Speech recognition features for EEG signal description in detection of neonatal seizures.

PubMed

Temko, A; Boylan, G; Marnane, W; Lightbody, G

2010-01-01

In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.

Defining greed.

PubMed

Seuntjens, Terri G; Zeelenberg, Marcel; Breugelmans, Seger M; van de Ven, Niels

2015-08-01

Although greed is both hailed as the motor of economic growth and blamed as the cause of economic crises, very little is known about its psychological underpinnings. Five studies explored lay conceptualizations of greed among U.S. and Dutch participants using a prototype analysis. Study 1 identified features related to greed. Study 2 determined the importance of these features; the most important features were classified as central (e.g., self-interested, never satisfied), whereas less important features were classified as peripheral (e.g., ambition, addiction). Subsequently, we found that, compared to peripheral features, participants recalled central features better (Study 3), faster (Study 4), and these central features were more present in real-life episodes of greed (Study 5). These findings provide a better understanding of the elements that make up the experience of greed and provide insights into how greed can be manipulated and measured in future research. © 2014 The British Psychological Society.

A Comparative Study of Coping Strategies and the Features of Interpersonal Relations and Life Orientations of People with Congenital and Acquired Diseases of the Musculoskeletal System

ERIC Educational Resources Information Center

Romanova, E. V.; Tolkacheva, O. N.

2016-01-01

The article presents the results of a comparative study of the features of the coping strategies, life orientations, and interpersonal relations of disabled people with acquired and congenital diseases of the musculoskeletal system. The authors discovered differences in interpersonal behavior in the area of control, and they revealed factors that…

Comparability of Computer-Based and Paper-Based Science Assessments

ERIC Educational Resources Information Center

Herrmann-Abell, Cari F.; Hardcastle, Joseph; DeBoer, George E.

2018-01-01

We compared students' performance on a paper-based test (PBT) and three computer-based tests (CBTs). The three computer-based tests used different test navigation and answer selection features, allowing us to examine how these features affect student performance. The study sample consisted of 9,698 fourth through twelfth grade students from across…

Plant species classification using flower images—A comparative study of local feature representations

PubMed Central

Seeland, Marco; Rzanny, Michael; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick

2017-01-01

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification. PMID:28234999

Diagnostic features of Alzheimer's disease extracted from PET sinograms

NASA Astrophysics Data System (ADS)

Sayeed, A.; Petrou, M.; Spyrou, N.; Kadyrov, A.; Spinks, T.

2002-01-01

Texture analysis of positron emission tomography (PET) images of the brain is a very difficult task, due to the poor signal to noise ratio. As a consequence, very few techniques can be implemented successfully. We use a new global analysis technique known as the Trace transform triple features. This technique can be applied directly to the raw sinograms to distinguish patients with Alzheimer's disease (AD) from normal volunteers. FDG-PET images of 18 AD and 10 normal controls obtained from the same CTI ECAT-953 scanner were used in this study. The Trace transform triple feature technique was used to extract features that were invariant to scaling, translation and rotation, referred to as invariant features, as well as features that were sensitive to rotation but invariant to scaling and translation, referred to as sensitive features in this study. The features were used to classify the groups using discriminant function analysis. Cross-validation tests using stepwise discriminant function analysis showed that combining both sensitive and invariant features produced the best results, when compared with the clinical diagnosis. Selecting the five best features produces an overall accuracy of 93% with sensitivity of 94% and specificity of 90%. This is comparable with the classification accuracy achieved by Kippenhan et al (1992), using regional metabolic activity.

The Word Shape Hypothesis Re-Examined: Evidence for an External Feature Advantage in Visual Word Recognition

ERIC Educational Resources Information Center

Beech, John R.; Mayall, Kate A.

2005-01-01

This study investigates the relative roles of internal and external letter features in word recognition. In Experiment 1 the efficacy of outer word fragments (words with all their horizontal internal features removed) was compared with inner word fragments (words with their outer features removed) as primes in a forward masking paradigm. These…

Learning representations for the early detection of sepsis with deep neural networks.

PubMed

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy

NASA Astrophysics Data System (ADS)

Cunliffe, Alexandra R.; Armato, Samuel G., III; Straus, Christopher; Malik, Renuka; Al-Hallaq, Hania A.

2014-09-01

This study examines the correlation between the radiologist-defined severity of normal tissue damage following radiation therapy (RT) for lung cancer treatment and a set of mathematical descriptors of computed tomography (CT) scan texture (‘texture features’). A pre-therapy CT scan and a post-therapy CT scan were retrospectively collected under IRB approval for each of the 25 patients who underwent definitive RT (median dose: 66 Gy). Sixty regions of interest (ROIs) were automatically identified in the non-cancerous lung tissue of each post-therapy scan. A radiologist compared post-therapy scan ROIs with pre-therapy scans and categorized each as containing no abnormality, mild abnormality, moderate abnormality, or severe abnormality. Twenty texture features that characterize gray-level intensity, region morphology, and gray-level distribution were calculated in post-therapy scan ROIs and compared with anatomically matched ROIs in the pre-therapy scan. Linear regression and receiver operating characteristic (ROC) analysis were used to compare the percent feature value change (ΔFV) between ROIs at each category of visible radiation damage. Most ROIs contained no (65%) or mild abnormality (30%). ROIs with moderate (3%) or severe (2%) abnormalities were observed in 9 patients. For 19 of 20 features, ΔFV was significantly different among severity levels. For 12 features, significant differences were observed at every level. Compared with regions with no abnormalities, ΔFV for these 12 features increased, on average, by 1.5%, 12%, and 30%, respectively, for mild, moderate, and severe abnormalitites. Area under the ROC curve was largest when comparing ΔFV in the highest severity level with the remaining three categories (mean AUC across features: 0.84). In conclusion, 19 features that characterized the severity of radiologic changes from pre-therapy scans were identified. These features may be used in future studies to quantify acute normal lung tissue damage following RT. Presented, in part at the IASLC 15th World Conference on Lung Conference, Sydney, AUS (2013).

Long-term information and distributed neural activation are relevant for the "internal features advantage" in face processing: electrophysiological and source reconstruction evidence.

PubMed

Olivares, Ela I; Saavedra, Cristina; Trujillo-Barreto, Nelson J; Iglesias, Jaime

2013-01-01

In face processing tasks, prior presentation of internal facial features, when compared with external ones, facilitates the recognition of subsequently displayed familiar faces. In a previous ERP study (Olivares & Iglesias, 2010) we found a visibly larger N400-like effect when identity mismatch familiar faces were preceded by internal features, as compared to prior presentation of external ones. In the present study we contrasted the processing of familiar and unfamiliar faces in the face-feature matching task to assess whether the so-called "internal features advantage" relies mainly on the use of stored face-identity-related information or if it might operate independently from stimulus familiarity. Our participants (N = 24) achieved better performance with internal features as primes and, significantly, with familiar faces. Importantly, ERPs elicited by identity mismatch complete faces displayed a negativity around 300-600 msec which was clearly enhanced for familiar faces primed by internal features when compared with the other experimental conditions. Source reconstruction showed incremented activity elicited by familiar stimuli in both posterior (ventral occipitotemporal) and more anterior (parahippocampal (ParaHIP) and orbitofrontal) brain regions. The activity elicited by unfamiliar stimuli was, in general, located in more posterior regions. Our findings suggest that the activation of multiple neural codes is required for optimal individuation in face-feature matching and that a cortical network related to long-term information for face-identity processing seems to support the internal feature effect. Copyright © 2013 Elsevier Ltd. All rights reserved.

Defining Features of Unhealthy Exercise Associated with Disordered Eating and Eating Disorder Diagnoses

PubMed Central

Holland, Lauren A.; Brown, Tiffany A.; Keel, Pamela K.

2013-01-01

Objectives The current study sought to compare different features of unhealthy exercise on associations with disordered eating and their ability to identify individuals with eating disorders. A secondary aim of the study was to compare prevalence and overlap of different aspects of unhealthy exercise and potential differences in their gender distribution. Design Cross-sectional epidemiological study. Methods A community-based sample of men (n=592) and women (n=1468) completed surveys of health and eating patterns, including questions regarding exercise habits and eating disorder symptoms. Results Compulsive and compensatory features of exercise were the best predictors of disordered eating and eating disorder diagnoses compared to exercise that was excessive in quantity. Further, compulsive and compensatory aspects of unhealthy exercise represented overlapping, yet distinct qualities in both men and women. Conclusions Including the compulsive quality among the defining features of unhealthy exercise may improve identification of eating disorders, particularly in men. Results suggest that the compensatory aspect of unhealthy exercise is not adequately captured by the compulsive aspect of unhealthy exercise. Thus, interventions that target unhealthy exercise behaviors among high-risk individuals, such as athletes, may benefit from addressing both the compulsive and compensatory aspects of unhealthy exercise. Future prospective longitudinal studies will aid in determining the direction of the association between these features of unhealthy exercise and the onset of eating pathology. PMID:24391457

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

Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

PubMed

Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

Internal versus external features in triggering the brain waveforms for conjunction and feature faces in recognition.

PubMed

Nie, Aiqing; Jiang, Jingguo; Fu, Qiao

2014-08-20

Previous research has found that conjunction faces (whose internal features, e.g. eyes, nose, and mouth, and external features, e.g. hairstyle and ears, are from separate studied faces) and feature faces (partial features of these are studied) can produce higher false alarms than both old and new faces (i.e. those that are exactly the same as the studied faces and those that have not been previously presented) in recognition. The event-related potentials (ERPs) that relate to conjunction and feature faces at recognition, however, have not been described as yet; in addition, the contributions of different facial features toward ERPs have not been differentiated. To address these issues, the present study compared the ERPs elicited by old faces, conjunction faces (the internal and the external features were from two studied faces), old internal feature faces (whose internal features were studied), and old external feature faces (whose external features were studied) with those of new faces separately. The results showed that old faces not only elicited an early familiarity-related FN400, but a more anterior distributed late old/new effect that reflected recollection. Conjunction faces evoked similar late brain waveforms as old internal feature faces, but not to old external feature faces. These results suggest that, at recognition, old faces hold higher familiarity than compound faces in the profiles of ERPs and internal facial features are more crucial than external ones in triggering the brain waveforms that are characterized as reflecting the result of familiarity.

What Top-Down Task Sets Do for Us: An ERP Study on the Benefits of Advance Preparation in Visual Search

ERIC Educational Resources Information Center

Eimer, Martin; Kiss, Monika; Nicholas, Susan

2011-01-01

When target-defining features are specified in advance, attentional target selection in visual search is controlled by preparatory top-down task sets. We used ERP measures to study voluntary target selection in the absence of such feature-specific task sets, and to compare it to selection that is guided by advance knowledge about target features.…

Hole Feature on Conical Face Recognition for Turning Part Model

NASA Astrophysics Data System (ADS)

Zubair, A. F.; Abu Mansor, M. S.

2018-03-01

Computer Aided Process Planning (CAPP) is the bridge between CAD and CAM and pre-processing of the CAD data in the CAPP system is essential. For CNC turning part, conical faces of part model is inevitable to be recognised beside cylindrical and planar faces. As the sinus cosines of the cone radius structure differ according to different models, face identification in automatic feature recognition of the part model need special intention. This paper intends to focus hole on feature on conical faces that can be detected by CAD solid modeller ACIS via. SAT file. Detection algorithm of face topology were generated and compared. The study shows different faces setup for similar conical part models with different hole type features. Three types of holes were compared and different between merge faces and unmerge faces were studied.

A Socio-Pragmatic Comparative Study of Ostensible Invitations in English and Farsi

ERIC Educational Resources Information Center

Salmani-Nodoushan, Mohammad Ali

2006-01-01

In their study in 1990, Clark and Isaacs identified five properties and seven defining features that distinguished between English ostensible and genuine invitations. To see if Persian ostensible and genuine invitations could be distinguished by the same features and properties, the present study was carried out. 45 field workers observed and…

Response of Phlebotomus papatasi to visual, physical and chemical attraction features in the field.

USDA-ARS?s Scientific Manuscript database

In this study, 27 CDC traps were modified with various attractive features and compared with a CDC trap with no light source or baits to evaluate the effects on attraction to Phlebotomus papatasi (Scopoli). Attractive features included CO2, lights, colored trap bodies, heat, moisture, chemical lures...

Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

PubMed

Shi, Bibo; Grimm, Lars J; Mazurowski, Maciej A; Baker, Jay A; Marks, Jeffrey R; King, Lorraine M; Maley, Carlo C; Hwang, E Shelley; Lo, Joseph Y

2018-03-01

The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. In this retrospective study, digital mammographic magnification views were collected for 99 subjects with DCIS at biopsy, 25 of which were subsequently upstaged to invasive cancer. A deep convolutional neural network model that was pretrained on nonmedical images (eg, animals, plants, instruments) was used as the feature extractor. Through a statistical pooling strategy, deep features were extracted at different levels of convolutional layers from the lesion areas, without sacrificing the original resolution or distorting the underlying topology. A multivariate classifier was then trained to predict which tumors contain occult invasive disease. This was compared with the performance of traditional "handcrafted" computer vision (CV) features previously developed specifically to assess mammographic calcifications. The generalization performance was assessed using Monte Carlo cross-validation and receiver operating characteristic curve analysis. Deep features were able to distinguish DCIS with occult invasion from pure DCIS, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval, 0.68-0.73). This performance was comparable with the handcrafted CV features (area under the curve = 0.68; 95% confidence interval, 0.66-0.71) that were designed with prior domain knowledge. Despite being pretrained on only nonmedical images, the deep features extracted from digital mammograms demonstrated comparable performance with handcrafted CV features for the challenging task of predicting DCIS upstaging. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Feature engineering for MEDLINE citation categorization with MeSH.

PubMed

Jimeno Yepes, Antonio Jose; Plaza, Laura; Carrillo-de-Albornoz, Jorge; Mork, James G; Aronson, Alan R

2015-04-08

Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system.

Comparative study of palm print authentication system using geometric features

NASA Astrophysics Data System (ADS)

Shreyas, Kamath K. M.; Rajeev, Srijith; Panetta, Karen; Agaian, Sos S.

2017-05-01

Biometrics, particularly palm print authentication has been a stimulating research area due to its abundance of features. Stable features and effective matching are the most crucial steps for an authentication system. In conventional palm print authentication systems, matching is based on flexion creases, friction ridges, and minutiae points. Currently, contactless palm print imaging is an emerging technology. However, they tend to involve fluctuations in the image quality and texture loss due to factors such as varying illumination conditions, occlusions, noise, pose, and ghosting. These variations decrease the performance of the authentication systems. Furthermore, real-time palm print authentication in large databases continue to be a challenging task. In order to effectively solve these problems, features which are invariant to these anomalies are required. This paper proposes a robust palm print matching framework by making a comparative study of different local geometric features such as Difference-of-Gaussian, Hessian, Hessian-Laplace, Harris-Laplace, and Multiscale Harris for feature detection. These detectors are coupled with Scale Invariant Feature Transformation (SIFT) descriptor to describe the identified features. Additionally, a two-stage refinement process is carried out to obtain the best stable matches. Computer simulations demonstrate that the accuracy of the system has increased effectively with an EER of 0.86% when Harris-Laplace detector is used on IITD database.

Combination of lateral and PA view radiographs to study development of knee OA and associated pain

NASA Astrophysics Data System (ADS)

Minciullo, Luca; Thomson, Jessie; Cootes, Timothy F.

