed

    Maguire, S A; Williams, B; Naughton, A M; Cowley, L E; Tempest, V; Mann, M K; Teague, M; Kemp, A M

    2015-09-01

    Interventions to minimize the long-term consequences of neglect or emotional abuse rely on prompt identification of these children. This systematic review of world literature (1947-2012) identifies features that children aged 5-14 years experiencing neglect or emotional abuse, as opposed to physical or sexual abuse, may exhibit. Searching 18 databases, utilizing over 100 keywords, supplemented by hand searching, 13,210 articles were identified and 111 underwent full critical appraisal by two independent trained reviewers. The 30 included studies highlighted behavioural features (15 studies), externalizing features being the most prominent (8/9 studies) and internalizing features noted in 4/6 studies. Four studies identified attention deficit hyperactivity disorder (ADHD) associated features: impulsivity, inattention or hyperactivity. Child difficulties in initiating or developing friendships were noted in seven studies. Of 13 studies addressing emotional well-being, three highlighted low self-esteem, with a perception of external control (1), or depression (6) including suicidality (1). A negative internal working model of the mother increased the likelihood of depression (1). In assessing cognition or academic performance, lower general intelligence (3/4) and reduced literacy and numeracy (2) were reported, but no observable effect on memory (3). School-aged children presenting with poor academic performance, ADHD symptomatology or abnormal behaviours warrant assessment of neglect or emotional abuse as a potential underlying cause. © 2015 John Wiley & Sons Ltd.

  8. Non-rigid ultrasound image registration using generalized relaxation labeling process

    NASA Astrophysics Data System (ADS)

    Lee, Jong-Ha; Seong, Yeong Kyeong; Park, MoonHo; Woo, Kyoung-Gu; Ku, Jeonghun; Park, Hee-Jun

    2013-03-01

    This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image's local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.

  9. Targeted Feature Detection for Data-Dependent Shotgun Proteomics.

    PubMed

    Weisser, Hendrik; Choudhary, Jyoti S

    2017-08-04

    Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known

  10. Which ante mortem clinical features predict progressive supranuclear palsy pathology?

    PubMed

    Respondek, Gesine; Kurz, Carolin; Arzberger, Thomas; Compta, Yaroslau; Englund, Elisabet; Ferguson, Leslie W; Gelpi, Ellen; Giese, Armin; Irwin, David J; Meissner, Wassilios G; Nilsson, Christer; Pantelyat, Alexander; Rajput, Alex; van Swieten, John C; Troakes, Claire; Josephs, Keith A; Lang, Anthony E; Mollenhauer, Brit; Müller, Ulrich; Whitwell, Jennifer L; Antonini, Angelo; Bhatia, Kailash P; Bordelon, Yvette; Corvol, Jean-Christophe; Colosimo, Carlo; Dodel, Richard; Grossman, Murray; Kassubek, Jan; Krismer, Florian; Levin, Johannes; Lorenzl, Stefan; Morris, Huw; Nestor, Peter; Oertel, Wolfgang H; Rabinovici, Gil D; Rowe, James B; van Eimeren, Thilo; Wenning, Gregor K; Boxer, Adam; Golbe, Lawrence I; Litvan, Irene; Stamelou, Maria; Höglinger, Günter U

    2017-07-01

    Progressive supranuclear palsy (PSP) is a neuropathologically defined disease presenting with a broad spectrum of clinical phenotypes. To identify clinical features and investigations that predict or exclude PSP pathology during life, aiming at an optimization of the clinical diagnostic criteria for PSP. We performed a systematic review of the literature published since 1996 to identify clinical features and investigations that may predict or exclude PSP pathology. We then extracted standardized data from clinical charts of patients with pathologically diagnosed PSP and relevant disease controls and calculated the sensitivity, specificity, and positive predictive value of key clinical features for PSP in this cohort. Of 4166 articles identified by the database inquiry, 269 met predefined standards. The literature review identified clinical features predictive of PSP, including features of the following 4 functional domains: ocular motor dysfunction, postural instability, akinesia, and cognitive dysfunction. No biomarker or genetic feature was found reliably validated to predict definite PSP. High-quality original natural history data were available from 206 patients with pathologically diagnosed PSP and from 231 pathologically diagnosed disease controls (54 corticobasal degeneration, 51 multiple system atrophy with predominant parkinsonism, 53 Parkinson's disease, 73 behavioral variant frontotemporal dementia). We identified clinical features that predicted PSP pathology, including phenotypes other than Richardson's syndrome, with varying sensitivity and specificity. Our results highlight the clinical variability of PSP and the high prevalence of phenotypes other than Richardson's syndrome. The features of variant phenotypes with high specificity and sensitivity should serve to optimize clinical diagnosis of PSP. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.

  11. Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

    PubMed Central

    Baydil, Banu; Daley, William P.; Larsen, Melinda; Yener, Bülent

    2012-01-01

    Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We

  12. Analysis of Nearly One Thousand Mammalian Mirtrons Reveals Novel Features of Dicer Substrates

    PubMed Central

    Shenker, Sol; Mohammed, Jaaved; Lai, Eric C.

    2015-01-01

    Mirtrons are microRNA (miRNA) substrates that utilize the splicing machinery to bypass the necessity of Drosha cleavage for their biogenesis. Expanding our recent efforts for mammalian mirtron annotation, we use meta-analysis of aggregate datasets to identify ~500 novel mouse and human introns that confidently generate diced small RNA duplexes. These comprise nearly 1000 total loci distributed in four splicing-mediated biogenesis subclasses, with 5'-tailed mirtrons as, by far, the dominant subtype. Thus, mirtrons surprisingly comprise a substantial fraction of endogenous Dicer substrates in mammalian genomes. Although mirtron-derived small RNAs exhibit overall expression correlation with their host mRNAs, we observe a subset with substantial differences that suggest regulated processing or accumulation. We identify characteristic sequence, length, and structural features of mirtron loci that distinguish them from bulk introns, and find that mirtrons preferentially emerge from genes with larger numbers of introns. While mirtrons generate miRNA-class regulatory RNAs, we also find that mirtrons exhibit many features that distinguish them from canonical miRNAs. We observe that conventional mirtron hairpins are substantially longer than Drosha-generated pre-miRNAs, indicating that the characteristic length of canonical pre-miRNAs is not a general feature of Dicer substrate hairpins. In addition, mammalian mirtrons exhibit unique patterns of ordered 5' and 3' heterogeneity, which reveal hidden complexity in miRNA processing pathways. These include broad 3'-uridylation of mirtron hairpins, atypically heterogeneous 5' termini that may result from exonucleolytic processing, and occasionally robust decapitation of the 5' guanine (G) of mirtron-5p species defined by splicing. Altogether, this study reveals that this extensive class of non-canonical miRNA bears a multitude of characteristic properties, many of which raise general mechanistic questions regarding the processing

  13. EEG feature selection method based on decision tree.

    PubMed

    Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

    2015-01-01

    This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

  14. The effect of feature selection methods on computer-aided detection of masses in mammograms

    NASA Astrophysics Data System (ADS)

    Hupse, Rianne; Karssemeijer, Nico

    2010-05-01

    In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

  15. Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

    PubMed

    Horikawa, Tomoyasu; Kamitani, Yukiyasu

    2017-01-01

    Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

  16. Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

    PubMed Central

    Smart, Otis; Burrell, Lauren

    2014-01-01

    Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

  17. Sensitivity to feature displacement in familiar and unfamiliar faces: beyond the internal/external feature distinction.

    PubMed

    Brooks, Kevin R; Kemp, Richard I

    2007-01-01

    Previous studies of face recognition and of face matching have shown a general improvement for the processing of internal features as a face becomes more familiar to the participant. In this study, we used a psychophysical two-alternative forced-choice paradigm to investigate thresholds for the detection of a displacement of the eyes, nose, mouth, or ears for familiar and unfamiliar faces. No clear division between internal and external features was observed. Rather, for familiar (compared to unfamiliar) faces participants were more sensitive to displacements of internal features such as the eyes or the nose; yet, for our third internal feature-the mouth no such difference was observed. Despite large displacements, many subjects were unable to perform above chance when stimuli involved shifts in the position of the ears. These results are consistent with the proposal that familiarity effects may be mediated by the construction of a robust representation of a face, although the involvement of attention in the encoding of face stimuli cannot be ruled out. Furthermore, these effects are mediated by information from a spatial configuration of features, rather than by purely feature-based information.

  18. New Dust Features Observed with ISO

    NASA Technical Reports Server (NTRS)

    Tielens, Alexander G. G. M.; Young, Richard E. (Technical Monitor)

    1997-01-01

    This paper will review our current knowledge of circumstellar and interstellar dust with the emphasis on infrared spectroscopy with ISO. Objects embedded in or located behind molecular clouds show a wealth of absorption features due to simple molecules in an icy mantle. The SWS on ISO has provided us, for the first time, with complete 3-45 um spectra which allow an inventory of interstellar ice. Among the species identified are H2O, CH3OH, CH4, CO2, CO, and OCS. These species are formed through simple reactions among gas phase species accreted on grain surfaces, possibly modified by FUV photolysis and warm-up (ie., outgassing). The implications of the observations for our understanding of these processes will be reviewed. The IR spectra of many UV bright objects are dominated by strong emission features at 3.3, 6.2, 7.7, and 11.3 micrometers. These are generally attributed to Polycyclic Aromatic Hydrocarbons (PAHs) molecules. The observational evidence will be reviewed. The emphasis will be on recent data which show widespread spectral variations, particularly among protoplanetary and planetary nebulae, and their implications. One of the most exciting, recent discoveries on interstellar and circumstellar dust has been the detection of spectral structure due to crystalline olivine and enstatite in a variety of objects surrounded by circumstellar silicates. These spectra will be reviewed and circumstellar silicate mineralogy will be discussed.

  19. Generalized Multilevel Structural Equation Modeling

    ERIC Educational Resources Information Center

    Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew

    2004-01-01

    A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…

  20. A feature based comparison of pen and swipe based signature characteristics.

    PubMed

    Robertson, Joshua; Guest, Richard

    2015-10-01

    Dynamic Signature Verification (DSV) is a biometric modality that identifies anatomical and behavioral characteristics when an individual signs their name. Conventionally signature data has been captured using pen/tablet apparatus. However, the use of other devices such as the touch-screen tablets has expanded in recent years affording the possibility of assessing biometric interaction on this new technology. To explore the potential of employing DSV techniques when a user signs or swipes with their finger, we report a study to correlate pen and finger generated features. Investigating the stability and correlation between a set of characteristic features recorded in participant's signatures and touch-based swipe gestures, a statistical analysis was conducted to assess consistency between capture scenarios. The results indicate that there is a range of static and dynamic features such as the rate of jerk, size, duration and the distance the pen traveled that can lead to interoperability between these two systems for input methods for use within a potential biometric context. It can be concluded that this data indicates that a general principle is that the same underlying constructional mechanisms are evident. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Clinical and pathological features of myeloid leukemia cutis*

    PubMed Central

    Li, Li; Wang, Yanan; Lian, Christine Guo; Hu, Nina; Jin, Hongzhong; Liu, Yuehua

    2018-01-01

    Background Myeloid leukemia cutis is the terminology used for cutaneous manifestations of myeloid leukemia. Objective The purpose of this study was to study the clinical, histopathological and immunohistochemical features of myeloid leukemia cutis. Methods This was a retrospective study of clinical and pathological features of 10 patients with myeloid leukemia cutis. Results One patient developed skin lesions before the onset of leukemia, seven patients developed skin infiltration within 4-72 months after the onset of leukemia, and two patients developed skin lesions and systemic leukemia simultaneously. Of these patients, five presented with generalized papules or nodules, and five with localized masses. The biopsy of skin lesions showed a large number of tumor cells within the dermis and subcutaneous fat layer. Immunohistochemical analysis showed strong reactivity to myeloperoxidase (MPO), CD15, CD43 and CD45 (LCA) in most cases. NPM1 (nucleophosmin I) and FLT3-ITD (Fms-like tyrosine kinase 3-internal tandem duplication) mutations were identified in one case. Five patients with acute myelogenous leukemia and one patient with chronic myelomonocytic leukemia died within two months to one year after the onset of skin lesions. Study limitations This was a retrospective and small sample study. Conclusions In patients with myelogenous leukemia, skin infiltration usually occurs after, but occasionally before, the appearance of hemogram and myelogram abnormalities, and the presence of skin infiltration is often associated with a poor prognosis and short survival time. myeloid leukemia cutis often presents as generalized or localized nodules or masses with characteristic pathological and histochemical findings. PMID:29723350

  2. Generalized worry disorder: a review of DSM-IV generalized anxiety disorder and options for DSM-V.

    PubMed

    Andrews, Gavin; Hobbs, Megan J; Borkovec, Thomas D; Beesdo, Katja; Craske, Michelle G; Heimberg, Richard G; Rapee, Ronald M; Ruscio, Ayelet Meron; Stanley, Melinda A

    2010-02-01

    Generalized anxiety disorder (GAD) has undergone a series of substantial classificatory changes since its first inclusion in DSM-III. The majority of these revisions have been in response to its poor inter-rater reliability and concerns that it may lack diagnostic validity. This article provides options for the revision of the DSM-IV GAD criteria for DSM-V. First, searches were conducted to identify the evidence that previous DSM Work Groups relied upon when revising the DSM-III-R GAD and the overanxious disorder classifications. Second, the literature pertaining to the DSM-IV criteria for GAD was examined. The review presents a number of options to be considered for DSM-V. One option is for GAD to be re-labeled in DSM-V as generalized worry disorder. This would reflect its hallmark feature. Proposed revisions would result in a disorder that is characterized by excessive anxiety and worry generalized to a number of events or activities for 3 months or more. Worry acts as a cognitive coping strategy that manifests in avoidant behaviors. The reliability and validity of the proposed changes could be investigated in DSM-V validity tests and field trials.

  3. Intrinsic and contextual features in object recognition.

    PubMed

    Schlangen, Derrick; Barenholtz, Elan

    2015-01-28

    The context in which an object is found can facilitate its recognition. Yet, it is not known how effective this contextual information is relative to the object's intrinsic visual features, such as color and shape. To address this, we performed four experiments using rendered scenes with novel objects. In each experiment, participants first performed a visual search task, searching for a uniquely shaped target object whose color and location within the scene was experimentally manipulated. We then tested participants' tendency to use their knowledge of the location and color information in an identification task when the objects' images were degraded due to blurring, thus eliminating the shape information. In Experiment 1, we found that, in the absence of any diagnostic intrinsic features, participants identified objects based purely on their locations within the scene. In Experiment 2, we found that participants combined an intrinsic feature, color, with contextual location in order to uniquely specify an object. In Experiment 3, we found that when an object's color and location information were in conflict, participants identified the object using both sources of information equally. Finally, in Experiment 4, we found that participants used whichever source of information-either color or location-was more statistically reliable in order to identify the target object. Overall, these experiments show that the context in which objects are found can play as important a role as intrinsic features in identifying the objects. © 2015 ARVO.

  4. Sparse Feature Selection Identifies H2A.Z as a Novel, Pattern-Specific Biomarker for Asymmetrically Self-Renewing Distributed Stem Cells

    PubMed Central

    Huh, Yang Hoon; Noh, Minsoo; Burden, Frank R.; Chen, Jennifer C.; Winkler, David A.; Sherley, James L.

    2015-01-01

    There is a long-standing unmet clinical need for biomarkers with high specificity for distributed stem cells (DSCs) in tissues, or for use in diagnostic and therapeutic cell preparations (e.g., bone marrow). Although DSCs are essential for tissue maintenance and repair, accurate determination of their numbers for medical applications has been problematic. Previous searches for biomarkers expressed specifically in DSCs were hampered by difficulty obtaining pure DSCs and by the challenges in mining complex molecular expression data. To identify DSC such useful and specific biomarkers, we combined a novel sparse feature selection method with combinatorial molecular expression data focused on asymmetric self-renewal, a conspicuous property of DSCs. The analysis identified reduced expression of the histone H2A variant H2A.Z as a superior molecular discriminator for DSC asymmetric self-renewal. Subsequent molecular expression studies showed H2A.Z to be a novel “pattern-specific biomarker” for asymmetrically self-renewing cells with sufficient specificity to count asymmetrically self-renewing DSCs in vitro and potentially in situ. PMID:25636161

  5. Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection.

    PubMed

    Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy

    2010-04-01

    A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.

  6. General fusion approaches for the age determination of latent fingerprint traces: results for 2D and 3D binary pixel feature fusion

    NASA Astrophysics Data System (ADS)

    Merkel, Ronny; Gruhn, Stefan; Dittmann, Jana; Vielhauer, Claus; Bräutigam, Anja

    2012-03-01

    Determining the age of latent fingerprint traces found at crime scenes is an unresolved research issue since decades. Solving this issue could provide criminal investigators with the specific time a fingerprint trace was left on a surface, and therefore would enable them to link potential suspects to the time a crime took place as well as to reconstruct the sequence of events or eliminate irrelevant fingerprints to ensure privacy constraints. Transferring imaging techniques from different application areas, such as 3D image acquisition, surface measurement and chemical analysis to the domain of lifting latent biometric fingerprint traces is an upcoming trend in forensics. Such non-destructive sensor devices might help to solve the challenge of determining the age of a latent fingerprint trace, since it provides the opportunity to create time series and process them using pattern recognition techniques and statistical methods on digitized 2D, 3D and chemical data, rather than classical, contact-based capturing techniques, which alter the fingerprint trace and therefore make continuous scans impossible. In prior work, we have suggested to use a feature called binary pixel, which is a novel approach in the working field of fingerprint age determination. The feature uses a Chromatic White Light (CWL) image sensor to continuously scan a fingerprint trace over time and retrieves a characteristic logarithmic aging tendency for 2D-intensity as well as 3D-topographic images from the sensor. In this paper, we propose to combine such two characteristic aging features with other 2D and 3D features from the domains of surface measurement, microscopy, photography and spectroscopy, to achieve an increase in accuracy and reliability of a potential future age determination scheme. Discussing the feasibility of such variety of sensor devices and possible aging features, we propose a general fusion approach, which might combine promising features to a joint age determination scheme

  7. Crowding with conjunctions of simple features.

    PubMed

    Põder, Endel; Wagemans, Johan

    2007-11-20

    Several recent studies have related crowding with the feature integration stage in visual processing. In order to understand the mechanisms involved in this stage, it is important to use stimuli that have several features to integrate, and these features should be clearly defined and measurable. In this study, Gabor patches were used as target and distractor stimuli. The stimuli differed in three dimensions: spatial frequency, orientation, and color. A group of 3, 5, or 7 objects was presented briefly at 4 deg eccentricity of the visual field. The observers' task was to identify the object located in the center of the group. A strong effect of the number of distractors was observed, consistent with various spatial pooling models. The analysis of incorrect responses revealed that these were a mix of feature errors and mislocalizations of the target object. Feature errors were not purely random, but biased by the features of distractors. We propose a simple feature integration model that predicts most of the observed regularities.

  8. General Relativistic Radiative Transfer and General Relativistic MHD Simulations of Accretion and Outflows of Black Holes

    NASA Technical Reports Server (NTRS)

    Fuerst, Steven V.; Mizuno, Yosuke; Nishikawa, Ken-Ichi; Wu, Kinwah

    2007-01-01

    We have calculated the emission from relativistic flows in black hole systems using a fully general relativistic radiative transfer, with flow structures obtained by general relativistic magnetohydrodynamic simulations. We consider thermal free-free emission and thermal synchrotron emission. Bright filament-like features are found protruding (visually) from the accretion disk surface, which are enhancements of synchrotron emission when the magnetic field is roughly aligned with the line-of-sight in the co-moving frame. The features move back and forth as the accretion flow evolves, but their visibility and morphology are robust. We propose that variations and location drifts of the features are responsible for certain X-ray quasi-periodic oscillations (QPOs) observed in black-hole X-ray binaries.

  9. General Relativistic Radiative Transfer and GeneralRelativistic MHD Simulations of Accretion and Outflows of Black Holes

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

    Fuerst, Steven V.; /KIPAC, Menlo Park; Mizuno, Yosuke

    2007-01-05

    We calculate the emission from relativistic flows in black hole systems using a fully general relativistic radiative transfer formulation, with flow structures obtained by general relativistic magneto-hydrodynamic simulations. We consider thermal free-free emission and thermal synchrotron emission. Bright filament-like features protrude (visually) from the accretion disk surface, which are enhancements of synchrotron emission where the magnetic field roughly aligns with the line-of-sight in the co-moving frame. The features move back and forth as the accretion flow evolves, but their visibility and morphology are robust. We propose that variations and drifts of the features produce certain X-ray quasi-periodic oscillations (QPOs) observedmore » in black-hole X-ray binaries.« less

  10. Maximum entropy methods for extracting the learned features of deep neural networks.

    PubMed

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  11. Targeted Feature Detection for Data-Dependent Shotgun Proteomics

    PubMed Central

    2017-01-01

    Label-free quantification of shotgun LC–MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification (“FFId”), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between “internal” and “external” (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the “uncertain” feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards

  12. An Educational System to Help Students Assess Website Features and Identify High-Risk Websites

    ERIC Educational Resources Information Center

    Kajiyama, Tomoko; Echizen, Isao

    2015-01-01

    Purpose: The purpose of this paper is to propose an effective educational system to help students assess Web site risk by providing an environment in which students can better understand a Web site's features and determine the risks of accessing the Web site for themselves. Design/methodology/approach: The authors have enhanced a prototype…

  13. Dynamics of feature categorization.

    PubMed

    Martí, Daniel; Rinzel, John

    2013-01-01

    In visual and auditory scenes, we are able to identify shared features among sensory objects and group them according to their similarity. This grouping is preattentive and fast and is thought of as an elementary form of categorization by which objects sharing similar features are clustered in some abstract perceptual space. It is unclear what neuronal mechanisms underlie this fast categorization. Here we propose a neuromechanistic model of fast feature categorization based on the framework of continuous attractor networks. The mechanism for category formation does not rely on learning and is based on biologically plausible assumptions, for example, the existence of populations of neurons tuned to feature values, feature-specific interactions, and subthreshold-evoked responses upon the presentation of single objects. When the network is presented with a sequence of stimuli characterized by some feature, the network sums the evoked responses and provides a running estimate of the distribution of features in the input stream. If the distribution of features is structured into different components or peaks (i.e., is multimodal), recurrent excitation amplifies the response of activated neurons, and categories are singled out as emerging localized patterns of elevated neuronal activity (bumps), centered at the centroid of each cluster. The emergence of bump states through sequential, subthreshold activation and the dependence on input statistics is a novel application of attractor networks. We show that the extraction and representation of multiple categories are facilitated by the rich attractor structure of the network, which can sustain multiple stable activity patterns for a robust range of connectivity parameters compatible with cortical physiology.

  14. 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.

  15. Aeolian features and processes at the Mars Pathfinder landing site

    USGS Publications Warehouse

    Greeley, Ronald; Kraft, Michael; Sullivan, Robert; Wilson, Gregory; Bridges, Nathan; Herkenhoff, Ken; Kuzmin, Ruslan O.; Malin, Michael; Ward, Wes

    1999-01-01

    The Mars Pathfinder landing site contains abundant features attributed to aeolian, or wind, processes. These include wind tails, drift deposits, duneforms of various types, ripplelike features, and ventifacts (the first clearly seen on Mars). Many of these features are consistant with formation involving sand-size particles. Although some features, such as dunes, could develop from saltating sand-size aggregates of finer grains, the discovery of ventifact flutes cut in rocks strongly suggests that at least some of the grains are crystalline, rather than aggregates. Excluding the ventifacts, the orientations of the wind-related features correlate well with the orientations of bright wind steaks seen on Viking Orbiter images in the general area. They also correlate with wind direction predictions from the NASA-Ames General Circulation Model (GCM) which show that the strongest winds in the area occur in the northern hemisphere winter and are directed toward 209°. Copyright 1999 by the American Geophysical Union.

  16. Using listener-based perceptual features as intermediate representations in music information retrieval.

    PubMed

    Friberg, Anders; Schoonderwaldt, Erwin; Hedblad, Anton; Fabiani, Marco; Elowsson, Anders

    2014-10-01

    The notion of perceptual features is introduced for describing general music properties based on human perception. This is an attempt at rethinking the concept of features, aiming to approach the underlying human perception mechanisms. Instead of using concepts from music theory such as tones, pitches, and chords, a set of nine features describing overall properties of the music was selected. They were chosen from qualitative measures used in psychology studies and motivated from an ecological approach. The perceptual features were rated in two listening experiments using two different data sets. They were modeled both from symbolic and audio data using different sets of computational features. Ratings of emotional expression were predicted using the perceptual features. The results indicate that (1) at least some of the perceptual features are reliable estimates; (2) emotion ratings could be predicted by a small combination of perceptual features with an explained variance from 75% to 93% for the emotional dimensions activity and valence; (3) the perceptual features could only to a limited extent be modeled using existing audio features. Results clearly indicated that a small number of dedicated features were superior to a "brute force" model using a large number of general audio features.

  17. MCNP4A: Features and philosophy

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

    Hendricks, J.S.

    This paper describes MCNP, states its philosophy, introduces a number of new features becoming available with version MCNP4A, and answers a number of questions asked by participants in the workshop. MCNP is a general-purpose three-dimensional neutron, photon and electron transport code. Its philosophy is ``Quality, Value and New Features.`` Quality is exemplified by new software quality assurance practices and a program of benchmarking against experiments. Value includes a strong emphasis on documentation and code portability. New features are the third priority. MCNP4A is now available at Los Alamos. New features in MCNP4A include enhanced statistical analysis, distributed processor multitasking, newmore » photon libraries, ENDF/B-VI capabilities, X-Windows graphics, dynamic memory allocation, expanded criticality output, periodic boundaries, plotting of particle tracks via SABRINA, and many other improvements. 23 refs.« less

  18. Hadoop neural network for parallel and distributed feature selection.

    PubMed

    Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

    2016-06-01

    In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  19. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

    PubMed

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Enhanced flyby science with onboard computer vision: Tracking and surface feature detection at small bodies

    NASA Astrophysics Data System (ADS)

    Fuchs, Thomas J.; Thompson, David R.; Bue, Brian D.; Castillo-Rogez, Julie; Chien, Steve A.; Gharibian, Dero; Wagstaff, Kiri L.

    2015-10-01

    Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.

  1. Ability of Slovakian Pupils to Identify Birds

    ERIC Educational Resources Information Center

    Prokop, Pavol; Rodak, Rastislav

    2009-01-01

    A pupil's ability to identify common organisms is necessary for acquiring further knowledge of biology. We investigated how pupils were able to identify 25 bird species following their song, growth habits, or both features presented simultaneously. Just about 19% of birds were successfully identified by song, about 39% by growth habit, and 45% of…

  2. qFeature

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

    2015-09-14

    This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

  3. Dermoscopic features of nail psoriasis treated with biologics.

    PubMed

    Hashimoto, Yuki; Uyama, Miki; Takada, Yuko; Yoshida, Kenji; Ishiko, Akira

    2017-05-01

    Although psoriatic nail lesions are small, they cause considerable discomfort for patients and adversely affect quality of life. Few studies have evaluated the dermoscopic features of psoriatic nails. The aim of this study was to clarify the dermoscopic features of nail psoriasis and identify those that reflect psoriatic activity. During biologic treatment of psoriasis, six patients with psoriatic nails twice underwent dermoscopic examination, with an interval of 17-42 weeks. We used the modified Nail Psoriasis Severity Index score and Psoriasis Area and Severity Index score to identify and assess dermoscopic features. We identified 10 dermoscopic findings, of which disappearance of diffuse scaling of the nail plate, transverse step-like notches and splinter hemorrhages of the nail bed, and appearance of erythematous borders of the onycholytic area were associated with improvement in Psoriasis Area and Severity Index score. Dermoscopy can detect nail changes during psoriasis treatment and should be used to evaluate treatment success. © 2017 Japanese Dermatological Association.