2017-03-01

Knee Osteoarthritis (OA) is the most common form of arthritis, affecting millions of people around the world. The effects of the disease have been studied using the shape and texture features of bones in PosteriorAnterior (PA) and Lateral radiographs separately. In this work we compare the utility of features from each view, and evaluate whether combining features from both is advantageous. We built a fully automated system to independently locate landmark points in both radiographic images using Random Forest Constrained Local Models. We extracted discriminative features from the two bony outlines using Appearance Models. The features were used to train Random Forest classifiers to solve three specific tasks: (i) OA classification, distinguishing patients with structural signs of OA from the others; (ii) predicting future onset of the disease and (iii) predicting which patients with no current pain will have a positive pain score later in a follow-up visit. Using a subset of the MOST dataset we show that the PA view has more discriminative features to classify and predict OA, while the lateral view contains features that achieve better performance in predicting pain, and that combining the features from both views gives a small improvement in accuracy of the classification compared to the individual views.

Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

PubMed

Martinez, Emmanuel; Alvarez, Mario Moises; Trevino, Victor

2010-08-01

Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

On the Formulation of Anisotropic-Polyaxial Failure Criteria: A Comparative Study

NASA Astrophysics Data System (ADS)

Parisio, Francesco; Laloui, Lyesse

2018-02-01

The correct representation of the failure of geomaterials that feature strength anisotropy and polyaxiality is crucial for many applications. In this contribution, we propose and evaluate through a comparative study a generalized framework that covers both features. Polyaxiality of strength is modeled with a modified Van Eekelen approach, while the anisotropy is modeled using a fabric tensor approach of the Pietruszczak and Mroz type. Both approaches share the same philosophy as they can be applied to simpler failure surfaces, allowing great flexibility in model formulation. The new failure surface is tested against experimental data and its performance compared against classical failure criteria commonly used in geomechanics. Our study finds that the global error between predictions and data is generally smaller for the proposed framework compared to other classical approaches.

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

PubMed Central

Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

2014-01-01

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

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.

Comparative evaluation of Populus variants total sugar release and structural features following pretreatment and digestion by two distinct biological systems

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

Thomas, Vanessa A.; Kothari, Ninad; Bhagia, Samarthya

Populus natural variants have been shown to realize a broad range of sugar yields during saccharification, however, the structural features responsible for higher sugar release from natural variants are not clear. In addition, the sugar release patterns resulting from digestion with two distinct biological systems, fungal enzymes and Clostridium thermocellum, have yet to be evaluated and compared. This study evaluates the effect of structural features of three natural variant Populus lines, which includes the line BESC standard, with respect to the overall process of sugar release for two different biological systems.

Comparative evaluation of Populus variants total sugar release and structural features following pretreatment and digestion by two distinct biological systems

DOE PAGES

Thomas, Vanessa A.; Kothari, Ninad; Bhagia, Samarthya; ...

2017-11-30

Populus natural variants have been shown to realize a broad range of sugar yields during saccharification, however, the structural features responsible for higher sugar release from natural variants are not clear. In addition, the sugar release patterns resulting from digestion with two distinct biological systems, fungal enzymes and Clostridium thermocellum, have yet to be evaluated and compared. This study evaluates the effect of structural features of three natural variant Populus lines, which includes the line BESC standard, with respect to the overall process of sugar release for two different biological systems.

Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta

NASA Astrophysics Data System (ADS)

Zhang, Hongsheng; Xu, Ru

2018-02-01

Integrating synthetic aperture radar (SAR) and optical data to improve urban land cover classification has been identified as a promising approach. However, which integration level is the most suitable remains unclear but important to many researchers and engineers. This study aimed to compare different integration levels for providing a scientific reference for a wide range of studies using optical and SAR data. SAR data from TerraSAR-X and ENVISAT ASAR in both WSM and IMP modes were used to be combined with optical data at pixel level, feature level and decision levels using four typical machine learning methods. The experimental results indicated that: 1) feature level that used both the original images and extracted features achieved a significant improvement of up to 10% compared to that using optical data alone; 2) different levels of fusion required different suitable methods depending on the data distribution and data resolution. For instance, support vector machine was the most stable at both the feature and decision levels, while random forest was suitable at the pixel level but not suitable at the decision level. 3) By examining the distribution of SAR features, some features (e.g., homogeneity) exhibited a close-to-normal distribution, explaining the improvement from the maximum likelihood method at the feature and decision levels. This indicated the benefits of using texture features from SAR data when being combined with optical data for land cover classification. Additionally, the research also shown that combining optical and SAR data does not guarantee improvement compared with using single data source for urban land cover classification, depending on the selection of appropriate fusion levels and fusion methods.

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

Dyadic differences in friendships of adolescents with chronic pain compared with pain-free peers.

PubMed

Forgeron, Paula A; Chambers, Christine T; Cohen, Janice; Dick, Bruce D; Finley, G Allen; Lamontagne, Christine

2018-06-01

A multisite cross-sectional study was conducted to examine dyadic friendship features between adolescents with chronic pain (ACP) and their friends compared with non-pain adolescent friendship dyads and the association of these friendship features with loneliness and depressive symptoms. Participants completed a battery of standardized measures to capture friendship features (friendship quality, closeness, and perceived social support from friends) and indices of social-emotional well-being. Sixty-one same sex friendship dyads (122 adolescents) participated; 30 friendship dyads included an adolescent with chronic pain and 52 dyads were female. Adolescents with chronic pain scored significantly higher on measures of loneliness and depressive symptoms compared with all other participants. Hierarchical Multiple Regression analysis revealed that friendship features predicted loneliness and depressive symptoms. Chronic pain predicted loneliness and depressive symptoms above and beyond friendship features. Actor Partner Interdependence Modeling found perceived social support from friends had differing associations on loneliness and depressive symptoms for dyads with a chronic pain member compared with pain-free control dyads. Friendship features were associated with loneliness and depressive symptoms for adolescents, but friendship features alone did not explain loneliness and depressive symptoms for ACP. Further research is needed to understand whether pain-related social support improves loneliness and depressive symptoms for ACP. Furthermore, a more nuanced understanding of loneliness in this population is warranted. Strategies to help ACP garner needed social support from friends are needed to decrease rates of loneliness to improve long-term outcomes.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

PubMed

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Comparison of the Physical Education and Sports School Students' Multiple Intelligence Areas According to Demographic Features

ERIC Educational Resources Information Center

Aslan, Cem Sinan

2016-01-01

The aim of this study is to compare the multiple intelligence areas of a group of physical education and sports students according to their demographic features. In the study, "Multiple Intelligence Scale", consisting of 27 items, whose Turkish validity and reliability study have been done by Babacan (2012) and which is originally owned…

Comparison of CT enterography and MR enterography imaging features of active Crohn disease in children and adolescents.

PubMed

Gale, Heather I; Sharatz, Steven M; Taphey, Mayureewan; Bradley, William F; Nimkin, Katherine; Gee, Michael S

2017-09-01

Assessment for active Crohn disease by CT enterography and MR enterography relies on identifying mural and perienteric imaging features. To evaluate the performance of established imaging features of active Crohn disease in children and adolescents on CT and MR enterography compared with histological reference. We included patients ages 18 years and younger who underwent either CT or MR enterography from 2007 to 2014 and had endoscopic biopsy within 28 days of imaging. Two pediatric radiologists blinded to the histological results reviewed imaging studies and scored the bowel for the presence or absence of mural features (wall thickening >3 mm, mural hyperenhancement) and perienteric features (mesenteric hypervascularity, edema, fibrofatty proliferation and lymphadenopathy) of active disease. We performed univariate analysis and multivariate logistic regression to compare imaging features with histological reference. We evaluated 452 bowel segments (135 from CT enterography, 317 from MR enterography) from 84 patients. Mural imaging features had the highest association with active inflammation both for MR enterography (wall thickening had 80% accuracy, 69% sensitivity and 91% specificity; mural hyperenhancement had 78%, 53% and 96%, respectively) and CT enterography (wall thickening had 84% accuracy, 72% sensitivity and 91% specificity; mural hyperenhancement had 76%, 51% and 91%, respectively), with perienteric imaging features performing significantly worse on MR enterography relative to CT enterography (P < 0.001). Mural features are predictors of active inflammation for both CT and MR enterography, while perienteric features can be distinguished better on CT enterography compared with MR enterography. This likely reflects the increased conspicuity of the mesentery on CT enterography and suggests that mural features are the most reliable imaging features of active Crohn disease in children and adolescents.

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.

PubMed

Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

2017-12-01

This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.

Feature Selection Methods for Zero-Shot Learning of Neural Activity.

PubMed

Caceres, Carlos A; Roos, Matthew J; Rupp, Kyle M; Milsap, Griffin; Crone, Nathan E; Wolmetz, Michael E; Ratto, Christopher R

2017-01-01

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

An Evaluation of Feature Learning Methods for High Resolution Image Classification

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Montoya, J.; Schindler, K.

2012-07-01

Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

PubMed

Zhang, Junming; Wu, Yan

2018-03-28

Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

PubMed

Zheng, Yuanjie; Keller, Brad M; Ray, Shonket; Wang, Yan; Conant, Emily F; Gee, James C; Kontos, Despina

2015-07-01

Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors.

Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier.

PubMed

Desbordes, Paul; Ruan, Su; Modzelewski, Romain; Pineau, Pascal; Vauclin, Sébastien; Gouel, Pierrick; Michel, Pierre; Di Fiore, Frédéric; Vera, Pierre; Gardin, Isabelle

2017-01-01

In oncology, texture features extracted from positron emission tomography with 18-fluorodeoxyglucose images (FDG-PET) are of increasing interest for predictive and prognostic studies, leading to several tens of features per tumor. To select the best features, the use of a random forest (RF) classifier was investigated. Sixty-five patients with an esophageal cancer treated with a combined chemo-radiation therapy were retrospectively included. All patients underwent a pretreatment whole-body FDG-PET. The patients were followed for 3 years after the end of the treatment. The response assessment was performed 1 month after the end of the therapy. Patients were classified as complete responders and non-complete responders. Sixty-one features were extracted from medical records and PET images. First, Spearman's analysis was performed to eliminate correlated features. Then, the best predictive and prognostic subsets of features were selected using a RF algorithm. These results were compared to those obtained by a Mann-Whitney U test (predictive study) and a univariate Kaplan-Meier analysis (prognostic study). Among the 61 initial features, 28 were not correlated. From these 28 features, the best subset of complementary features found using the RF classifier to predict response was composed of 2 features: metabolic tumor volume (MTV) and homogeneity from the co-occurrence matrix. The corresponding predictive value (AUC = 0.836 ± 0.105, Se = 82 ± 9%, Sp = 91 ± 12%) was higher than the best predictive results found using the Mann-Whitney test: busyness from the gray level difference matrix (P < 0.0001, AUC = 0.810, Se = 66%, Sp = 88%). The best prognostic subset found using RF was composed of 3 features: MTV and 2 clinical features (WHO status and nutritional risk index) (AUC = 0.822 ± 0.059, Se = 79 ± 9%, Sp = 95 ± 6%), while no feature was significantly prognostic according to the Kaplan-Meier analysis. The RF classifier can improve predictive and prognostic values compared to the Mann-Whitney U test and the univariate Kaplan-Meier survival analysis when applied to several tens of features in a limited patient database.

Study of reflection and transport in the microwave photo-excited GaAs/AlGaAs two dimensional electron system

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

Ye, Tianyu; Mani, Ramesh G.; Wegscheider, Werner

2013-12-04

We present the results of a concurrent experimental study of microwave reflection and transport in the GaAs/AlGaAs two dimensional electron gas system and correlate observed features in the reflection with the observed transport features. The experimental results are compared with expectations based on theory.

No evidence for intervention-dependent influence of methodological features on treatment effect.

PubMed

Jacobs, Wilco C H; Kruyt, Moyo C; Moojen, Wouter A; Verbout, Ab J; Oner, F Cumhur

2013-12-01

The goal of this systematic review was to evaluate if the influence of methodological features on treatment effect differs between types of intervention. MEDLINE, Embase, Web of Science, Cochrane methodology register, and reference lists were searched for meta-epidemiologic studies on the influence of methodological features on treatment effect. Studies analyzing influence of methodological features related to internal validity were included. We made a distinction among surgical, pharmaceutical, and therapeutical as separate types of intervention. Heterogeneity was calculated to identify differences among these types. Fourteen meta-epidemiologic studies were found with 51 estimates of influence of methodological features on treatment effect. Heterogeneity was observed among the intervention types for randomization. Surgical intervention studies showed a larger treatment effect when randomized; this was in contrast to pharmaceutical studies that found the opposite. For allocation concealment and double blinding, the influence of methodological features on the treatment effect was comparable across different types of intervention. For the remaining methodological features, there were insufficient observations. The influence of allocation concealment and double blinding on the treatment effect is consistent across studies of different interventional types. The influence of randomization although, may be different between surgical and nonsurgical studies. Copyright © 2013 Elsevier Inc. All rights reserved.

Pressure-Relief Features of Fixed and Autotitrating Continuous Positive Airway Pressure May Impair Their Efficacy: Evaluation with a Respiratory Bench Model

PubMed Central

Zhu, Kaixian; Aouf, Sami; Roisman, Gabriel; Escourrou, Pierre

2016-01-01

Study Objectives: Pressure-relief features are aimed at improving the patient's comfort during continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea. The objective of this study was to determine the effect of these therapy features on fixed CPAP and autotitrating CPAP (APAP) treatment efficacy. Methods: Seven pressure-relief features applied by three CPAP devices were included in our study (Remstar Auto: C-Flex 3, C-Flex+ 3, A-Flex 3, P-Flex; AirSense 10: EPR 3; Prisma 20A: SoftPAP 2 and 3). In fixed CPAP, the devices were subjected to a 10-min bench-simulated obstructive apnea sequence (initial apnea-hypopnea index, AHI = 60/h) with and without pressure-relief features. In APAP, the sequence was lengthened to 4.2 h (initial AHI = 58.6/h). The residual AHI and mean/median pressure were compared with and without pressure-relief features. Results: Compared to conventional CPAP, where pressure was adjusted to be just sufficient to control the simulated obstructive events, C-Flex+ 3, P-Flex, and EPR 3 failed to normalize the breathing flow and did not reduce the AHI. The mean pressures with the three features, respectively, were 1.8, 2.6, and 2.6 cmH2O lower than the conventional CPAP. Compared to conventional APAP, similar levels of control were observed with pressure-relief features, apart from P-Flex where the delivered mean pressure was lower and residual AHI greater. The device-reported mean/median pressures in APAP with A-Flex 3, P-Flex, EPR 3, and SoftPAP 3 were higher than that measured on the bench. Conclusions: Pressure-relief features may attenuate CPAP efficacy if not adjusted for at the time of their introduction. In clinical practice, efficacy can be ensured by increasing the therapeutic pressure delivered by fixed CPAP or by enabling the pressure-relief features prior to initial pressure titration. Device-reported pressures in APAP devices with pressure relief activated may overstate delivered pressures. Citation: Zhu K, Aouf S, Roisman G, Escourrou P. Pressure-relief features of fixed and autotitrating continuous positive airway pressure may impair their efficacy: evaluation with a respiratory bench model. J Clin Sleep Med 2016;12(3):385–392. PMID:26564383

New Features for Neuron Classification.

PubMed

Hernández-Pérez, Leonardo A; Delgado-Castillo, Duniel; Martín-Pérez, Rainer; Orozco-Morales, Rubén; Lorenzo-Ginori, Juan V

2018-04-28

This paper addresses the problem of obtaining new neuron features capable of improving results of neuron classification. Most studies on neuron classification using morphological features have been based on Euclidean geometry. Here three one-dimensional (1D) time series are derived from the three-dimensional (3D) structure of neuron instead, and afterwards a spatial time series is finally constructed from which the features are calculated. Digitally reconstructed neurons were separated into control and pathological sets, which are related to three categories of alterations caused by epilepsy, Alzheimer's disease (long and local projections), and ischemia. These neuron sets were then subjected to supervised classification and the results were compared considering three sets of features: morphological, features obtained from the time series and a combination of both. The best results were obtained using features from the time series, which outperformed the classification using only morphological features, showing higher correct classification rates with differences of 5.15, 3.75, 5.33% for epilepsy and Alzheimer's disease (long and local projections) respectively. The morphological features were better for the ischemia set with a difference of 3.05%. Features like variance, Spearman auto-correlation, partial auto-correlation, mutual information, local minima and maxima, all related to the time series, exhibited the best performance. Also we compared different evaluators, among which ReliefF was the best ranked.

A study of extreme carbon stars. I - Silicon carbide emission features

NASA Technical Reports Server (NTRS)

Cohen, M.

1984-01-01

10-micron spectra of many extreme carbon stars reveal a prominent emission feature near 11 microns. This is compared with laboratory spectra of SiC grains. Two distinct types of features are found, perhaps indicative of different mechanisms of grain formation in different stars. Estimates are made of probable column densities and total masses of SiC in the circumstellar shells.

Ultrastructure of canine vasoformative tumors.

PubMed

Madewell, B R; Griffey, S M; Munn, R J

1992-01-01

The transmission electron microscope was used to examine 20 spontaneous canine hemangiosarcomas or hemangiopericytomas in order to define their fine ultrastructural features, and to compare those features with descriptions of human counterpart neoplasms. From specimen to specimen the neoplasms examined showed considerable structural heterogeneity but, in composite, appeared similar to the prototype human tumors. These data suggest that the canine hemangiosarcoma and hemangiopericytoma might serve as comparative models for studies of the morphogenesis of vasoformative neoplasms.

Fetal alcohol spectrum disorders--a case-control study from India.

PubMed

Nayak, Raghavendra; Murthy, Pratima; Girimaji, Satish; Navaneetham, Jamuna

2012-02-01

Maternal alcohol abuse during pregnancy can lead to fetal neurotoxicity and fetal alcohol spectrum disorder (FASD). To compare the clinical features and neurobehavioral profiles of children exposed to alcohol during pregnancy with controls. Children exposed to alcohol in utero (n = 26) and 27-years age- and sex-matched controls were compared on FAS facial features, minor physical anomalies (MPAs), anthropometric measures, behavioral problems and intellectual functioning. MPAs were more common in cases (p = 0.001). Among FAS facial features, only philtrum smoothness varied significantly between the groups (p = 0.001). Behavioral problems (on Childhood Behavior Check List) were more pronounced (p = 0.001) and intellectual functioning significantly poorer in cases (p = 0.001) compared to controls. Children prenatally exposed to alcohol manifest several neurobehavioral problems compared to controls. Underlying malnutrition may have altered some of the clinical findings.

Visual feature integration with an attention deficit.

PubMed

Arguin, M; Cavanagh, P; Joanette, Y

1994-01-01

Treisman's feature integration theory proposes that the perception of illusory conjunctions of correctly encoded visual features is due to the failure of an attentional process. This hypothesis was examined by studying brain-damaged subjects who had previously been shown to have difficulty in attending to contralesional stimulation. These subjects exhibited a massive feature integration deficit for contralesional stimulation relative to ipsilesional displays. In contrast, both normal age-matched controls and brain-damaged subjects who did not exhibit any evidence of an attention deficit showed comparable feature integration performance with left- and right-hemifield stimulation. These observations indicate the crucial function of attention for visual feature integration in normal perception.

The Role of Attention in the Binding of Surface Features to Locations

PubMed Central

Hyun, Joo-seok; Woodman, Geoffrey F.; Luck, Steven J.

2013-01-01

Previous studies have proposed that attention is not necessary for detecting simple features but is necessary for binding them to spatial locations. The present study tested this hypothesis, using the N2pc component of the event-related potential waveform as a measure of the allocation of attention. A simple feature detection condition, in which observers reported whether a target color was present or not, was compared with feature-location binding conditions, in which observers reported the location of the target color. A larger N2pc component was observed in the binding conditions than in the detection condition, indicating that additional attentional resources are needed to bind a feature to a location than to detect the feature independently of its location. This finding supports theories of attention in which attention plays a special role in binding features. PMID:24235876

Home and Community Environmental Features, Activity Performance, and Community Participation among Older Adults with Functional Limitations

PubMed Central

Yang, Hsiang-Yu; Sanford, Jon A.

2012-01-01

This paper describes relationships among home and community environmental features, activity performance in the home, and community participation potential to support aging in place. A subset of data on older adults with functional limitations (N = 122), sixty three (63) with mobility and 59 with other limitations, were utilized in this study from a larger project's subject pool. Results showed significant and positive correlations between environmental barriers, activity dependence and difficulty at home, and less community participation in the mobility limitation group. While kitchen and bathroom features were most limiting to home performance, bathtub or shower was the only home feature, and destination social environment was the only community feature, that explained community participation. Compared to environmental features, home performance explained much more community participation. Study results provide detailed information about environmental features as well as types of home activities that can be prioritized as interventions for aging in place. PMID:22162808

Using high spectral resolution spectrophotometry to study broad mineral absorption features on Mars

NASA Technical Reports Server (NTRS)

Blaney, D. L.; Crisp, D.

1993-01-01

Traditionally telescopic measurements of mineralogic absorption features have been made using relatively low to moderate (R=30-300) spectral resolution. Mineralogic absorption features tend to be broad so high resolution spectroscopy (R greater than 10,000) does not provide significant additional compositional information. Low to moderate resolution spectroscopy allows an observer to obtain data over a wide wavelength range (hundreds to thousands of wavenumbers) compared to the several wavenumber intervals that are collected using high resolution spectrometers. However, spectrophotometry at high resolution has major advantages over lower resolution spectroscopy in situations that are applicable to studies of the Martian surface, i.e., at wavelengths where relatively weak surface absorption features and atmospheric gas absorption features both occur.

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.

Treatment recommendations for DSM-5-defined mixed features.

PubMed

Rosenblat, Joshua D; McIntyre, Roger S

2017-04-01

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) mixed features specifier provides a less restrictive definition of mixed mood states, compared to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR), including mood episodes that manifest with subthreshold symptoms of the opposite mood state. A limited number of studies have assessed the efficacy of treatments specifically for DSM-5-defined mixed features in mood disorders. As such, there is currently an inadequate amount of data to appropriately inform evidence-based treatment guidelines of DSM-5 defined mixed features. However, given the high prevalence and morbidity of mixed features, treatment recommendations based on the currently available evidence along with expert opinion may be of benefit. This article serves to provide these interim treatment recommendations while humbly acknowledging the limited amount of evidence currently available. Second-generation antipsychotics (SGAs) appear to have the greatest promise in the treatment of bipolar disorder (BD) with mixed features. Conventional mood stabilizing agents (ie, lithium and divalproex) may also be of benefit; however, they have been inadequately studied. In the treatment of major depressive disorder (MDD) with mixed features, the comparable efficacy of antidepressants versus other treatments, such as SGAs, remains unknown. As such, antidepressants remain first-line treatment of MDD with or without mixed features; however, there are significant safety concerns associated with antidepressant monotherapy when mixed features are present, which merits increased monitoring. Lurasidone is the only SGA monotherapy that has been shown to be efficacious specifically in the treatment of MDD with mixed features. Further research is needed to accurately determine the efficacy, safety, and tolerability of treatments specifically for mood episodes with mixed features to adequately inform future treatment guidelines.

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

NASA Astrophysics Data System (ADS)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

A window-based time series feature extraction method.

PubMed

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

PubMed

Khondoker, Mizanur; Dobson, Richard; Skirrow, Caroline; Simmons, Andrew; Stahl, Daniel

2016-10-01

Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features. For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study. © The Author(s) 2013.

Feature Selection Methods for Zero-Shot Learning of Neural Activity

PubMed Central

Caceres, Carlos A.; Roos, Matthew J.; Rupp, Kyle M.; Milsap, Griffin; Crone, Nathan E.; Wolmetz, Michael E.; Ratto, Christopher R.

2017-01-01

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy. PMID:28690513

Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation: A Preliminary Study

PubMed Central

Kontos, Despina; Bakic, Predrag R.; Carton, Ann-Katherine; Troxel, Andrea B.; Conant, Emily F.; Maidment, Andrew D.A.

2009-01-01

Rationale and Objectives Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superimposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superimposition, offering superior parenchymal texture visualization compared to mammography. Our study investigates the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation. Materials and Methods DBT and digital mammography (DM) images of 39 women were analyzed. Texture features, shown in studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. We compared the relative performance of DBT and DM texture features in correlating with two measures of breast cancer risk: (i) the Gail and Claus risk estimates, and (ii) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk. Results No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density (PD) than DM (p ≤0.05). When dividing our study population in groups of increasing breast PD, the DBT texture features appeared to be more discriminative, having regression lines with overall lower p-values, steeper slopes, and higher R2 estimates. Conclusion Although preliminary, our results suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation. PMID:19201357

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

Characterizing mammographic images by using generic texture features

PubMed Central

2012-01-01

Introduction Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. Methods A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. Results Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. Conclusions Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy. PMID:22490545

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

A structural SVM approach for reference parsing.

PubMed

Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

2011-06-09

Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

Networking Course Syllabus in Accredited Library and Information Science Programs: A Comparative Analysis Study

ERIC Educational Resources Information Center

Abouserie, Hossam Eldin Mohamed Refaat

2009-01-01

The study investigated networking courses offered in accredited Library and Information Science schools in the United States in 2009. The study analyzed and compared network syllabi according to Course Syllabus Evaluation Rubric to obtain in-depth understanding of basic features and characteristics of networking courses taught. The study embraced…

A liver cirrhosis classification on B-mode ultrasound images by the use of higher order local autocorrelation features

NASA Astrophysics Data System (ADS)

Sasaki, Kenya; Mitani, Yoshihiro; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao

2017-02-01

In this paper, in order to classify liver cirrhosis on regions of interest (ROIs) images from B-mode ultrasound images, we have proposed to use the higher order local autocorrelation (HLAC) features. In a previous study, we tried to classify liver cirrhosis by using a Gabor filter based approach. However, the classification performance of the Gabor feature was poor from our preliminary experimental results. In order accurately to classify liver cirrhosis, we examined to use the HLAC features for liver cirrhosis classification. The experimental results show the effectiveness of HLAC features compared with the Gabor feature. Furthermore, by using a binary image made by an adaptive thresholding method, the classification performance of HLAC features has improved.

Sex Determination by Biometry of Anterior Features of Human Hip Bones in South Indian Population.

PubMed

Rajasekhar, Sssn; Vasudha, T K; Aravindhan, K

2017-06-01

Sex determination is the first step in establishing the identity of skeletal remains. Many studies included biometry of posterior features of hip bone. Very few studies are reported involving the biometry of anterior features of the hip bone. Anterior features of hip bone are important especially, if there is damage to the posterior features of hip bone in cases involving deliberate disfigurement of the body to resist identification of the crime in medicolegal cases. The present study was done to evaluate the effectiveness of anterior border parameters of the hip bone for prediction of sex using discriminant function analysis in South Indian population. A total of 206 dry bones were used (121 male and 85 female) and parameters like the distance between pubic tubercle and anterior rim of acetabulum, vertical acetabular diameter, transverse acetabular diameter, and the distance between pubic tubercle to highest point on the iliopubic eminence were measured using Vernier calipers. Normally distributed variables were compared using Students t-test to analyse the significance. There was significant difference between the male and female hip bones of the observed variables with p-value less than 0.05. In parameters like the distance between pubic tubercle to anterior rim of acetabulum and distance between the highest points on iliopubic eminence to pubic tubercle; the values were more in female when compared to males. In parameters like vertical and transverse acetabular diameters; the values in males were more when compared to females. These parameters of hip bone can be utilised for sex determination in South Indian population.

Imaging Features of Radiofrequency Ablation with Heat-Deployed Liposomal Doxorubicin in Hepatic Tumors

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

Hong, Cheng William, E-mail: williamhongcheng@gmail.com; Chow, Lucy, E-mail: lucychow282@gmail.com; Turkbey, Evrim B., E-mail: evrimbengi@yahoo.com

2016-03-15

IntroductionThe imaging features of unresectable hepatic malignancies in patients who underwent radiofrequency ablation (RFA) in combination with lyso-thermosensitive liposomal doxorubicin (LTLD) were determined.Materials and MethodsA phase I dose escalation study combining RFA with LTLD was performed with peri- and post- procedural CT and MRI. Imaging features were analyzed and measured in terms of ablative zone size and surrounding penumbra size. The dynamic imaging appearance was described qualitatively immediately following the procedure and at 1-month follow-up. The control group receiving liver RFA without LTLD was compared to the study group in terms of imaging features and post-ablative zone size dynamics atmore » follow-up.ResultsPost-treatment scans of hepatic lesions treated with RFA and LTLD have distinctive imaging characteristics when compared to those treated with RFA alone. The addition of LTLD resulted in a regular or smooth enhancing rim on T1W MRI which often correlated with increased attenuation on CT. The LTLD-treated ablation zones were stable or enlarged at follow-up four weeks later in 69 % of study subjects as opposed to conventional RFA where the ablation zone underwent involution compared to imaging acquired immediately after the procedure.ConclusionThe imaging features following RFA with LTLD were different from those after standard RFA and can mimic residual or recurrent tumor. Knowledge of the subtle findings between the two groups can help avoid misinterpretation and proper identification of treatment failure in this setting. Increased size of the LTLD-treated ablation zone after RFA suggests the ongoing drug-induced biological effects.« less

Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind.