  4. Feature-oriented regional modeling and simulations in the Gulf of Maine and Georges Bank

    NASA Astrophysics Data System (ADS)

    Gangopadhyay, Avijit; Robinson, Allan R.; Haley, Patrick J.; Leslie, Wayne G.; Lozano, Carlos J.; Bisagni, James J.; Yu, Zhitao

    2003-03-01

    The multiscale synoptic circulation system in the Gulf of Maine and Georges Bank (GOMGB) region is presented using a feature-oriented approach. Prevalent synoptic circulation structures, or 'features', are identified from previous observational studies. These features include the buoyancy-driven Maine Coastal Current, the Georges Bank anticyclonic frontal circulation system, the basin-scale cyclonic gyres (Jordan, Georges and Wilkinson), the deep inflow through the Northeast Channel (NEC), the shallow outflow via the Great South Channel (GSC), and the shelf-slope front (SSF). Their synoptic water-mass ( T- S) structures are characterized and parameterized in a generalized formulation to develop temperature-salinity feature models. A synoptic initialization scheme for feature-oriented regional modeling and simulation (FORMS) of the circulation in the coastal-to-deep region of the GOMGB system is then developed. First, the temperature and salinity feature-model profiles are placed on a regional circulation template and then objectively analyzed with appropriate background climatology in the coastal region. Furthermore, these fields are melded with adjacent deep-ocean regional circulation (Gulf Stream Meander and Ring region) along and across the SSF. These initialization fields are then used for dynamical simulations via the primitive equation model. Simulation results are analyzed to calibrate the multiparameter feature-oriented modeling system. Experimental short-term synoptic simulations are presented for multiple resolutions in different regions with and without atmospheric forcing. The presented 'generic and portable' methodology demonstrates the potential of applying similar FORMS in many other regions of the Global Coastal Ocean.

  5. Computation and evaluation of features of surface electromyogram to identify the force of muscle contraction and muscle fatigue.

    PubMed

    Arjunan, Sridhar P; Kumar, Dinesh K; Naik, Ganesh

    2014-01-01

    The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study: normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P < 0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P < 0.01). Both of these features were not affected by the intersubject variations (P > 0.05).

  6. Computation and Evaluation of Features of Surface Electromyogram to Identify the Force of Muscle Contraction and Muscle Fatigue

    PubMed Central

    Arjunan, Sridhar P.; Kumar, Dinesh K.; Naik, Ganesh

    2014-01-01

    The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study: normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P < 0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P < 0.01). Both of these features were not affected by the intersubject variations (P > 0.05). PMID:24995275

  7. Online feature selection with streaming features.

    PubMed

    Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

    2013-05-01

    We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

  8. Recovering faces from memory: the distracting influence of external facial features.

    PubMed

    Frowd, Charlie D; Skelton, Faye; Atherton, Chris; Pitchford, Melanie; Hepton, Gemma; Holden, Laura; McIntyre, Alex H; Hancock, Peter J B

    2012-06-01

    Recognition memory for unfamiliar faces is facilitated when contextual cues (e.g., head pose, background environment, hair and clothing) are consistent between study and test. By contrast, inconsistencies in external features, especially hair, promote errors in unfamiliar face-matching tasks. For the construction of facial composites, as carried out by witnesses and victims of crime, the role of external features (hair, ears, and neck) is less clear, although research does suggest their involvement. Here, over three experiments, we investigate the impact of external features for recovering facial memories using a modern, recognition-based composite system, EvoFIT. Participant-constructors inspected an unfamiliar target face and, one day later, repeatedly selected items from arrays of whole faces, with "breeding," to "evolve" a composite with EvoFIT; further participants (evaluators) named the resulting composites. In Experiment 1, the important internal-features (eyes, brows, nose, and mouth) were constructed more identifiably when the visual presence of external features was decreased by Gaussian blur during construction: higher blur yielded more identifiable internal-features. In Experiment 2, increasing the visible extent of external features (to match the target's) in the presented face-arrays also improved internal-features quality, although less so than when external features were masked throughout construction. Experiment 3 demonstrated that masking external-features promoted substantially more identifiable images than using the previous method of blurring external-features. Overall, the research indicates that external features are a distractive rather than a beneficial cue for face construction; the results also provide a much better method to construct composites, one that should dramatically increase identification of offenders.

  9. A feature-based approach to modeling protein–protein interaction hot spots

    PubMed Central

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-01-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to π–related interactions, especially π · · · π interactions. PMID:19273533

  10. A feature-based approach to modeling protein-protein interaction hot spots.

    PubMed

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-05-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

  11. Controllable Edge Feature Sharpening for Dental Applications

    PubMed Central

    2014-01-01

    This paper presents a new approach to sharpen blurred edge features in scanned tooth preparation surfaces generated by structured-light scanners. It aims to efficiently enhance the edge features so that the embedded feature lines can be easily identified in dental CAD systems, and to avoid unnatural oversharpening geometry. We first separate the feature regions using graph-cut segmentation, which does not require a user-defined threshold. Then, we filter the face normal vectors to propagate the geometry from the smooth region to the feature region. In order to control the degree of the sharpness, we propose a feature distance measure which is based on normal tensor voting. Finally, the vertex positions are updated according to the modified face normal vectors. We have applied the approach to scanned tooth preparation models. The results show that the blurred edge features are enhanced without unnatural oversharpening geometry. PMID:24741376

  12. Controllable edge feature sharpening for dental applications.

    PubMed

    Fan, Ran; Jin, Xiaogang

    2014-01-01

    This paper presents a new approach to sharpen blurred edge features in scanned tooth preparation surfaces generated by structured-light scanners. It aims to efficiently enhance the edge features so that the embedded feature lines can be easily identified in dental CAD systems, and to avoid unnatural oversharpening geometry. We first separate the feature regions using graph-cut segmentation, which does not require a user-defined threshold. Then, we filter the face normal vectors to propagate the geometry from the smooth region to the feature region. In order to control the degree of the sharpness, we propose a feature distance measure which is based on normal tensor voting. Finally, the vertex positions are updated according to the modified face normal vectors. We have applied the approach to scanned tooth preparation models. The results show that the blurred edge features are enhanced without unnatural oversharpening geometry.

  13. Quantum-enhanced feature selection with forward selection and backward elimination

    NASA Astrophysics Data System (ADS)

    He, Zhimin; Li, Lvzhou; Huang, Zhiming; Situ, Haozhen

    2018-07-01

    Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one.

  14. Identifying the Minimum Model Features to Replicate Historic Morphodynamics of a Juvenile Delta

    NASA Astrophysics Data System (ADS)

    Czapiga, M. J.; Parker, G.

    2017-12-01

    We introduce a quasi-2D morphodynamic delta model that improves on past models that require many simplifying assumptions, e.g. a single channel representative of a channel network, fixed channel width, and spatially uniform deposition. Our model is useful for studying long-term progradation rates of any generic micro-tidal delta system with specification of: characteristic grain size, input water and sediment discharges and basin morphology. In particular, we relax the assumption of a single, implicit channel sweeping across the delta topset in favor of an implicit channel network. This network, coupled with recent research on channel-forming Shields number, quantitative assessments of the lateral depositional length of sand (corresponding loosely to levees) and length between bifurcations create a spatial web of deposition within the receiving basin. The depositional web includes spatial boundaries for areas infilling with sands carried as bed material load, as well as those filling via passive deposition of washload mud. Our main goal is to identify the minimum features necessary to accurately model the morphodynamics of channel number, width, depth, and overall delta progradation rate in a juvenile delta. We use the Wax Lake Delta in Louisiana as a test site due to its rapid growth in the last 40 years. Field data including topset/island bathymetry, channel bathymetry, topset/island width, channel width, number of channels, and radial topset length are compiled from US Army Corps of Engineers data for 1989, 1998, and 2006. Additional data is extracted from a DEM from 2015. These data are used as benchmarks for the hindcast model runs. The morphology of Wax Lake Delta is also strongly affected by a pre-delta substrate that acts as a lower "bedrock" boundary. Therefore, we also include closures for a bedrock-alluvial transition and an excess shear rate-law incision model to estimate bedrock incision. The model's framework is generic, but inclusion of individual

  15. Clinical Features and Awareness of Hand Eczema in Korea

    PubMed Central

    Park, Jae Beom; Lee, Seung Ho; Kim, Kea Jeung; Lee, Ga-Young; Yang, Jun-Mo; Kim, Do Won; Lee, Seok Jong; Lee, Cheol Heon; Park, Eun Joo; Kim, Kyu Han; Eun, Hee Chul; Chang, Sung Eun; Moon, Kee Chan; Kim, Seong Hyun; Kim, Seong Jin; Kim, Byung-Soo; Lee, Jun Young; Kim, Hyung-Ok; Kang, Hoon; Lee, Min Geol; Kim, Soo-Chan; Ro, Young Suck; Ko, Joo Yeon; Park, Mi Youn; Kim, Myung Hwa; Shin, Jeong Hyun; Choi, Hae Young; Hong, Chang Kwun; Lee, Sung Yul; Bak, Hana

    2016-01-01

    Background Hand eczema is one of the most common skin disorders and negatively affects quality of life. However, a large-scale multicenter study investigating the clinical features of patients with hand eczema has not yet been conducted in Korea. Objective To identify the prevalence of various hand diseases, which is defined as all cutaneous disease occurring in hands, and to investigate the clinical features of patients with hand eczema and the awareness about hand eczema in the general population and to compare the prevalence of hand eczema between health care providers and non-health care providers. Methods To estimate the prevalence of hand diseases, we analyzed the medical records of patients from 24 medical centers. Patients were assessed by online and offline questionnaires. A 1,000 from general population and 913 hand eczema patients answered the questionnaire, for a total of 1,913 subjects. Results The most common hand disease was irritant contact dermatitis. In an online survey, the lifetime prevalence of hand eczema was 31.2%. Hand eczema was more likely to occur in females (66.0%) and younger (20~39 years, 53.9%). Health care providers and housewives were the occupations most frequently associated with hand eczema. Winter (33.6%) was the most common season which people experienced aggravation. The 63.0% and 67.0% answered that hand eczema hinders their personal relationship and negatively affects daily living activities, respectively. Conclusion Hand eczema is a very common disease and hinders the quality of life. The appropriate identification of hand eczema is necessary to implement effective and efficient treatment. PMID:27274632

  16. Feature Masking in Computer Game Promotes Visual Imagery

    ERIC Educational Resources Information Center

    Smith, Glenn Gordon; Morey, Jim; Tjoe, Edwin

    2007-01-01

    Can learning of mental imagery skills for visualizing shapes be accelerated with feature masking? Chemistry, physics fine arts, military tactics, and laparoscopic surgery often depend on mentally visualizing shapes in their absence. Does working with "spatial feature-masks" (skeletal shapes, missing key identifying portions) encourage people to…

  17. Optimization Of Feature Weight TheVoting Feature Intervals 5 Algorithm Using Partical Swarm Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Hayana Hasibuan, Eka; Mawengkang, Herman; Efendi, Syahril

    2017-12-01

    The use of Partical Swarm Optimization Algorithm in this research is to optimize the feature weights on the Voting Feature Interval 5 algorithm so that we can find the model of using PSO algorithm with VFI 5. Optimization of feature weight on Diabetes or Dyspesia data is considered important because it is very closely related to the livelihood of many people, so if there is any inaccuracy in determining the most dominant feature weight in the data will cause death. Increased accuracy by using PSO Algorithm ie fold 1 from 92.31% to 96.15% increase accuracy of 3.8%, accuracy of fold 2 on Algorithm VFI5 of 92.52% as well as generated on PSO Algorithm means accuracy fixed, then in fold 3 increase accuracy of 85.19% Increased to 96.29% Accuracy increased by 11%. The total accuracy of all three trials increased by 14%. In general the Partical Swarm Optimization algorithm has succeeded in increasing the accuracy to several fold, therefore it can be concluded the PSO algorithm is well used in optimizing the VFI5 Classification Algorithm.

  18. Feature highlighting enhances learning of a complex natural-science category.

    PubMed

    Miyatsu, Toshiya; Gouravajhala, Reshma; Nosofsky, Robert M; McDaniel, Mark A

    2018-04-26

    Learning naturalistic categories, which tend to have fuzzy boundaries and vary on many dimensions, can often be harder than learning well defined categories. One method for facilitating the category learning of naturalistic stimuli may be to provide explicit feature descriptions that highlight the characteristic features of each category. Although this method is commonly used in textbooks and classrooms, theoretically it remains uncertain whether feature descriptions should advantage learning complex natural-science categories. In three experiments, participants were trained on 12 categories of rocks, either without or with a brief description highlighting key features of each category. After training, they were tested on their ability to categorize both old and new rocks from each of the categories. Providing feature descriptions as a caption under a rock image failed to improve category learning relative to providing only the rock image with its category label (Experiment 1). However, when these same feature descriptions were presented such that they were explicitly linked to the relevant parts of the rock image (feature highlighting), participants showed significantly higher performance on both immediate generalization to new rocks (Experiment 2) and generalization after a 2-day delay (Experiment 3). Theoretical and practical implications are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  19. SOCIODEMOGRAPHIC DATA USED FOR IDENTIFYING ...

    EPA Pesticide Factsheets

    Due to unique social and demographic characteristics, various segments of the population may experience exposures different from those of the general population, which, in many cases, may be greater. When risk assessments do not characterize subsets of the general population, the populations that may experience the greatest risk remain unidentified. When such populations are not identified, the social and demographic data relevant to these populations is not considered when preparing exposure estimates, which can underestimate exposure and risk estimates for at-risk populations. Thus, it is necessary for risk or exposure assessors characterizing a diverse population, to first identify and then enumerate certain groups within the general population who are at risk for greater contaminant exposures. The document entitled Sociodemographic Data Used for Identifying Potentially Highly Exposed Populations (also referred to as the Highly Exposed Populations document), assists assessors in identifying and enumerating potentially highly exposed populations. This document presents data relating to factors which potentially impact an individual or group's exposure to environmental contaminants based on activity patterns (how time is spent), microenvironments (locations where time is spent), and other socio-demographic data such as age, gender, race and economic status. Populations potentially more exposed to various chemicals of concern, relative to the general population

  20. Characteristic Features and Contributory Factors in Fatal Ciguatera Fish Poisoning—Implications for Prevention and Public Education

    PubMed Central

    Chan, Thomas Y. K.

    2016-01-01

    In this review, the main objective was to describe the characteristic features of fatal ciguatera fish poisoning and identify contributory factors, with a view to promote prevention and public education. Ciguatera-related deaths, although rare, have been reported from the Pacific, Caribbean, and Indian Ocean regions. The clinical features were generally dominated by convulsions and coma, with various focal neurological signs. Several contributory factors could be identified, including consumption of ciguatoxin (CTX)-rich fish parts (viscera and head) in larger amounts, the most ciguatoxic fish species (e.g., Gymnothorax flavimarginatus) and reef fish collected after storms and individuals' susceptibility. Mass ciguatera fish poisoning with mortalities also occurred when G. flavimarginatus and other ciguatoxic fish species were shared in gatherings and parties. The characteristic features of fatal ciguatera fish poisoning must be recognized early. The public should be repeatedly reminded to avoid eating the most ciguatoxic fish species and the CTX-rich parts of reef fish. To prevent mass poisoning in gatherings and parties, the most ciguatoxic fish species and potentially toxic fish species must be avoided. Particularly after hits by disastrous storms, it is important to monitor the toxicity of reef fish and the incidence rates of ciguatera. PMID:26787145

  1. Characteristic Features and Contributory Factors in Fatal Ciguatera Fish Poisoning--Implications for Prevention and Public Education.

    PubMed

    Chan, Thomas Y K

    2016-04-01

    In this review, the main objective was to describe the characteristic features of fatal ciguatera fish poisoning and identify contributory factors, with a view to promote prevention and public education. Ciguatera-related deaths, although rare, have been reported from the Pacific, Caribbean, and Indian Ocean regions. The clinical features were generally dominated by convulsions and coma, with various focal neurological signs. Several contributory factors could be identified, including consumption of ciguatoxin (CTX)-rich fish parts (viscera and head) in larger amounts, the most ciguatoxic fish species (e.g.,Gymnothorax flavimarginatus) and reef fish collected after storms and individuals' susceptibility. Mass ciguatera fish poisoning with mortalities also occurred when G. flavimarginatus and other ciguatoxic fish species were shared in gatherings and parties. The characteristic features of fatal ciguatera fish poisoning must be recognized early. The public should be repeatedly reminded to avoid eating the most ciguatoxic fish species and the CTX-rich parts of reef fish. To prevent mass poisoning in gatherings and parties, the most ciguatoxic fish species and potentially toxic fish species must be avoided. Particularly after hits by disastrous storms, it is important to monitor the toxicity of reef fish and the incidence rates of ciguatera. © The American Society of Tropical Medicine and Hygiene.

  2. Newly identified psychiatric illness in one general practice: 12-month outcome and the influence of patients' personality.

    PubMed Central

    Wright, A F; Anderson, A J

    1995-01-01

    BACKGROUND. Relatively little is known about the natural history and outcome of psychological problems in patients who present to general practitioners. Only a small proportion of such patients are seen by specialists. Clinical experience suggests that patient personality is one of the factors influencing outcome in patients diagnosed as having psychiatric illness. AIM. This study set out to examine prospectively the progress and 12-month outcome of patients with newly identified psychiatric illness, and the association of patients' personality with outcome. METHOD. One hundred and seventy one patients with clinically significant psychiatric illness attending one practice in a Scottish new town were followed up prospectively (96 presented with psychological symptoms and 75 with somatic symptoms), and were compared with a group of 127 patients with chronic physical illness. Patients were assessed in terms of psychiatric state, social problems and personality using both computer-based and pencil and paper tests in addition to clinical assessments at each consultation during the follow-up year and structured interview one year after recruitment. RESULTS. Most of the improvement in psychiatric state scores on the 28-item general health questionnaire occurred in the first six months of the illness. Of the 171 patients with psychiatric illness 34% improved quickly and remained well, 54% had an intermittent course but had improved at 12-month follow up while 12% pursued a chronic course without improvement. The mean number of consultations in the follow-up year was 8.4 for patients presenting with psychological symptoms, 7.2 for those presenting with somatic symptoms and 6.6 for patients with chronic physical illness. The Eysenck N score proved a strong predictor of the outcome of new psychiatric illness. CONCLUSION. Only one in three patients with newly identified psychiatric illness improved quickly and and remained well, reflecting the importance of continuing care of

  3. A multiple maximum scatter difference discriminant criterion for facial feature extraction.

    PubMed

    Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei

    2007-12-01

    Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.

  4. Common structural features of cholesterol binding sites in crystallized soluble proteins

    PubMed Central

    Bukiya, Anna N.; Dopico, Alejandro M.

    2017-01-01

    Cholesterol-protein interactions are essential for the architectural organization of cell membranes and for lipid metabolism. While cholesterol-sensing motifs in transmembrane proteins have been identified, little is known about cholesterol recognition by soluble proteins. We reviewed the structural characteristics of binding sites for cholesterol and cholesterol sulfate from crystallographic structures available in the Protein Data Bank. This analysis unveiled key features of cholesterol-binding sites that are present in either all or the majority of sites: i) the cholesterol molecule is generally positioned between protein domains that have an organized secondary structure; ii) the cholesterol hydroxyl/sulfo group is often partnered by Asn, Gln, and/or Tyr, while the hydrophobic part of cholesterol interacts with Leu, Ile, Val, and/or Phe; iii) cholesterol hydrogen-bonding partners are often found on α-helices, while amino acids that interact with cholesterol’s hydrophobic core have a slight preference for β-strands and secondary structure-lacking protein areas; iv) the steroid’s C21 and C26 constitute the “hot spots” most often seen for steroid-protein hydrophobic interactions; v) common “cold spots” are C8–C10, C13, and C17, at which contacts with the proteins were not detected. Several common features we identified for soluble protein-steroid interaction appear evolutionarily conserved. PMID:28420706

  5. Features selection and classification to estimate elbow movements

    NASA Astrophysics Data System (ADS)

    Rubiano, A.; Ramírez, J. L.; El Korso, M. N.; Jouandeau, N.; Gallimard, L.; Polit, O.

    2015-11-01

    In this paper, we propose a novel method to estimate the elbow motion, through the features extracted from electromyography (EMG) signals. The features values are normalized and then compared to identify potential relationships between the EMG signal and the kinematic information as angle and angular velocity. We propose and implement a method to select the best set of features, maximizing the distance between the features that correspond to flexion and extension movements. Finally, we test the selected features as inputs to a non-linear support vector machine in the presence of non-idealistic conditions, obtaining an accuracy of 99.79% in the motion estimation results.

  6. [Depressive disorders in primary care: Clinical features and sociodemographic characteristics].

    PubMed

    Oneib, B; Sabir, M; Otheman, Y; Ouanass, A

    2018-06-01

    Our aim was to determine the reason for consultation and the clinical features of depressive disorders according to the diagnostic and statistical manual (DSM) 4th edition IV R in primary care and to identify if there is an association between sociodemographic characteristics and depressive pattern. In a cross-sectional study conducted to determinate the prevalence of depressive disorders in primary care, at three urban centers in two cities Salé and Oujda by five physicians, we recruited primary care 396 patients of whom 58 were depressed, among these patients we screened for depressive disorders, their clinical features, the melancholic characteristics and suicidal ideation using the Mini International Neuropsychiatric Interview. Mean age of the 58 depressive patients was 46±15 years. They were predominantly female, inactive and of low socio-economic level. Approximately one-third of the patients were illiterate and single. The symptoms frequently encountered were sadness (63.7%), anhedonia (62%), insomnia (45.7%), anorexia (60.9%), psychomotor retardation (60.9%) and asthenia (73.9%). Somatic symptoms were present 99%, the most common complaint was pain that exhibited 68.6% prevalence. Suicidal ideations were found in 36.2% of these depressive patients. The accuracy of the clinical features of patients with depression in primary care will facilitate the detection of these disorders by general practitioners and improve management of depression. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

  7. The General Assessment of Personality Disorder (GAPD) as an instrument for assessing the core features of personality disorders.

    PubMed

    Berghuis, Han; Kamphuis, Jan H; Verheul, Roel; Larstone, Roseann; Livesley, John

    2013-01-01

    This study presents a psychometric evaluation of the General Assessment of Personality Disorder (GAPD), a self-report questionnaire for assessing the core components of personality dysfunction on the basis of Livesley's (2003) adaptive failure model. Analysis of samples from a general (n = 196) and a clinical population (n = 280) from Canada and the Netherlands, respectively, found a very similar two-component structure consistent with the two core components of personality dysfunction proposed by the model, namely, self-pathology and interpersonal dysfunction. Moreover, the GAPD discriminated between patients diagnosed with and without Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV-TR) personality disorder(s) and demonstrated discriminative power in detecting the severity of personality pathology. Correlations with a DSM-IV symptom measure and a pathological traits model suggest partial conceptual overlap. Although further testing is indicated, the present findings suggest the GAPD is suitable for assessing the core components of personality dysfunction. It may contribute to a two-step integrated assessment of personality pathology that assesses both personality dysfunction and personality traits. The core features of personality disorder can be defined as disorders in the self and in the capacity for interpersonal functioning. A clinically useful operationalization of disordered functioning of personality is needed to determine the maladaptivity of personality traits. An integrated assessment of personality (dys)functioning and personality traits provides a more comprehensive clinical picture of the patient, which may aid treatment planning. Copyright © 2012 John Wiley & Sons, Ltd.

  8. Vegetation-terrain feature relationships in southeast Arizona

    NASA Technical Reports Server (NTRS)

    Schrumpf, B. J. (Principal Investigator); Mouat, D. A.

    1972-01-01

    There are no author-identified significant results in this report. Studies of relationships of vegetation distribution to geomorphic characteristics of the landscape and of plant phenological patterns to vegetation identification of satellite imagery indicate that there exists positive relationships between certain plant species and certain terrain features. Not all species were found to exhibit positive relationships with all terrain feature variables, but enough positive relationships seem to exist to indicate that terrain feature variable-vegetation relationship studies have a definite place in plant ecological investigations. Even more importantly, the vegetation groups examined appeared to be successfully discriminated by the terrain feature variables. This would seem to indicate that spatial interpretations of vegetation groups may be possible. While vegetational distributions aren't determined by terrain feature differences, terrain features do mirror factors which directly influence vegetational response and hence distribution. As a result, those environmental features which can be readily and rapidly ascertained on relatively small-scale imagery may prove to be valuable indicators of vegetation distribution.

  9. Electroencephalographic features of benign adult familial myoclonic epilepsy.

    PubMed

    Toyota, Tomoko; Akamatsu, Naoki; Tanaka, Akihiro; Tsuji, Sadatoshi; Uozumi, Takenori

    2014-02-01

    To investigate electroencephalographic (EEG) features of benign adult familial myoclonic epilepsy (BAFME). We reviewed interictal EEG features in patients with BAFME treated between April 2005 and November 2012 at a tertiary referral center. The diagnostic criteria for BAFME were the presence of infrequent generalized tonic-clonic seizures, myoclonus or myoclonic seizures, and autosomal dominant inheritance. Interictal EEG findings of epilepsy with generalized tonic-clonic seizure only (EGTCS) were reviewed for comparison. We randomly selected 10 generalized spike/polyspike and wave complexes (GSW) for each BAFME patient and measured the duration of them. Photic stimulation and hyperventilation were performed in all. Nineteen (eight men, 11 women) patients with BAFME were included in this study. The mean frequency of GSW was 4.3±1.0Hz (mean±SD, n=14) in BAFME and 3.2±0.8Hz (n=10) in EGTCS. There was a statistically significant difference (p=0.008) between the two. Photoparoxysmal responses (PPR) were noted in 18 (95%) patients with BAFME but 1 (10%) with EGTCS. Faster frequency of GSW, compared with that in EGTCS, accompanied by PPR may be characteristic EEG features of BAFME. These findings may lead the diagnosis of BAFME. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  10. Feature integration theory revisited: dissociating feature detection and attentional guidance in visual search.

    PubMed

    Chan, Louis K H; Hayward, William G

    2009-02-01

    In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed results are difficult to explain in its absence. The present study measured dimension-specific performance during detection and localization, tasks that require operation of dimensional modules and the master map, respectively. Results showed a dissociation between tasks in terms of both dimension-switching costs and cross-dimension attentional capture, reflecting a dimension-specific nature for detection tasks and a dimension-general nature for localization tasks. In a feature-discrimination task, results precluded an explanation based on response mode. These results are interpreted to support FIT's postulation that different mechanisms are involved in parallel and focal attention searches. This indicates that the FIT architecture should be adopted to explain the current results and that a variety of visual attention findings can be addressed within this framework. Copyright 2009 APA, all rights reserved.

  11. Global map of eolian features on Mars.

    USGS Publications Warehouse

    Ward, A.W.; Doyle, K.B.; Helm, P.J.; Weisman, M.K.; Witbeck, N.E.