PubMed

Shrivastava, Vimal K; Londhe, Narendra D; Sonawane, Rajendra S; Suri, Jasjit S

2016-04-01

Psoriasis is an autoimmune skin disease with red and scaly plaques on skin and affecting about 125 million people worldwide. Currently, dermatologist use visual and haptic methods for diagnosis the disease severity. This does not help them in stratification and risk assessment of the lesion stage and grade. Further, current methods add complexity during monitoring and follow-up phase. The current diagnostic tools lead to subjectivity in decision making and are unreliable and laborious. This paper presents a first comparative performance study of its kind using principal component analysis (PCA) based CADx system for psoriasis risk stratification and image classification utilizing: (i) 11 higher order spectra (HOS) features, (ii) 60 texture features, and (iii) 86 color feature sets and their seven combinations. Aggregate 540 image samples (270 healthy and 270 diseased) from 30 psoriasis patients of Indian ethnic origin are used in our database. Machine learning using PCA is used for dominant feature selection which is then fed to support vector machine classifier (SVM) to obtain optimized performance. Three different protocols are implemented using three kinds of feature sets. Reliability index of the CADx is computed. Among all feature combinations, the CADx system shows optimal performance of 100% accuracy, 100% sensitivity and specificity, when all three sets of feature are combined. Further, our experimental result with increasing data size shows that all feature combinations yield high reliability index throughout the PCA-cutoffs except color feature set and combination of color and texture feature sets. HOS features are powerful in psoriasis disease classification and stratification. Even though, independently, all three set of features HOS, texture, and color perform competitively, but when combined, the machine learning system performs the best. The system is fully automated, reliable and accurate. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Study on the traditional pattern retrieval method of minorities in Gansu province

NASA Astrophysics Data System (ADS)

Zheng, Gang; Wang, Beizhan; Sun, Yuchun; Xu, Jin

2018-03-01

The traditional patterns of ethnic minorities in gansu province are ethnic arts with strong ethnic characteristics. It is the crystallization of the hard work and wisdom of minority nationalities in gansu province. Unique traditional patterns of ethnic minorities in Gansu province with rich ethnic folk arts, is the crystallization of geographical environment in Gansu minority diligence and wisdom. By using the Surf feature point identification algorithm, the feature point extractor in OpenCV is used to extract the feature points. And the feature points are applied to compare the pattern features to find patterns similar to the artistic features. The application of this method can quickly or efficiently extract pattern information in a database.

iFER: facial expression recognition using automatically selected geometric eye and eyebrow features

NASA Astrophysics Data System (ADS)

Oztel, Ismail; Yolcu, Gozde; Oz, Cemil; Kazan, Serap; Bunyak, Filiz

2018-03-01

Facial expressions have an important role in interpersonal communications and estimation of emotional states or intentions. Automatic recognition of facial expressions has led to many practical applications and became one of the important topics in computer vision. We present a facial expression recognition system that relies on geometry-based features extracted from eye and eyebrow regions of the face. The proposed system detects keypoints on frontal face images and forms a feature set using geometric relationships among groups of detected keypoints. Obtained feature set is refined and reduced using the sequential forward selection (SFS) algorithm and fed to a support vector machine classifier to recognize five facial expression classes. The proposed system, iFER (eye-eyebrow only facial expression recognition), is robust to lower face occlusions that may be caused by beards, mustaches, scarves, etc. and lower face motion during speech production. Preliminary experiments on benchmark datasets produced promising results outperforming previous facial expression recognition studies using partial face features, and comparable results to studies using whole face information, only slightly lower by ˜ 2.5 % compared to the best whole face facial recognition system while using only ˜ 1 / 3 of the facial region.

Extraction of Late Summer Sea Ice Properties from Polarimetric SAR Features in C- and X-Band

NASA Astrophysics Data System (ADS)

Fors, Ane S.; Brekke, Camilla; Gerland, Sebastian; Doulgeris, Anthony P.; Eltoft, Torbjørn

2015-04-01

In this study we examine the potential use of six polarimetric features for interpretation of late summer sea ice types. Five high-resolution C and X-band scenes were recorded in the Fram Strait covering fast first-year and old sea ice. In addition sea ice thickness, surface roughness and melt pond fraction were collected during a helicopter flight at the study area. From the SAR scenes, six polarimetric features were extracted. Along sections of the track of the helicopter flight, the mean of the SAR features were compared to mean values of the properties measured during the helicopter flight. The results reveal relations between several of the SAR features and the geophysical properties measured in C-band, and weak relations in X-band.

A multicenter tractography study of deep white matter tracts in bipolar I disorder: psychotic features and interhemispheric disconnectivity.

PubMed

Sarrazin, Samuel; Poupon, Cyril; Linke, Julia; Wessa, Michèle; Phillips, Mary; Delavest, Marine; Versace, Amelia; Almeida, Jorge; Guevara, Pamela; Duclap, Delphine; Duchesnay, Edouard; Mangin, Jean-François; Le Dudal, Katia; Daban, Claire; Hamdani, Nora; D'Albis, Marc-Antoine; Leboyer, Marion; Houenou, Josselin

2014-04-01

Tractography studies investigating white matter (WM) abnormalities in patients with bipolar disorder have yielded heterogeneous results owing to small sample sizes. The small size limits their generalizability, a critical issue for neuroimaging studies of biomarkers of bipolar I disorder (BPI). To study WM abnormalities using whole-brain tractography in a large international multicenter sample of BPI patients and to compare these alterations between patients with or without a history of psychotic features during mood episodes. A cross-sectional, multicenter, international, Q-ball imaging tractography study comparing 118 BPI patients and 86 healthy control individuals. In addition, among the patient group, we compared those with and without a history of psychotic features. University hospitals in France, Germany, and the United States contributed participants. Participants underwent assessment using the Diagnostic Interview for Genetic Studies at the French sites or the Structured Clinical Interview for DSM-IV at the German and US sites. Diffusion-weighted magnetic resonance images were acquired using the same acquisition parameters and scanning hardware at each site. We reconstructed 22 known deep WM tracts using Q-ball imaging tractography and an automatized segmentation technique. Generalized fractional anisotropy values along each reconstructed WM tract. Compared with controls, BPI patients had significant reductions in mean generalized fractional anisotropy values along the body and the splenium of the corpus callosum, the left cingulum, and the anterior part of the left arcuate fasciculus when controlling for age, sex, and acquisition site (corrected for multiple testing). Patients with a history of psychotic features had a lower mean generalized fractional anisotropy value than those without along the body of the corpus callosum (corrected for multiple testing). In this multicenter sample, BPI patients had reduced WM integrity in interhemispheric, limbic, and arcuate WM tracts. Interhemispheric pathways are more disrupted in patients with than in those without psychotic symptoms. Together these results highlight the existence of an anatomic disconnectivity in BPI and further underscore a role for interhemispheric disconnectivity in the pathophysiological features of psychosis in BPI.

The surprisingly high human efficiency at learning to recognize faces

PubMed Central

Peterson, Matthew F.; Abbey, Craig K.; Eckstein, Miguel P.

2009-01-01

We investigated the ability of humans to optimize face recognition performance through rapid learning of individual relevant features. We created artificial faces with discriminating visual information heavily concentrated in single features (nose, eyes, chin or mouth). In each of 2500 learning blocks a feature was randomly selected and retained over the course of four trials, during which observers identified randomly sampled, noisy face images. Observers learned the discriminating feature through indirect feedback, leading to large performance gains. Performance was compared to a learning Bayesian ideal observer, resulting in unexpectedly high learning compared to previous studies with simpler stimuli. We explore various explanations and conclude that the higher learning measured with faces cannot be driven by adaptive eye movement strategies but can be mostly accounted for by suboptimalities in human face discrimination when observers are uncertain about the discriminating feature. We show that an initial bias of humans to use specific features to perform the task even though they are informed that each of four features is equally likely to be the discriminatory feature would lead to seemingly supra-optimal learning. We also examine the possibility of inefficient human integration of visual information across the spatially distributed facial features. Together, the results suggest that humans can show large performance improvement effects in discriminating faces as they learn to identify the feature containing the discriminatory information. PMID:19000918

Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.

PubMed

Lin, Xiaohui; Li, Chao; Zhang, Yanhui; Su, Benzhe; Fan, Meng; Wei, Hai

2017-12-26

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.

Analysis of pseudocolor transformations of ERTS-1 images of Southern California area. [geological faults and lineaments

NASA Technical Reports Server (NTRS)

Merifield, P. M. (Principal Investigator); Lamar, D. L.; Stratton, R. H.; Lamar, J. V.; Gazley, C., Jr.

1974-01-01

The author has identified the following significant results. Representative faults and lineaments, natural features on the Mojave Desert, and cultural features of the southern California area were studied on ERTS-1 images. The relative appearances of the features were compared on a band 4 and 5 subtraction image, its pseudocolor transformation, and pseudocolor images of bands 4, 5, and 7. Selected features were also evaluated in a test given students at the University of California, Los Angeles. Observations and the test revealed no significant improvement in the ability to detect and locate faults and lineaments on the pseudocolor transformations. With the exception of dry lake surfaces, no enhancement of the features studied was observed on the bands 4 and 5 subtraction images. Geologic and geographic features characterized by minor tonal differences on relatively flat surfaces were enhanced on some of the pseudocolor images.

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

PubMed

Guo, Jian; Qian, Kun; Zhang, Gongxuan; Xu, Huijie; Schuller, Björn

2017-12-01

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

Application of IRS-1D data in water erosion features detection (case study: Nour roud catchment, Iran).

PubMed

Solaimani, K; Amri, M A Hadian

2008-08-01

The aim of this study was capability of Indian Remote Sensing (IRS) data of 1D to detecting erosion features which were created from run-off. In this study, ability of PAN digital data of IRS-1D satellite was evaluated for extraction of erosion features in Nour-roud catchment located in Mazandaran province, Iran, using GIS techniques. Research method has based on supervised digital classification, using MLC algorithm and also visual interpretation, using PMU analysis and then these were evaluated and compared. Results indicated that opposite of digital classification, with overall accuracy 40.02% and kappa coefficient 31.35%, due to low spectral resolution; visual interpretation and classification, due to high spatial resolution (5.8 m), prepared classifying erosion features from this data, so that these features corresponded with the lithology, slope and hydrograph lines using GIS, so closely that one can consider their boundaries overlapped. Also field control showed that this data is relatively fit for using this method in investigation of erosion features and specially, can be applied to identify large erosion features.

SU-D-BRA-07: A Phantom Study to Assess the Variability in Radiomics Features Extracted From Cone-Beam CT Images

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

Fave, X; Fried, D; UT Health Science Center Graduate School of Biomedical Sciences, Houston, TX

2015-06-15

Purpose: Several studies have demonstrated the prognostic potential for texture features extracted from CT images of non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine if these features could be extracted with high reproducibility from cone-beam CT (CBCT) images in order for features to be easily tracked throughout a patient’s treatment. Methods: Two materials in a radiomics phantom, designed to approximate NSCLC tumor texture, were used to assess the reproducibility of 26 features. This phantom was imaged on 9 CBCT scanners, including Elekta and Varian machines. Thoracic and head imaging protocols were acquired on eachmore » machine. CBCT images from 27 NSCLC patients imaged using the thoracic protocol on Varian machines were obtained for comparison. The variance for each texture measured from these patients was compared to the variance in phantom values for different manufacturer/protocol subsets. Levene’s test was used to identify features which had a significantly smaller variance in the phantom scans versus the patient data. Results: Approximately half of the features (13/26 for material1 and 15/26 for material2) had a significantly smaller variance (p<0.05) between Varian thoracic scans of the phantom compared to patient scans. Many of these same features remained significant for the head scans on Varian (12/26 and 8/26). However, when thoracic scans from Elekta and Varian were combined, only a few features were still significant (4/26 and 5/26). Three features (skewness, coarsely filtered mean and standard deviation) were significant in almost all manufacturer/protocol subsets. Conclusion: Texture features extracted from CBCT images of a radiomics phantom are reproducible and show significantly less variation than the same features measured from patient images when images from the same manufacturer or with similar parameters are used. Reproducibility between CBCT scanners may be high enough to allow the extraction of meaningful texture values for patients. This project was funded in part by the Cancer Prevention Research Institute of Texas (CPRIT). Xenia Fave is a recipient of the American Association of Physicists in Medicine Graduate Fellowship.« less

The effects of acute alcohol intoxication on the cognitive mechanisms underlying false facial recognition.

PubMed

Colloff, Melissa F; Flowe, Heather D

2016-06-01

False face recognition rates are sometimes higher when faces are learned while under the influence of alcohol. Alcohol myopia theory (AMT) proposes that acute alcohol intoxication during face learning causes people to attend to only the most salient features of a face, impairing the encoding of less salient facial features. Yet, there is currently no direct evidence to support this claim. Our objective was to test whether acute alcohol intoxication impairs face learning by causing subjects to attend to a salient (i.e., distinctive) facial feature over other facial features, as per AMT. We employed a balanced placebo design (N = 100). Subjects in the alcohol group were dosed to achieve a blood alcohol concentration (BAC) of 0.06 %, whereas the no alcohol group consumed tonic water. Alcohol expectancy was controlled. Subjects studied faces with or without a distinctive feature (e.g., scar, piercing). An old-new recognition test followed. Some of the test faces were "old" (i.e., previously studied), and some were "new" (i.e., not previously studied). We varied whether the new test faces had a previously studied distinctive feature versus other familiar characteristics. Intoxicated and sober recognition accuracy was comparable, but subjects in the alcohol group made more positive identifications overall compared to the no alcohol group. The results are not in keeping with AMT. Rather, a more general cognitive mechanism appears to underlie false face recognition in intoxicated subjects. Specifically, acute alcohol intoxication during face learning results in more liberal choosing, perhaps because of an increased reliance on familiarity.

Automatic change detection: does the auditory system use representations of individual stimulus features or gestalts?

PubMed

Deacon, D; Nousak, J M; Pilotti, M; Ritter, W; Yang, C M

1998-07-01

The effects of global and feature-specific probabilities of auditory stimuli were manipulated to determine their effects on the mismatch negativity (MMN) of the human event-related potential. The question of interest was whether the automatic comparison of stimuli indexed by the MMN was performed on representations of individual stimulus features or on gestalt representations of their combined attributes. The design of the study was such that both feature and gestalt representations could have been available to the comparator mechanism generating the MMN. The data were consistent with the interpretation that the MMN was generated following an analysis of stimulus features.

A morphological comparison of narrow, low-gradient streams traversing wetland environments to alluvial streams.

PubMed

Jurmu, Michael C

2002-12-01

Twelve morphological features from research on alluvial streams are compared in four narrow, low-gradient wetland streams located in different geographic regions (Connecticut, Indiana, and Wisconsin, USA). All four reaches differed in morphological characteristics in five of the features compared (consistent bend width, bend cross-sectional shape, riffle width compared to pool width, greatest width directly downstream of riffles, and thalweg location), while three reaches differed in two comparisons (mean radius of curvature to width ratio and axial wavelength to width ratio). The remaining five features compared had at least one reach where different characteristics existed. This indicates the possibility of varying morphology for streams traversing wetland areas further supporting the concept that the unique qualities of wetland environments might also influence the controls on fluvial dynamics and the development of streams. If certain morphological features found in streams traversing wetland areas differ from current fluvial principles, then these varying features should be incorporated into future wetland stream design and creation projects. The results warrant further research on other streams traversing wetlands to determine if streams in these environments contain unique morphology and further investigation of the impact of low-energy fluvial processes on morphological development. Possible explanations for the morphology deviations in the study streams and some suggestions for stream design in wetland areas based upon the results and field observations are also presented.

MO-DE-207B-01: JACK FOWLER JUNIOR INVESTIGATOR COMPETITION WINNER: Between Somatic Mutations and PET-Based Radiomic Features in Non-Small Cell Lung Cancer

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

Yip, S; Coroller, T; Rios Velazquez, E

Purpose: Although PET-based radiomic features have been proposed to quantify tumor heterogeneity and shown promise in outcome prediction, little is known about their relationship with tumor genetics. This study assessed the association of [{sup 18}F]fluorodeoxyglucose (FDG)-PET-based radiomic features with non-small cell lung cancer (NSCLC) mutations. Methods: 348 NSCLC patients underwent FDG-PET/CT scans before treatment and were tested for genetic mutations. 13% (44/348) and 28% (96/348) patients were found to harbor EGFR (EGFR+) and KRAS (KRAS+) mutations, respectively. We evaluated nineteen PET-based radiomic features quantifying phenotypic traits, and compared them with conventional PET features (metabolic tumor volume (MTV) and maximum-SUV). Themore » association between the feature values and mutation status was evaluated using the Wilcoxcon-rank-sum-test. The ability of each measure to predict mutations was assessed by the area under the receiver operating curve (AUC). Noether’s test was used to determine if the AUCs were significantly from random (AUC=0.50). All p-values were corrected for multiple testing by controlling the false discovery rate (FDR{sub Wilcoxon} and FDR{sub Noether}) of 10%. Results: Eight radiomic features, MTV, and maximum-SUV, were significantly associated with the EGFR mutation (FDR{sub Wilcoxon}=0.01–0.10). However, KRAS+ demonstrated no significantly distinctive imaging features compared to KRAS− (FDR{sub Wilcoxon}≥0.92). EGFR+ and EGFR− were significantly discriminated by conventional PET features (AUC=0.61, FDR{sub Noether}=0.04 for MTV and AUC=0.64, FDR{sub Noether}=0.01 for maximum-SUV). Eight radiomic features were significantly predictive for EGFR+ compared to EGFR− (AUC=0.59–0.67, FDR{sub Noether}=0.0032–0.09). Normalized-inverse-difference-moment outperformed all features in predicting EGFR mutation (AUC=0.67, FDR{sub Noether}=0.0032). Moreover, only the radiomic feature normalized-inverse-difference-moment could significantly predict KRAS+ from EGFR+ (AUC=0.65, FDR{sub Noether}=0.05). All measures failed to predict KRAS+ from KRAS− (AUC=0.50–0.54, FDR{sub Noether}≥0.92). Conclusion: PET imaging features were strongly associated with EGFR mutations in NSCLC. Radiomic features have great potential in predicting EGFR mutations. Our study may help develop a non-invasive imaging biomarker for EGFR mutation. R.M. has consulting interests with Amgen.« less

A quick eye to anger: An investigation of a differential effect of facial features in detecting angry and happy expressions.

PubMed

Lo, L Y; Cheng, M Y

2017-06-01

Detection of angry and happy faces is generally found to be easier and faster than that of faces expressing emotions other than anger or happiness. This can be explained by the threatening account and the feature account. Few empirical studies have explored the interaction between these two accounts which are seemingly, but not necessarily, mutually exclusive. The present studies hypothesised that prominent facial features are important in facilitating the detection process of both angry and happy expressions; yet the detection of happy faces was more facilitated by the prominent features than angry faces. Results confirmed the hypotheses and indicated that participants reacted faster to the emotional expressions with prominent features (in Study 1) and the detection of happy faces was more facilitated by the prominent feature than angry faces (in Study 2). The findings are compatible with evolutionary speculation which suggests that the angry expression is an alarming signal of potential threats to survival. Compared to the angry faces, the happy faces need more salient physical features to obtain a similar level of processing efficiency. © 2015 International Union of Psychological Science.

Case study of 3D fingerprints applications

PubMed Central

Liu, Feng; Liang, Jinrong; Shen, Linlin; Yang, Meng; Zhang, David; Lai, Zhihui

2017-01-01

Human fingers are 3D objects. More information will be provided if three dimensional (3D) fingerprints are available compared with two dimensional (2D) fingerprints. Thus, this paper firstly collected 3D finger point cloud data by Structured-light Illumination method. Additional features from 3D fingerprint images are then studied and extracted. The applications of these features are finally discussed. A series of experiments are conducted to demonstrate the helpfulness of 3D information to fingerprint recognition. Results show that a quick alignment can be easily implemented under the guidance of 3D finger shape feature even though this feature does not work for fingerprint recognition directly. The newly defined distinctive 3D shape ridge feature can be used for personal authentication with Equal Error Rate (EER) of ~8.3%. Also, it is helpful to remove false core point. Furthermore, a promising of EER ~1.3% is realized by combining this feature with 2D features for fingerprint recognition which indicates the prospect of 3D fingerprint recognition. PMID:28399141

Feature reconstruction of LFP signals based on PLSR in the neural information decoding study.

PubMed

Yonghui Dong; Zhigang Shang; Mengmeng Li; Xinyu Liu; Hong Wan

2017-07-01

To solve the problems of Signal-to-Noise Ratio (SNR) and multicollinearity when the Local Field Potential (LFP) signals is used for the decoding of animal motion intention, a feature reconstruction of LFP signals based on partial least squares regression (PLSR) in the neural information decoding study is proposed in this paper. Firstly, the feature information of LFP coding band is extracted based on wavelet transform. Then the PLSR model is constructed by the extracted LFP coding features. According to the multicollinearity characteristics among the coding features, several latent variables which contribute greatly to the steering behavior are obtained, and the new LFP coding features are reconstructed. Finally, the K-Nearest Neighbor (KNN) method is used to classify the reconstructed coding features to verify the decoding performance. The results show that the proposed method can achieve the highest accuracy compared to the other three methods and the decoding effect of the proposed method is robust.

Case study of 3D fingerprints applications.

PubMed

Liu, Feng; Liang, Jinrong; Shen, Linlin; Yang, Meng; Zhang, David; Lai, Zhihui

2017-01-01

Human fingers are 3D objects. More information will be provided if three dimensional (3D) fingerprints are available compared with two dimensional (2D) fingerprints. Thus, this paper firstly collected 3D finger point cloud data by Structured-light Illumination method. Additional features from 3D fingerprint images are then studied and extracted. The applications of these features are finally discussed. A series of experiments are conducted to demonstrate the helpfulness of 3D information to fingerprint recognition. Results show that a quick alignment can be easily implemented under the guidance of 3D finger shape feature even though this feature does not work for fingerprint recognition directly. The newly defined distinctive 3D shape ridge feature can be used for personal authentication with Equal Error Rate (EER) of ~8.3%. Also, it is helpful to remove false core point. Furthermore, a promising of EER ~1.3% is realized by combining this feature with 2D features for fingerprint recognition which indicates the prospect of 3D fingerprint recognition.

Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats

PubMed Central

Rosselli, Federica B.; Alemi, Alireza; Ansuini, Alessio; Zoccolan, Davide

2015-01-01

In recent years, a number of studies have explored the possible use of rats as models of high-level visual functions. One central question at the root of such an investigation is to understand whether rat object vision relies on the processing of visual shape features or, rather, on lower-order image properties (e.g., overall brightness). In a recent study, we have shown that rats are capable of extracting multiple features of an object that are diagnostic of its identity, at least when those features are, structure-wise, distinct enough to be parsed by the rat visual system. In the present study, we have assessed the impact of object structure on rat perceptual strategy. We trained rats to discriminate between two structurally similar objects, and compared their recognition strategies with those reported in our previous study. We found that, under conditions of lower stimulus discriminability, rat visual discrimination strategy becomes more view-dependent and subject-dependent. Rats were still able to recognize the target objects, in a way that was largely tolerant (i.e., invariant) to object transformation; however, the larger structural and pixel-wise similarity affected the way objects were processed. Compared to the findings of our previous study, the patterns of diagnostic features were: (i) smaller and more scattered; (ii) only partially preserved across object views; and (iii) only partially reproducible across rats. On the other hand, rats were still found to adopt a multi-featural processing strategy and to make use of part of the optimal discriminatory information afforded by the two objects. Our findings suggest that, as in humans, rat invariant recognition can flexibly rely on either view-invariant representations of distinctive object features or view-specific object representations, acquired through learning. PMID:25814936

18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer.

PubMed

Tsujikawa, Tetsuya; Rahman, Tasmiah; Yamamoto, Makoto; Yamada, Shizuka; Tsuyoshi, Hideaki; Kiyono, Yasushi; Kimura, Hirohiko; Yoshida, Yoshio; Okazawa, Hidehiko

2017-11-01

The aims of our study were to find the textural features on 18 F-FDG PET/CT which reflect the different histological architectures between cervical cancer subtypes and to make a visual assessment of the association between 18 F-FDG PET textural features in cervical cancer. Eighty-three cervical cancer patients [62 squamous cell carcinomas (SCCs) and 21 non-SCCs (NSCCs)] who had undergone pretreatment 18 F-FDG PET/CT were enrolled. A texture analysis was performed on PET/CT images, from which 18 PET radiomics features were extracted including first-order features such as standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), second- and high-order textural features using SUV histogram, normalized gray-level co-occurrence matrix (NGLCM), and neighborhood gray-tone difference matrix, respectively. These features were compared between SCC and NSCC using a Bonferroni adjusted P value threshold of 0.0028 (0.05/18). To assess the association between PET features, a heat map analysis with hierarchical clustering, one of the radiomics approaches, was performed. Among 18 PET features, correlation, a second-order textural feature derived from NGLCM, was a stable parameter and it was the only feature which showed a robust trend toward significant difference between SCC and NSCC. Cervical SCC showed a higher correlation (0.70 ± 0.07) than NSCC (0.64 ± 0.07, P = 0.0030). The other PET features did not show any significant differences between SCC and NSCC. A higher correlation in SCC might reflect higher structural integrity and stronger spatial/linear relationship of cancer cells compared with NSCC. A heat map with a PET feature dendrogram clearly showed 5 distinct clusters, where correlation belonged to a cluster including MTV and TLG. However, the association between correlation and MTV/TLG was not strong. Correlation was a relatively independent PET feature in cervical cancer. 18 F-FDG PET textural features might reflect the differences in histological architecture between cervical cancer subtypes. PET radiomics approaches reveal the association between PET features and will be useful for finding a single feature or a combination of features leading to precise diagnoses, potential prognostic models, and effective therapeutic strategies.

Two decades (1993-2012) of adult intensive care unit design: a comparative study of the physical design features of the best practice examples.

PubMed

Rashid, Mahbub

2014-01-01

In 2006, Critical Care Nursing Quarterly published a study of the physical design features of a set of best practice example adult intensive care units (ICUs). These adult ICUs were awarded between 1993 and 2003 by the Society of Critical Care Medicine (SCCM), the American Association of Critical-Care Nurses, and the American Institute of Architects/Academy of Architecture for Health for their efforts to promote the critical care unit environment through design. Since 2003, several more adult ICUs were awarded by the same organizations for similar efforts. This study includes these newer ICUs along with those of the previous study to cover a period of 2 decades from 1993 to 2012. Like the 2006 study, this study conducts a systematic content analysis of the materials submitted by the award-winning adult ICUs. On the basis of the analysis, the study compares the 1993-2002 and 2003-2012 adult ICUs in relation to construction type, unit specialty, unit layout, unit size, patient room size and design, support and service area layout, and family space design. The study also compares its findings with the 2010 Guidelines for Design and Construction of Health Care Facilities of the Facility Guidelines Institute and the 2012 Guidelines for Intensive Care Unit Design of the SCCM. The study indicates that the award-winning ICUs of both decades used several design features that were associated with positive outcomes in research studies. The study also indicates that the award-winning ICUs of the second decade used more evidence-based design features than those of the first decades. In most cases, these ICUs exceeded the requirements of the Facility Guidelines Institute Guidelines to meet those of the SCCM Guidelines. Yet, the award-winning ICUs of both decades also used several features that had very little or no supporting research evidence. Since they all were able to create an optimal critical care environment for which they were awarded, having knowledge of the physical design of these award-winning ICUs may help design better ICUs.

Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.

PubMed

Hsu, Wei-Yen

2013-12-01

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.

Comparison of image features calculated in different dimensions for computer-aided diagnosis of lung nodules

NASA Astrophysics Data System (ADS)

Xu, Ye; Lee, Michael C.; Boroczky, Lilla; Cann, Aaron D.; Borczuk, Alain C.; Kawut, Steven M.; Powell, Charles A.

2009-02-01

Features calculated from different dimensions of images capture quantitative information of the lung nodules through one or multiple image slices. Previously published computer-aided diagnosis (CADx) systems have used either twodimensional (2D) or three-dimensional (3D) features, though there has been little systematic analysis of the relevance of the different dimensions and of the impact of combining different dimensions. The aim of this study is to determine the importance of combining features calculated in different dimensions. We have performed CADx experiments on 125 pulmonary nodules imaged using multi-detector row CT (MDCT). The CADx system computed 192 2D, 2.5D, and 3D image features of the lesions. Leave-one-out experiments were performed using five different combinations of features from different dimensions: 2D, 3D, 2.5D, 2D+3D, and 2D+3D+2.5D. The experiments were performed ten times for each group. Accuracy, sensitivity and specificity were used to evaluate the performance. Wilcoxon signed-rank tests were applied to compare the classification results from these five different combinations of features. Our results showed that 3D image features generate the best result compared with other combinations of features. This suggests one approach to potentially reducing the dimensionality of the CADx data space and the computational complexity of the system while maintaining diagnostic accuracy.

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

NASA Astrophysics Data System (ADS)

Kokaly, Raymond F.; Skidmore, Andrew K.

2015-12-01

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic Csbnd H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).

Efficacy of texture, shape, and intensity features for robust posterior-fossa tumor segmentation in MRI

NASA Astrophysics Data System (ADS)

Ahmed, S.; Iftekharuddin, K. M.; Ogg, R. J.; Laningham, F. H.

2009-02-01

Our previous works suggest that fractal-based texture features are very useful for detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. In this work, we investigate and compare efficacy of our texture features such as fractal and multifractional Brownian motion (mBm), and intensity along with another useful level-set based shape feature in PF tumor segmentation. We study feature selection and ranking using Kullback -Leibler Divergence (KLD) and subsequent tumor segmentation; all in an integrated Expectation Maximization (EM) framework. We study the efficacy of all four features in both multimodality as well as disparate MRI modalities such as T1, T2 and FLAIR. Both KLD feature plots and information theoretic entropy measure suggest that mBm feature offers the maximum separation between tumor and non-tumor tissues in T1 and FLAIR MRI modalities. The same metrics show that intensity feature offers the maximum separation between tumor and non-tumor tissue in T2 MRI modality. The efficacies of these features are further validated in segmenting PF tumor using both single modality and multimodality MRI for six pediatric patients with over 520 real MR images.

Lumbar hernia in South Korea: different from that in foreign literature?

PubMed

Park, S H; Chung, H S; Song, S H

2015-10-01

This study aimed to analyze the clinical features of lumbar hernia reported in South Korea and compare these features with those reported in foreign literature. From January 1968 through December 2013, 13 cases reported in South Korea were included in the study. The variables compared were age, sex, main symptoms at hospital visit, etiology, location, herniated contents, lateralization, defect size, diagnostic methods, surgical methods, surgical opinions, and recurrence. In the South Korean cases, women outnumbered men (3.3:1) and no significant differences were found in the herniated side (left:right, 1.1:1). In contrast, in the foreign cases, men outnumbered women (3:1) and left-sided hernia was dominant (2:1). Moreover, in most of the foreign cases, patients were aged 50-70 years, whereas in the South Korean cases, none of the patients were in their 50 s. However, no substantial differences were found in etiology, anatomical locations, symptoms, and herniated contents. This research revealed that few clinical features of lumbar hernias in South Korea differ from those reported in foreign literature. Thirteen cases were analyzed in the present study, and results obtained from such a small sample size cannot be generalized with certainty. Therefore, more cases should be collected for a definitive analysis. Despite this limitation, this study is important because it is the first attempt to collect and analyze the clinical features of lumbar hernia in South Korea. This study will serve as a basis for future studies investigating the clinical features of lumbar hernia cases in South Korea.

A Point-of-Purchase Intervention Featuring In-Person Supermarket Education Affects Healthful Food Purchases

ERIC Educational Resources Information Center

Milliron, Brandy-Joe; Woolf, Kathleen; Appelhans, Bradley M.