    1985-01-01

    Ten basic categories of eolian features on Mars were identified from a survey of Mariner 9 and Viking orbiter images. The ten features mapped are 1) light streaks (including frost streaks), 2) dark streaks, 3) sand sheets or splotches, 4) barchan dunes, 5) transverse dunes, 6) crescentic dunes, 7) anomalous dunes, 8) yardangs, 9) wind grooves, and 10) deflation pits. The features were mapped in groups, not as individual landforms, and recorded according to their geographic positions and orientations on maps of 1:12.5 million or 1:25 million scale. -from Authors

  12. Previously Identified Deficiencies Not Corrected in the General Fund Enterprise Business System Program

    DTIC Science & Technology

    2011-06-15

    Army AAA Report No. A-2009-0226- FFM , “Examination of Federal Financial Management Improvement Act Compliance - Test Validation General Fund Enterprise...Business System Release 1.2,” September 30, 2009 AAA Report No. A-2009-0231- FFM , “General Fund Enterprise Business System - Federal Financial...Management Improvement Act Compliance Examination of Release 1.3 Functionality,” September 30, 2009 AAA Report No. A-2009-0232- FFM , “General Fund

  13. CONTRASTIVE CULTURAL FEATURES IN FL TEACHING.

    ERIC Educational Resources Information Center

    FISCHER, MILLA

    CONTRASTIVE CULTURAL FEATURES SHOULD BE INCLUDED WITHIN THE FRAMEWORK OF THE GRAMMATICAL LESSON AS A MEANS OF COUNTERBALANCING THE GENERALLY UNSATISFACTORY MATERIAL USED FOR RUSSIAN TEXTS. LESSONS FOR AMERICAN STUDENTS LEARNING RUSSIAN SHOULD INCLUDE PHONOLOGICAL DRILLS ON VOWEL LENGTHS, DISTRIBUTION OF VOICED OBSTRUENTS, AND OBSTRUENT CLUSTERS,…

  14. Features and heterogeneities in growing network models

    NASA Astrophysics Data System (ADS)

    Ferretti, Luca; Cortelezzi, Michele; Yang, Bin; Marmorini, Giacomo; Bianconi, Ginestra

    2012-06-01

    Many complex networks from the World Wide Web to biological networks grow taking into account the heterogeneous features of the nodes. The feature of a node might be a discrete quantity such as a classification of a URL document such as personal page, thematic website, news, blog, search engine, social network, etc., or the classification of a gene in a functional module. Moreover the feature of a node can be a continuous variable such as the position of a node in the embedding space. In order to account for these properties, in this paper we provide a generalization of growing network models with preferential attachment that includes the effect of heterogeneous features of the nodes. The main effect of heterogeneity is the emergence of an “effective fitness” for each class of nodes, determining the rate at which nodes acquire new links. The degree distribution exhibits a multiscaling behavior analogous to the the fitness model. This property is robust with respect to variations in the model, as long as links are assigned through effective preferential attachment. Beyond the degree distribution, in this paper we give a full characterization of the other relevant properties of the model. We evaluate the clustering coefficient and show that it disappears for large network size, a property shared with the Barabási-Albert model. Negative degree correlations are also present in this class of models, along with nontrivial mixing patterns among features. We therefore conclude that both small clustering coefficients and disassortative mixing are outcomes of the preferential attachment mechanism in general growing networks.

  15. Distinguishing General and Specific Personality Disorder Features and Implications for Substance Dependence Comorbidity

    PubMed Central

    Jahng, Seungmin; Trull, Timothy J.; Wood, Phillip K.; Tragesser, Sarah L.; Tomko, Rachel; Grant, Julia D.; Bucholz, Kathleen K.; Sher, Kenneth J.

    2014-01-01

    Clinical and population-based samples show high comorbidity between Substance Use Disorders (SUDs) and Axis II Personality Disorders (PDs). However, Axis II disorders are frequently comorbid with each other, and existing research has generally failed to distinguish the extent to which SUD/PD comorbidity is general or specific with respect to both specific types of PDs and specific types of SUDs. We sought to determine whether ostensibly specific comorbid substance dependence-Axis II diagnoses (e.g., alcohol use dependence and borderline personality disorder) are reflective of more pervasive or general personality pathology or whether the comorbidity is specific to individual PDs. Face-to-face interview data from Wave 1 and Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions were analyzed. Participants included 34,653 adults living in households in the United States. We used hierarchical factor models to statistically partition general and specific personality disorder dimensions while simultaneously testing for specific PD-substance dependence relations. Results indicated that substance dependence-Axis II comorbidity is characterized by general (pervasive) pathology and by Cluster B PD pathology over and above the relationship to the general PD factor. Further, these relations between PD factors and substance dependence diagnoses appeared to largely account for the comorbidity among substance dependence diagnoses in the younger but not older participants. Our findings suggest that a failure to consider the general PD factor, which we interpret as reflecting interpersonal dysfunction, can lead to potential mischaracterizations of the nature of certain PD and SUD comorbidities. PMID:21604829

  16. Feature Selection for Ridge Regression with Provable Guarantees.

    PubMed

    Paul, Saurabh; Drineas, Petros

    2016-04-01

    We introduce single-set spectral sparsification as a deterministic sampling-based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

  17. Automatic Image Registration of Multimodal Remotely Sensed Data with Global Shearlet Features

    NASA Technical Reports Server (NTRS)

    Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

    2015-01-01

    Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

  18. Modeling crash injury severity by road feature to improve safety.

    PubMed

    Penmetsa, Praveena; Pulugurtha, Srinivas S

    2018-01-02

    The objective of this research is 2-fold: to (a) model and identify critical road features (or locations) based on crash injury severity and compare it with crash frequency and (b) model and identify drivers who are more likely to contribute to crashes by road feature. Crash data from 2011 to 2013 were obtained from the Highway Safety Information System (HSIS) for the state of North Carolina. Twenty-three different road features were considered, analyzed, and compared with each other as well as no road feature. A multinomial logit (MNL) model was developed and odds ratios were estimated to investigate the effect of road features on crash injury severity. Among the many road features, underpass, end or beginning of a divided highway, and on-ramp terminal on crossroad are the top 3 critical road features. Intersection crashes are frequent but are not highly likely to result in severe injuries compared to critical road features. Roundabouts are least likely to result in both severe and moderate injuries. Female drivers are more likely to be involved in crashes at intersections (4-way and T) compared to male drivers. Adult drivers are more likely to be involved in crashes at underpasses. Older drivers are 1.6 times more likely to be involved in a crash at the end or beginning of a divided highway. The findings from this research help to identify critical road features that need to be given priority. As an example, additional advanced warning signs and providing enlarged or highly retroreflective signs that grab the attention of older drivers may help in making locations such as end or beginning of a divided highway much safer. Educating drivers about the necessary skill sets required at critical road features in addition to engineering solutions may further help them adopt safe driving behaviors on the road.

  19. [Development of a pharmacological curriculum for general practice: Identifying and prescribing orally administered pharmacological substances with relevance for general practice].

    PubMed

    Straßner, Cornelia; Kaufmann-Kolle, Petra; Flum, Elisabeth; Schwill, Simon; Brandt, Bettina; Steinhäuser, Jost

    2017-05-01

    General practitioners (GPs) are among the specialists who prescribe the highest number of medication. Therefore the improvement of pharmacological competencies is an important part of the GP specialist training. The self-concept of general practice stating that GPs are the first contact persons for all health problems makes it challenging to define and acquire competencies for specialist training. While the "Competence-based Curriculum" developed by the German College of General Practitioners and Family Physicians defines diagnoses, reasons for counselling and competencies which are essential for general practice, a similar orientation guide is lacking for the pharmacological field. The aim of this study is to define and characterize pharmacological substances which every GP should know so well that he or she is able to conduct counselling and monitoring. We analysed private and public health insurance prescriptions of all general practices participating in the CONTENT project in the period from 2009 to 2014. The analysis was limited to substances with oral application which were prescribed at least once by at least 25 % (n = 11) of the practices. While the 100 most frequent prescriptions were included due to their frequency, less frequently prescribed substances were assessed concerning their relevance for general practice in a rating procedure. The substances included were classified by diagnoses and reasons for counselling. We analysed 1,912,896 prescriptions from 44 practices and 112,535 patients on the basis of the Anatomical Therapeutic Chemical (ATC) classification system. After applying the inclusion criteria, 453 substances were left, 302 of which were considered relevant for general practice and could be assigned to 45 diagnoses / reasons for counselling. The result of this study could be considered a working draft for a pharmacological curriculum for general practice, which may complement the "Competence-based Curriculum" in the medium term. Copyright

  20. Discriminative and informative features for biomolecular text mining with ensemble feature selection.

    PubMed

    Van Landeghem, Sofie; Abeel, Thomas; Saeys, Yvan; Van de Peer, Yves

    2010-09-15

    In the field of biomolecular text mining, black box behavior of machine learning systems currently limits understanding of the true nature of the predictions. However, feature selection (FS) is capable of identifying the most relevant features in any supervised learning setting, providing insight into the specific properties of the classification algorithm. This allows us to build more accurate classifiers while at the same time bridging the gap between the black box behavior and the end-user who has to interpret the results. We show that our FS methodology successfully discards a large fraction of machine-generated features, improving classification performance of state-of-the-art text mining algorithms. Furthermore, we illustrate how FS can be applied to gain understanding in the predictions of a framework for biomolecular event extraction from text. We include numerous examples of highly discriminative features that model either biological reality or common linguistic constructs. Finally, we discuss a number of insights from our FS analyses that will provide the opportunity to considerably improve upon current text mining tools. The FS algorithms and classifiers are available in Java-ML (http://java-ml.sf.net). The datasets are publicly available from the BioNLP'09 Shared Task web site (http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/SharedTask/).

  1. Patterns of Dysmorphic Features in Schizophrenia

    PubMed Central

    Scutt, L.E.; Chow, E.W.C.; Weksberg, R.; Honer, W.G.; Bassett, Anne S.

    2011-01-01

    Congenital dysmorphic features are prevalent in schizophrenia and may reflect underlying neurodevelopmental abnormalities. A cluster analysis approach delineating patterns of dysmorphic features has been used in genetics to classify individuals into more etiologically homogeneous subgroups. In the present study, this approach was applied to schizophrenia, using a sample with a suspected genetic syndrome as a testable model. Subjects (n = 159) with schizophrenia or schizoaffective disorder were ascertained from chronic patient populations (random, n=123) or referred with possible 22q11 deletion syndrome (referred, n = 36). All subjects were evaluated for presence or absence of 70 reliably assessed dysmorphic features, which were used in a three-step cluster analysis. The analysis produced four major clusters with different patterns of dysmorphic features. Significant between-cluster differences were found for rates of 37 dysmorphic features (P < 0.05), median number of dysmorphic features (P = 0.0001), and validating features not used in the cluster analysis: mild mental retardation (P = 0.001) and congenital heart defects (P = 0.002). Two clusters (1 and 4) appeared to represent more developmental subgroups of schizophrenia with elevated rates of dysmorphic features and validating features. Cluster 1 (n = 27) comprised mostly referred subjects. Cluster 4 (n= 18) had a different pattern of dysmorphic features; one subject had a mosaic Turner syndrome variant. Two other clusters had lower rates and patterns of features consistent with those found in previous studies of schizophrenia. Delineating patterns of dysmorphic features may help identify subgroups that could represent neurodevelopmental forms of schizophrenia with more homogeneous origins. PMID:11803519

  2. Models of clinical reasoning with a focus on general practice: A critical review

    PubMed Central

    YAZDANI, SHAHRAM; HOSSEINZADEH, MOHAMMAD; HOSSEINI, FAKHROLSADAT

    2017-01-01

    Introduction: Diagnosis lies at the heart of general practice. Every day general practitioners (GPs) visit patients with a wide variety of complaints and concerns, with often minor but sometimes serious symptoms. General practice has many features which differentiate it from specialty care setting, but during the last four decades little attention was paid to clinical reasoning in general practice. Therefore, we aimed to critically review the clinical reasoning models with a focus on the clinical reasoning in general practice or clinical reasoning of general practitioners to find out to what extent the existing models explain the clinical reasoning specially in primary care and also identity the gaps of the model for use in primary care settings. Methods: A systematic search to find models of clinical reasoning were performed. To have more precision, we excluded the studies that focused on neurobiological aspects of reasoning, reasoning in disciplines other than medicine decision making or decision analysis on treatment or management plan. All the articles and documents were first scanned to see whether they include important relevant contents or any models. The selected studies which described a model of clinical reasoning in general practitioners or with a focus on general practice were then reviewed and appraisal or critics of other authors on these models were included. The reviewed documents on the model were synthesized. Results: Six models of clinical reasoning were identified including hypothetic-deductive model, pattern recognition, a dual process diagnostic reasoning model, pathway for clinical reasoning, an integrative model of clinical reasoning, and model of diagnostic reasoning strategies in primary care. Only one model had specifically focused on general practitioners reasoning. Conclusion: A Model of clinical reasoning that included specific features of general practice to better help the general practitioners with the difficulties of clinical

  3. Models of clinical reasoning with a focus on general practice: A critical review.

    PubMed

    Yazdani, Shahram; Hosseinzadeh, Mohammad; Hosseini, Fakhrolsadat

    2017-10-01

    Diagnosis lies at the heart of general practice. Every day general practitioners (GPs) visit patients with a wide variety of complaints and concerns, with often minor but sometimes serious symptoms. General practice has many features which differentiate it from specialty care setting, but during the last four decades little attention was paid to clinical reasoning in general practice. Therefore, we aimed to critically review the clinical reasoning models with a focus on the clinical reasoning in general practice or clinical reasoning of general practitioners to find out to what extent the existing models explain the clinical reasoning specially in primary care and also identity the gaps of the model for use in primary care settings. A systematic search to find models of clinical reasoning were performed. To have more precision, we excluded the studies that focused on neurobiological aspects of reasoning, reasoning in disciplines other than medicine decision making or decision analysis on treatment or management plan. All the articles and documents were first scanned to see whether they include important relevant contents or any models. The selected studies which described a model of clinical reasoning in general practitioners or with a focus on general practice were then reviewed and appraisal or critics of other authors on these models were included. The reviewed documents on the model were synthesized. Six models of clinical reasoning were identified including hypothetic-deductive model, pattern recognition, a dual process diagnostic reasoning model, pathway for clinical reasoning, an integrative model of clinical reasoning, and model of diagnostic reasoning strategies in primary care. Only one model had specifically focused on general practitioners reasoning. A Model of clinical reasoning that included specific features of general practice to better help the general practitioners with the difficulties of clinical reasoning in this setting is needed.

  4. Identification of cardiac rhythm features by mathematical analysis of vector fields.

    PubMed

    Fitzgerald, Tamara N; Brooks, Dana H; Triedman, John K

    2005-01-01

    Automated techniques for locating cardiac arrhythmia features are limited, and cardiologists generally rely on isochronal maps to infer patterns in the cardiac activation sequence during an ablation procedure. Velocity vector mapping has been proposed as an alternative method to study cardiac activation in both clinical and research environments. In addition to the visual cues that vector maps can provide, vector fields can be analyzed using mathematical operators such as the divergence and curl. In the current study, conduction features were extracted from velocity vector fields computed from cardiac mapping data. The divergence was used to locate ectopic foci and wavefront collisions, and the curl to identify central obstacles in reentrant circuits. Both operators were applied to simulated rhythms created from a two-dimensional cellular automaton model, to measured data from an in situ experimental canine model, and to complex three-dimensional human cardiac mapping data sets. Analysis of simulated vector fields indicated that the divergence is useful in identifying ectopic foci, with a relatively small number of vectors and with errors of up to 30 degrees in the angle measurements. The curl was useful for identifying central obstacles in reentrant circuits, and the number of velocity vectors needed increased as the rhythm became more complex. The divergence was able to accurately identify canine in situ pacing sites, areas of breakthrough activation, and wavefront collisions. In data from human arrhythmias, the divergence reliably estimated origins of electrical activity and wavefront collisions, but the curl was less reliable at locating central obstacles in reentrant circuits, possibly due to the retrospective nature of data collection. The results indicate that the curl and divergence operators applied to velocity vector maps have the potential to add valuable information in cardiac mapping and can be used to supplement human pattern recognition.

  5. A method for data‐driven exploration to pinpoint key features in medical data and facilitate expert review

    PubMed Central

    Juhlin, Kristina; Norén, G. Niklas

    2017-01-01

    Abstract Purpose To develop a method for data‐driven exploration in pharmacovigilance and illustrate its use by identifying the key features of individual case safety reports related to medication errors. Methods We propose vigiPoint, a method that contrasts the relative frequency of covariate values in a data subset of interest to those within one or more comparators, utilizing odds ratios with adaptive statistical shrinkage. Nested analyses identify higher order patterns, and permutation analysis is employed to protect against chance findings. For illustration, a total of 164 000 adverse event reports related to medication errors were characterized and contrasted to the other 7 833 000 reports in VigiBase, the WHO global database of individual case safety reports, as of May 2013. The initial scope included 2000 features, such as patient age groups, reporter qualifications, and countries of origin. Results vigiPoint highlighted 109 key features of medication error reports. The most prominent were that the vast majority of medication error reports were from the United States (89% compared with 49% for other reports in VigiBase); that the majority of reports were sent by consumers (53% vs 17% for other reports); that pharmacists (12% vs 5.3%) and lawyers (2.9% vs 1.5%) were overrepresented; and that there were more medication error reports than expected for patients aged 2‐11 years (10% vs 5.7%), particularly in Germany (16%). Conclusions vigiPoint effectively identified key features of medication error reports in VigiBase. More generally, it reduces lead times for analysis and ensures reproducibility and transparency. An important next step is to evaluate its use in other data. PMID:28815800

  6. Polycystic ovary syndrome: perceptions and attitudes of women and primary health care physicians on features of PCOS and renaming the syndrome.

    PubMed

    Teede, Helena; Gibson-Helm, Melanie; Norman, Robert J; Boyle, Jacqueline

    2014-01-01

    Polycystic ovary syndrome (PCOS) is an under-recognized, common, and complex endocrinopathy. The name PCOS is a misnomer, and there have been calls for a change to reflect the broader clinical syndrome. The aim of the study was to determine perceptions held by women and primary health care physicians around key clinical features of PCOS and attitudes toward current and alternative names for the syndrome. We conducted a cross-sectional study utilizing a devised questionnaire. Participants were recruited throughout Australia via professional associations, women's health organizations, and a PCOS support group. Fifty-seven women with PCOS and 105 primary care physicians participated in the study. Perceptions of key clinical PCOS features and attitudes toward current and alternative syndrome names were investigated. Irregular periods were identified as a key clinical feature of PCOS by 86% of the women with PCOS and 90% of the primary care physicians. In both groups, 60% also identified hormone imbalance as a key feature. Among women with PCOS, 47% incorrectly identified ovarian cysts as key, 48% felt the current name is confusing, and 51% supported a change. Most primary care physicians agreed that the name is confusing (74%) and needs changing (81%); however, opinions on specific alternative names were divided. The name "polycystic ovary syndrome" is perceived as confusing, and there is general support for a change to reflect the broader clinical syndrome. Engagement of primary health care physicians and consumers is strongly recommended to ensure that an alternative name enhances understanding and recognition of the syndrome and its complex features.

  7. Finger vein recognition based on the hyperinformation feature

    NASA Astrophysics Data System (ADS)

    Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Yang, Lu

    2014-01-01

    The finger vein is a promising biometric pattern for personal identification due to its advantages over other existing biometrics. In finger vein recognition, feature extraction is a critical step, and many feature extraction methods have been proposed to extract the gray, texture, or shape of the finger vein. We treat them as low-level features and present a high-level feature extraction framework. Under this framework, base attribute is first defined to represent the characteristics of a certain subcategory of a subject. Then, for an image, the correlation coefficient is used for constructing the high-level feature, which reflects the correlation between this image and all base attributes. Since the high-level feature can reveal characteristics of more subcategories and contain more discriminative information, we call it hyperinformation feature (HIF). Compared with low-level features, which only represent the characteristics of one subcategory, HIF is more powerful and robust. In order to demonstrate the potential of the proposed framework, we provide a case study to extract HIF. We conduct comprehensive experiments to show the generality of the proposed framework and the efficiency of HIF on our databases, respectively. Experimental results show that HIF significantly outperforms the low-level features.

  8. Probing features in inflaton potential and reionization history with future CMB space observations

    NASA Astrophysics Data System (ADS)

    Hazra, Dhiraj Kumar; Paoletti, Daniela; Ballardini, Mario; Finelli, Fabio; Shafieloo, Arman; Smoot, George F.; Starobinsky, Alexei A.

    2018-02-01

    We consider the prospects of probing features in the primordial power spectrum with future Cosmic Microwave Background (CMB) polarization measurements. In the scope of the inflationary scenario, such features in the spectrum can be produced by local non-smooth pieces in an inflaton potential (smooth and quasi-flat in general) which in turn may originate from fast phase transitions during inflation in other quantum fields interacting with the inflaton. They can fit some outliers in the CMB temperature power spectrum which are unaddressed within the standard inflationary ΛCDM model. We consider Wiggly Whipped Inflation (WWI) as a theoretical framework leading to improvements in the fit to the Planck 2015 temperature and polarization data in comparison with the standard inflationary models, although not at a statistically significant level. We show that some type of features in the potential within the WWI models, leading to oscillations in the primordial power spectrum that extend to intermediate and small scales can be constrained with high confidence (at 3σ or higher confidence level) by an instrument as the Cosmic ORigins Explorer (CORE). In order to investigate the possible confusion between inflationary features and footprints from the reionization era, we consider an extended reionization history with monotonic increase of free electrons with decrease in redshift. We discuss the present constraints on this model of extended reionization and future predictions with CORE. We also project, to what extent, this extended reionization can create confusion in identifying inflationary features in the data.

  9. Identifying Key Features of Student Performance in Educational Video Games and Simulations through Cluster Analysis

    ERIC Educational Resources Information Center

    Kerr, Deirdre; Chung, Gregory K. W. K.

    2012-01-01

    The assessment cycle of "evidence-centered design" (ECD) provides a framework for treating an educational video game or simulation as an assessment. One of the main steps in the assessment cycle of ECD is the identification of the key features of student performance. While this process is relatively simple for multiple choice tests, when…

  10. Reliability of resting-state microstate features in electroencephalography.

    PubMed

    Khanna, Arjun; Pascual-Leone, Alvaro; Farzan, Faranak

    2014-01-01

    Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's α > 0.8, SEM ≈ 10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α > 0.9). All features had high test-retest reliability with 19 and 8 electrodes. High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health.

  11. Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.

    PubMed

    Zhang, Jian; Gao, Bo; Chai, Haiting; Ma, Zhiqiang; Yang, Guifu

    2016-08-26

    DNA-binding proteins (DBPs) play fundamental roles in many biological processes. Therefore, the developing of effective computational tools for identifying DBPs is becoming highly desirable. In this study, we proposed an accurate method for the prediction of DBPs. Firstly, we focused on the challenge of improving DBP prediction accuracy with information solely from the sequence. Secondly, we used multiple informative features to encode the protein. These features included evolutionary conservation profile, secondary structure motifs, and physicochemical properties. Thirdly, we introduced a novel improved Binary Firefly Algorithm (BFA) to remove redundant or noisy features as well as select optimal parameters for the classifier. The experimental results of our predictor on two benchmark datasets outperformed many state-of-the-art predictors, which revealed the effectiveness of our method. The promising prediction performance on a new-compiled independent testing dataset from PDB and a large-scale dataset from UniProt proved the good generalization ability of our method. In addition, the BFA forged in this research would be of great potential in practical applications in optimization fields, especially in feature selection problems. A highly accurate method was proposed for the identification of DBPs. A user-friendly web-server named iDbP (identification of DNA-binding Proteins) was constructed and provided for academic use.

  12. Cocrystal dissociation in the presence of water: a general approach for identifying stable cocrystal forms.

    PubMed

    Eddleston, Mark D; Madusanka, Nadeesh; Jones, William

    2014-09-01

    In previous studies, cocrystals have been shown to be susceptible to dissociation at high humidity because of differences in the solubilities of the two coformer molecules, especially when these molecules can form hydrates. Contrastingly, however, the propensity of the pharmaceutically active compound caffeine to hydrate formation is reduced by cocrystallization with oxalic acid. Here, the stability of the oxalic acid cocrystal of caffeine is investigated from a thermodynamic perspective through the use of aqueous slurries of caffeine hydrate and oxalic acid dihydrate. Conversion to the anhydrous caffeine-oxalic acid cocrystal occurred under these conditions confirming that this form is thermodynamically stable in an aqueous environment. The slurry methodology was further developed as a general approach to screening for cocrystals that are not susceptible to dissociation at high humidity. In this manner, cocrystals of the hydrate-forming molecules theophylline, carbamazepine, and piroxicam that are stable at high humidity, indefinitely avoiding hydrate formation, were identified. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

  13. Data for Quaternary faults, liquefaction features, and possible tectonic features in the Central and Eastern United States, east of the Rocky Mountain Front

    USGS Publications Warehouse

    Crone, Anthony J.; Wheeler, Russell L.

    2000-01-01

    (Classes C and D). The correlation between historical seismicity and Quaternary faults and liquefaction features in the CEUS is generally poor, which probably reflects the long return times between successive movements on individual structures. Some Quaternary faults and liquefaction features are located in aseismic areas or where historical seismicity is sparse. These relations indicate that the record of historical seismicity does not identify all potential seismic sources in the CEUS. Furthermore, geological studies of some currently aseismic faults have shown that the faults have generated strong earthquakes in the geologically recent past. Thus, the combination of geological information and seismological data can provide better insight into potential earthquake sources and thereby, contribute to better, more comprehensive seismic-hazard assessments.

  14. Multiclass Bayes error estimation by a feature space sampling technique

    NASA Technical Reports Server (NTRS)

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

    1979-01-01

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

  15. Identifying patient safety problems associated with information technology in general practice: an analysis of incident reports.

    PubMed

    Magrabi, Farah; Liaw, Siaw Teng; Arachi, Diana; Runciman, William; Coiera, Enrico; Kidd, Michael R

    2016-11-01

    To identify the categories of problems with information technology (IT), which affect patient safety in general practice. General practitioners (GPs) reported incidents online or by telephone between May 2012 and November 2013. Incidents were reviewed against an existing classification for problems associated with IT and the clinical process impacted. 87 GPs across Australia. Types of problems, consequences and clinical processes. GPs reported 90 incidents involving IT which had an observable impact on the delivery of care, including actual patient harm as well as near miss events. Practice systems and medications were the most affected clinical processes. Problems with IT disrupted clinical workflow, wasted time and caused frustration. Issues with user interfaces, routine updates to software packages and drug databases, and the migration of records from one package to another generated clinical errors that were unique to IT; some could affect many patients at once. Human factors issues gave rise to some errors that have always existed with paper records but are more likely to occur and cause harm with IT. Such errors were linked to slips in concentration, multitasking, distractions and interruptions. Problems with patient identification and hybrid records generated errors that were in principle no different to paper records. Problems associated with IT include perennial risks with paper records, but additional disruptions in workflow and hazards for patients unique to IT, occasionally affecting multiple patients. Surveillance for such hazards may have general utility, but particularly in the context of migrating historical records to new systems and software updates to existing systems. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  16. Rapid Processing of a Global Feature in the ON Visual Pathways of Behaving Monkeys.