2012-01-01

Objective: This study tested the efficacy of a multicomponent supermarket point-of-purchase intervention featuring in-person nutrition education on the nutrient composition of food purchases. Design: The design was a randomized trial comparing the intervention with usual care (no treatment). Setting and Participants: A supermarket in a…

Effects of Interactive Vocabulary Instruction on the Vocabulary Learning and Reading Comprehension of Junior-High Learning Disabled Students.

ERIC Educational Resources Information Center

Bos, Candace S.; Anders, Patricia L.

1990-01-01

The study, involving 61 learning-disabled junior high students, compared the short-term and long-term effectiveness of definition instruction with interactive vocabulary strategies (semantic mapping, semantic feature analysis, and semantic/syntactic feature analysis). Students participating in the interactive strategies demonstrated greater…

Diversity in Adoption of Linguistic Features of London English by Chinese and Bangladeshi Adolescents

ERIC Educational Resources Information Center

Pennington, Martha C.; Lau, Lawrence; Sachdev, Itesh

2011-01-01

This comparative study, conducted in multicultural London, investigates the occurrence in interviews with a researcher and in constructed same-sex peer conversations of five linguistic features characteristic of London English in the speech of two groups of British-born adolescents: ethnic Bangladeshis and ethnic Chinese of Cantonese heritage. The…

Children's Performance in Mental Rotation Tasks: Orientation-Free Features Flatten the Slope

ERIC Educational Resources Information Center

Perrucci, Vittore; Agnoli, Franca; Albiero, Paolo

2008-01-01

Studies of the development of mental rotation have yielded conflicting results, apparently because different mental rotation tasks draw on different cognitive abilities. Children may compare two stimuli at different orientations without mental rotation if the stimuli contain orientation-free features. Two groups of children (78 6-year-olds and 92…

Histopathology of acute acalculous cholecystitis in critically ill patients.

PubMed

Laurila, J J; Ala-Kokko, T I; Laurila, P A; Saarnio, J; Koivukangas, V; Syrjälä, H; Karttunen, T J

2005-11-01

To illustrate the histopathological features of acute acalculous cholecystitis (AAC) of critically ill patients and to compare them with those of acute calculous cholecystitis (ACC) and normal gallbladders. We studied 34 gallbladders with AAC and compared them with 28 cases of ACC and 14 normal gallbladders. Histological features were systematically evaluated. Typical features in AAC were bile infiltration, leucocyte margination of blood vessels and lymphatic dilation. Bile infiltration in the gallbladder wall was more common and extended wider and deeper into the muscle layer in AAC compared with ACC. Epithelial degeneration and defects and widespread occurrence of inflammatory cells were typical features in ACC. Necrosis in the muscle layer was also more common and extended wider and deeper in ACC. There were no differences in the occurrence of capillary thromboses, lymphatic follicles or Rokitansky-Aschoff sinuses between the AAC and ACC samples. There are characteristic differences in histopathology between AAC and ACC, although due to overlap, none appeared to be specific as such for either condition. These results suggest that AAC is largely a manifestation of systemic critical illness, whereas ACC is a local disease of the gallbladder.

The effect of emergent features on judgments of quantity in configural and separable displays.

PubMed

Peebles, David

2008-06-01

Two experiments investigated effects of emergent features on perceptual judgments of comparative magnitude in three diagrammatic representations: kiviat charts, bar graphs, and line graphs. Experiment 1 required participants to compare individual values; whereas in Experiment 2 participants had to integrate several values to produce a global comparison. In Experiment 1, emergent features of the diagrams resulted in significant distortions of magnitude judgments, each related to a common geometric illusion. Emergent features are also widely believed to underlie the general superiority of configural displays, such as kiviat charts, for tasks requiring the integration of information. Experiment 2 tested the extent of this benefit using diagrams with a wide range of values. Contrary to the results of previous studies, the configural display produced the poorest performance compared to the more separable displays. Moreover, the pattern of responses suggests that kiviat users switched from an integration strategy to a sequential one depending on the shape of the diagram. The experiments demonstrate the powerful interaction between emergent visual properties and cognition and reveal limits to the benefits of configural displays for integration tasks. (c) 2008 APA, all rights reserved

Glycosyl-Nucleolipids as new bioinspired amphiphiles.

PubMed

Latxague, Laurent; Patwa, Amit; Amigues, Eric; Barthélémy, Philippe

2013-09-30

Four new Glycosyl-NucleoLipid (GNL) analogs featuring either a single fluorocarbon or double hydrocarbon chains were synthesized in good yields from azido thymidine as starting material. Physicochemical studies (surface tension measurements, differential scanning calorimetry) indicate that hydroxybutanamide-based GNLs feature endothermic phase transition temperatures like the previously reported double chain glycerol-based GNLs. The second generation of GNFs featuring a free nucleobase reported here presents a better surface activity (lower glim) compared to the first generation of GNFs.

The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

PubMed

Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

2018-01-25

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

Comparative study on the performance of textural image features for active contour segmentation.

PubMed

Moraru, Luminita; Moldovanu, Simona

2012-07-01

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

Terrestrial Analogs for Surface Properties Associated with Impact Cratering on the Moon - Self-secondary Impact Features at Kings Bowl, Idaho

NASA Astrophysics Data System (ADS)

Matiella Novak, M. A.; Zanetti, M.; Neish, C.; Kukko, A.; Fan, K.; Heldmann, J.; Hughes, S. S.

2017-12-01

The Kings Bowl (KB) eruptive fissure and lava field, located in the southern end of Craters of the Moon National Monument, Idaho, is an ideal location for planetary analogue field studies of surface properties related to volcanic and impact processes. Here we look at possible impact features present in the KB lava field near the main vent that resulted in squeeze-ups of molten lava from beneath a semi-solid lava lake crust. These may have been caused by the ejection of blocks during the phreatic eruption that formed the Kings Bowl pit, and their subsequent impact into a partially solidified lava pond. We compare and contrast these features with analogous self-secondary impact features, such as irregular, rimless secondary craters ("splash craters") observed in lunar impact melt deposits, to better understand how self-secondary impacts determine the surface properties of volcanic and impact crater terrains. We do this by analyzing field measurements of these features, as well as high-resolution DEM data collected through the Kinematic LiDAR System (KLS), both of which give us feature dimensions and distributions. We then compare these data with self-secondary impact features on the Moon and related surface roughness constrained through Lunar Reconnaissance Orbiter observations (Mini-RF and LROC NACs). Possible self-secondary impact features can be found in association with many lunar impact craters. These are formed when ballistic ejecta from the crater falls onto the ejecta blanket and melt surrounding the newly formed crater. Self-secondary impact features involving impact melt deposits are particularly useful to study because the visibly smooth melt texture serves to highlight the impact points in spacecraft imagery. The unusual morphology of some of these features imply that they formed when the melt had not yet completely solidified, strongly suggesting a source of impactors from the primary crater itself. We will also discuss ongoing efforts to integrate field and LiDAR data collected at KB with virtual reality environments as another technique for advancing exploration efforts through analogue field studies of impact features.

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2017-05-01

Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning in the Making: A Comparative Case Study of Three Makerspaces

ERIC Educational Resources Information Center

Sheridan, Kimberly M.; Halverson, Erica Rosenfeld; Litts, Breanne K.; Brahms, Lisa; Jacobs-Priebe, Lynette; Owens, Trevor

2014-01-01

Through a comparative case study, Sheridan and colleagues explore how makerspaces may function as learning environments. Drawing on field observations, interviews, and analysis of artifacts, videos, and other documents, the authors describe features of three makerspaces and how participants learn and develop through complex design and making…

A Comparison and Analog-Based Analysis of Sinuous Channels on the Rift Aprons of Ascraeus Mons and Pavonis Mons Volcanoes, Mars

NASA Technical Reports Server (NTRS)

Collins, A.; de Wet, A.; Bleacher, J.; Schierl, Z.; Schwans, B.

2012-01-01

The origin of sinuous channels on the flanks of the Tharsis volcanoes on Mars is debated among planetary scientists. Some argue a volcanic genesis [1] while others have suggested a fluvial basis [2-4]. The majority of the studies thus far have focused on channels on the rift apron of Ascraeus Mons. Here, however, we broadly examine the channels on the rift apron of Pavonis Mons and compare them with those studied channels around Ascraeus. We compare the morphologies of features from both of these volcanoes with similar features of known volcanic origin on the island of Hawai i. We show that the morphologies between these two volcanoes in the Tharsis province are very similar and were likely formed by comparable processes, as previous authors have suggested [5]. We show that, although the morphologies of many of the channels around these volcanoes show some parallels to terrestrial fluvial systems, these morphologies can also be formed by volcanic processes. The context of these features suggests that volcanic processes were the more likely cause of these channels.

MIBG avidity correlates with clinical features, tumor biology, and outcomes in neuroblastoma: A report from the Children's Oncology Group.

PubMed

DuBois, Steven G; Mody, Rajen; Naranjo, Arlene; Van Ryn, Collin; Russ, Douglas; Oldridge, Derek; Kreissman, Susan; Baker, David L; Parisi, Marguerite; Shulkin, Barry L; Bai, Harrison; Diskin, Sharon J; Batra, Vandana; Maris, John M; Park, Julie R; Matthay, Katherine K; Yanik, Gregory

2017-11-01

Prior studies suggest that neuroblastomas that do not accumulate metaiodobenzylguanidine (MIBG) on diagnostic imaging (MIBG non-avid) may have more favorable features compared with MIBG avid tumors. We compared clinical features, biologic features, and clinical outcomes between patients with MIBG nonavid and MIBG avid neuroblastoma. Patients had metastatic high- or intermediate-risk neuroblastoma and were treated on Children's Oncology Group protocols A3973 or A3961. Comparisons of clinical and biologic features according to MIBG avidity were made with chi-squared or Fisher exact tests. Event-free (EFS) and overall (OS) survival compared using log-rank tests and modeled using Cox models. Thirty of 343 patients (8.7%) had MIBG nonavid disease. Patients with nonavid tumors were less likely to have adrenal primary tumors (34.5 vs. 57.2%; P = 0.019), bone metastases (36.7 vs. 61.7%; P = 0.008), or positive urine catecholamines (66.7 vs. 91.0%; P < 0.001) compared with patients with MIBG avid tumors. Nonavid tumors were more likely to be MYCN amplified (53.8 vs. 32.6%; P = 0.030) and had lower norepinephrine transporter expression. Patients with MIBG nonavid disease had a 5-year EFS of 50.0% compared with 38.7% for patients with MIBG avid disease (P = 0.028). On multivariate testing in high-risk patients, MIBG avidity was the sole adverse prognostic factor for EFS identified (hazard ratio 1.77; 95% confidence interval 1.04-2.99; P = 0.034). Patients with MIBG nonavid neuroblastoma have lower rates of adrenal primary tumors, bone metastasis, and catecholamine secretion. Despite being more likely to have MYCN-amplified tumors, these patients have superior outcomes compared with patients with MIBG avid disease. © 2017 Wiley Periodicals, Inc.

Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.

PubMed

Morris, Jeffrey S

2012-01-01

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry ( Cromwell ) and 2D gel electrophoresis ( Pinnacle ) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods presented are applied to two specific proteomic technologies, MALDI-TOF and 2D gel electrophoresis, these methods and the other principles discussed in the paper apply much more broadly to other expression proteomics technologies.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Characteristics of circular features on comet 67P/Churyumov-Gerasimenko

NASA Astrophysics Data System (ADS)

Deller, J. F.; Güttler, C.; Tubiana, C.; Hofmann, M.; Sierks, H.

2017-09-01

Comet 67P/Churyumov-Gerasimenko shows a large variety of circular structures such as pits, elevated roundish features in Imhotep, and even a single occurrence of a plausible fresh impact crater. Imaging the pits in the Ma'at region, aiming to understand their structure and origin drove the design of the final descent trajectory of the Rosetta spacecraft. The high-resolution images obtained during the last mission phase allow us to study these pits as exemplary circular features. A complete catalogue of circular features gives us the possibility to compare and classify these structures systematically.

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 appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.

Classifying four-category visual objects using multiple ERP components in single-trial ERP.

PubMed

Qin, Yu; Zhan, Yu; Wang, Changming; Zhang, Jiacai; Yao, Li; Guo, Xiaojuan; Wu, Xia; Hu, Bin

2016-08-01

Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.

An integrative view of storage of low- and high-level visual dimensions in visual short-term memory.

PubMed

Magen, Hagit

2017-03-01

Efficient performance in an environment filled with complex objects is often achieved through the temporal maintenance of conjunctions of features from multiple dimensions. The most striking finding in the study of binding in visual short-term memory (VSTM) is equal memory performance for single features and for integrated multi-feature objects, a finding that has been central to several theories of VSTM. Nevertheless, research on binding in VSTM focused almost exclusively on low-level features, and little is known about how items from low- and high-level visual dimensions (e.g., colored manmade objects) are maintained simultaneously in VSTM. The present study tested memory for combinations of low-level features and high-level representations. In agreement with previous findings, Experiments 1 and 2 showed decrements in memory performance when non-integrated low- and high-level stimuli were maintained simultaneously compared to maintaining each dimension in isolation. However, contrary to previous findings the results of Experiments 3 and 4 showed decrements in memory performance even when integrated objects of low- and high-level stimuli were maintained in memory, compared to maintaining single-dimension objects. Overall, the results demonstrate that low- and high-level visual dimensions compete for the same limited memory capacity, and offer a more comprehensive view of VSTM.

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.

Personality features in ultra-high risk for psychosis: a comparative study with schizophrenia and control subjects using the Temperament and Character Inventory-Revised (TCI-R).

PubMed

Fresán, Ana; León-Ortiz, Pablo; Robles-García, Rebeca; Azcárraga, Mariana; Guizar, Diana; Reyes-Madrigal, Francisco; Tovilla-Zárate, Carlos Alfonso; de la Fuente-Sandoval, Camilo

2015-02-01

Several variables have been identified as risk factors for conversion to overt psychosis in ultra-high risk for psychosis (UHR) individuals. Although almost two-thirds of them do not experience a transition to psychosis, they still exhibit functional disabilities. Other subjective developmental features may be useful for a more precise identification of individuals at UHR. Avoidant behaviors are consistently reported in schizophrenia and in UHR individuals and may be the reflection of a pattern of personality. Thus, personality features in UHR individuals deserves further research. The objective of the present study was to compare temperament and character dimensions between UHR individuals, patients with schizophrenia and healthy controls. One hundred participants (25 UHR individuals, 25 schizophrenia patients and 50 control subjects) where evaluated with the Temperament and Character Inventory-Revised (TCI-R). Univariate ANOVAs followed by Bonferroni tests were used. UHR individuals and schizophrenia patients exhibited higher levels of Harm Avoidance (HA) when compared to control subjects. For HA1 Anticipatory worry vs Uninhibited optimism and HA4 Fatigability & asthenia, UHR and schizophrenia groups showed similar scores and both groups were higher compared to control subjects. With respect to Cooperativeness (CO), UHR and schizophrenia reported lower scores than control subjects, in particular CO2 Empathy vs Social disinterest and CO3 Helpfulness vs unhelpfulness. This study replicates and extends the consideration of HA as a psychopathological related endophenotype and gives us further information of the possible role of personality features in the expression of some of the social dysfunctions observed both in prodromal subjects and schizophrenia patients. Copyright © 2014 Elsevier Ltd. All rights reserved.

On the use of information theory for the analysis of synchronous nociceptive withdrawal reflexes and somatosensory evoked potentials elicited by graded electrical stimulation.

PubMed

Arguissain, Federico G; Biurrun Manresa, José A; Mørch, Carsten D; Andersen, Ole K

2015-01-30

To date, few studies have combined the simultaneous acquisition of nociceptive withdrawal reflexes (NWR) and somatosensory evoked potentials (SEPs). In fact, it is unknown whether the combination of these two signals acquired simultaneously could provide additional information on somatosensory processing at spinal and supraspinal level compared to individual NWR and SEP signals. By using the concept of mutual information (MI), it is possible to quantify the relation between electrical stimuli and simultaneous elicited electrophysiological responses in humans based on the estimated stimulus-response signal probability distributions. All selected features from NWR and SEPs were informative in regard to the stimulus when considered individually. Specifically, the information carried by NWR features was significantly higher than the information contained in the SEP features (p<0.05). Moreover, the joint information carried by the combination of features showed an overall redundancy compared to the sum of the individual contributions. Comparison with existing methods MI can be used to quantify the information that single-trial NWR and SEP features convey, as well as the information carried jointly by NWR and SEPs. This is a model-free approach that considers linear and non-linear correlations at any order and is not constrained by parametric assumptions. The current study introduces a novel approach that allows the quantification of the individual and joint information content of single-trial NWR and SEP features. This methodology could be used to decode and interpret spinal and supraspinal interaction in studies modulating the responsiveness of the nociceptive system. Copyright © 2014 Elsevier B.V. All rights reserved.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

NASA Astrophysics Data System (ADS)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Materials and Morphology Study for Templated Hydrogen Solidification

DOE PAGES

Shin, Swanee J.; Kozioziemski, Bernard J.

2017-11-29

In this work, we performed a series of experiments to elucidate the characteristics of a good template for solid hydrogen nucleation. Zinc stands out among several materials with comparable size and shape. Nucleation could be observed to occur on top of sharp features, such as grain boundaries and cracks, but our attempts proved unsuccessful to fabricate or replicate such features. The variations of the supercooling (ΔT) values measured for comparable samples and the dependence of ΔT on the cell temperature cycling revealed that templated nucleation of solid hydrogen is a very delicate process.

Linear and Non-Linear Visual Feature Learning in Rat and Humans

PubMed Central

Bossens, Christophe; Op de Beeck, Hans P.

2016-01-01

The visual system processes visual input in a hierarchical manner in order to extract relevant features that can be used in tasks such as invariant object recognition. Although typically investigated in primates, recent work has shown that rats can be trained in a variety of visual object and shape recognition tasks. These studies did not pinpoint the complexity of the features used by these animals. Many tasks might be solved by using a combination of relatively simple features which tend to be correlated. Alternatively, rats might extract complex features or feature combinations which are nonlinear with respect to those simple features. In the present study, we address this question by starting from a small stimulus set for which one stimulus-response mapping involves a simple linear feature to solve the task while another mapping needs a well-defined nonlinear combination of simpler features related to shape symmetry. We verified computationally that the nonlinear task cannot be trivially solved by a simple V1-model. We show how rats are able to solve the linear feature task but are unable to acquire the nonlinear feature. In contrast, humans are able to use the nonlinear feature and are even faster in uncovering this solution as compared to the linear feature. The implications for the computational capabilities of the rat visual system are discussed. PMID:28066201

A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.

PubMed

Echegaray, Sebastian; Nair, Viswam; Kadoch, Michael; Leung, Ann; Rubin, Daniel; Gevaert, Olivier; Napel, Sandy

2016-12-01

Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.

The reflectivity, wettability and scratch durability of microsurface features molded in the injection molding process using a dynamic tool tempering system

NASA Astrophysics Data System (ADS)

Kuhn, Sascha; Burr, August; Kübler, Michael; Deckert, Matthias; Bleesen, Christoph

2011-02-01

In this paper the replication qualities of periodically and randomly arranged micro-features molded in the injection molding process and their effects on surface properties are studied. The features are molded in PC, PMMA and PP at different mold wall temperatures in order to point out the necessity and profitability of a variotherm mold wall temperature control system. A one-dimensional heat conduction model is proposed to predict the cycle times of the variotherm injection molding processes. With regard to these processes, the molding results are compared to the molded surface feature heights using an atomic force microscope. In addition, the effects of the molded surface features on macroscopic surfaces are characterized in terms of light reflection using a spectrometer and in terms of water wettability by measuring the static contact angle. Furthermore, due to the sensitivity of the surface features on the molded parts, their durability is compared in a scratch test with a diamond tip. This leads to successful implementation in applications in which the optical appearance, in terms of gloss and reflection, and the water repellence, in terms of drag flow and adhesion, are of importance.

Shared Features of L2 Writing: Intergroup Homogeneity and Text Classification

ERIC Educational Resources Information Center

Crossley, Scott A.; McNamara, Danielle S.

2011-01-01

This study investigates intergroup homogeneity within high intermediate and advanced L2 writers of English from Czech, Finnish, German, and Spanish first language backgrounds. A variety of linguistic features related to lexical sophistication, syntactic complexity, and cohesion were used to compare texts written by L1 speakers of English to L2…

The Multi-Feature Hypothesis: Connectionist Guidelines for L2 Task Design

ERIC Educational Resources Information Center

Moonen, Machteld; de Graaff, Rick; Westhoff, Gerard; Brekelmans, Mieke

2014-01-01

This study focuses on the effects of task type on the retention and ease of activation of second language (L2) vocabulary, based on the multi-feature hypothesis (Moonen, De Graaff, & Westhoff, 2006). Two tasks were compared: a writing task and a list-learning task. It was hypothesized that performing the writing task would yield higher…

Poverty Is Not a Human Characteristic: A Retrospective Study of Comprehending and Educating Impoverished Children

ERIC Educational Resources Information Center

Holmlund, Kerstin

2012-01-01

This article describes and compares the differences between a feature-oriented understanding and a relational understanding of a child's behavior and the different ways of educating children which these two empirical and theoretical perspectives offer. The feature-oriented perspective focuses on the nature and character of impoverished children as…

Developing an Approach for Comparing Students' Multimodal Text Creations: A Case Study

ERIC Educational Resources Information Center

Levy, Mike; Kimber, Kay

2009-01-01

Classroom teachers routinely make judgments on the quality of their students' work based on their recognition of how effectively the student has assembled key features of the genre or the medium. Yet how readily can teachers talk about the features of student-created multimodal texts in ways that can improve learning and performance? This article…

Diagnostic specificity of poor premorbid adjustment: Comparison of schizophrenia, schizoaffective disorder, and mood disorder with psychotic features

PubMed Central

Tarbox, Sarah I.; Brown, Leslie H.; Haas, Gretchen L.

2012-01-01

Individuals with schizophrenia have significant deficits in premorbid social and academic adjustment compared to individuals with non-psychotic diagnoses. However, it is unclear how severity and developmental trajectory of premorbid maladjustment compare across psychotic disorders. This study examined the association between premorbid functioning (in childhood, early adolescence, and late adolescence) and psychotic disorder diagnosis in a first-episode sample of 105 individuals: schizophrenia (n=68), schizoaffective disorder (n=22), and mood disorder with psychotic features (n=15). Social and academic maladjustment was assessed using the Cannon-Spoor Premorbid Adjustment Scale. Worse social functioning in late adolescence was associated with higher odds of schizophrenia compared to odds of either schizoaffective disorder or mood disorder with psychotic features, independently of child and early adolescent maladjustment. Greater social dysfunction in childhood was associated with higher odds of schizoaffective disorder compared to odds of schizophrenia. Premorbid decline in academic adjustment was observed for all groups, but did not predict diagnosis at any stage of development. Results suggest that social functioning is disrupted in the premorbid phase of both schizophrenia and schizoaffective disorder, but remains fairly stable in mood disorders with psychotic features. Disparities in the onset and time course of social dysfunction suggest important developmental differences between schizophrenia and schizoaffective disorder. PMID:22858353

Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

PubMed Central

Padmanaban, Subash; Baker, Justin; Greger, Bradley

2018-01-01

Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve the performance of the decoding algorithm. The aim of our study was to compare the effects of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis (PCA), and Mutual Information Maximization on SVM classification performance for a dexterous decoding task. Approach: A nonhuman primate (NHP) was trained to perform small coordinated movements—similar to typing. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials (AP) during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon AP firing rates. We used the SVM classification to examine the functional parameters of (i) robustness to simulated failure and (ii) longevity of classification. We also compared the effect of using isolated-neuron and multi-unit firing rates as the feature vector supplied to the SVM. Main results: The average decoding accuracy for multi-unit features and single-unit features using Mutual Information Maximization (MIM) across 47 sessions was 96.74 ± 3.5% and 97.65 ± 3.36% respectively. The reduction in decoding accuracy between using 100% of the features and 10% of features based on MIM was 45.56% (from 93.7 to 51.09%) and 4.75% (from 95.32 to 90.79%) for multi-unit and single-unit features respectively. MIM had best performance compared to other feature selection methods. Significance: These results suggest improved decoding performance can be achieved by using optimally selected features. The results based on clinically relevant performance metrics also suggest that the decoding algorithm can be made robust by using optimal features and feature selection algorithms. We believe that even a few percent increase in performance is important and improves the decoding accuracy of the machine learning algorithm potentially increasing the ease of use of a brain machine interface. PMID:29467602

Clinical and genetic features of diuretic-associated gout: a case-control study.

PubMed

Mitnala, Sirisha; Phipps-Green, Amanda; Franklin, Christopher; Horne, Anne; Stamp, Lisa K; Merriman, Tony R; Dalbeth, Nicola

2016-07-01

Hyperuricaemia and gout are well-recognized complications of diuretic use. The aim of this study was to examine the clinical and genetic features of diuretic-associated gout. Participants (n = 1365) fulfilling the 1977 ARA gout classification criteria, recruited from primary and secondary care, attended a study visit that included a detailed clinical assessment. Use of diuretic therapy was recorded during the study visit, and was confirmed by electronic dispensing data [n = 426 (31.2%) on diuretics]. Gout-associated single nucleotide polymorphisms were genotyped. Clinical and genetic features of diuretic-associated gout were analysed using a case-control study design (diuretics vs no diuretics). In the diuretic group there were more women, higher rates of comorbid conditions, higher BMI and lower estimated glomerular filtration rate compared with those not taking diuretics. Gout disease duration, frequency of gout flares and presence of tophi were similar in the two groups. Patients on diuretics had higher age of gout presentation and higher recorded serum urate. The ABCG2 rs2231142 risk allele was present less frequently in the diuretic group (36.1%) compared with those not on diuretics (47.6%, P = 1.2 × 10(-4)). The differences in ABCG2 were observed in both men and women with gout. Diuretic-associated gout represents a medically complex condition. Although age of gout onset is later and serum urate concentrations are higher in those on diuretics, other clinical features of gout are similar. The observed differences in the ABCG2 risk allele frequency suggest that some genetic factors play a less dominant role in diuretic-associated gout compared with primary gout. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Linguistic labels, dynamic visual features, and attention in infant category learning.

PubMed

Deng, Wei Sophia; Sloutsky, Vladimir M

2015-06-01

How do words affect categorization? According to some accounts, even early in development words are category markers and are different from other features. According to other accounts, early in development words are part of the input and are akin to other features. The current study addressed this issue by examining the role of words and dynamic visual features in category learning in 8- to 12-month-old infants. Infants were familiarized with exemplars from one category in a label-defined or motion-defined condition and then tested with prototypes from the studied category and from a novel contrast category. Eye-tracking results indicated that infants exhibited better category learning in the motion-defined condition than in the label-defined condition, and their attention was more distributed among different features when there was a dynamic visual feature compared with the label-defined condition. These results provide little evidence for the idea that linguistic labels are category markers that facilitate category learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Linguistic Labels, Dynamic Visual Features, and Attention in Infant Category Learning

PubMed Central

Deng, Wei (Sophia); Sloutsky, Vladimir M.

2015-01-01

How do words affect categorization? According to some accounts, even early in development, words are category markers and are different from other features. According to other accounts, early in development, words are part of the input and are akin to other features. The current study addressed this issue by examining the role of words and dynamic visual features in category learning in 8- to 12- month infants. Infants were familiarized with exemplars from one category in a label-defined or motion-defined condition and then tested with prototypes from the studied category and from a novel contrast category. Eye tracking results indicated that infants exhibited better category learning in the motion-defined than in the label-defined condition and their attention was more distributed among different features when there was a dynamic visual feature compared to the label-defined condition. These results provide little evidence for the idea that linguistic labels are category markers that facilitate category learning. PMID:25819100

Maternal Identity of Hearing Mothers of Deaf Adolescents. Empirical Studies: An Interpersonal Approach

ERIC Educational Resources Information Center

Kobosko, Joanna; Zalewska, Marina

2011-01-01

The maternal identity of mothers of adolescents who are deaf has certain specific features compared with mothers of adolescents who have typical hearing. That is, maternal identity differs with respect to distinctiveness, self-representation, and representation of mother-child relationships. A study using a comparative paradigm was conducted. The…

A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

PubMed

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2015-08-01

In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme.

Preattentive representation of feature conjunctions for concurrent spatially distributed auditory objects.

PubMed

Takegata, Rika; Brattico, Elvira; Tervaniemi, Mari; Varyagina, Olga; Näätänen, Risto; Winkler, István

2005-09-01

The role of attention in conjoining features of an object has been a topic of much debate. Studies using the mismatch negativity (MMN), an index of detecting acoustic deviance, suggested that the conjunctions of auditory features are preattentively represented in the brain. These studies, however, used sequentially presented sounds and thus are not directly comparable with visual studies of feature integration. Therefore, the current study presented an array of spatially distributed sounds to determine whether the auditory features of concurrent sounds are correctly conjoined without focal attention directed to the sounds. Two types of sounds differing from each other in timbre and pitch were repeatedly presented together while subjects were engaged in a visual n-back working-memory task and ignored the sounds. Occasional reversals of the frequent pitch-timbre combinations elicited MMNs of a very similar amplitude and latency irrespective of the task load. This result suggested preattentive integration of auditory features. However, performance in a subsequent target-search task with the same stimuli indicated the occurrence of illusory conjunctions. The discrepancy between the results obtained with and without focal attention suggests that illusory conjunctions may occur during voluntary access to the preattentively encoded object representations.

The Role of Hospital Design in Reducing Anxiety for Pediatric Patients.

PubMed

Cartland, Jenifer; Ruch-Ross, Holly S; Carr, Lauren; Hall, Audrey; Olsen, Richard; Rosendale, Ellen; Ruohonen, Susan

2018-01-01

To study the impact of hospital design on patient and family experiences during and after hospitalization. Hospitalization can be psychologically traumatic for children. Few research studies have studied the role of the design of the hospital environment in mitigating that traumatic experience. The study employs a two-group posttest and follow-up design to compare the impact of hospitalization on child anxiety and parent stress. It compares the experiences of children (ages 3-17) hospitalized at a new facility designed to support child-centered care and with family-friendly features with an older facility that did not have these features. The new facility was a replacement of the old one, so that many challenges to comparison are addressed. Controlling for the facts of hospitalization, patient demographics, and the child's typical anxiety level, children in the new facility experienced less anxiety than in the old facility. The study does not provide evidence that the hospital design reduced the psychological sequelae of hospitalization. Parents and children found different features of the hospital to be restorative. The study supports the use of Ulrich's theory of supportive design to children's healthcare environments, though what is experienced as supportive design will vary by the developmental stage of the child.