    PubMed

    Huang, Jun; Yang, Yan; Zhou, Ke; Zhao, Xudong; Zhou, Quan; Zhu, Hong; Yang, Yingshan; Zhang, Chunming; Zhou, Yifeng; Zhou, Wu

    2017-01-01

    Visual objects are recognized by their features. Whereas, some features are based on simple components (i.e., local features, such as orientation of line segments), some features are based on the whole object (i.e., global features, such as an object having a hole in it). Over the past five decades, behavioral, physiological, anatomical, and computational studies have established a general model of vision, which starts from extracting local features in the lower visual pathways followed by a feature integration process that extracts global features in the higher visual pathways. This local-to-global model is successful in providing a unified account for a vast sets of perception experiments, but it fails to account for a set of experiments showing human visual systems' superior sensitivity to global features. Understanding the neural mechanisms underlying the "global-first" process will offer critical insights into new models of vision. The goal of the present study was to establish a non-human primate model of rapid processing of global features for elucidating the neural mechanisms underlying differential processing of global and local features. Monkeys were trained to make a saccade to a target in the black background, which was different from the distractors (white circle) in color (e.g., red circle target), local features (e.g., white square target), a global feature (e.g., white ring with a hole target) or their combinations (e.g., red square target). Contrary to the predictions of the prevailing local-to-global model, we found that (1) detecting a distinction or a change in the global feature was faster than detecting a distinction or a change in color or local features; (2) detecting a distinction in color was facilitated by a distinction in the global feature, but not in the local features; and (3) detecting the hole was interfered by the local features of the hole (e.g., white ring with a squared hole). These results suggest that monkey ON visual systems

  17. Identifying unproven cancer treatments on the health web: addressing accuracy, generalizability and scalability.

    PubMed

    Aphinyanaphongs, Yin; Fu, Lawrence D; Aliferis, Constantin F

    2013-01-01

    Building machine learning models that identify unproven cancer treatments on the Health Web is a promising approach for dealing with the dissemination of false and dangerous information to vulnerable health consumers. Aside from the obvious requirement of accuracy, two issues are of practical importance in deploying these models in real world applications. (a) Generalizability: The models must generalize to all treatments (not just the ones used in the training of the models). (b) Scalability: The models can be applied efficiently to billions of documents on the Health Web. First, we provide methods and related empirical data demonstrating strong accuracy and generalizability. Second, by combining the MapReduce distributed architecture and high dimensionality compression via Markov Boundary feature selection, we show how to scale the application of the models to WWW-scale corpora. The present work provides evidence that (a) a very small subset of unproven cancer treatments is sufficient to build a model to identify unproven treatments on the web; (b) unproven treatments use distinct language to market their claims and this language is learnable; (c) through distributed parallelization and state of the art feature selection, it is possible to prepare the corpora and build and apply models with large scalability.

  18. Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

    Hinders, Mark K.; Miller, Corey A.

    2014-02-18

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

  19. Feature selection using probabilistic prediction of support vector regression.

    PubMed

    Yang, Jian-Bo; Ong, Chong-Jin

    2011-06-01

    This paper presents a new wrapper-based feature selection method for support vector regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiments show that the proposed method generally performs better than, or at least as well as, the existing methods, with notable advantage when the dataset is sparse.

  20. Point cloud registration from local feature correspondences-Evaluation on challenging datasets.

    PubMed

    Petricek, Tomas; Svoboda, Tomas

    2017-01-01

    Registration of laser scans, or point clouds in general, is a crucial step of localization and mapping with mobile robots or in object modeling pipelines. A coarse alignment of the point clouds is generally needed before applying local methods such as the Iterative Closest Point (ICP) algorithm. We propose a feature-based approach to point cloud registration and evaluate the proposed method and its individual components on challenging real-world datasets. For a moderate overlap between the laser scans, the method provides a superior registration accuracy compared to state-of-the-art methods including Generalized ICP, 3D Normal-Distribution Transform, Fast Point-Feature Histograms, and 4-Points Congruent Sets. Compared to the surface normals, the points as the underlying features yield higher performance in both keypoint detection and establishing local reference frames. Moreover, sign disambiguation of the basis vectors proves to be an important aspect in creating repeatable local reference frames. A novel method for sign disambiguation is proposed which yields highly repeatable reference frames.

  1. The Mysterious 6565 Å Absorption Feature of the Galactic Halo

    NASA Astrophysics Data System (ADS)

    Sethi, Shiv K.; Shchekinov, Yuri; Nath, Biman B.

    2017-12-01

    We consider various possible scenarios to explain the recent observation of what has been called a broad Hα absorption in our Galactic halo, with peak optical depth τ ≃ 0.01 and equivalent width W≃ 0.17 \\mathringA . We show that the absorbed feature cannot arise from the circumgalactic and ISM Hα absorption. As the observed absorption feature is quite broad ({{Δ }}λ ≃ 30 \\mathringA ), we also consider CNO lines that lie close to Hα as possible alternatives to explain the feature. We show that such lines could also not account for the observed feature. Instead, we suggest that it could arise from diffuse interstellar bands (DIBs) carriers or polyaromatic hydrocarbons (PAHs) absorption. While we identify several such lines close to the Hα transition, we are unable to determine the molecule responsible for the observed feature, partly because of selection effects that prevent us from identifying DIBs/PAHs features close to Hα using local observations. Deep integration of a few extragalactic sources with high spectral resolution might allow us to distinguish between different possible explanations.

  2. Auditory emotion recognition impairments in Schizophrenia: Relationship to acoustic features and cognition

    PubMed Central

    Gold, Rinat; Butler, Pamela; Revheim, Nadine; Leitman, David; Hansen, John A.; Gur, Ruben; Kantrowitz, Joshua T.; Laukka, Petri; Juslin, Patrik N.; Silipo, Gail S.; Javitt, Daniel C.

    2013-01-01

    Objective Schizophrenia is associated with deficits in ability to perceive emotion based upon tone of voice. The basis for this deficit, however, remains unclear and assessment batteries remain limited. We evaluated performance in schizophrenia on a novel voice emotion recognition battery with well characterized physical features, relative to impairments in more general emotional and cognitive function. Methods We studied in a primary sample of 92 patients relative to 73 controls. Stimuli were characterized according to both intended emotion and physical features (e.g., pitch, intensity) that contributed to the emotional percept. Parallel measures of visual emotion recognition, pitch perception, general cognition, and overall outcome were obtained. More limited measures were obtained in an independent replication sample of 36 patients, 31 age-matched controls, and 188 general comparison subjects. Results Patients showed significant, large effect size deficits in voice emotion recognition (F=25.4, p<.00001, d=1.1), and were preferentially impaired in recognition of emotion based upon pitch-, but not intensity-features (group X feature interaction: F=7.79, p=.006). Emotion recognition deficits were significantly correlated with pitch perception impairments both across (r=56, p<.0001) and within (r=.47, p<.0001) group. Path analysis showed both sensory-specific and general cognitive contributions to auditory emotion recognition deficits in schizophrenia. Similar patterns of results were observed in the replication sample. Conclusions The present study demonstrates impairments in auditory emotion recognition in schizophrenia relative to acoustic features of underlying stimuli. Furthermore, it provides tools and highlights the need for greater attention to physical features of stimuli used for study of social cognition in neuropsychiatric disorders. PMID:22362394

  3. Generalized Processing Tree Models: Jointly Modeling Discrete and Continuous Variables.

    PubMed

    Heck, Daniel W; Erdfelder, Edgar; Kieslich, Pascal J

    2018-05-24

    Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories.

  4. Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations

    PubMed Central

    Gurunathan, Rajalakshmi; Van Emden, Bernard; Panchanathan, Sethuraman; Kumar, Sudhir

    2004-01-01

    Background Modern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns. Results The effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches. Conclusions We conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns

  5. Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

    NASA Astrophysics Data System (ADS)

    Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

    2018-06-01

    With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

  6. Informative Feature Selection for Object Recognition via Sparse PCA

    DTIC Science & Technology

    2011-04-07

    constraint on images collected from low-power camera net- works instead of high-end photography is that establishing wide-baseline feature correspondence of...variable selection tool for selecting informative features in the object images captured from low-resolution cam- era sensor networks. Firstly, we...More examples can be found in Figure 4 later. 3. Identifying Informative Features Classical PCA is a well established tool for the analysis of high

  7. Feature-Based Morphometry: Discovering Group-related Anatomical Patterns

    PubMed Central

    Toews, Matthew; Wells, William; Collins, D. Louis; Arbel, Tal

    2015-01-01

    This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1). PMID:19853047

  8. Generalized Models for Rock Joint Surface Shapes

    PubMed Central

    Du, Shigui; Hu, Yunjin; Hu, Xiaofei

    2014-01-01

    Generalized models of joint surface shapes are the foundation for mechanism studies on the mechanical effects of rock joint surface shapes. Based on extensive field investigations of rock joint surface shapes, generalized models for three level shapes named macroscopic outline, surface undulating shape, and microcosmic roughness were established through statistical analyses of 20,078 rock joint surface profiles. The relative amplitude of profile curves was used as a borderline for the division of different level shapes. The study results show that the macroscopic outline has three basic features such as planar, arc-shaped, and stepped; the surface undulating shape has three basic features such as planar, undulating, and stepped; and the microcosmic roughness has two basic features such as smooth and rough. PMID:25152901

  9. Feature engineering for drug name recognition in biomedical texts: feature conjunction and feature selection.

    PubMed

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong; Fan, Xiaoming

    2015-01-01

    Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.

  10. Windblown Features on Venus and Geological Mapping

    NASA Technical Reports Server (NTRS)

    Greeley, Ronald

    1999-01-01

    The objectives of this study were to: 1) develop a global data base of aeolian features by searching Magellan coverage for possible time-variable wind streaks, 2) analyze the data base to characterize aeolian features and processes on Venus, 3) apply the analysis to assessments of wind patterns near the surface and for comparisons with atmospheric circulation models, 4) analyze shuttle radar data acquired for aeolian features on Earth to determine their radar characteristics, and 5) conduct geological mapping of two quadrangles. Wind, or aeolian, features are observed on Venus and aeolian processes play a role in modifying its surface. Analysis of features resulting from aeolian processes provides insight into characteristics of both the atmosphere and the surface. Wind related features identified on Venus include erosional landforms (yardangs), depositional dune fields, and features resulting from the interaction of the atmosphere and crater ejecta at the time of impact. The most abundant aeolian features are various wind streaks. Their discovery on Venus afforded the opportunity to learn about the interaction of the atmosphere and surface, both for the identification of sediments and in mapping near-surface winds.

  11. Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

    PubMed Central

    Zhao, Yu-Xiang; Chou, Chien-Hsing

    2016-01-01

    In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

  12. Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

    PubMed

    Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

    2014-08-01

    Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature

  13. Selective attention to temporal features on nested time scales.

    PubMed

    Henry, Molly J; Herrmann, Björn; Obleser, Jonas

    2015-02-01

    Meaningful auditory stimuli such as speech and music often vary simultaneously along multiple time scales. Thus, listeners must selectively attend to, and selectively ignore, separate but intertwined temporal features. The current study aimed to identify and characterize the neural network specifically involved in this feature-selective attention to time. We used a novel paradigm where listeners judged either the duration or modulation rate of auditory stimuli, and in which the stimulation, working memory demands, response requirements, and task difficulty were held constant. A first analysis identified all brain regions where individual brain activation patterns were correlated with individual behavioral performance patterns, which thus supported temporal judgments generically. A second analysis then isolated those brain regions that specifically regulated selective attention to temporal features: Neural responses in a bilateral fronto-parietal network including insular cortex and basal ganglia decreased with degree of change of the attended temporal feature. Critically, response patterns in these regions were inverted when the task required selectively ignoring this feature. The results demonstrate how the neural analysis of complex acoustic stimuli with multiple temporal features depends on a fronto-parietal network that simultaneously regulates the selective gain for attended and ignored temporal features. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Integrating postgraduate and undergraduate general practice education: qualitative study.

    PubMed

    O'Regan, Andrew; Culhane, Aidan; Dunne, Colum; Griffin, Michael; McGrath, Deirdre; Meagher, David; O'Dwyer, Pat; Cullen, Walter

    2013-05-01

    Educational activity in general practice has increased considerably in the past 20 years. Vertical integration, whereby practices support students and trainees at different stages, may enhance general practices' capacity to fulfil this role. To explore the potential for vertical integration in undergraduate and postgraduate education in general practice, by describing the experience of (and attitudes towards) 'vertical integration in general practice education' among key stakeholder groups. Qualitative study of GPs, practice staff, GPs-in-training and medical students involving focus groups which were thematically analysed. We identified four overarching themes: (1) Important practical features of vertical integration are interaction between learners at different stages, active involvement in clinical teams and interagency collaboration; (2) Vertical integration may benefit GPs/practices, students and patients through improved practice systems, exposure to team-working and multi-morbidity and opportunistic health promotion, respectively; (3) Capacity issues may challenge its implementation; (4) Strategies such as recognising and addressing diverse learner needs and inter-agency collaboration can promote vertical integration. Vertical integration, whereby practices support students and trainees at different stages, may enhance general practices' teaching capacity. Recognising the diverse educational needs of learners at different stages and collaboration between agencies responsible for the planning and delivery of specialist training and medical degree programmes would appear to be important.

  15. Clinical features of movement disorders.

    PubMed

    Yung, C Y

    1983-08-01

    The descriptive aspects of all types of movement disorders and their related syndromes and terminologies used in the literature are reviewed and described. This comprises the features of (a) movement disorders secondary to neurological diseases affecting the extrapyramidal motor system, such as: athetosis, chorea, dystonia, hemiballismus, myoclonus, tremor, tics and spasm, (b) drug induced movement disorders, such as: akathisia, akinesia, hyperkinesia, dyskinesias, extrapyramidal syndrome, and tardive dyskinesia, and (c) abnormal movements in psychiatric disorders, such as: mannerism, stereotyped behaviour and psychomotor retardation. It is intended to bring about a more comprehensive overview of these movement disorders from a phenomenological perspective, so that clinicians can familiarize with these features for diagnosis. Some general statements are made in regard to some of the characteristics of movement disorders.

  16. Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

    PubMed

    Gutta, Sandeep; Cheng, Qi

    2016-03-01

    Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

  17. Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

    PubMed

    Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S

    2007-09-01

    The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after

  18. Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

    PubMed Central

    Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

    2017-01-01

    Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone. PMID:29123329

  19. NetProt: Complex-based Feature Selection.

    PubMed

    Goh, Wilson Wen Bin; Wong, Limsoon

    2017-08-04

    Protein complex-based feature selection (PCBFS) provides unparalleled reproducibility with high phenotypic relevance on proteomics data. Currently, there are five PCBFS paradigms, but not all representative methods have been implemented or made readily available. To allow general users to take advantage of these methods, we developed the R-package NetProt, which provides implementations of representative feature-selection methods. NetProt also provides methods for generating simulated differential data and generating pseudocomplexes for complex-based performance benchmarking. The NetProt open source R package is available for download from https://github.com/gohwils/NetProt/releases/ , and online documentation is available at http://rpubs.com/gohwils/204259 .

  20. Identification of S-glutathionylation sites in species-specific proteins by incorporating five sequence-derived features into the general pseudo-amino acid composition.

    PubMed

    Zhao, Xiaowei; Ning, Qiao; Ai, Meiyue; Chai, Haiting; Yang, Guifu

    2016-06-07

    As a selective and reversible protein post-translational modification, S-glutathionylation generates mixed disulfides between glutathione (GSH) and cysteine residues, and plays an important role in regulating protein activity, stability, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-Glutathionylated sites is crucial. Experimental identification of S-glutathionylated sites is labor-intensive and time consuming, so establishing an effective computational method is much desirable due to their convenient and fast speed. Therefore, in this study, a new bioinformatics tool named SSGlu (Species-Specific identification of Protein S-glutathionylation Sites) was developed to identify species-specific protein S-glutathionylated sites, utilizing support vector machines that combine multiple sequence-derived features with a two-step feature selection. By 5-fold cross validation, the performance of SSGlu was measured with an AUC of 0.8105 and 0.8041 for Homo sapiens and Mus musculus, respectively. Additionally, SSGlu was compared with the existing methods, and the higher MCC and AUC of SSGlu demonstrated that SSGlu was very promising to predict S-glutathionylated sites. Furthermore, a site-specific analysis showed that S-glutathionylation intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, SSGlu is freely accessible at http://59.73.198.144:8080/SSGlu/. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Ordinal feature selection for iris and palmprint recognition.

    PubMed

    Sun, Zhenan; Wang, Libin; Tan, Tieniu

    2014-09-01

    Ordinal measures have been demonstrated as an effective feature representation model for iris and palmprint recognition. However, ordinal measures are a general concept of image analysis and numerous variants with different parameter settings, such as location, scale, orientation, and so on, can be derived to construct a huge feature space. This paper proposes a novel optimization formulation for ordinal feature selection with successful applications to both iris and palmprint recognition. The objective function of the proposed feature selection method has two parts, i.e., misclassification error of intra and interclass matching samples and weighted sparsity of ordinal feature descriptors. Therefore, the feature selection aims to achieve an accurate and sparse representation of ordinal measures. And, the optimization subjects to a number of linear inequality constraints, which require that all intra and interclass matching pairs are well separated with a large margin. Ordinal feature selection is formulated as a linear programming (LP) problem so that a solution can be efficiently obtained even on a large-scale feature pool and training database. Extensive experimental results demonstrate that the proposed LP formulation is advantageous over existing feature selection methods, such as mRMR, ReliefF, Boosting, and Lasso for biometric recognition, reporting state-of-the-art accuracy on CASIA and PolyU databases.

  2. Identification of informative features for predicting proinflammatory potentials of engine exhausts.

    PubMed

    Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

    2017-08-18

    The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

  3. A keyword spotting model using perceptually significant energy features

    NASA Astrophysics Data System (ADS)

    Umakanthan, Padmalochini

    The task of a keyword recognition system is to detect the presence of certain words in a conversation based on the linguistic information present in human speech. Such keyword spotting systems have applications in homeland security, telephone surveillance and human-computer interfacing. General procedure of a keyword spotting system involves feature generation and matching. In this work, new set of features that are based on the psycho-acoustic masking nature of human speech are proposed. After developing these features a time aligned pattern matching process was implemented to locate the words in a set of unknown words. A word boundary detection technique based on frame classification using the nonlinear characteristics of speech is also addressed in this work. Validation of this keyword spotting model was done using widely acclaimed Cepstral features. The experimental results indicate the viability of using these perceptually significant features as an augmented feature set in keyword spotting.

  4. Demersal fish assemblages on seamounts and other rugged features in the northeastern Caribbean

    NASA Astrophysics Data System (ADS)

    Quattrini, Andrea M.; Demopoulos, Amanda W. J.; Singer, Randal; Roa-Varon, Adela; Chaytor, Jason D.

    2017-05-01

    Recent investigations of demersal fish communities in deepwater (>50 m) habitats have considerably increased our knowledge of the factors that influence the assemblage structure of fishes across mesophotic to deep-sea depths. While different habitat types influence deepwater fish distribution, whether different types of rugged seafloor features provide functionally equivalent habitat for fishes is poorly understood. In the northeastern Caribbean, different types of rugged features (e.g., seamounts, banks, canyons) punctuate insular margins, and thus create a remarkable setting in which to compare demersal fish communities across various features. Concurrently, several water masses are vertically layered in the water column, creating strong stratification layers corresponding to specific abiotic conditions. In this study, we examined differences among fish assemblages across different features (e.g., seamount, canyon, bank/ridge) and water masses at depths ranging from 98 to 4060 m in the northeastern Caribbean. We conducted 26 remotely operated vehicle dives across 18 sites, identifying 156 species of which 42% of had not been previously recorded from particular depths or localities in the region. While rarefaction curves indicated fewer species at seamounts than at other features in the NE Caribbean, assemblage structure was similar among the different types of features. Thus, similar to seamount studies in other regions, seamounts in the Anegada Passage do not harbor distinct communities from other types of rugged features. Species assemblages, however, differed among depths, with zonation generally corresponding to water mass boundaries in the region. High species turnover occurred at depths <1200 m, and may be driven by changes in water mass characteristics including temperature (4.8-24.4 °C) and dissolved oxygen (2.2-9.5 mg per l). Our study suggests the importance of water masses in influencing community structure of benthic fauna, while considerably adding

  5. Demersal fish assemblages on seamounts and other rugged features in the northeastern Caribbean

    USGS Publications Warehouse

    Quattrini, Andrea M.; Demopoulos, Amanda W. J.; Singer, Randal; Roa-Varon, Adela; Chaytor, Jason D.

    2017-01-01

    Recent investigations of demersal fish communities in deepwater (>50 m) habitats have considerably increased our knowledge of the factors that influence the assemblage structure of fishes across mesophotic to deep-sea depths. While different habitat types influence deepwater fish distribution, whether different types of rugged seafloor features provide functionally equivalent habitat for fishes is poorly understood. In the northeastern Caribbean, different types of rugged features (e.g., seamounts, banks, canyons) punctuate insular margins, and thus create a remarkable setting in which to compare demersal fish communities across various features. Concurrently, several water masses are vertically layered in the water column, creating strong stratification layers corresponding to specific abiotic conditions. In this study, we examined differences among fish assemblages across different features (e.g., seamount, canyon, bank/ridge) and water masses at depths ranging from 98 to 4060 m in the northeastern Caribbean. We conducted 26 remotely operated vehicle dives across 18 sites, identifying 156 species of which 42% of had not been previously recorded from particular depths or localities in the region. While rarefaction curves indicated fewer species at seamounts than at other features in the NE Caribbean, assemblage structure was similar among the different types of features. Thus, similar to seamount studies in other regions, seamounts in the Anegada Passage do not harbor distinct communities from other types of rugged features. Species assemblages, however, differed among depths, with zonation generally corresponding to water mass boundaries in the region. High species turnover occurred at depths <1200 m, and may be driven by changes in water mass characteristics including temperature (4.8–24.4 °C) and dissolved oxygen (2.2–9.5 mg per l). Our study suggests the importance of water masses in influencing community structure of benthic fauna, while

  6. EFS: an ensemble feature selection tool implemented as R-package and web-application.

    PubMed

    Neumann, Ursula; Genze, Nikita; Heider, Dominik

    2017-01-01

    Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.

  7. A formal theory of feature binding in object perception.

    PubMed

    Ashby, F G; Prinzmetal, W; Ivry, R; Maddox, W T

    1996-01-01

    Visual objects are perceived correctly only if their features are identified and then bound together. Illusory conjunctions result when feature identification is correct but an error occurs during feature binding. A new model is proposed that assumes feature binding errors occur because of uncertainty about the location of visual features. This model accounted for data from 2 new experiments better than a model derived from A. M. Treisman and H. Schmidt's (1982) feature integration theory. The traditional method for detecting the occurrence of true illusory conjunctions is shown to be fundamentally flawed. A reexamination of 2 previous studies provided new insights into the role of attention and location information in object perception and a reinterpretation of the deficits in patients who exhibit attentional disorders.

  8. Structural Features of Algebraic Quantum Notations

    ERIC Educational Resources Information Center

    Gire, Elizabeth; Price, Edward

    2015-01-01

    The formalism of quantum mechanics includes a rich collection of representations for describing quantum systems, including functions, graphs, matrices, histograms of probabilities, and Dirac notation. The varied features of these representations affect how computations are performed. For example, identifying probabilities of measurement outcomes…

  9. Quantitative profiling of immune repertoires for minor lymphocyte counts using unique molecular identifiers.

    PubMed

    Egorov, Evgeny S; Merzlyak, Ekaterina M; Shelenkov, Andrew A; Britanova, Olga V; Sharonov, George V; Staroverov, Dmitriy B; Bolotin, Dmitriy A; Davydov, Alexey N; Barsova, Ekaterina; Lebedev, Yuriy B; Shugay, Mikhail; Chudakov, Dmitriy M

    2015-06-15

    Emerging high-throughput sequencing methods for the analyses of complex structure of TCR and BCR repertoires give a powerful impulse to adaptive immunity studies. However, there are still essential technical obstacles for performing a truly quantitative analysis. Specifically, it remains challenging to obtain comprehensive information on the clonal composition of small lymphocyte populations, such as Ag-specific, functional, or tissue-resident cell subsets isolated by sorting, microdissection, or fine needle aspirates. In this study, we report a robust approach based on unique molecular identifiers that allows profiling Ag receptors for several hundred to thousand lymphocytes while preserving qualitative and quantitative information on clonal composition of the sample. We also describe several general features regarding the data analysis with unique molecular identifiers that are critical for accurate counting of starting molecules in high-throughput sequencing applications. Copyright © 2015 by The American Association of Immunologists, Inc.

  10. Selective processing of multiple features in the human brain: effects of feature type and salience.

    PubMed

    McGinnis, E Menton; Keil, Andreas

    2011-02-09

    Identifying targets in a stream of items at a given constant spatial location relies on selection of aspects such as color, shape, or texture. Such attended (target) features of a stimulus elicit a negative-going event-related brain potential (ERP), termed Selection Negativity (SN), which has been used as an index of selective feature processing. In two experiments, participants viewed a series of Gabor patches in which targets were defined as a specific combination of color, orientation, and shape. Distracters were composed of different combinations of color, orientation, and shape of the target stimulus. This design allows comparisons of items with and without specific target features. Consistent with previous ERP research, SN deflections extended between 160-300 ms. Data from the subsequent P3 component (300-450 ms post-stimulus) were also examined, and were regarded as an index of target processing. In Experiment A, predominant effects of target color on SN and P3 amplitudes were found, along with smaller ERP differences in response to variations of orientation and shape. Manipulating color to be less salient while enhancing the saliency of the orientation of the Gabor patch (Experiment B) led to delayed color selection and enhanced orientation selection. Topographical analyses suggested that the location of SN on the scalp reliably varies with the nature of the to-be-attended feature. No interference of non-target features on the SN was observed. These results suggest that target feature selection operates by means of electrocortical facilitation of feature-specific sensory processes, and that selective electrocortical facilitation is more effective when stimulus saliency is heightened.

  11. Surprise! Infants consider possible bases of generalization for a single input example.

    PubMed

    Gerken, LouAnn; Dawson, Colin; Chatila, Razanne; Tenenbaum, Josh

    2015-01-01

    Infants have been shown to generalize from a small number of input examples. However, existing studies allow two possible means of generalization. One is via a process of noting similarities shared by several examples. Alternatively, generalization may reflect an implicit desire to explain the input. The latter view suggests that generalization might occur when even a single input example is surprising, given the learner's current model of the domain. To test the possibility that infants are able to generalize based on a single example, we familiarized 9-month-olds with a single three-syllable input example that contained either one surprising feature (syllable repetition, Experiment 1) or two features (repetition and a rare syllable, Experiment 2). In both experiments, infants generalized only to new strings that maintained all of the surprising features from familiarization. This research suggests that surprise can promote very rapid generalization. © 2014 John Wiley & Sons Ltd.

  12. Prototype Effect and the Persuasiveness of Generalizations.

    PubMed

    Dahlman, Christian; Sarwar, Farhan; Bååth, Rasmus; Wahlberg, Lena; Sikström, Sverker

    An argument that makes use of a generalization activates the prototype for the category used in the generalization. We conducted two experiments that investigated how the activation of the prototype affects the persuasiveness of the argument. The results of the experiments suggest that the features of the prototype overshadow and partly overwrite the actual facts of the case. The case is, to some extent, judged as if it had the features of the prototype instead of the features it actually has. This prototype effect increases the persuasiveness of the argument in situations where the audience finds the judgment more warranted for the prototype than for the actual case (positive prototype effect), but decreases persuasiveness in situations where the audience finds the judgment less warranted for the prototype than for the actual case (negative prototype effect).

  13. A framework for semisupervised feature generation and its applications in biomedical literature mining.

    PubMed

    Li, Yanpeng; Hu, Xiaohua; Lin, Hongfei; Yang, Zhihao

    2011-01-01

    Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is: EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the performance of these low-frequency features can be greatly boosted and new information from unlabeled can be incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER), protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets.

  14. Features of Online Health Communities for Adolescents With Type 1 Diabetes

    PubMed Central

    Ho, Yun-Xian; O’Connor, Brendan H.; Mulvaney, Shelagh A.

    2014-01-01

    The aim of this exploratory study was to examine diabetes online health communities (OHCs) available to adolescents with type 1 diabetes (T1D). We sought to identify and classify site features and relate them to evidence-based processes for improving self-management. We reviewed 18 OHCs and identified the following five feature categories: social learning and networking, information, guidance, engagement, and personal health data sharing. While features that have been associated with improved self-management were present, such as social learning, results suggest that more guidance or structure would be helpful to ensure that those processes were focused on promoting positive beliefs and behaviors. Enhancing guidance-related features and structure to existing OHCs could provide greater opportunity for effective diabetes self-management support. To support clinical recommendations, more research is needed to quantitatively relate features and participation in OHCs to patient outcomes. PMID:24473058

  15. FSMRank: feature selection algorithm for learning to rank.

    PubMed

    Lai, Han-Jiang; Pan, Yan; Tang, Yong; Yu, Rong

    2013-06-01

    In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate O(1/T(2)). We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.

  16. Vertical Feature Mask Feature Classification Flag Extraction

    Atmospheric Science Data Center

    2013-03-28

      Vertical Feature Mask Feature Classification Flag Extraction This routine demonstrates extraction of the ... in a CALIPSO Lidar Level 2 Vertical Feature Mask feature classification flag value. It is written in Interactive Data Language (IDL) ...

  17. Engineering nanoscale surface features to sustain microparticle rolling in flow.

    PubMed

    Kalasin, Surachate; Santore, Maria M

    2015-05-26

    Nanoscopic features of channel walls are often engineered to facilitate microfluidic transport, for instance when surface charge enables electro-osmosis or when grooves drive mixing. The dynamic or rolling adhesion of flowing microparticles on a channel wall holds potential to accomplish particle sorting or to selectively transfer reactive species or signals between the wall and flowing particles. Inspired by cell rolling under the direction of adhesion molecules called selectins, we present an engineered platform in which the rolling of flowing microparticles is sustained through the incorporation of entirely synthetic, discrete, nanoscale, attractive features into the nonadhesive (electrostatically repulsive) surface of a flow channel. Focusing on one example or type of nanoscale feature and probing the impact of broad systematic variations in surface feature loading and processing parameters, this study demonstrates how relatively flat, weakly adhesive nanoscale features, positioned with average spacings on the order of tens of nanometers, can produce sustained microparticle rolling. We further demonstrate how the rolling velocity and travel distance depend on flow and surface design. We identify classes of related surfaces that fail to support rolling and present a state space that identifies combinations of surface and processing variables corresponding to transitions between rolling, free particle motion, and arrest. Finally we identify combinations of parameters (surface length scales, particle size, flow rates) where particles can be manipulated with size-selectivity.

  18. Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data.

    PubMed

    Ozery-Flato, Michal; Yanover, Chen; Gottlieb, Assaf; Weissbrod, Omer; Parush Shear-Yashuv, Naama; Goldschmidt, Yaara

    2017-01-01

    We present a framework for feature engineering, tailored for longitudinal structured data, such as electronic health records (EHRs). To fast-track feature engineering and extraction, the framework combines general-use plug-in extractors, a multi-cohort management mechanism, and modular memoization. Using this framework, we rapidly extracted thousands of features from diverse and large healthcare data sources in multiple projects.

  19. The Features of Female Managers' Personality Traits in Organization

    ERIC Educational Resources Information Center

    Gabdreeva, Guzel Sh.; Khalfieva, Alisa R.

    2016-01-01

    The relevance of the "female" management features study is driven by the active penetration of women to management in various fields and the emergence of a new social category "Business-women". The article contains the results of a study aimed to identify the features of personal properties and structure of low-level,…

  20. Algorithm-Dependent Generalization Bounds for Multi-Task Learning.

    PubMed

    Liu, Tongliang; Tao, Dacheng; Song, Mingli; Maybank, Stephen J

    2017-02-01

    Often, tasks are collected for multi-task learning (MTL) because they share similar feature structures. Based on this observation, in this paper, we present novel algorithm-dependent generalization bounds for MTL by exploiting the notion of algorithmic stability. We focus on the performance of one particular task and the average performance over multiple tasks by analyzing the generalization ability of a common parameter that is shared in MTL. When focusing on one particular task, with the help of a mild assumption on the feature structures, we interpret the function of the other tasks as a regularizer that produces a specific inductive bias. The algorithm for learning the common parameter, as well as the predictor, is thereby uniformly stable with respect to the domain of the particular task and has a generalization bound with a fast convergence rate of order O(1/n), where n is the sample size of the particular task. When focusing on the average performance over multiple tasks, we prove that a similar inductive bias exists under certain conditions on the feature structures. Thus, the corresponding algorithm for learning the common parameter is also uniformly stable with respect to the domains of the multiple tasks, and its generalization bound is of the order O(1/T), where T is the number of tasks. These theoretical analyses naturally show that the similarity of feature structures in MTL will lead to specific regularizations for predicting, which enables the learning algorithms to generalize fast and correctly from a few examples.

  1. Relating interesting quantitative time series patterns with text events and text features

    NASA Astrophysics Data System (ADS)

    Wanner, Franz; Schreck, Tobias; Jentner, Wolfgang; Sharalieva, Lyubka; Keim, Daniel A.

    2013-12-01

    In many application areas, the key to successful data analysis is the integrated analysis of heterogeneous data. One example is the financial domain, where time-dependent and highly frequent quantitative data (e.g., trading volume and price information) and textual data (e.g., economic and political news reports) need to be considered jointly. Data analysis tools need to support an integrated analysis, which allows studying the relationships between textual news documents and quantitative properties of the stock market price series. In this paper, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which reflect quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a-priori method. First, based on heuristics we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a-priori method supports the discovery of such sequential temporal patterns. Then, various text features like the degree of sentence nesting, noun phrase complexity, the vocabulary richness, etc. are extracted from the news to obtain meta patterns. Meta patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time-, cluster- and sequence visualization and analysis functionality. We provide two case studies, showing the effectiveness of our combined quantitative and textual analysis work flow. The workflow can also be generalized to other

  2. The effects of variations in parameters and algorithm choices on calculated radiomics feature values: initial investigations and comparisons to feature variability across CT image acquisition conditions

    NASA Astrophysics Data System (ADS)

    Emaminejad, Nastaran; Wahi-Anwar, Muhammad; Hoffman, John; Kim, Grace H.; Brown, Matthew S.; McNitt-Gray, Michael

    2018-02-01

    Translation of radiomics into clinical practice requires confidence in its interpretations. This may be obtained via understanding and overcoming the limitations in current radiomic approaches. Currently there is a lack of standardization in radiomic feature extraction. In this study we examined a few factors that are potential sources of inconsistency in characterizing lung nodules, such as 1)different choices of parameters and algorithms in feature calculation, 2)two CT image dose levels, 3)different CT reconstruction algorithms (WFBP, denoised WFBP, and Iterative). We investigated the effect of variation of these factors on entropy textural feature of lung nodules. CT images of 19 lung nodules identified from our lung cancer screening program were identified by a CAD tool and contours provided. The radiomics features were extracted by calculating 36 GLCM based and 4 histogram based entropy features in addition to 2 intensity based features. A robustness index was calculated across different image acquisition parameters to illustrate the reproducibility of features. Most GLCM based and all histogram based entropy features were robust across two CT image dose levels. Denoising of images slightly improved robustness of some entropy features at WFBP. Iterative reconstruction resulted in improvement of robustness in a fewer times and caused more variation in entropy feature values and their robustness. Within different choices of parameters and algorithms texture features showed a wide range of variation, as much as 75% for individual nodules. Results indicate the need for harmonization of feature calculations and identification of optimum parameters and algorithms in a radiomics study.

  3. Robust extrema features for time-series data analysis.

    PubMed

    Vemulapalli, Pramod K; Monga, Vishal; Brennan, Sean N

    2013-06-01

    The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.

  4. General Practice Clinical Data Help Identify Dementia Hotspots: A Novel Geospatial Analysis Approach.

    PubMed

    Bagheri, Nasser; Wangdi, Kinley; Cherbuin, Nicolas; Anstey, Kaarin J

    2018-01-01

    We have a poor understanding of whether dementia clusters geographically, how this occurs, and how dementia may relate to socio-demographic factors. To shed light on these important questions, this study aimed to compute a dementia risk score for individuals to assess spatial variation of dementia risk, identify significant clusters (hotspots), and explore their association with socioeconomic status. We used clinical records from 16 general practices (468 Statistical Area level 1 s, N = 14,746) from the city of west Adelaide, Australia for the duration of 1 January 2012 to 31 December 2014. Dementia risk was estimated using The Australian National University-Alzheimer's Disease Risk Index. Hotspot analyses were applied to examine potential clusters in dementia risk at small area level. Significant hotspots were observed in eastern and southern areas while coldspots were observed in the western area within the study perimeter. Additionally, significant hotspots were observed in low socio-economic communities. We found dementia risk scores increased with age, sex (female), high cholesterol, no physical activity, living alone (widow, divorced, separated, or never married), and co-morbidities such as diabetes and depression. Similarly, smoking was associated with a lower dementia risk score. The identification of dementia risk clusters may provide insight into possible geographical variations in risk factors for dementia and quantify these risks at the community level. As such, this research may enable policy makers to tailor early prevention strategies to the correct individuals within their precise locations.

  5. Insight into resolution enhancement in generalized two-dimensional correlation spectroscopy.

    PubMed

    Ma, Lu; Sikirzhytski, Vitali; Hong, Zhenmin; Lednev, Igor K; Asher, Sanford A

    2013-03-01

    Generalized two-dimensional correlation spectroscopy (2D-COS) can be used to enhance spectral resolution in order to help differentiate highly overlapped spectral bands. Despite the numerous extensive 2D-COS investigations, the origin of the 2D spectral resolution enhancement mechanism(s) is not completely understood. In the work here, we studied the 2D-COS of simulated spectra in order to develop new insights into the dependence of 2D-COS spectral features on the overlapping band separations, their intensities and bandwidths, and their band intensity change rates. We found that the features in the 2D-COS maps that are derived from overlapping bands were determined by the spectral normalized half-intensities and the total intensity changes of the correlated bands. We identified the conditions required to resolve overlapping bands. In particular, 2D-COS peak resolution requires that the normalized half-intensities of a correlating band have amplitudes between the maxima and minima of the normalized half-intensities of the overlapping bands.

  6. Establishing the situated features associated with perceived stress

    PubMed Central

    Lebois, Lauren A.M.; Hertzog, Christopher; Slavich, George M.; Barrett, Lisa Feldman; Barsalou, Lawrence W.

    2016-01-01

    We propose that the domain general process of categorization contributes to the perception of stress. When a situation contains features associated with stressful experiences, it is categorized as stressful. From the perspective of situated cognition, the features used to categorize experiences as stressful are the features typically true of stressful situations. To test this hypothesis, we asked participants to evaluate the perceived stress of 572 imagined situations, and to also evaluate each situation for how much it possessed 19 features potentially associated with stressful situations and their processing (e.g., self-threat, familiarity, visual imagery, outcome certainty). Following variable reduction through factor analysis, a core set of 8 features associated with stressful situations—expectation violation, self-threat, coping efficacy, bodily experience, arousal, negative valence, positive valence, and perseveration—all loaded on a single Core Stress Features factor. In a multilevel model, this factor and an Imagery factor explained 88% of the variance in judgments of perceived stress, with significant random effects reflecting differences in how individual participants categorized stress. These results support the hypothesis that people categorize situations as stressful to the extent that typical features of stressful situations are present. To our knowledge, this is the first attempt to establish a comprehensive set of features that predicts perceived stress. PMID:27288834

  7. IDGenerator: unique identifier generator for epidemiologic or clinical studies.

    PubMed

    Olden, Matthias; Holle, Rolf; Heid, Iris M; Stark, Klaus

    2016-09-15

    Creating study identifiers and assigning them to study participants is an important feature in epidemiologic studies, ensuring the consistency and privacy of the study data. The numbering system for identifiers needs to be random within certain number constraints, to carry extensions coding for organizational information, or to contain multiple layers of numbers per participant to diversify data access. Available software can generate globally-unique identifiers, but identifier-creating tools meeting the special needs of epidemiological studies are lacking. We have thus set out to develop a software program to generate IDs for epidemiological or clinical studies. Our software IDGenerator creates unique identifiers that not only carry a random identifier for a study participant, but also support the creation of structured IDs, where organizational information is coded into the ID directly. This may include study center (for multicenter-studies), study track (for studies with diversified study programs), or study visit (baseline, follow-up, regularly repeated visits). Our software can be used to add a check digit to the ID to minimize data entry errors. It facilitates the generation of IDs in batches and the creation of layered IDs (personal data ID, study data ID, temporary ID, external data ID) to ensure a high standard of data privacy. The software is supported by a user-friendly graphic interface that enables the generation of IDs in both standard text and barcode 128B format. Our software IDGenerator can create identifiers meeting the specific needs for epidemiologic or clinical studies to facilitate study organization and data privacy. IDGenerator is freeware under the GNU General Public License version 3; a Windows port and the source code can be downloaded at the Open Science Framework website: https://osf.io/urs2g/ .

  8. Joint recognition and discrimination in nonlinear feature space

    NASA Astrophysics Data System (ADS)

    Talukder, Ashit; Casasent, David P.

    1997-09-01

    A new general method for linear and nonlinear feature extraction is presented. It is novel since it provides both representation and discrimination while most other methods are concerned with only one of these issues. We call this approach the maximum representation and discrimination feature (MRDF) method and show that the Bayes classifier and the Karhunen- Loeve transform are special cases of it. We refer to our nonlinear feature extraction technique as nonlinear eigen- feature extraction. It is new since it has a closed-form solution and produces nonlinear decision surfaces with higher rank than do iterative methods. Results on synthetic databases are shown and compared with results from standard Fukunaga- Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem (discrimination) and to the classification and pose estimation of two similar objects (representation and discrimination).

  9. Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

    PubMed Central

    Gandomkar, Ziba; Brennan, Patrick C.; Mello-Thoms, Claudia

    2017-01-01

    Context: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Aims: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. Materials and Methods: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. Statistical Analysis Used: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. Results: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Conclusions: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic

  10. Genetic (idiopathic) epilepsy with photosensitive seizures includes features of both focal and generalized seizures.

    PubMed

    Xue, Jiao; Gong, Pan; Yang, Haipo; Liu, Xiaoyan; Jiang, Yuwu; Zhang, Yuehua; Yang, Zhixian

    2018-04-19

    Clinically, some patients having genetic (idiopathic) epilepsy with photosensitive seizures were difficult to be diagnosed. We aimed to discuss whether the genetic (idiopathic) epilepsy with photosensitive seizures is a focal entity, a generalized entity or a continuum. Twenty-two patients with idiopathic epilepsies and photoconvulsive response (PCR) were retrospectively recruited. In the medical records, the seizure types included "generalized tonic-clonic seizures (GTCS)" in 15, "partial secondarily GTCS (PGTCS)" in 3, partial seizures (PS) in 3, myoclonic seizures in 2, eyelid myoclonus in one, and only febrile seizures in one. Seizure types of PCR included GTCS (1/22), PGTCS (6/22), PS (9/22), electrical seizures (ES) (3/22) and GTCS/PGTCS (3/22). Combined the medical history with PCR results, they were diagnosed as: idiopathic (photosensitive) occipital lobe epilepsy (I(P)OE) in 12, genetic (idiopathic) generalized epilepsy (GGE) in one, GGE/I(P)OE in 5, pure photosensitive seizure in one, and epilepsy with undetermined generalized or focal seizure in 3. So, the dichotomy between generalized and focal seizures might have been out of date regarding to pathophysiological advances in epileptology. To some extent, it would be better to recognize the idiopathic epilepsy with photosensitive seizures as a continuum between focal and generalized seizures.

  11. General Features of GRB 030329 in the EMBH Model

    NASA Astrophysics Data System (ADS)

    Bernardini, Maria Grazia; Bianco, Carlo Luciano; Ruffini, Remo; Xue, She-Sheng; Chardonnet, Pascal; Fraschetti, Federico

    2006-02-01

    GRB 030329 is considered within the EMBH model. We determine the three free parameters and deduce its luminosity in given energy bands comparing it with the observations. The observed substructures are compared with the predictions of the model: by applying the result that substructures observed in the extended afterglow peak emission (E-APE) do indeed originate in the collision of the accelerated baryonic matter (ABM) pulse with the inhomogeneities in the interstellar medium around the black-hole, masks of density inhomogeneities are considered in order to reproduce the observed temporal substructures. The induced supernova concept is applied to this system and the general consequences that we are witnessing are the formation of a cosmological thriptych of a black hole originating the GRB 030329, the supernova SN2003dh and a young neutron star. Analogies to the system GRB 980425-SN1998bw are outlined.

  12. Interactions between space-based and feature-based attention.

    PubMed

    Leonard, Carly J; Balestreri, Angela; Luck, Steven J

    2015-02-01

    Although early research suggested that attention to nonspatial features (i.e., red) was confined to stimuli appearing at an attended spatial location, more recent research has emphasized the global nature of feature-based attention. For example, a distractor sharing a target feature may capture attention even if it occurs at a task-irrelevant location. Such findings have been used to argue that feature-based attention operates independently of spatial attention. However, feature-based attention may nonetheless interact with spatial attention, yielding larger feature-based effects at attended locations than at unattended locations. The present study tested this possibility. In 2 experiments, participants viewed a rapid serial visual presentation (RSVP) stream and identified a target letter defined by its color. Target-colored distractors were presented at various task-irrelevant locations during the RSVP stream. We found that feature-driven attentional capture effects were largest when the target-colored distractor was closer to the attended location. These results demonstrate that spatial attention modulates the strength of feature-based attention capture, calling into question the prior evidence that feature-based attention operates in a global manner that is independent of spatial attention.

  13. Image counter-forensics based on feature injection

    NASA Astrophysics Data System (ADS)

    Iuliani, M.; Rossetto, S.; Bianchi, T.; De Rosa, Alessia; Piva, A.; Barni, M.

    2014-02-01

    Starting from the concept that many image forensic tools are based on the detection of some features revealing a particular aspect of the history of an image, in this work we model the counter-forensic attack as the injection of a specific fake feature pointing to the same history of an authentic reference image. We propose a general attack strategy that does not rely on a specific detector structure. Given a source image x and a target image y, the adversary processes x in the pixel domain producing an attacked image ~x, perceptually similar to x, whose feature f(~x) is as close as possible to f(y) computed on y. Our proposed counter-forensic attack consists in the constrained minimization of the feature distance Φ(z) =│ f(z) - f(y)│ through iterative methods based on gradient descent. To solve the intrinsic limit due to the numerical estimation of the gradient on large images, we propose the application of a feature decomposition process, that allows the problem to be reduced into many subproblems on the blocks the image is partitioned into. The proposed strategy has been tested by attacking three different features and its performance has been compared to state-of-the-art counter-forensic methods.

  14. Maximum likelihood clustering with dependent feature trees

    NASA Technical Reports Server (NTRS)

    Chittineni, C. B. (Principal Investigator)

    1981-01-01

    The decomposition of mixture density of the data into its normal component densities is considered. The densities are approximated with first order dependent feature trees using criteria of mutual information and distance measures. Expressions are presented for the criteria when the densities are Gaussian. By defining different typs of nodes in a general dependent feature tree, maximum likelihood equations are developed for the estimation of parameters using fixed point iterations. The field structure of the data is also taken into account in developing maximum likelihood equations. Experimental results from the processing of remotely sensed multispectral scanner imagery data are included.

  15. Identifying priority medicines policy issues for New Zealand: a general inductive study

    PubMed Central

    Babar, Zaheer-Ud-Din; Francis, Susan

    2014-01-01

    Objectives To identify priority medicines policy issues for New Zealand. Setting Stakeholders from a broad range of healthcare and policy institutions including primary, secondary and tertiary care. Participants Exploratory, semistructured interviews were conducted with 20 stakeholders throughout New Zealand. Primary and secondary outcome measures The interviews were digitally recorded, transcribed and coded into INVIVO 10, then compared and grouped for similarity of theme. Perceptions, experiences and opinions regarding New Zealand's medicines policy issues were recorded. Results A large proportion of stakeholders appeared to be unaware of New Zealand's (NZ) medicines policy. In general, the policy was considered to offer consistency to guide decision-making. In the context of Pharmaceutical Management Agency's (PHARMAC's) fixed budget for procuring and subsidising medicines, there was reasonable satisfaction with the range of medicines available—rare disorder medicines being the clear exception. Concerns raised were by whom and how decisions are made and whether desired health outcomes are being measured. Other concerns included inconsistencies in evidence and across health technologies. Despite attempts to improve the situation, lower socioeconomic groups (including rural residents) Māori and Pacific ethnicities and people with rare disorders face challenges with regards to accessing medicines. Other barriers include, convenience to and affordability of prescribers and the increase of prescription fees from NZ$3 to NZ$5. Concerns related to the PHARMAC of New Zealand included: a constraining budget; non-transparency of in-house analysis; lack of consistency in recommendations between the Pharmacology and Therapeutics Advisory Committee. Constraints and inefficiencies also exist in the submission process to access high-cost medicines. Conclusions The results suggest reasonable satisfaction with the availability of subsidised medicines. However, some of the

  16. Which features of primary care affect unscheduled secondary care use? A systematic review

    PubMed Central

    Huntley, Alyson; Lasserson, Daniel; Wye, Lesley; Morris, Richard; Checkland, Kath; England, Helen; Salisbury, Chris; Purdy, Sarah

    2014-01-01

    Objectives To conduct a systematic review to identify studies that describe factors and interventions at primary care practice level that impact on levels of utilisation of unscheduled secondary care. Setting Observational studies at primary care practice level. Participants Studies included people of any age of either sex living in Organisation for Economic Co-operation and Development (OECD) countries with any health condition. Primary and secondary outcome measures The primary outcome measure was unscheduled secondary care as measured by emergency department attendance and emergency hospital admissions. Results 48 papers were identified describing potential influencing features on emergency department visits (n=24 studies) and emergency admissions (n=22 studies). Patient factors associated with both outcomes were increased age, reduced socioeconomic status, lower educational attainment, chronic disease and multimorbidity. Features of primary care affecting unscheduled secondary care were more complex. Being able to see the same healthcare professional reduced unscheduled secondary care. Generally, better access was associated with reduced unscheduled care in the USA. Proximity to healthcare provision influenced patterns of use. Evidence relating to quality of care was limited and mixed. Conclusions The majority of research was from different healthcare systems and limited in the extent to which it can inform policy. However, there is evidence that continuity of care is associated with reduced emergency department attendance and emergency hospital admissions. PMID:24860000

  17. Childhood Precursors of Adult Borderline Personality Disorder Features: A Longitudinal Study.

    PubMed

    Cramer, Phebe

    2016-07-01

    This study identifies childhood personality traits that are precursors of adult Borderline Personality Disorder (BPD) features. In a longitudinal study, childhood personality traits were assessed at age 11 (N = 100) using the California Child Q-set (CCQ: Block and Block, 1980). A number of these Q-items were found to be significantly correlated (p < 0.001) with a prototype-based measure of BPD features at age 23. Factor analysis of these Q-items suggested that they could be characterized by two underlying personality dimensions: Impulsivity and Nonconformity/Aggression. The findings thus provide evidence that childhood personality traits predict adult BPD features. Identifying such childhood precursors provides an opportunity for early intervention.

  18. Automatic Feature Extraction from Planetary Images

    NASA Technical Reports Server (NTRS)

    Troglio, Giulia; Le Moigne, Jacqueline; Benediktsson, Jon A.; Moser, Gabriele; Serpico, Sebastiano B.

    2010-01-01

    With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images.

  19. Feature instructions improve face-matching accuracy

    PubMed Central

    Bindemann, Markus

    2018-01-01

    Identity comparisons of photographs of unfamiliar faces are prone to error but important for applied settings, such as person identification at passport control. Finding techniques to improve face-matching accuracy is therefore an important contemporary research topic. This study investigated whether matching accuracy can be improved by instruction to attend to specific facial features. Experiment 1 showed that instruction to attend to the eyebrows enhanced matching accuracy for optimized same-day same-race face pairs but not for other-race faces. By contrast, accuracy was unaffected by instruction to attend to the eyes, and declined with instruction to attend to ears. Experiment 2 replicated the eyebrow-instruction improvement with a different set of same-race faces, comprising both optimized same-day and more challenging different-day face pairs. These findings suggest that instruction to attend to specific features can enhance face-matching accuracy, but feature selection is crucial and generalization across face sets may be limited. PMID:29543822

  20. Educational interventions for general practitioners to identify and manage depression as a suicide risk factor in young people: a systematic review and meta-analysis protocol.

    PubMed

    Tait, Lynda; Michail, Maria

    2014-12-15

    Suicide is a major public health problem and globally is the second leading cause of death in young adults. Globally, there are 164,000 suicides per year in young people under 25 years. Depression is a strong risk factor for suicide. Evidence shows that 45% of those completing suicide, including young adults, contact their general practitioner rather than a mental health professional in the month before their death. Further evidence indicates that risk factors or early warning signs of suicide in young people go undetected and untreated by general practitioners. Healthcare-based suicide prevention interventions targeted at general practitioners are designed to increase identification of at-risk young people. The rationale of this type of intervention is that early identification and improved clinical management of at-risk individuals will reduce morbidity and mortality. This systematic review will synthesise evidence on the effectiveness of education interventions for general practitioners in identifying and managing depression as a suicide risk factor in young people. We shall conduct a systematic review and meta-analysis following the Cochrane Handbook for Systematic Reviews of Interventions guidelines and conform to the reporting guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement recommendations. Electronic databases will be systematically searched for randomised controlled trials and quasi-experimental studies investigating the effectiveness of interventions for general practitioners in identifying and managing depression as a suicide risk factor in young people in comparison to any other intervention, no intervention, usual care or waiting list. Grey literature will be searched by screening trial registers. Only studies published in English will be included. No date restrictions will be applied. Two authors will independently screen titles and abstracts of potential studies. The primary outcome is

  1. Image retrieval for identifying house plants

    NASA Astrophysics Data System (ADS)

    Kebapci, Hanife; Yanikoglu, Berrin; Unal, Gozde

    2010-02-01

    We present a content-based image retrieval system for plant identification which is intended for providing users with a simple method to locate information about their house plants. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging. We studied the suitability of various well-known color, texture and shape features for this problem, as well as introducing some new ones. The features are extracted from the general plant region that is segmented from the background using the max-flow min-cut technique. Results on a database of 132 different plant images show promise (in about 72% of the queries, the correct plant image is retrieved among the top-15 results).

  2. Generalized overgrowth syndromes with prenatal onset.

    PubMed

    Yachelevich, Naomi

    2015-04-01

    Children with generalized overgrowth syndromes are large at birth, or have excessive postnatal growth. Many of these syndromes are associated with an increase in neoplasia. Consideration of the possibility of overgrowth syndrome in a pediatric patient who presents with increased growth parameters, variable malformations and neurodevelopmental phenotype, and distinctive features, is important for medical management, reproductive counseling, and tumor surveillance for some of the disorders. This review describes the clinical features and surveillance recommendations for the common generalized overgrowth syndromes the pediatrician may encounter. It also provides a glimpse into advances of recent years in understanding the molecular mechanisms responsible for the disrupted growth regulation in these disorders. Copyright © 2015 Mosby, Inc. All rights reserved.

  3. Analysis of geometric moments as features for firearm identification.

    PubMed

    Md Ghani, Nor Azura; Liong, Choong-Yeun; Jemain, Abdul Aziz

    2010-05-20

    The task of identifying firearms from forensic ballistics specimens is exacting in crime investigation since the last two decades. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint'. These fingerprints transfer when a firearm is fired to the fired bullet and cartridge case. The components that are involved in producing these unique characteristics are the firing chamber, breech face, firing pin, ejector, extractor and the rifling of the barrel. These unique characteristics are the critical features in identifying firearms. It allows investigators to decide on which particular firearm that has fired the bullet. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. Therefore, the main objective of this study is the extraction and identification of suitable features from firing pin impression of cartridge case images for firearm recognition. Some previous studies have shown that firing pin impression of cartridge case is one of the most important characteristics used for identifying an individual firearm. In this study, data are gathered using 747 cartridge case images captured from five different pistols of type 9mm Parabellum Vektor SP1, made in South Africa. All the images of the cartridge cases are then segmented into three regions, forming three different set of images, i.e. firing pin impression image, centre of firing pin impression image and ring of firing pin impression image. Then geometric moments up to the sixth order were generated from each part of the images to form a set of numerical features. These 48 features were found to be significantly different using the MANOVA test. This high dimension of features is then reduced into only 11 significant features using correlation analysis. Classification results using cross-validation under discriminant analysis show that 96.7% of the images were classified correctly. These results demonstrate

  4. Essential Features of Tier 2 Social-Behavioral Interventions

    ERIC Educational Resources Information Center

    Yong, Minglee; Cheney, Douglas A.

    2013-01-01

    The purpose of this study is to identify the essential features of Tier 2 interventions conducted within multitier systems of behavior support in schools. A systematic literature search identified 12 empirical studies that were coded and scored according to a list of Tier 2 specific RE-AIM criteria, related to the Reach, Effectiveness, Adoption,…

  5. 14 CFR 35.7 - Features and characteristics.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. No. 35-8, 73 FR 63346, Oct. 24...

  6. 14 CFR 35.7 - Features and characteristics.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. 35-8, 73 FR 63346, Oct. 24, 2008] ...

  7. 14 CFR 35.7 - Features and characteristics.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. 35-8, 73 FR 63346, Oct. 24, 2008] ...

  8. 14 CFR 35.7 - Features and characteristics.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. No. 35-8, 73 FR 63346, Oct. 24...

  9. 14 CFR 35.7 - Features and characteristics.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... AIRWORTHINESS STANDARDS: PROPELLERS General § 35.7 Features and characteristics. (a) The propeller may not have... propeller. The applicant must make changes to the design and conduct additional tests that the Administrator finds necessary to establish the airworthiness of the propeller. [Amdt. 35-8, 73 FR 63346, Oct. 24, 2008] ...

  10. Early Readers and Electronic Texts: CD-ROM Storybook Features That Influence Reading Behaviors

    ERIC Educational Resources Information Center

    Lefever-Davis, Shirley; Pearman, Cathy

    2005-01-01

    This research explores the impact of CD-ROM storybook features on the reading behaviors of 6- and 7-year-old students with limited exposure to CD-ROM storybooks. Six categories of behaviors were identified: tracking, electronic feature dependency, distractibility, spectator stance, electronic feature limitations, and electronic features as tools.…

  11. Acoustic Features Influence Musical Choices Across Multiple Genres.

    PubMed

    Barone, Michael D; Bansal, Jotthi; Woolhouse, Matthew H

    2017-01-01

    Based on a large behavioral dataset of music downloads, two analyses investigate whether the acoustic features of listeners' preferred musical genres influence their choice of tracks within non-preferred, secondary musical styles. Analysis 1 identifies feature distributions for pairs of genre-defined subgroups that are distinct. Using correlation analysis, these distributions are used to test the degree of similarity between subgroups' main genres and the other music within their download collections. Analysis 2 explores the issue of main-to-secondary genre influence through the production of 10 feature-influence matrices, one per acoustic feature, in which cell values indicate the percentage change in features for genres and subgroups compared to overall population averages. In total, 10 acoustic features and 10 genre-defined subgroups are explored within the two analyses. Results strongly indicate that the acoustic features of people's main genres influence the tracks they download within non-preferred, secondary musical styles. The nature of this influence and its possible actuating mechanisms are discussed with respect to research on musical preference, personality, and statistical learning.

  12. Impact of feature saliency on visual category learning.

    PubMed

    Hammer, Rubi

    2015-01-01

    People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the 'essence' of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies.

  13. Impact of feature saliency on visual category learning

    PubMed Central

    Hammer, Rubi

    2015-01-01

    People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the ‘essence’ of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies. PMID:25954220

  14. Acoustic Features Influence Musical Choices Across Multiple Genres

    PubMed Central

    Barone, Michael D.; Bansal, Jotthi; Woolhouse, Matthew H.

    2017-01-01

    Based on a large behavioral dataset of music downloads, two analyses investigate whether the acoustic features of listeners' preferred musical genres influence their choice of tracks within non-preferred, secondary musical styles. Analysis 1 identifies feature distributions for pairs of genre-defined subgroups that are distinct. Using correlation analysis, these distributions are used to test the degree of similarity between subgroups' main genres and the other music within their download collections. Analysis 2 explores the issue of main-to-secondary genre influence through the production of 10 feature-influence matrices, one per acoustic feature, in which cell values indicate the percentage change in features for genres and subgroups compared to overall population averages. In total, 10 acoustic features and 10 genre-defined subgroups are explored within the two analyses. Results strongly indicate that the acoustic features of people's main genres influence the tracks they download within non-preferred, secondary musical styles. The nature of this influence and its possible actuating mechanisms are discussed with respect to research on musical preference, personality, and statistical learning. PMID:28725200

  15. Infrastructure features outperform environmental variables explaining rabbit abundance around motorways.

    PubMed

    Planillo, Aimara; Malo, Juan E

    2018-01-01

    Human disturbance is widespread across landscapes in the form of roads that alter wildlife populations. Knowing which road features are responsible for the species response and their relevance in comparison with environmental variables will provide useful information for effective conservation measures. We sampled relative abundance of European rabbits, a very widespread species, in motorway verges at regional scale, in an area with large variability in environmental and infrastructure conditions. Environmental variables included vegetation structure, plant productivity, distance to water sources, and altitude. Infrastructure characteristics were the type of vegetation in verges, verge width, traffic volume, and the presence of embankments. We performed a variance partitioning analysis to determine the relative importance of two sets of variables on rabbit abundance. Additionally, we identified the most important variables and their effects model averaging after model selection by AICc on hypothesis-based models. As a group, infrastructure features explained four times more variability in rabbit abundance than environmental variables, being the effects of the former critical in motorway stretches located in altered landscapes with no available habitat for rabbits, such as agricultural fields. Model selection and Akaike weights showed that verge width and traffic volume are the most important variables explaining rabbit abundance index, with positive and negative effects, respectively. In the light of these results, the response of species to the infrastructure can be modulated through the modification of motorway features, being some of them manageable in the design phase. The identification of such features leads to suggestions for improvement through low-cost corrective measures and conservation plans. As a general indication, keeping motorway verges less than 10 m wide will prevent high densities of rabbits and avoid the unwanted effects that rabbit populations

  16. Gene expression profiling identifies inflammation and angiogenesis as distinguishing features of canine hemangiosarcoma.

    PubMed

    Tamburini, Beth A; Phang, Tzu L; Fosmire, Susan P; Scott, Milcah C; Trapp, Susan C; Duckett, Megan M; Robinson, Sally R; Slansky, Jill E; Sharkey, Leslie C; Cutter, Gary R; Wojcieszyn, John W; Bellgrau, Donald; Gemmill, Robert M; Hunter, Lawrence E; Modiano, Jaime F

    2010-11-09

    The etiology of hemangiosarcoma remains incompletely understood. Its common occurrence in dogs suggests predisposing factors favor its development in this species. These factors could represent a constellation of heritable characteristics that promote transformation events and/or facilitate the establishment of a microenvironment that is conducive for survival of malignant blood vessel-forming cells. The hypothesis for this study was that characteristic molecular features distinguish hemangiosarcoma from non-malignant endothelial cells, and that such features are informative for the etiology of this disease. We first investigated mutations of VHL and Ras family genes that might drive hemangiosarcoma by sequencing tumor DNA and mRNA (cDNA). Protein expression was examined using immunostaining. Next, we evaluated genome-wide gene expression profiling using the Affymetrix Canine 2.0 platform as a global approach to test the hypothesis. Data were evaluated using routine bioinformatics and validation was done using quantitative real time RT-PCR. Each of 10 tumor and four non-tumor samples analyzed had wild type sequences for these genes. At the genome wide level, hemangiosarcoma cells clustered separately from non-malignant endothelial cells based on a robust signature that included genes involved in inflammation, angiogenesis, adhesion, invasion, metabolism, cell cycle, signaling, and patterning. This signature did not simply reflect a cancer-associated angiogenic phenotype, as it also distinguished hemangiosarcoma from non-endothelial, moderately to highly angiogenic bone marrow-derived tumors (lymphoma, leukemia, osteosarcoma). The data show that inflammation and angiogenesis are important processes in the pathogenesis of vascular tumors, but a definitive ontogeny of the cells that give rise to these tumors remains to be established. The data do not yet distinguish whether functional or ontogenetic plasticity creates this phenotype, although they suggest that cells

  17. Gene expression profiling identifies inflammation and angiogenesis as distinguishing features of canine hemangiosarcoma

    PubMed Central

    2010-01-01

    Background The etiology of hemangiosarcoma remains incompletely understood. Its common occurrence in dogs suggests predisposing factors favor its development in this species. These factors could represent a constellation of heritable characteristics that promote transformation events and/or facilitate the establishment of a microenvironment that is conducive for survival of malignant blood vessel-forming cells. The hypothesis for this study was that characteristic molecular features distinguish hemangiosarcoma from non-malignant endothelial cells, and that such features are informative for the etiology of this disease. Methods We first investigated mutations of VHL and Ras family genes that might drive hemangiosarcoma by sequencing tumor DNA and mRNA (cDNA). Protein expression was examined using immunostaining. Next, we evaluated genome-wide gene expression profiling using the Affymetrix Canine 2.0 platform as a global approach to test the hypothesis. Data were evaluated using routine bioinformatics and validation was done using quantitative real time RT-PCR. Results Each of 10 tumor and four non-tumor samples analyzed had wild type sequences for these genes. At the genome wide level, hemangiosarcoma cells clustered separately from non-malignant endothelial cells based on a robust signature that included genes involved in inflammation, angiogenesis, adhesion, invasion, metabolism, cell cycle, signaling, and patterning. This signature did not simply reflect a cancer-associated angiogenic phenotype, as it also distinguished hemangiosarcoma from non-endothelial, moderately to highly angiogenic bone marrow-derived tumors (lymphoma, leukemia, osteosarcoma). Conclusions The data show that inflammation and angiogenesis are important processes in the pathogenesis of vascular tumors, but a definitive ontogeny of the cells that give rise to these tumors remains to be established. The data do not yet distinguish whether functional or ontogenetic plasticity creates this

  18. Is the Binding of Visual Features in Working Memory Resource-Demanding?

    ERIC Educational Resources Information Center

    Allen, Richard J.; Baddeley, Alan D.; Hitch, Graham J.

    2006-01-01

    The episodic buffer component of working memory is assumed to play a role in the binding of features into chunks. A series of experiments compared memory for arrays of colors or shapes with memory for bound combinations of these features. Demanding concurrent verbal tasks were used to investigate the role of general attentional processes,…

  19. Identifying metastatic breast tumors using textural kinetic features of a contrast based habitat in DCE-MRI

    NASA Astrophysics Data System (ADS)

    Chaudhury, Baishali; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Gatenby, Robert A.; Gillies, Robert J.; Drukteinis, Jennifer S.

    2015-03-01

    The ability to identify aggressive tumors from indolent tumors using quantitative analysis on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) would dramatically change the breast cancer treatment paradigm. With this prognostic information, patients with aggressive tumors that have the ability to spread to distant sites outside of the breast could be selected for more aggressive treatment and surveillance regimens. Conversely, patients with tumors that do not have the propensity to metastasize could be treated less aggressively, avoiding some of the morbidity associated with surgery, radiation and chemotherapy. We propose a computer aided detection framework to determine which breast cancers will metastasize to the loco-regional lymph nodes as well as which tumors will eventually go on to develop distant metastses using quantitative image analysis and radiomics. We defined a new contrast based tumor habitat and analyzed textural kinetic features from this habitat for classification purposes. The proposed tumor habitat, which we call combined-habitat, is derived from the intersection of two individual tumor sub-regions: one that exhibits rapid initial contrast uptake and the other that exhibits rapid delayed contrast washout. Hence the combined-habitat represents the tumor sub-region within which the pixels undergo both rapid initial uptake and rapid delayed washout. We analyzed a dataset of twenty-seven representative two dimensional (2D) images from volumetric DCE-MRI of breast tumors, for classification of tumors with no lymph nodes from tumors with positive number of axillary lymph nodes. For this classification an accuracy of 88.9% was achieved. Twenty of the twenty-seven patients were analyzed for classification of distant metastatic tumors from indolent cancers (tumors with no lymph nodes), for which the accuracy was 84.3%.

  20. Karst features detection and mapping using airphotos, DSMs and GIS techniques

    NASA Astrophysics Data System (ADS)

    Kakavas, M. P.; Nikolakopoulos, K. G.; Zagana, E.

    2015-10-01

    The aim of this work is to detect and qualify natural karst depressions in the Aitoloakarnania Prefecture, Western Greece, using remote sensing data in conjunction with the Geographical Information Systems - GIS. The study area is a part of the Ionian geotectonic zone, and its geological background consists of the Triassic Evaporates. The Triassic carbonate breccias where formed as a result of the tectonic and orogenetic setting of the external Hellenides and the diaper phenomena of the Triassic Evaporates. The landscape characterized by exokarst features closed depressions in the Triassic carbonate breccias. At the threshold of this study, an in situ observation was performed in order to identify dolines and swallow holes. The creation of sinkholes, in general, is based on the collapse of the surface layer due to chemical dissolution of carbonate rocks. In the current study airphotos stereopairs, DSMs and GIS were combined in order to detect and map the karst features. Thirty seven airphotos were imported in Leica Photogrammetry Suite and a stereo model of the study area was created. Then in 3D view possible karst features were detected and digitized. Those sites were verified during the in situ survey. ASTER GDEM, SRTM DEM, high resolution airphoto DSM created from the Greek Cadastral and a DEM from digitized contours from the 1/50,000 topographic were also evaluated in GIS environment for the automatic detection of the karst depressions. The results are presented in this study.

  1. Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

    PubMed

    Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

    We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

  2. Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification

    PubMed Central

    Feng, Yang; Jiang, Jiancheng; Tong, Xin

    2015-01-01

    We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing. PMID:27185970

  3. Novel Myopia Genes and Pathways Identified From Syndromic Forms of Myopia

    PubMed Central

    Loughman, James; Wildsoet, Christine F.; Williams, Cathy; Guggenheim, Jeremy A.

    2018-01-01

    Purpose To test the hypothesis that genes known to cause clinical syndromes featuring myopia also harbor polymorphisms contributing to nonsyndromic refractive errors. Methods Clinical phenotypes and syndromes that have refractive errors as a recognized feature were identified using the Online Mendelian Inheritance in Man (OMIM) database. One hundred fifty-four unique causative genes were identified, of which 119 were specifically linked with myopia and 114 represented syndromic myopia (i.e., myopia and at least one other clinical feature). Myopia was the only refractive error listed for 98 genes and hyperopia and the only refractive error noted for 28 genes, with the remaining 28 genes linked to phenotypes with multiple forms of refractive error. Pathway analysis was carried out to find biological processes overrepresented within these sets of genes. Genetic variants located within 50 kb of the 119 myopia-related genes were evaluated for involvement in refractive error by analysis of summary statistics from genome-wide association studies (GWAS) conducted by the CREAM Consortium and 23andMe, using both single-marker and gene-based tests. Results Pathway analysis identified several biological processes already implicated in refractive error development through prior GWAS analyses and animal studies, including extracellular matrix remodeling, focal adhesion, and axon guidance, supporting the research hypothesis. Novel pathways also implicated in myopia development included mannosylation, glycosylation, lens development, gliogenesis, and Schwann cell differentiation. Hyperopia was found to be linked to a different pattern of biological processes, mostly related to organogenesis. Comparison with GWAS findings further confirmed that syndromic myopia genes were enriched for genetic variants that influence refractive errors in the general population. Gene-based analyses implicated 21 novel candidate myopia genes (ADAMTS18, ADAMTS2, ADAMTSL4, AGK, ALDH18A1, ASXL1, COL4A1

  4. Random forest feature selection approach for image segmentation

    NASA Astrophysics Data System (ADS)

    Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina; Vaida, Mircea Florin

    2017-03-01

    In the field of image segmentation, discriminative models have shown promising performance. Generally, every such model begins with the extraction of numerous features from annotated images. Most authors create their discriminative model by using many features without using any selection criteria. A more reliable model can be built by using a framework that selects the important variables, from the point of view of the classification, and eliminates the unimportant once. In this article we present a framework for feature selection and data dimensionality reduction. The methodology is built around the random forest (RF) algorithm and its variable importance evaluation. In order to deal with datasets so large as to be practically unmanageable, we propose an algorithm based on RF that reduces the dimension of the database by eliminating irrelevant features. Furthermore, this framework is applied to optimize our discriminative model for brain tumor segmentation.

  5. Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics.

    PubMed

    Piao, Guanying; Wu, Jiarui

    2013-11-01

    To analyze the differentially expressed genes and identify featured biomarkers from prostatic carcinoma. The software "Significance Analysis of Microarray" (SAM) was used to identify the differentially coexpressed genes (DCGs). The DCGs existed in two datasets were analyzed by GO (Gene Ontology) functional annotation. A total of 389 DCGs were obtained. By GO analysis, we found these DCGs were closely related with the acinus development, TGF-β receptor and signal transduction pathways. Furthermore, five featured biomarkers were discovered by interaction analysis. These important signal pathways and oncogenes may provide potential therapeutic targets for prostatic carcinoma.

  6. Automated feature detection and identification in digital point-ordered signals

    DOEpatents

    Oppenlander, Jane E.; Loomis, Kent C.; Brudnoy, David M.; Levy, Arthur J.

    1998-01-01

    A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.

  7. Prevalence of histological features of idiopathic noncirrhotic portal hypertension in general population: a retrospective study of incidental liver biopsies.

    PubMed

    Zuo, Chunlai; Chumbalkar, Vaibhav; Ells, Peter F; Bonville, Daniel J; Lee, Hwajeong

    2017-09-01

    Idiopathic noncirrhotic portal hypertension (INCPH) is associated with histologic changes secondary to obliterative portal venopathy without cirrhosis. We studied the prevalence of individual histological features of INCPH in liver biopsies obtained incidentally during unrelated elective procedures and in elective liver biopsies with the diagnosis of fatty liver disease. A total of 53 incidental liver biopsies obtained intraoperatively during unrelated elective procedures and an additional 28 elective biopsies with the diagnosis of fatty liver disease without portal hypertension and cirrhosis were studied. Various histologic features of INCPH were evaluated. Shunt vessel (30%), phlebosclerosis (27%), increased number of portal vessels (19%) and incomplete septa (17%) were common in these liver biopsies after confounding factors such as co-existing fatty liver disease or fibrosis were excluded. At least one feature of INCPH was noted in 90% of the biopsies. Eight (10%) biopsies showed 5-6 features of INCPH. In total, 11 (14%) of 81 patients had risk factors associated with INCPH, including hypercoagulability, autoimmune disease, exposure to drugs, and infections. No patient had portal hypertension at the end of the follow-up. The histologic features of INCPH are seen in incidental liver biopsies and fatty liver disease without portal hypertension. Ten percent of the biopsies show 5-6 features of INCPH without portal hypertension. Interpreting histologic features in the right clinical context is important for proper patient care.

  8. Geometric quantification of features in large flow fields.

    PubMed

    Kendall, Wesley; Huang, Jian; Peterka, Tom

    2012-01-01

    Interactive exploration of flow features in large-scale 3D unsteady-flow data is one of the most challenging visualization problems today. To comprehensively explore the complex feature spaces in these datasets, a proposed system employs a scalable framework for investigating a multitude of characteristics from traced field lines. This capability supports the examination of various neighborhood-based geometric attributes in concert with other scalar quantities. Such an analysis wasn't previously possible because of the large computational overhead and I/O requirements. The system integrates visual analytics methods by letting users procedurally and interactively describe and extract high-level flow features. An exploration of various phenomena in a large global ocean-modeling simulation demonstrates the approach's generality and expressiveness as well as its efficacy.

  9. The gate studies: Assessing the potential of future small general aviation turbine engines

    NASA Technical Reports Server (NTRS)

    Strack, W. C.

    1979-01-01

    Four studies were completed that explore the opportunities for future General Aviation turbine engines (GATE) in the 150-1000 SHP class. These studies forecasted the potential impact of advanced technology turbine engines in the post-1988 market, identified important aircraft and missions, desirable engine sizes, engine performance, and cost goals. Parametric evaluations of various engine cycles, configurations, design features, and advanced technology elements defined baseline conceptual engines for each of the important missions identified by the market analysis. Both fixed-wing and helicopter aircraft, and turboshaft, turboprop, and turbofan engines were considered. Sizable performance gains (e.g., 20% SFC decrease), and large engine cost reductions of sufficient magnitude to challenge the reciprocating engine in the 300-500 SHP class were predicted.

  10. Functional Investigations of HNF1A Identify Rare Variants as Risk Factors for Type 2 Diabetes in the General Population

    PubMed Central

    Najmi, Laeya Abdoli; Aukrust, Ingvild; Flannick, Jason; Molnes, Janne; Burtt, Noel; Molven, Anders; Groop, Leif; Altshuler, David; Johansson, Stefan; Njølstad, Pål Rasmus

    2017-01-01

    Variants in HNF1A encoding hepatocyte nuclear factor 1α (HNF-1A) are associated with maturity-onset diabetes of the young form 3 (MODY 3) and type 2 diabetes. We investigated whether functional classification of HNF1A rare coding variants can inform models of diabetes risk prediction in the general population by analyzing the effect of 27 HNF1A variants identified in well-phenotyped populations (n = 4,115). Bioinformatics tools classified 11 variants as likely pathogenic and showed no association with diabetes risk (combined minor allele frequency [MAF] 0.22%; odds ratio [OR] 2.02; 95% CI 0.73–5.60; P = 0.18). However, a different set of 11 variants that reduced HNF-1A transcriptional activity to <60% of normal (wild-type) activity was strongly associated with diabetes in the general population (combined MAF 0.22%; OR 5.04; 95% CI 1.99–12.80; P = 0.0007). Our functional investigations indicate that 0.44% of the population carry HNF1A variants that result in a substantially increased risk for developing diabetes. These results suggest that functional characterization of variants within MODY genes may overcome the limitations of bioinformatics tools for the purposes of presymptomatic diabetes risk prediction in the general population. PMID:27899486

  11. Nonclassical crystallization in vivo et in vitro (II): Nanogranular features in biomimetic minerals disclose a general colloid-mediated crystal growth mechanism.

    PubMed

    Rodríguez-Navarro, Carlos; Ruiz-Agudo, Encarnación; Harris, Joe; Wolf, Stephan E

    2016-11-01

    Recent research has shown that biominerals and their biomimetics (i) typically form via an amorphous precursor phase, and (ii) commonly display a nanogranular texture. Apparently, these two key features are closely related, underlining the fact that the formation of biominerals and their biomimetics does not necessarily follow classical crystallization routes, and leaves a characteristic nanotextural imprint which may help to disclose their origins and formation mechanisms. Here we present a general overview of the current theories and models of nonclassical crystallization and their applicability for the advance of our current understanding of biomineralization and biomimetic mineralization. We pay particular attention to the link between nonclassical crystallization routes and the resulting nanogranular textures of biomimetic CaCO 3 mineral structures. After a general introductory section, we present an overview of classical nucleation and crystal growth theories and their limitations. Then, we introduce the Ostwald's step rule as a general framework to explain nonclassical crystallization. Subsequently, we describe nonclassical crystallization routes involving stable prenucleation clusters, dense liquid and solid amorphous precursor phases, as well as current nonclassical crystal growth models. The latter include oriented attachment, mesocrystallization and the new model based on the colloidal growth of crystals via attachment of amorphous nanoparticles. Biomimetic examples of nanostructured CaCO 3 minerals formed via these nonclassical routes are presented which help us to show that colloid-mediated crystal growth can be regarded as a wide-spread growth mechanism. Implications of these observations for the advance in the current understanding on the formation of biomimetic materials and biominerals are finally outlined. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Mentoring for population health in general practice divisions.

    PubMed

    Moss, John R; Mickan, Sharon M; Fuller, Jeffrey D; Procter, Nicholas G; Waters, Barb A; O'Rourke, Peter K

    2006-02-01

    This paper describes the implementation and evaluation of a three-way model of service development mentoring. This population health mentoring program was funded by the Commonwealth Department of Health and Ageing to enable staff from eight Divisions of General Practice in South Australia to gain a sound understanding of population health concepts relevant to their workplace. The distinguishing features of service development mentoring were that the learning was grounded within an individual's work setting and experience; there was an identified population health problem or issue confronting the Division of General Practice; and there was an expectation of enhanced organisational performance. A formal evaluation found a consensus among all learners that mentoring was a positive and worthwhile experience, where they had achieved what they had set out to do. Mentors found the model of learning agreeable and effective. Division executive officers recognised enhanced skills among their "learner" colleagues, and commented positively on the benefits to their organisations through the development of well researched and relevant projects, with the potential to improve the efficiency of their population health activities.

  13. Microbiological Features of KPC-Producing Enterobacter Isolates Identified in a U.S. Hospital System

    PubMed Central

    Ahn, Chulsoo; Syed, Alveena; Hu, Fupin; O’Hara, Jessica A.; Rivera, Jesabel I.; Doi, Yohei

    2014-01-01

    Microbiological data regarding KPC-producing Enterobacter spp. are scarce. In this study, 11 unique KPC-producing Enterobacter isolates were identified among 44 ertapenem-non-susceptible Enterobacter isolates collected between 2009 and 2013 at a hospital system in Western Pennsylvania. All cases were healthcare-associated and occurred in medically complex patients. While pulsed-field gel electrophoresis (PFGE) showed diverse restriction patterns overall, multilocus sequence typing (MLST) identified Enterobacter cloacae isolates with sequence types (STs) 93 and 171 from two hospitals each. The levels of carbapenem minimum inhibitory concentrations were highly variable. All isolates remained susceptible to colistin, tigecycline, and the majority to amikacin and doxycycline. A blaKPC-carrying IncN plasmid conferring trimethoprim-sulfamethoxazole resistance was identified in three of the isolates. Spread of blaKPC in Enterobacter spp. appears to be due to a combination of plasmid-mediated and clonal processes. PMID:25053203

  14. Identification of particle-laden flow features from wavelet decomposition

    NASA Astrophysics Data System (ADS)

    Jackson, A.; Turnbull, B.

    2017-12-01

    A wavelet decomposition based technique is applied to air pressure data obtained from laboratory-scale powder snow avalanches. This technique is shown to be a powerful tool for identifying both repeatable and chaotic features at any frequency within the signal. Additionally, this technique is demonstrated to be a robust method for the removal of noise from the signal as well as being capable of removing other contaminants from the signal. Whilst powder snow avalanches are the focus of the experiments analysed here, the features identified can provide insight to other particle-laden gravity currents and the technique described is applicable to a wide variety of experimental signals.

  15. Discriminative Features Mining for Offline Handwritten Signature Verification

    NASA Astrophysics Data System (ADS)

    Neamah, Karrar; Mohamad, Dzulkifli; Saba, Tanzila; Rehman, Amjad

    2014-03-01

    Signature verification is an active research area in the field of pattern recognition. It is employed to identify the particular person with the help of his/her signature's characteristics such as pen pressure, loops shape, speed of writing and up down motion of pen, writing speed, pen pressure, shape of loops, etc. in order to identify that person. However, in the entire process, features extraction and selection stage is of prime importance. Since several signatures have similar strokes, characteristics and sizes. Accordingly, this paper presents combination of orientation of the skeleton and gravity centre point to extract accurate pattern features of signature data in offline signature verification system. Promising results have proved the success of the integration of the two methods.

  16. Prevalence and correlates of binge eating disorder related features in the community.

    PubMed

    Mustelin, Linda; Bulik, Cynthia M; Kaprio, Jaakko; Keski-Rahkonen, Anna

    2017-02-01

    Binge eating disorder (BED) is associated with high levels of obesity and psychological suffering, but little is known about 1) the distribution of features of BED in the general population and 2) their consequences for weight development and psychological distress in young adulthood. We investigated the prevalence of features of BED and their association with body mass index (BMI) and psychological distress among men (n = 2423) and women (n = 2825) from the longitudinal community-based FinnTwin16 cohort (born 1975-1979). Seven eating-related cognitions and behaviors similar to the defining features of BED were extracted from the Eating Disorder Inventory-2 and were assessed at a mean age of 24. BMI and psychological distress, measured with the General Health Questionnaire, were assessed at ages 24 and 34. We assessed prevalence of the features and their association with BMI and psychological distress cross-sectionally and prospectively. More than half of our participants reported at least one feature of BED; clustering of several features in one individual was less common, particularly among men. The most frequently reported feature was 'stuffing oneself with food', whereas the least common was 'eating or drinking in secrecy'. All individual features of BED and their clustering particularly were associated with higher BMI and more psychological distress cross-sectionally. Prospectively, the clustering of features of BED predicted increase in psychological distress but not additional weight gain when baseline BMI was accounted for. In summary, although some features of BED were common, the clustering of several features in one individual was not. The features were cumulatively associated with BMI and psychological distress and predicted further increase in psychological distress over ten years of follow-up. Copyright © 2016. Published by Elsevier Ltd.

  17. Vehicle license plate recognition based on geometry restraints and multi-feature decision

    NASA Astrophysics Data System (ADS)

    Wu, Jianwei; Wang, Zongyue

    2005-10-01

    Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

  18. Distinctive Features of Japanese Education. NIER Occasional Paper 01/91.

    ERIC Educational Resources Information Center

    National Inst. for Educational Research, Tokyo (Japan).

    For the past decade there has been a surge of international interest in Japanese education in the wake of its economic and technological successes. This paper discusses eight distinctive features of Japanese education, identifying their advantages and disadvantages and how they have been brought about. These eight features of Japanese schooling…

  19. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.

    PubMed

    Chen, Wei; Feng, Peng-Mian; Lin, Hao; Chou, Kuo-Chen

    2014-01-01

    In eukaryotic genes, exons are generally interrupted by introns. Accurately removing introns and joining exons together are essential processes in eukaryotic gene expression. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapid and effective detection of splice sites that play important roles in gene structure annotation and even in RNA splicing. Although a series of computational methods were proposed for splice site identification, most of them neglected the intrinsic local structural properties. In the present study, a predictor called "iSS-PseDNC" was developed for identifying splice sites. In the new predictor, the sequences were formulated by a novel feature-vector called "pseudo dinucleotide composition" (PseDNC) into which six DNA local structural properties were incorporated. It was observed by the rigorous cross-validation tests on two benchmark datasets that the overall success rates achieved by iSS-PseDNC in identifying splice donor site and splice acceptor site were 85.45% and 87.73%, respectively. It is anticipated that iSS-PseDNC may become a useful tool for identifying splice sites and that the six DNA local structural properties described in this paper may provide novel insights for in-depth investigations into the mechanism of RNA splicing.

  20. Connecting infrared spectra with plant traits to identify species

    NASA Astrophysics Data System (ADS)

    Buitrago, Maria F.; Skidmore, Andrew K.; Groen, Thomas A.; Hecker, Christoph A.

    2018-05-01

    Plant traits are used to define species, but also to evaluate the health status of forests, plantations and crops. Conventional methods of measuring plant traits (e.g. wet chemistry), although accurate, are inefficient and costly when applied over large areas or with intensive sampling. Spectroscopic methods, as used in the food industry and mineralogy, are nowadays applied to identify plant traits, however, most studies analysed visible to near infrared, while infrared spectra of longer wavelengths have been little used for identifying the spectral differences between plant species. This study measured the infrared spectra (1.4-16.0 μm) on individual, fresh leaves of 19 species (from herbaceous to woody species), as well as 14 leaf traits for each leaf. The results describe at which wavelengths in the infrared the leaves' spectra can differentiate most effectively between these plant species. A Quadratic Discrimination Analysis (QDA) shows that using five bands in the SWIR or the LWIR is enough to accurately differentiate these species (Kappa: 0.93, 0.94 respectively), while the MWIR has a lower classification accuracy (Kappa: 0.84). This study also shows that in the infrared spectra of fresh leaves, the identified species-specific features are correlated with leaf traits as well as changes in their values. Spectral features in the SWIR (1.66, 1.89 and 2.00 μm) are common to all species and match the main features of pure cellulose and lignin spectra. The depth of these features varies with changes of cellulose and leaf water content and can be used to differentiate species in this region. In the MWIR and LWIR, the absorption spectra of leaves are formed by key species-specific traits including lignin, cellulose, water, nitrogen and leaf thickness. The connection found in this study between leaf traits, features and spectral signatures are novel tools to assist when identifying plant species by spectroscopy and remote sensing.

  1. Correlative feature analysis of FFDM images

    NASA Astrophysics Data System (ADS)

    Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

    2008-03-01

    Identifying the corresponding image pair of a lesion is an essential step for combining information from different views of the lesion to improve the diagnostic ability of both radiologists and CAD systems. Because of the non-rigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this study, we present a computerized framework that differentiates the corresponding images from different views of a lesion from non-corresponding ones. A dual-stage segmentation method, which employs an initial radial gradient index(RGI) based segmentation and an active contour model, was initially applied to extract mass lesions from the surrounding tissues. Then various lesion features were automatically extracted from each of the two views of each lesion to quantify the characteristics of margin, shape, size, texture and context of the lesion, as well as its distance to nipple. We employed a two-step method to select an effective subset of features, and combined it with a BANN to obtain a discriminant score, which yielded an estimate of the probability that the two images are of the same physical lesion. ROC analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing between corresponding and non-corresponding pairs. By using a FFDM database with 124 corresponding image pairs and 35 non-corresponding pairs, the distance feature yielded an AUC (area under the ROC curve) of 0.8 with leave-one-out evaluation by lesion, and the feature subset, which includes distance feature, lesion size and lesion contrast, yielded an AUC of 0.86. The improvement by using multiple features was statistically significant as compared to single feature performance. (p<0.001)

  2. New method for identifying features of an image on a digital video display

    NASA Astrophysics Data System (ADS)

    Doyle, Michael D.

    1991-04-01

    The MetaMap process extends the concept of direct manipulation human-computer interfaces to new limits. Its specific capabilities include the correlation of discrete image elements to relevant text information and the correlation of these image features to other images as well as to program control mechanisms. The correlation is accomplished through reprogramming of both the color map and the image so that discrete image elements comprise unique sets of color indices. This process allows the correlation to be accomplished with very efficient data storage and program execution times. Image databases adapted to this process become object-oriented as a result. Very sophisticated interrelationships can be set up between images text and program control mechanisms using this process. An application of this interfacing process to the design of an interactive atlas of medical histology as well as other possible applications are described. The MetaMap process is protected by U. S. patent #4

  3. The General Factor of Personality: A General Critique.

    PubMed

    Revelle, William; Wilt, Joshua

    2013-10-01

    Recently, it has been proposed that all non-cognitive measures of personality share a general factor of personality. A problem with many of these studies is a lack of clarity in defining a general factor. In this paper we address the multiple ways in which a general factor has been identified and argue that many of these approaches find factors that are not in fact general. Through the use of artificial examples, we show that a general factor is not: The first factor or component of a correlation or covariance matrix.The first factor resulting from a bifactor rotation or biquartimin transformationNecessarily the result of a confirmatory factor analysis forcing a bifactor solution We consider how the definition of what constitutes a general factor can lead to confusion, and we will demonstrate alternative ways of estimating the general factor saturation that are more appropriate.

  4. The General Factor of Personality: A General Critique

    PubMed Central

    Revelle, William; Wilt, Joshua

    2013-01-01

    Recently, it has been proposed that all non-cognitive measures of personality share a general factor of personality. A problem with many of these studies is a lack of clarity in defining a general factor. In this paper we address the multiple ways in which a general factor has been identified and argue that many of these approaches find factors that are not in fact general. Through the use of artificial examples, we show that a general factor is not: The first factor or component of a correlation or covariance matrix.The first factor resulting from a bifactor rotation or biquartimin transformationNecessarily the result of a confirmatory factor analysis forcing a bifactor solution We consider how the definition of what constitutes a general factor can lead to confusion, and we will demonstrate alternative ways of estimating the general factor saturation that are more appropriate. PMID:23956474

  5. Identifying parameter regions for multistationarity

    PubMed Central

    Conradi, Carsten; Mincheva, Maya; Wiuf, Carsten

    2017-01-01

    Mathematical modelling has become an established tool for studying the dynamics of biological systems. Current applications range from building models that reproduce quantitative data to identifying systems with predefined qualitative features, such as switching behaviour, bistability or oscillations. Mathematically, the latter question amounts to identifying parameter values associated with a given qualitative feature. We introduce a procedure to partition the parameter space of a parameterized system of ordinary differential equations into regions for which the system has a unique or multiple equilibria. The procedure is based on the computation of the Brouwer degree, and it creates a multivariate polynomial with parameter depending coefficients. The signs of the coefficients determine parameter regions with and without multistationarity. A particular strength of the procedure is the avoidance of numerical analysis and parameter sampling. The procedure consists of a number of steps. Each of these steps might be addressed algorithmically using various computer programs and available software, or manually. We demonstrate our procedure on several models of gene transcription and cell signalling, and show that in many cases we obtain a complete partitioning of the parameter space with respect to multistationarity. PMID:28972969

  6. CIRCAL-2 - General-purpose on-line circuit design.

    NASA Technical Reports Server (NTRS)

    Dertouzos, M. L.; Jessel, G. P.; Stinger, J. R.

    1972-01-01

    CIRCAL-2 is a second-generation general-purpose on-line circuit-design program with the following main features: (1) multiple-analysis capability; (2) uniform and general data structures for handling text editing, network representations, and output results, regardless of analysis; (3) special techniques and structures for minimizing and controlling user-program interaction; (4) use of functionals for the description of hysteresis and heat effects; and (5) ability to define optimization procedures that 'replace' the user. The paper discusses the organization of CIRCAL-2, the aforementioned main features, and their consequences, such as a set of network elements and models general enough for most analyses and a set of functions tailored to circuit-design requirements. The presentation is descriptive, concentrating on conceptual rather than on program implementation details.

  7. Using earthquake clusters to identify fracture zones at Puna geothermal field, Hawaii

    NASA Astrophysics Data System (ADS)

    Lucas, A.; Shalev, E.; Malin, P.; Kenedi, C. L.

    2010-12-01

    production area for the power plant. Most of the clusters had linear features when their Hypoinverse locations were plotted. The concentration of individual linear features was higher in the PGS than the surrounding ERZ. The resolution of the features was resolved further by relocating each individual cluster through the catalog double difference method. Mapping of the linear features showed that a number of the larger features ran rift parallel. However a large number of rift perpendicular features were also identified. In the area where the anomalous (N-S) shear wave polarization was observed, a number of linear features with a similar orientation were identified. We assume that events occurring on the same fracture zone have similar source mechanisms and thus similar waveforms. It is concluded that the linear features identified by earthquake clustering are fracture zones. The orientation and concentration of the fracture zones is consistent with that of the shear wave splitting polarizations.

  8. Using Manipulated Photographs to Identify Features of Streetscapes That May Encourage Older Adults to Walk for Transport

    PubMed Central

    Van Cauwenberg, Jelle; Van Holle, Veerle; De Bourdeaudhuij, Ilse; Clarys, Peter; Nasar, Jack; Salmon, Jo; Goubert, Liesbet; Deforche, Benedicte

    2014-01-01

    Experimental evidence of environmental features important for physical activity is challenging to procure in real world settings. The current study aimed to investigate the causal effects of environmental modifications on a photographed street's appeal for older adults' walking for transport. Secondly, we examined whether these effects differed according to gender, functional limitations, and current level of walking for transport. Thirdly, we examined whether different environmental modifications interacted with each other. Qualitative responses were also reported to gain deeper insight into the observed quantitative relationships. Two sets of 16 panoramic photographs of a streetscape were created, in which six environmental factors were manipulated (sidewalk evenness, traffic level, general upkeep, vegetation, separation from traffic, and benches). Sixty older adults sorted these photographs on appeal for walking for transport on a 7-point scale and reported qualitative information on the reasons for their rankings. Sidewalk evenness appeared to have the strongest influence on a street's appeal for transport-related walking. The effect of sidewalk evenness was even stronger when the street's overall upkeep was good and when traffic was absent. Absence of traffic, presence of vegetation, and separation from traffic also increased a street's appeal for walking for transport. There were no moderating effects by gender or functional limitations. The presence of benches increased the streetscape's appeal among participants who already walked for transport at least an hour/week. The protocols and methods used in the current study carry the potential to further our understanding of environment-PA relationships. Our findings indicated sidewalk evenness as the most important environmental factor influencing a street's appeal for walking for transport among older adults. However, future research in larger samples and in real-life settings is needed to confirm current

  9. Using manipulated photographs to identify features of streetscapes that may encourage older adults to walk for transport.

    PubMed

    Van Cauwenberg, Jelle; Van Holle, Veerle; De Bourdeaudhuij, Ilse; Clarys, Peter; Nasar, Jack; Salmon, Jo; Goubert, Liesbet; Deforche, Benedicte

    2014-01-01

    Experimental evidence of environmental features important for physical activity is challenging to procure in real world settings. The current study aimed to investigate the causal effects of environmental modifications on a photographed street's appeal for older adults' walking for transport. Secondly, we examined whether these effects differed according to gender, functional limitations, and current level of walking for transport. Thirdly, we examined whether different environmental modifications interacted with each other. Qualitative responses were also reported to gain deeper insight into the observed quantitative relationships. Two sets of 16 panoramic photographs of a streetscape were created, in which six environmental factors were manipulated (sidewalk evenness, traffic level, general upkeep, vegetation, separation from traffic, and benches). Sixty older adults sorted these photographs on appeal for walking for transport on a 7-point scale and reported qualitative information on the reasons for their rankings. Sidewalk evenness appeared to have the strongest influence on a street's appeal for transport-related walking. The effect of sidewalk evenness was even stronger when the street's overall upkeep was good and when traffic was absent. Absence of traffic, presence of vegetation, and separation from traffic also increased a street's appeal for walking for transport. There were no moderating effects by gender or functional limitations. The presence of benches increased the streetscape's appeal among participants who already walked for transport at least an hour/week. The protocols and methods used in the current study carry the potential to further our understanding of environment-PA relationships. Our findings indicated sidewalk evenness as the most important environmental factor influencing a street's appeal for walking for transport among older adults. However, future research in larger samples and in real-life settings is needed to confirm current

  10. Mapping soil features from multispectral scanner data

    NASA Technical Reports Server (NTRS)

    Kristof, S. J.; Zachary, A. L.

    1974-01-01

    In being able to identify quickly gross variations in soil features, the computer-aided classification of multispectral scanner data can be an effective aid to soil surveying. Variations in soil tone are easily seen as well as variations in features related to soil tone, e.g., drainage patterns and organic matter content. Changes in surface texture also affect the reflectance properties of soils. Inasmuch as conventional soil classes are based on both surface and subsurface soil characteristics, the technique described here can be expected only to augment and not replace traditional soil mapping.

  11. PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables.

    PubMed

    Pounds, Stan; Cheng, Cheng; Cao, Xueyuan; Crews, Kristine R; Plunkett, William; Gandhi, Varsha; Rubnitz, Jeffrey; Ribeiro, Raul C; Downing, James R; Lamba, Jatinder

    2009-08-15

    In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables. Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis. Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org.

  12. Correlative feature analysis on FFDM

    PubMed Central

    Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

    2008-01-01

    Identifying the corresponding images of a lesion in different views is an essential step in improving the diagnostic ability of both radiologists and computer-aided diagnosis (CAD) systems. Because of the nonrigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this pilot study, we present a computerized framework that differentiates between corresponding images of the same lesion in different views and noncorresponding images, i.e., images of different lesions. A dual-stage segmentation method, which employs an initial radial gradient index (RGI) based segmentation and an active contour model, is applied to extract mass lesions from the surrounding parenchyma. Then various lesion features are automatically extracted from each of the two views of each lesion to quantify the characteristics of density, size, texture and the neighborhood of the lesion, as well as its distance to the nipple. A two-step scheme is employed to estimate the probability that the two lesion images from different mammographic views are of the same physical lesion. In the first step, a correspondence metric for each pairwise feature is estimated by a Bayesian artificial neural network (BANN). Then, these pairwise correspondence metrics are combined using another BANN to yield an overall probability of correspondence. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing corresponding pairs from noncorresponding pairs. Using a FFDM database with 123 corresponding image pairs and 82 noncorresponding pairs, the distance feature yielded an area under the ROC curve (AUC) of 0.81±0.02 with leave-one-out (by physical lesion) evaluation, and the feature metric subset, which included distance, gradient texture, and ROI-based correlation, yielded an AUC of 0.87±0.02. The improvement by using multiple feature metrics was statistically

  13. Significant Wilderness Qualities: Can They Be Identified and Monitored? Proceedings of the Annual NOLS [National Outdoor Leadership School] Wilderness Research Colloquium (3rd, Shoshone National Forest, Wyoming, August 10-15, 1987).

    ERIC Educational Resources Information Center

    Cole, David N., Comp.; Lucas, Robert C., Comp.

    This report is a compilation of the papers presented at a colloquium on wilderness management and a synopsis of discussions held during the conference. The conference theme was how to determine and monitor the most significant features and qualities of the wilderness resource. Generally, participants identify solitude; pollution-free air and…

  14. ANALYSIS OF CLINICAL AND DERMOSCOPIC FEATURES FOR BASAL CELL CARCINOMA NEURAL NETWORK CLASSIFICATION

    PubMed Central

    Cheng, Beibei; Stanley, R. Joe; Stoecker, William V; Stricklin, Sherea M.; Hinton, Kristen A.; Nguyen, Thanh K.; Rader, Ryan K.; Rabinovitz, Harold S.; Oliviero, Margaret; Moss, Randy H.

    2012-01-01

    Background Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the United States. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including Evolving Artificial Neural Networks and Evolving Artificial Neural Network Ensembles. Results Experiment results based on ten-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process. PMID:22724561

  15. Feature relevance assessment for the semantic interpretation of 3D point cloud data

    NASA Astrophysics Data System (ADS)

    Weinmann, M.; Jutzi, B.; Mallet, C.

    2013-10-01

    The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

  16. Distinctive Features of NREM Parasomnia Behaviors in Parkinson’s Disease and Multiple System Atrophy

    PubMed Central

    Ratti, Pietro-Luca; Sierra-Peña, Maria; Manni, Raffaele; Simonetta-Moreau, Marion; Bastin, Julien; Mace, Harrison; Rascol, Olivier; David, Olivier

    2015-01-01

    Objective To characterize parasomnia behaviors on arousal from NREM sleep in Parkinson’s Disease (PD) and Multiple System Atrophy (MSA). Methods From 30 patients with PD, Dementia with Lewy Bodies/Dementia associated with PD, or MSA undergoing nocturnal video-polysomnography for presumed dream enactment behavior, we were able to select 2 PD and 2 MSA patients featuring NREM Parasomnia Behviors (NPBs). We identified episodes during which the subjects seemed to enact dreams or presumed dream-like mentation (NPB arousals) versus episodes with physiological movements (no-NPB arousals). A time-frequency analysis (Morlet Wavelet Transform) of the scalp EEG signals around each NPB and no- NPB arousal onset was performed, and the amplitudes of the spectral frequencies were compared between NPB and no-NPB arousals. Results 19 NPBs were identified, 12 of which consisting of ‘elementary’ NPBs while 7 resembling confusional arousals. With quantitative EEG analysis, we found an amplitude reduction in the 5-6 Hz band 40 seconds before NPBs arousal as compared to no-NPB arousals at F4 and C4 derivations (p<0.01). Conclusions Many PD and MSA patients feature various NREM sleep-related behaviors, with clinical and electrophysiological differences and similarities with arousal parasomnias in the general population. Significance This study help bring to attention an overlooked phenomenon in neurodegenerative diseases. PMID:25756280

  17. Communication: Finding destructive interference features in molecular transport junctions.

    PubMed

    Reuter, Matthew G; Hansen, Thorsten

    2014-11-14

    Associating molecular structure with quantum interference features in electrode-molecule-electrode transport junctions has been difficult because existing guidelines for understanding interferences only apply to conjugated hydrocarbons. Herein we use linear algebra and the Landauer-Büttiker theory for electron transport to derive a general rule for predicting the existence and locations of interference features. Our analysis illustrates that interferences can be directly determined from the molecular Hamiltonian and the molecule-electrode couplings, and we demonstrate its utility with several examples.

  18. Mining online e-liquid reviews for opinion polarities about e-liquid features.

    PubMed

    Chen, Zhipeng; Zeng, Daniel D

    2017-07-07

    In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. We collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users' preference on this feature. The popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit. We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users.

  19. Slow feature analysis: unsupervised learning of invariances.

    PubMed

    Wiskott, Laurenz; Sejnowski, Terrence J

    2002-04-01

    Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

  20. 21 CFR 211.42 - Design and construction features.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 21 Food and Drugs 4 2010-04-01 2010-04-01 false Design and construction features. 211.42 Section 211.42 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL CURRENT GOOD MANUFACTURING PRACTICE FOR FINISHED PHARMACEUTICALS Buildings and Facilities...

  1. Arabic writer identification based on diacritic's features

    NASA Astrophysics Data System (ADS)

    Maliki, Makki; Al-Jawad, Naseer; Jassim, Sabah A.

    2012-06-01

    Natural languages like Arabic, Kurdish, Farsi (Persian), Urdu, and any other similar languages have many features, which make them different from other languages like Latin's script. One of these important features is diacritics. These diacritics are classified as: compulsory like dots which are used to identify/differentiate letters, and optional like short vowels which are used to emphasis consonants. Most indigenous and well trained writers often do not use all or some of these second class of diacritics, and expert readers can infer their presence within the context of the writer text. In this paper, we investigate the use of diacritics shapes and other characteristic as parameters of feature vectors for Arabic writer identification/verification. Segmentation techniques are used to extract the diacritics-based feature vectors from examples of Arabic handwritten text. The results of evaluation test will be presented, which has been carried out on an in-house database of 50 writers. Also the viability of using diacritics for writer recognition will be demonstrated.

  2. A Study for the Feature Selection to Identify GIEMSA-Stained Human Chromosomes Based on Artificial Neural Network

    DTIC Science & Technology

    2001-10-25

    neural network (ANN) has been adopted for the human chromosome classification. It is important to select optimum features for training neural network...Many studies for computer-based chromosome analysis have shown that it is possible to classify chromosomes into 24 subgroups. In addition, artificial

  3. Identifying Advanced Technologies for Education's Future.

    ERIC Educational Resources Information Center

    Moore, Gwendolyn B.; Yin, Robert K.

    A study to determine how three advanced technologies might be applied to the needs of special education students helped inspire the development of a new method for identifying such applications. This new method, named the "Hybrid Approach," combines features of the two traditional methods: technology-push and demand-pull. Technology-push involves…

  4. Image processing tool for automatic feature recognition and quantification

    DOEpatents

    Chen, Xing; Stoddard, Ryan J.

    2017-05-02

    A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

  5. Forecast of the general aviation air traffic control environment for the 1980's

    NASA Technical Reports Server (NTRS)

    Hoffman, W. C.; Hollister, W. M.

    1976-01-01

    The critical information required for the design of a reliable, low cost, advanced avionics system which would enhance the safety and utility of general aviation is stipulated. Sufficient data is accumulated upon which industry can base the design of a reasonably priced system having the capability required by general aviation in and beyond the 1980's. The key features of the Air Traffic Control (ATC) system are: a discrete address beacon system, a separation assurance system, area navigation, a microwave landing system, upgraded ATC automation, airport surface traffic control, a wake vortex avoidance system, flight service stations, and aeronautical satellites. The critical parameters that are necessary for component design are identified. The four primary functions of ATC (control, surveillance, navigation, and communication) and their impact on the onboard avionics system design are assessed.

  6. COMPARISON OF VISUAL PROGNOSIS AND CLINICAL FEATURES OF CYTOMEGALOVIRUS RETINITIS IN HIV AND NON-HIV PATIENTS.

    PubMed

    Kim, Dong Yoon; Jo, Jaehyuck; Joe, Soo Geun; Kim, June-Gone; Yoon, Young Hee; Lee, Joo Yong

    2017-02-01

    To compare the visual prognosis and clinical features of cytomegalovirus (CMV) retinitis between HIV and non-HIV patients. Retrospective cross-sectional study on patients diagnosed with CMV retinitis. Depending on the presence of HIV infection, best-corrected visual acuity (VA) and clinical feature of CMV retinitis were analyzed. The clinical characteristics associated with poor visual prognosis after antiviral treatment were also identified. A total of 78 eyes (58 patients) with CMV retinitis were included in this study: 21 eyes and 57 eyes in HIV and non-HIV patients, respectively. Best-corrected VA was not significantly different between HIV and non-HIV patients. The rate of foveal involvement, retinal detachment, involved zone, and mortality did not significantly differ between the two groups. Visual acuity after antiviral treatment was significantly worse (pretreatment logarithm of the minimal angle of resolution best-corrected VA, 0.54 ± 0.67 [Snellen VA, 20/63]; posttreatment logarithm of the minimal angle of resolution best-corrected VA, 0.77 ± 0.94 [Snellen VA, 20/125]; P = 0.014). Poor visual prognosis was significantly associated with Zone 1 involvement, retinal detachment, and a poor general condition. The overall visual prognosis and the clinical features of CMV retinitis do not differ between HIV and non-HIV patients. The visual prognosis of CMV retinitis still remains quite poor despite advancements in antiviral treatment. This poor prognosis after antiviral treatment is associated with retinal detachment during follow-up, Zone 1 involvement, and the poor general condition of the patient.

  7. Identification and Description of Alternative Means of Accomplishing IMS Operational Features.

    ERIC Educational Resources Information Center

    Dave, Ashok

    The operational features of feasible alternative configurations for a computer-based instructional management system are identified. Potential alternative means and components of accomplishing these features are briefly described. Included are aspects of data collection, data input, data transmission, data reception, scanning and processing,…

  8. Small blob identification in medical images using regional features from optimum scale.

    PubMed

    Zhang, Min; Wu, Teresa; Bennett, Kevin M

    2015-04-01

    Recent advances in medical imaging technology have greatly enhanced imaging-based diagnosis which requires computational effective and accurate algorithms to process the images (e.g., measure the objects) for quantitative assessment. In this research, we are interested in one type of imaging objects: small blobs. Examples of small blob objects are cells in histopathology images, glomeruli in MR images, etc. This problem is particularly challenging because the small blobs often have in homogeneous intensity distribution and an indistinct boundary against the background. Yet, in general, these blobs have similar sizes. Motivated by this finding, we propose a novel detector termed Hessian-based Laplacian of Gaussian (HLoG) using scale space theory as the foundation. Like most imaging detectors, an image is first smoothed via LoG. Hessian analysis is then launched to identify the single optimal scale on which a presegmentation is conducted. The advantage of the Hessian process is that it is capable of delineating the blobs. As a result, regional features can be retrieved. These features enable an unsupervised clustering algorithm for postpruning which should be more robust and sensitive than the traditional threshold-based postpruning commonly used in most imaging detectors. To test the performance of the proposed HLoG, two sets of 2-D grey medical images are studied. HLoG is compared against three state-of-the-art detectors: generalized LoG, Radial-Symmetry and LoG using precision, recall, and F-score metrics.We observe that HLoG statistically outperforms the compared detectors.

  9. A TALE OF THREE MYSTERIOUS SPECTRAL FEATURES IN CARBON-RICH EVOLVED STARS: THE 21 μm, 30 μm, AND “UNIDENTIFIED INFRARED” EMISSION FEATURES

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

    Mishra, Ajay; Li, Aigen; Jiang, B. W., E-mail: amishra@mail.missouri.edu, E-mail: lia@missouri.edu, E-mail: bjiang@bnu.edu.cn

    2015-03-20

    The mysterious “21 μm” emission feature seen almost exclusively in the short-lived protoplanetary nebula (PPN) phase of stellar evolution remains unidentified since its discovery two decades ago. This feature is always accompanied by the equally mysterious, unidentified “30 μm” feature and the so-called “unidentified infrared” (UIR) features at 3.3, 6.2, 7.7, 8.6, and 11.3 μm which are generally attributed to polycyclic aromatic hydrocarbon (PAH) molecules. The 30 μm feature is commonly observed in all stages of stellar evolution from the asymptotic giant branch through PPN to the planetary nebula phase. We explore the interrelations among the mysterious 21, 30 μm,more » and UIR features of the 21 μm sources. We derive the fluxes emitted in the observed UIR, 21, and 30 μm features from published Infrared Space Observatory or Spitzer/IRS spectra. We find that none of these spectral features correlate with each other. This argues against a common carrier (e.g., thiourea) for both the 21 μm feature and the 30 μm feature. This also does not support large PAH clusters as a possible carrier for the 21 μm feature.« less

  10. Key Program Features to Enhance the School-to-Career Transition for Youth with Disabilities

    ERIC Educational Resources Information Center

    Doren, Bonnie; Yan, Min-Chi; Tu, Wei-Mo

    2013-01-01

    The purpose of the article was to identify key features within research-based school-to-career programs that were linked to positive employment outcomes for youth disabilities. Three key program features were identified and discussed that could be incorporated into the practices and programs of schools and communities to support the employment…

  11. Analyzing linear spatial features in ecology.

    PubMed

    Buettel, Jessie C; Cole, Andrew; Dickey, John M; Brook, Barry W

    2018-06-01

    The spatial analysis of dimensionless points (e.g., tree locations on a plot map) is common in ecology, for instance using point-process statistics to detect and compare patterns. However, the treatment of one-dimensional linear features (fiber processes) is rarely attempted. Here we appropriate the methods of vector sums and dot products, used regularly in fields like astrophysics, to analyze a data set of mapped linear features (logs) measured in 12 × 1-ha forest plots. For this demonstrative case study, we ask two deceptively simple questions: do trees tend to fall downhill, and if so, does slope gradient matter? Despite noisy data and many potential confounders, we show clearly that topography (slope direction and steepness) of forest plots does matter to treefall. More generally, these results underscore the value of mathematical methods of physics to problems in the spatial analysis of linear features, and the opportunities that interdisciplinary collaboration provides. This work provides scope for a variety of future ecological analyzes of fiber processes in space. © 2018 by the Ecological Society of America.

  12. Dimensional Reduction for the General Markov Model on Phylogenetic Trees.

    PubMed

    Sumner, Jeremy G

    2017-03-01

    We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.

  13. A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

    PubMed

    Torija, Antonio J; Ruiz, Diego P

    2015-02-01

    The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

  14. National Institute of General Medical Sciences

    MedlinePlus

    ... Over Navigation Links National Institute of General Medical Sciences Site Map Staff Search My Order Search the ... NIGMS Website Research Funding Research Training News & Meetings Science Education About NIGMS Feature Slides View All Slides ...

  15. An Analysis of the Formal Features of "Reality-Based" Television Programs.

    ERIC Educational Resources Information Center

    Neapolitan, D. M.

    Reality-based television programs showcase actual footage or recreate actual events, and include programs such as "America's Most Wanted" and "Rescue 911." To identify the features that typify reality-based television programs, this study conducted an analysis of formal features used in reality-based programs. Formal features…

  16. Which features of primary care affect unscheduled secondary care use? A systematic review.

    PubMed

    Huntley, Alyson; Lasserson, Daniel; Wye, Lesley; Morris, Richard; Checkland, Kath; England, Helen; Salisbury, Chris; Purdy, Sarah

    2014-05-23

    To conduct a systematic review to identify studies that describe factors and interventions at primary care practice level that impact on levels of utilisation of unscheduled secondary care. Observational studies at primary care practice level. Studies included people of any age of either sex living in Organisation for Economic Co-operation and Development (OECD) countries with any health condition. The primary outcome measure was unscheduled secondary care as measured by emergency department attendance and emergency hospital admissions. 48 papers were identified describing potential influencing features on emergency department visits (n=24 studies) and emergency admissions (n=22 studies). Patient factors associated with both outcomes were increased age, reduced socioeconomic status, lower educational attainment, chronic disease and multimorbidity. Features of primary care affecting unscheduled secondary care were more complex. Being able to see the same healthcare professional reduced unscheduled secondary care. Generally, better access was associated with reduced unscheduled care in the USA. Proximity to healthcare provision influenced patterns of use. Evidence relating to quality of care was limited and mixed. The majority of research was from different healthcare systems and limited in the extent to which it can inform policy. However, there is evidence that continuity of care is associated with reduced emergency department attendance and emergency hospital admissions. 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.

  17. Confirmation of a traveling feature in Saturn's rings in Cassini Imaging Science Subsystem data

    NASA Astrophysics Data System (ADS)

    Aye, K. M.; Rehnberg, M.; Esposito, L. W.

    2017-12-01

    Introduction: Using Cassini UVIS occultation data, a traveling wave feature has been identified in the Saturn rings that is most likely caused by the radial positions swap of the moons Janus and Epimetheus [1]. The hypothesis is that non-linear interferences between the density waves when being relocated by the moon swap create a solitary wave that is traveling outward through the rings. The observations in [1] further lead to the derivation of values for the radial travel speeds of the identified traveling features, from 39.6 km/yr for the Janus 5:4 resonance up to 45.8 for the Janus 4:3 resonance. Previous confirmations in ISS data: Work in [1] also identified the feature in Cassini Imaging Science Subsystem (ISS) data that was taken around the time of the UVIS occultations where the phenomenon was first discovered, so far one ISS image for each Janus resonances 2:1, 4:3, 5:4, and 6:5. Searches performed in ISS data: Filtering all existing ISS data down to the best resolutions that include both a clearly identifiable minimum and maximum ring radius, we have visually inspected approx. 200 images, both with and without known resonances within the image, but unbeknownst to the inspector. Identification of a feature of interest happens when train waves are being interrupted by anomalies. Comparing the radial locations of identified ISS features with those in UV data of [1], we have identified several at the same radii. Considering the vast differences in radial resolution, we conclude that the traveling feature causes observable anomalies at both small scales of meters, up to large scales of hundreds of meters to kilometers.References: [1] Rehnberg, M.E., Esposito, L.W., Brown, Z.L., Albers, N., Sremčević, M., Stewart, G.R., 2016. A Traveling Feature in Saturn's Rings. Icarus, accepted in June 2016. [2] K.-Michael Aye (2016, November 11). michaelaye/pyciss: . v0.6.0 Zenodo. https://doi.org/10.5281/zenodo.596802

  18. Searching for a traveling feature in Saturn's rings in Cassini Imaging Science Subsystem data

    NASA Astrophysics Data System (ADS)

    Aye, Klaus-Michael; Rehnberg, Morgan; Brown, Zarah; Esposito, Larry W.

    2016-10-01

    Introduction: Using Cassini UVIS occultation data, a traveling wave feature has been identified in the Saturn rings that is most likely caused by the radial positions swap of the moons Janus and Epimetheus [1]. The hypothesis is that non-linear interferences between the linear density waves when being relocated by the moon swap create a solitary wave that is traveling outward through the rings. The observations in [1] further lead to the derivation of values for the radial travel speeds of the identified traveling features, from 39.6 km/yr for the Janus 5:4 resonance up to 45.8 for the Janus 4:3 resonance.Previous confirmations in ISS data: Work in [1] also identified the feature in Cassini Imaging Science Subsystem (ISS) data that was taken around the time of the UVIS occultations where the phenomenon was first discovered, so far one ISS image for each Janus resonances 2:1, 4:3, 5:4, and 6:5.Search guided by predicted locations: Using the observation-fitted radial velocities from [1], we can extrapolate these to identify Saturn radii at which the traveling feature should be found at later times. Using this and new image analysis and plotting tools available in [2], we have identified a potential candidate feature in an ISS image that was taken 2.5 years after the feature causing moon swap in January 2006. We intend to expand our search by identifying candidate ISS data by a meta-database search constraining the radius at future times corresponding to the predicted future locations of the hypothesized solitary wave and present our findings at this conference.References: [1] Rehnberg, M.E., Esposito, L.W., Brown, Z.L., Albers, N., Sremčević, M., Stewart, G.R., 2016. A Traveling Feature in Saturn's Rings. Icarus, accepted in June 2016. [2] K.-Michael Aye. (2016). pyciss: v0.5.0. Zenodo. 10.5281/zenodo.53092

  19. Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

    PubMed

    Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E

    2016-08-01

    Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

  20. Human olfactory consciousness and cognition: its unusual features may not result from unusual functions but from limited neocortical processing resources

    PubMed Central

    Stevenson, Richard J.; Attuquayefio, Tuki

    2013-01-01

    Human and animal olfactory perception is shaped both by functional demands and by various environmental constraints seemingly peculiar to chemical stimuli. These demands and constraints may have generated a sensory system that is cognitively distinct from the major senses. In this article we identify these various functional demands and constraints, and examine whether they can be used to account for olfaction's unique cognitive features on a case-by-case basis. We then use this as grounds to argue that specific conscious processes do have functional value, a finding that naturally emerges when a comparative approach to consciousness across the senses is adopted. More generally, we conclude that certain peculiar features of olfactory cognition may owe more to limited neocortical processing resources, than they do to the challenges faced by perceiving chemical stimuli. PMID:24198808

  1. Features versus context: An approach for precise and detailed detection and delineation of faces and facial features.

    PubMed

    Ding, Liya; Martinez, Aleix M

    2010-11-01

    The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose, and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose, and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature, we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult, however, because the context and the texture of the facial features vary widely under changing expression, pose, and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide

  2. The Role of Linguistic Labels in Inductive Generalization

    ERIC Educational Resources Information Center

    Deng, W.; Sloutsky, Vladimir M.

    2013-01-01

    What is the role of linguistic labels in inductive generalization? According to one approach labels denote categories and differ from object features, whereas according to another approach labels start out as features and may become category markers in the course of development. This issue was addressed in four experiments with 4- and 5-year-olds…

  3. An unusual landslide feature on Mars

    NASA Technical Reports Server (NTRS)

    Veverka, J.; Liang, T.

    1975-01-01

    A flow feature on a crater wall, characteristic of a landslide, has been identified in a Mariner 9 high resolution photograph. Although other evidence of mass wasting is common in Mariner 9 photography, the case presented appears unique. A tentative conclusion is that, at least in some cases, Martian soil exhibits significant internal friction in mass movements.

  4. Insight into Resolution Enhancement in Generalized Two-Dimensional Correlation Spectroscopy

    PubMed Central

    Ma, Lu; Sikirzhytski, Vitali; Hong, Zhenmin; Lednev, Igor K.; Asher, Sanford A.

    2014-01-01

    Generalized two-dimensional correlation spectroscopy (2D COS) can be used to enhance spectral resolution in order to help differentiate highly overlapped spectral bands. Despite the numerous extensive 2D COS investigations, the origin of the 2D spectral resolution enhancement mechanism(s) are not completely understood. In the work here we studied the 2D COS of simulated spectra in order to develop new insights into the dependence of the 2D COS spectral features on the overlapping band separations, their intensities and bandwidths, and their band intensity change rates. We find that the features in the 2D COS maps that derive from overlapping bands are determined by the spectral normalized half-intensities and the total intensity changes of the correlated bands. We identify the conditions required to resolve overlapping bands. In particular, 2D COS peak resolution requires that the normalized half-intensities of a correlating band have amplitudes between the maxima and minima of the normalized half-intensities of the overlapping bands. PMID:23452492

  5. Oversampling the Minority Class in the Feature Space.

    PubMed

    Perez-Ortiz, Maria; Gutierrez, Pedro Antonio; Tino, Peter; Hervas-Martinez, Cesar

    2016-09-01

    The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the classes will be linearly separable and synthetically generated patterns will lie on the minority class region. Since the feature space is not directly accessible, we use the empirical feature space (EFS) (a Euclidean space isomorphic to the feature space) for oversampling purposes. The proposed method is framed in the context of support vector machines, where the imbalanced data sets can pose a serious hindrance. The idea is investigated in three scenarios: 1) oversampling in the full and reduced-rank EFSs; 2) a kernel learning technique maximizing the data class separation to study the influence of the feature space structure (implicitly defined by the kernel function); and 3) a unified framework for preferential oversampling that spans some of the previous approaches in the literature. We support our investigation with extensive experiments over 50 imbalanced data sets.

  6. A prototype feature system for feature retrieval using relationships

    USGS Publications Warehouse

    Choi, J.; Usery, E.L.

    2009-01-01

    Using a feature data model, geographic phenomena can be represented effectively by integrating space, theme, and time. This paper extends and implements a feature data model that supports query and visualization of geographic features using their non-spatial and temporal relationships. A prototype feature-oriented geographic information system (FOGIS) is then developed and storage of features named Feature Database is designed. Buildings from the U.S. Marine Corps Base, Camp Lejeune, North Carolina and subways in Chicago, Illinois are used to test the developed system. The results of the applications show the strength of the feature data model and the developed system 'FOGIS' when they utilize non-spatial and temporal relationships in order to retrieve and visualize individual features.

  7. Intrinsic two-dimensional features as textons

    NASA Technical Reports Server (NTRS)

    Barth, E.; Zetzsche, C.; Rentschler, I.

    1998-01-01

    We suggest that intrinsic two-dimensional (i2D) features, computationally defined as the outputs of nonlinear operators that model the activity of end-stopped neurons, play a role in preattentive texture discrimination. We first show that for discriminable textures with identical power spectra the predictions of traditional models depend on the type of nonlinearity and fail for energy measures. We then argue that the concept of intrinsic dimensionality, and the existence of end-stopped neurons, can help us to understand the role of the nonlinearities. Furthermore, we show examples in which models without strong i2D selectivity fail to predict the correct ranking order of perceptual segregation. Our arguments regarding the importance of i2D features resemble the arguments of Julesz and co-workers regarding textons such as terminators and crossings. However, we provide a computational framework that identifies textons with the outputs of nonlinear operators that are selective to i2D features.

  8. What's growing on General Practitioner's stethoscope?

    PubMed

    Carducci, A; Cargnelutti, M; Tassinari, F; Bizzarro, A; Cordio, G; Carletti, S; Maccarini, L; Pelissero, G

    2016-01-01

    Non-critical medical devices, as stethoscopes, have long been considered as vectors in microorganisms' transmission. Cleaning standards for non-critical medical equipment are often unclear. This study was designed to assess the attitude of General Practitioners (GPs) towards cleaning their stethoscope and the degree of microbiological contamination of doctor's instrument in outpatient setting. Observational, crossover study. A structured questionnaire was administered to GPs to test their knowledge about medical instrument's cleanliness recommendations and the surface of the diaphragm of their stethoscopes was analyzed for bacteriological isolates using mass spectrometry technique. Most of GPs declared they don't know cleaning recommendations for non-critical medical devices and a relevant bacterial growth was identified on the majority of the stethoscopes' membranes. Almost all microbiological isolates resulted typically found in cutaneous flora. We can't state that the GP's stethoscopes feature a risk of transmission for microbiological pathogens; anyway, because of the level of contamination we observed, cleaning recommendations to disinfect instruments on a regular basis should be better indicated.

  9. Citing geospatial feature inventories with XML manifests

    NASA Astrophysics Data System (ADS)

    Bose, R.; McGarva, G.

    2006-12-01

    Today published scientific papers include a growing number of citations for online information sources that either complement or replace printed journals and books. We anticipate this same trend for cartographic citations used in the geosciences, following advances in web mapping and geographic feature-based services. Instead of using traditional libraries to resolve citations for print material, the geospatial citation life cycle will include requesting inventories of objects or geographic features from distributed geospatial data repositories. Using a case study from the UK Ordnance Survey MasterMap database, which is illustrative of geographic object-based products in general, we propose citing inventories of geographic objects using XML feature manifests. These manifests: (1) serve as a portable listing of sets of versioned features; (2) could be used as citations within the identification portion of an international geospatial metadata standard; (3) could be incorporated into geospatial data transfer formats such as GML; but (4) can be resolved only with comprehensive, curated repositories of current and historic data. This work has implications for any researcher who foresees the need to make or resolve references to online geospatial databases.

  10. Courseware Components and Features: Preferences of Faculty in the Human Sciences

    ERIC Educational Resources Information Center

    Causin, Gina Fe G.; Robertson, Lona J.; Ryan, Bill

    2008-01-01

    This project gathered information on the important components and features of distance education courseware identified by faculty teaching in the Great Plains Interactive Distance Education Alliance. Respondents indicated that they were most interested in features that helped with course management, allowed them to update and post course materials…

  11. Behavioral mechanisms of context fear generalization in mice

    PubMed Central

    Huckleberry, Kylie A.; Ferguson, Laura B.

    2016-01-01

    There is growing interest in generalization of learned contextual fear, driven in part by the hypothesis that mood and anxiety disorders stem from impaired hippocampal mechanisms of fear generalization and discrimination. However, there has been relatively little investigation of the behavioral and procedural mechanisms that might control generalization of contextual fear. We assessed the relative contribution of different contextual features to context fear generalization and characterized how two common conditioning protocols—foreground (uncued) and background (cued) contextual fear conditioning—affected context fear generalization. In one experiment, mice were fear conditioned in context A, and then tested for contextual fear both in A and in an alternate context created by changing a subset of A's elements. The results suggest that floor configuration and odor are more salient features than chamber shape. A second experiment compared context fear generalization in background and foreground context conditioning. Although foreground conditioning produced more context fear than background conditioning, the two procedures produced equal amounts of generalized fear. Finally, results indicated that the order of context tests (original first versus alternate first) significantly modulates context fear generalization, perhaps because the original and alternate contexts are differentially sensitive to extinction. Overall, results demonstrate that context fear generalization is sensitive to procedural variations and likely reflects the operation of multiple interacting psychological and neural mechanisms. PMID:27918275

  12. Chronic Anger as a Precursor to Adult Antisocial Personality Features: The Moderating Influence of Cognitive Control

    PubMed Central

    Hawes, Samuel W.; Perlman, Susan B.; Byrd, Amy L.; Raine, Adrian; Loeber, Rolf; Pardini, Dustin A.

    2015-01-01

    Anger is among the earliest occurring symptoms of mental health, yet we know little about its developmental course. Further, no studies have examined whether youth with persistent anger are at an increased risk of exhibiting antisocial personality features in adulthood, or how cognitive control abilities may protect these individuals from developing such maladaptive outcomes. Method Trajectories of anger were delineated among 503 boys using annual assessments from childhood to middle adolescence (~ages 7–14). Associations between these trajectories and features of antisocial personality in young adulthood (~age 28) were examined, including whether cognitive control moderates this association. Results Five trajectories of anger were identified (i.e., Childhood-Onset, Childhood-Limited, Adolescent-Onset, Moderate, and Low). Boys in the Childhood-Onset group exhibited the highest adulthood antisocial personality features (e.g., psychopathy, aggression, criminal charges). However, boys in this group were buffered from these problems if they had higher levels of cognitive control during adolescence. Findings were consistent across measures from multiple informants, replicated across distinct time periods, and remained when controlling for general intelligence and prior antisocial behavior. Conclusions This is the first study to document the considerable heterogeneity in the developmental course of anger from childhood to adolescence. As hypothesized, good cognitive control abilities protected youth with persistent anger problems from developing antisocial personality features in adulthood. Clinical implications and future directions are discussed. PMID:26618654

  13. Chronic anger as a precursor to adult antisocial personality features: The moderating influence of cognitive control.

    PubMed

    Hawes, Samuel W; Perlman, Susan B; Byrd, Amy L; Raine, Adrian; Loeber, Rolf; Pardini, Dustin A

    2016-01-01

    Anger is among the earliest occurring symptoms of mental health, yet we know little about its developmental course. Further, no studies have examined whether youth with persistent anger are at an increased risk of exhibiting antisocial personality features in adulthood, or how cognitive control abilities may protect these individuals from developing such maladaptive outcomes. Trajectories of anger were delineated among 503 boys using annual assessments from childhood to middle adolescence (ages ∼7-14). Associations between these trajectories and features of antisocial personality in young adulthood (age ∼28) were examined, including whether cognitive control moderates this association. Five trajectories of anger were identified (i.e., childhood-onset, childhood-limited, adolescent-onset, moderate, and low). Boys in the childhood-onset group exhibited the highest adulthood antisocial personality features (e.g., psychopathy, aggression, criminal charges). However, boys in this group were buffered from these problems if they had higher levels of cognitive control during adolescence. Findings were consistent across measures from multiple informants, replicated across distinct time periods, and remained when controlling for general intelligence and prior antisocial behavior. This is the first study to document the considerable heterogeneity in the developmental course of anger from childhood to adolescence. As hypothesized, good cognitive control abilities protected youth with persistent anger problems from developing antisocial personality features in adulthood. Clinical implications and future directions are discussed. (c) 2016 APA, all rights reserved.

  14. Automated detection of qualitative spatio-temporal features in electrocardiac activation maps.

    PubMed

    Ironi, Liliana; Tentoni, Stefania

    2007-02-01

    This paper describes a piece of work aiming at the realization of a tool for the automated interpretation of electrocardiac maps. Such maps can capture a number of electrical conduction pathologies, such as arrhytmia, that can be missed by the analysis of traditional electrocardiograms. But, their introduction into the clinical practice is still far away as their interpretation requires skills that belongs to very few experts. Then, an automated interpretation tool would bridge the gap between the established research outcome and clinical practice with a consequent great impact on health care. Qualitative spatial reasoning can play a crucial role in the identification of spatio-temporal patterns and salient features that characterize the heart electrical activity. We adopted the spatial aggregation (SA) conceptual framework and an interplay of numerical and qualitative information to extract features from epicardial maps, and to make them available for reasoning tasks. Our focus is on epicardial activation isochrone maps as they are a synthetic representation of spatio-temporal aspects of the propagation of the electrical excitation. We provide a computational SA-based methodology to extract, from 3D epicardial data gathered over time, (1) the excitation wavefront structure, and (2) the salient features that characterize wavefront propagation and visually correspond to specific geometric objects. The proposed methodology provides a robust and efficient way to identify salient pieces of information in activation time maps. The hierarchical structure of the abstracted geometric objects, crucial in capturing the prominent information, facilitates the definition of general rules necessary to infer the correlation between pathophysiological patterns and wavefront structure and propagation.

  15. Feature selection in feature network models: finding predictive subsets of features with the Positive Lasso.

    PubMed

    Frank, Laurence E; Heiser, Willem J

    2008-05-01

    A set of features is the basis for the network representation of proximity data achieved by feature network models (FNMs). Features are binary variables that characterize the objects in an experiment, with some measure of proximity as response variable. Sometimes features are provided by theory and play an important role in the construction of the experimental conditions. In some research settings, the features are not known a priori. This paper shows how to generate features in this situation and how to select an adequate subset of features that takes into account a good compromise between model fit and model complexity, using a new version of least angle regression that restricts coefficients to be non-negative, called the Positive Lasso. It will be shown that features can be generated efficiently with Gray codes that are naturally linked to the FNMs. The model selection strategy makes use of the fact that FNM can be considered as univariate multiple regression model. A simulation study shows that the proposed strategy leads to satisfactory results if the number of objects is less than or equal to 22. If the number of objects is larger than 22, the number of features selected by our method exceeds the true number of features in some conditions.

  16. Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

    PubMed Central

    Mohammad-Noori, Morteza; Beer, Michael A.

    2014-01-01

    Abstract Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. PMID:25033408

  17. Enhanced regulatory sequence prediction using gapped k-mer features.

    PubMed

    Ghandi, Mahmoud; Lee, Dongwon; Mohammad-Noori, Morteza; Beer, Michael A

    2014-07-01

    Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem.

  18. On the Relation between the General Affective Meaning and the Basic Sublexical, Lexical, and Inter-lexical Features of Poetic Texts—A Case Study Using 57 Poems of H. M. Enzensberger

    PubMed Central

    Ullrich, Susann; Aryani, Arash; Kraxenberger, Maria; Jacobs, Arthur M.; Conrad, Markus

    2017-01-01

    The literary genre of poetry is inherently related to the expression and elicitation of emotion via both content and form. To explore the nature of this affective impact at an extremely basic textual level, we collected ratings on eight different general affective meaning scales—valence, arousal, friendliness, sadness, spitefulness, poeticity, onomatopoeia, and liking—for 57 German poems (“die verteidigung der wölfe”) which the contemporary author H. M. Enzensberger had labeled as either “friendly,” “sad,” or “spiteful.” Following Jakobson's (1960) view on the vivid interplay of hierarchical text levels, we used multiple regression analyses to explore the specific influences of affective features from three different text levels (sublexical, lexical, and inter-lexical) on the perceived general affective meaning of the poems using three types of predictors: (1) Lexical predictor variables capturing the mean valence and arousal potential of words; (2) Inter-lexical predictors quantifying peaks, ranges, and dynamic changes within the lexical affective content; (3) Sublexical measures of basic affective tone according to sound-meaning correspondences at the sublexical level (see Aryani et al., 2016). We find the lexical predictors to account for a major amount of up to 50% of the variance in affective ratings. Moreover, inter-lexical and sublexical predictors account for a large portion of additional variance in the perceived general affective meaning. Together, the affective properties of all used textual features account for 43–70% of the variance in the affective ratings and still for 23–48% of the variance in the more abstract aesthetic ratings. In sum, our approach represents a novel method that successfully relates a prominent part of variance in perceived general affective meaning in this corpus of German poems to quantitative estimates of affective properties of textual components at the sublexical, lexical, and inter-lexical level

  19. A Motion-Based Feature for Event-Based Pattern Recognition

    PubMed Central

    Clady, Xavier; Maro, Jean-Matthieu; Barré, Sébastien; Benosman, Ryad B.

    2017-01-01

    This paper introduces an event-based luminance-free feature from the output of asynchronous event-based neuromorphic retinas. The feature consists in mapping the distribution of the optical flow along the contours of the moving objects in the visual scene into a matrix. Asynchronous event-based neuromorphic retinas are composed of autonomous pixels, each of them asynchronously generating “spiking” events that encode relative changes in pixels' illumination at high temporal resolutions. The optical flow is computed at each event, and is integrated locally or globally in a speed and direction coordinate frame based grid, using speed-tuned temporal kernels. The latter ensures that the resulting feature equitably represents the distribution of the normal motion along the current moving edges, whatever their respective dynamics. The usefulness and the generality of the proposed feature are demonstrated in pattern recognition applications: local corner detection and global gesture recognition. PMID:28101001

  20. Print advertisements for Alzheimer's disease drugs: informational and transformational features.

    PubMed

    Gooblar, Jonathan; Carpenter, Brian D

    2013-06-01

    We examined print advertisements for Alzheimer's disease drugs published in journals and magazines between January 2008 and February 2012, using an informational versus transformational theoretical framework to identify objective and persuasive features. In 29 unique advertisements, we used qualitative methods to code and interpret identifying information, charts, benefit and side effect language, and persuasive appeals embedded in graphics and narratives. Most elements contained a mixture of informational and transformational features. Charts were used infrequently, but when they did appear the accompanying text often exaggerated the data. Benefit statements covered an array of symptoms, drug properties, and caregiver issues. Side effect statements often used positive persuasive appeals. Graphics and narrative features emphasized positive emotions and outcomes. We found subtle and sophisticated attempts both to educate and to persuade readers. It is important for consumers and prescribing physicians to read print advertisements critically so that they can make informed treatment choices.