SU-F-R-40: Robustness Test of Computed Tomography Textures of Lung Tissues to Varying Scanning Protocols Using a Realistic Phantom Environment

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

Lee, S; Markel, D; Hegyi, G

2016-06-15

Purpose: The reliability of computed tomography (CT) textures is an important element of radiomics analysis. This study investigates the dependency of lung CT textures on different breathing phases and changes in CT image acquisition protocols in a realistic phantom setting. Methods: We investigated 11 CT texture features for radiation-induced lung disease from 3 categories (first-order, grey level co-ocurrence matrix (GLCM), and Law’s filter). A biomechanical swine lung phantom was scanned at two breathing phases (inhale/exhale) and two scanning protocols set for PET/CT and diagnostic CT scanning. Lung volumes acquired from the CT images were divided into 2-dimensional sub-regions with amore » grid spacing of 31 mm. The distribution of the evaluated texture features from these sub-regions were compared between the two scanning protocols and two breathing phases. The significance of each factor on feature values were tested at 95% significance level using analysis of covariance (ANCOVA) model with interaction terms included. Robustness of a feature to a scanning factor was defined as non-significant dependence on the factor. Results: Three GLCM textures (variance, sum entropy, difference entropy) were robust to breathing changes. Two GLCM (variance, sum entropy) and 3 Law’s filter textures (S5L5, E5L5, W5L5) were robust to scanner changes. Moreover, the two GLCM textures (variance, sum entropy) were consistent across all 4 scanning conditions. First-order features, especially Hounsfield unit intensity features, presented the most drastic variation up to 39%. Conclusion: Amongst the studied features, GLCM and Law’s filter texture features were more robust than first-order features. However, the majority of the features were modified by either breathing phase or scanner changes, suggesting a need for calibration when retrospectively comparing scans obtained at different conditions. Further investigation is necessary to identify the sensitivity of individual image acquisition parameters.« less

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Detection of desmoplastic melanoma with dermoscopy and reflectance confocal microscopy.

PubMed

Maher, N G; Solinas, A; Scolyer, R A; Puig, S; Pellacani, G; Guitera, P

2017-12-01

Desmoplastic melanoma (DM) is frequently misdiagnosed clinically and often associated with melanoma in situ (MIS). To improve the detection of DM using dermoscopy and reflectance confocal microscopy (RCM). A descriptive analysis of DM dermoscopy features and a case-control study within a melanoma population for RCM feature evaluation was performed blindly, using data obtained between 2005 and 2015. After retrospectively identifying all DM cases with RCM data over the study period (n = 16), a control group of non-DM melanoma patients with RCM data, in a ratio of at least 3 : 1, was selected. The control group was matched by age and primary tumour site location, divided into non-DM invasive melanomas (n = 27) and MIS (n = 27). Invasive melanomas were selected according to the melanoma subtypes associated with the DM cases. The main outcomes were the frequency of melanoma-specific features on dermoscopy for DM; and the odds ratios of RCM features to distinguish DM from MIS and/or other invasive melanomas; or MIS from the combined invasive melanoma group. At least one of the 14 melanoma-specific features evaluated on dermoscopy was found in 100% of DMs (n = 15 DM with dermoscopy). Known RCM melanoma predictors were commonly found in the DMs, such as pagetoid cells (100%) and cell atypia (100%). The RCM feature of spindle cells in the superficial dermis was more common in DM compared with the entire melanoma control group (OR 3.82, 95% CI 1.01-14.90), and particularly compared to MIS (OR 5.48, 95% CI 1.11-32.36). Nucleated cells in the dermis and the RCM correlate of dermal inflammation were also significant RCM features favouring DM over MIS, as well as invasive melanoma over MIS. Dermoscopy and RCM may be useful tools for the identification of DM. Certain RCM features may help distinguish DM from MIS and other invasive melanomas. Larger studies are warranted. © 2017 European Academy of Dermatology and Venereology.

Automatic classification of animal vocalizations

NASA Astrophysics Data System (ADS)

Clemins, Patrick J.

2005-11-01

Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysis techniques in recent years. Some common tasks in bioacoustics are repertoire determination, call detection, individual identification, stress detection, and behavior correlation. Each research study, however, uses a wide variety of different measured variables, called features, and classification systems to accomplish these tasks. The well-established field of human speech processing has developed a number of different techniques to perform many of the aforementioned bioacoustics tasks. Melfrequency cepstral coefficients (MFCCs) and perceptual linear prediction (PLP) coefficients are two popular feature sets. The hidden Markov model (HMM), a statistical model similar to a finite autonoma machine, is the most commonly used supervised classification model and is capable of modeling both temporal and spectral variations. This research designs a framework that applies models from human speech processing for bioacoustic analysis tasks. The development of the generalized perceptual linear prediction (gPLP) feature extraction model is one of the more important novel contributions of the framework. Perceptual information from the species under study can be incorporated into the gPLP feature extraction model to represent the vocalizations as the animals might perceive them. By including this perceptual information and modifying parameters of the HMM classification system, this framework can be applied to a wide range of species. The effectiveness of the framework is shown by analyzing African elephant and beluga whale vocalizations. The features extracted from the African elephant data are used as input to a supervised classification system and compared to results from traditional statistical tests. The gPLP features extracted from the beluga whale data are used in an unsupervised classification system and the results are compared to labels assigned by experts. The development of a framework from which to build animal vocalization classifiers will provide bioacoustics researchers with a consistent platform to analyze and classify vocalizations. A common framework will also allow studies to compare results across species and institutions. In addition, the use of automated classification techniques can speed analysis and uncover behavioral correlations not readily apparent using traditional techniques.

Gender discrimination of eyes and mouths by individuals with autism.

PubMed

Best, Catherine A; Minshew, Nancy J; Strauss, Mark S

2010-04-01

Evidence remains mixed about whether individuals with autism look less to eyes and whether they look more at mouths. Few studies have examined how spontaneous attention to facial features relates to face processing abilities. This study tested the ability to discriminate gender from facial features, namely eyes and mouths, by comparing accuracy scores of 17 children with autism and 15 adults with autism to 17 typically developing children and 15 typically developing adults. Results indicated that all participants regardless of diagnosis discriminated gender more accurately from eyes than from mouths. However, results indicated that compared to adults without autism, adults with autism were significantly worse at discriminating gender from eyes.

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.

PubMed

Qin, Jiang-Bo; Liu, Zhenyu; Zhang, Hui; Shen, Chen; Wang, Xiao-Chun; Tan, Yan; Wang, Shuo; Wu, Xiao-Feng; Tian, Jie

2017-05-07

BACKGROUND Gliomas are the most common primary brain neoplasms. Misdiagnosis occurs in glioma grading due to an overlap in conventional MRI manifestations. The aim of the present study was to evaluate the power of radiomic features based on multiple MRI sequences - T2-Weighted-Imaging-FLAIR (FLAIR), T1-Weighted-Imaging-Contrast-Enhanced (T1-CE), and Apparent Diffusion Coefficient (ADC) map - in glioma grading, and to improve the power of glioma grading by combining features. MATERIAL AND METHODS Sixty-six patients with histopathologically proven gliomas underwent T2-FLAIR and T1WI-CE sequence scanning with some patients (n=63) also undergoing DWI scanning. A total of 114 radiomic features were derived with radiomic methods by using in-house software. All radiomic features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Features with significant statistical differences were selected for receiver operating characteristic (ROC) curve analysis. The relationships between significantly different radiomic features and glial fibrillary acidic protein (GFAP) expression were evaluated. RESULTS A total of 8 radiomic features from 3 MRI sequences displayed significant differences between LGGs and HGGs. FLAIR GLCM Cluster Shade, T1-CE GLCM Entropy, and ADC GLCM Homogeneity were the best features to use in differentiating LGGs and HGGs in each MRI sequence. The combined feature was best able to differentiate LGGs and HGGs, which improved the accuracy of glioma grading compared to the above features in each MRI sequence. A significant correlation was found between GFAP and T1-CE GLCM Entropy, as well as between GFAP and ADC GLCM Homogeneity. CONCLUSIONS The combined radiomic feature had the highest efficacy in distinguishing LGGs from HGGs.

On the use of feature selection to improve the detection of sea oil spills in SAR images

NASA Astrophysics Data System (ADS)

Mera, David; Bolon-Canedo, Veronica; Cotos, J. M.; Alonso-Betanzos, Amparo

2017-03-01

Fast and effective oil spill detection systems are crucial to ensure a proper response to environmental emergencies caused by hydrocarbon pollution on the ocean's surface. Typically, these systems uncover not only oil spills, but also a high number of look-alikes. The feature extraction is a critical and computationally intensive phase where each detected dark spot is independently examined. Traditionally, detection systems use an arbitrary set of features to discriminate between oil spills and look-alikes phenomena. However, Feature Selection (FS) methods based on Machine Learning (ML) have proved to be very useful in real domains for enhancing the generalization capabilities of the classifiers, while discarding the existing irrelevant features. In this work, we present a generic and systematic approach, based on FS methods, for choosing a concise and relevant set of features to improve the oil spill detection systems. We have compared five FS methods: Correlation-based feature selection (CFS), Consistency-based filter, Information Gain, ReliefF and Recursive Feature Elimination for Support Vector Machine (SVM-RFE). They were applied on a 141-input vector composed of features from a collection of outstanding studies. Selected features were validated via a Support Vector Machine (SVM) classifier and the results were compared with previous works. Test experiments revealed that the classifier trained with the 6-input feature vector proposed by SVM-RFE achieved the best accuracy and Cohen's kappa coefficient (87.1% and 74.06% respectively). This is a smaller feature combination with similar or even better classification accuracy than previous works. The presented finding allows to speed up the feature extraction phase without reducing the classifier accuracy. Experiments also confirmed the significance of the geometrical features since 75.0% of the different features selected by the applied FS methods as well as 66.67% of the proposed 6-input feature vector belong to this category.

Does my face FIT?: a face image task reveals structure and distortions of facial feature representation.

PubMed

Fuentes, Christina T; Runa, Catarina; Blanco, Xenxo Alvarez; Orvalho, Verónica; Haggard, Patrick

2013-01-01

Despite extensive research on face perception, few studies have investigated individuals' knowledge about the physical features of their own face. In this study, 50 participants indicated the location of key features of their own face, relative to an anchor point corresponding to the tip of the nose, and the results were compared to the true location of the same individual's features from a standardised photograph. Horizontal and vertical errors were analysed separately. An overall bias to underestimate vertical distances revealed a distorted face representation, with reduced face height. Factor analyses were used to identify separable subconfigurations of facial features with correlated localisation errors. Independent representations of upper and lower facial features emerged from the data pattern. The major source of variation across individuals was in representation of face shape, with a spectrum from tall/thin to short/wide representation. Visual identification of one's own face is excellent, and facial features are routinely used for establishing personal identity. However, our results show that spatial knowledge of one's own face is remarkably poor, suggesting that face representation may not contribute strongly to self-awareness.

Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

PubMed Central

White, James Robert; Nagarajan, Niranjan; Pop, Mihai

2009-01-01

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them. We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/. PMID:19360128

An age-related deficit in spatial-feature reference memory in homing pigeons (Columba livia).

PubMed

Coppola, Vincent J; Flaim, Mary E; Carney, Samantha N; Bingman, Verner P

2015-03-01

Age-related memory decline in mammals has been well documented. By contrast, very little is known about memory decline in birds as they age. In the current study we trained younger and older homing pigeons on a reference memory task in which a goal location could be encoded by spatial and feature cues. Consistent with a previous working memory study, the results revealed impaired acquisition of combined spatial-feature reference memory in older compared to younger pigeons. Following memory acquisition, we used cue-conflict probe trials to provide an initial assessment of possible age-related differences in cue preference. Both younger and older pigeons displayed a similarly modest preference for feature over spatial cues. Copyright © 2014 Elsevier B.V. All rights reserved.

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

PubMed

Shim, Miseon; Hwang, Han-Jeong; Kim, Do-Won; Lee, Seung-Hwan; Im, Chang-Hwan

2016-10-01

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG features can potentially be applied to the differential diagnosis of schizophrenia, most studies have used only sensor-level EEG features such as ERP peak amplitude and power spectrum for machine learning-based diagnosis of schizophrenia. In this study, we used both sensor-level and source-level features extracted from EEG signals recorded during an auditory oddball task for the classification of patients with schizophrenia and healthy controls. EEG signals were recorded from 34 patients with schizophrenia and 34 healthy controls while each subject was asked to attend to oddball tones. Our results demonstrated higher classification accuracy when source-level features were used together with sensor-level features, compared to when only sensor-level features were used. In addition, the selected sensor-level features were mostly found in the frontal area, and the selected source-level features were mostly extracted from the temporal area, which coincide well with the well-known pathological region of cognitive processing in patients with schizophrenia. Our results suggest that our approach would be a promising tool for the computer-aided diagnosis of schizophrenia. Copyright © 2016 Elsevier B.V. All rights reserved.

The drug target genes show higher evolutionary conservation than non-target genes.

PubMed

Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

2016-01-26

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Creating global comparative analyses of tectonic rifts, monogenetic volcanism and inverted relief

NASA Astrophysics Data System (ADS)

van Wyk de Vries, Benjamin

2016-04-01

I have been all around the world, and to other planets and have travelled from the present to the Archaean and back to seek out the most significant tectonic rifts, monogenetic volcanoes and examples of inverted relief. I have done this to provide a broad foundation of the comparative analysis for the Chaîne des Puys - Limagne fault nomination to UNESCO world Heritage. This would have been an impossible task, if not for the cooperation of the scientific community and for Google Earth, Google Maps and academic search engines. In preparing global comparisons of geological features, these quite recently developed tools provide a powerful way to find and describe geological features. The ability to do scientific crowd sourcing, rapidly discussing with colleagues about features, allows large numbers of areas to be checked and the open GIS tools (such as Google Earth) allow a standardised description. Search engines also allow the literature on areas to be checked and compared. I will present a comparative study of rifts of the world, monogenetic volcanic field and inverted relief, integrated to analyse the full geological system represented by the Chaîne des Puys - Limagne fault. The analysis confirms that the site is an exceptional example of the first steps of continental drift in a mountain rift setting, and that this is necessarily seen through the combined landscape of tectonic, volcanic and geomorphic features. The analysis goes further to deepen the understanding of geological systems and stresses the need for more study on geological heritage using such a global and broad systems approach.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Combining heterogenous features for 3D hand-held object recognition

NASA Astrophysics Data System (ADS)

Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Correlation of tumor-infiltrating lymphocytes to histopathological features and molecular phenotypes in canine mammary carcinoma: A morphologic and immunohistochemical morphometric study.

PubMed

Kim, Jong-Hyuk; Chon, Seung-Ki; Im, Keum-Soon; Kim, Na-Hyun; Sur, Jung-Hyang

2013-04-01

Abundant lymphocyte infiltration is frequently found in canine malignant mammary tumors, but the pathological features and immunophenotypes associated with the infiltration remain to be elucidated. The aim of the present study was to evaluate the relationship between lymphocyte infiltration, histopathological features, and molecular phenotype in canine mammary carcinoma (MC). The study was done with archived formalin-fixed, paraffin-embedded samples (n = 47) by histologic and immunohistochemical methods. The degree of lymphocyte infiltration was evaluated by morphologic analysis, and the T- and B-cell populations as well as the T/B-cell ratio were evaluated by morphometric analysis; results were compared with the histologic features and molecular phenotypes. The degree of lymphocyte infiltration was significantly higher in MCs with lymphatic invasion than in those without lymphatic invasion (P < 0.0001) and in tumors of high histologic grade compared with those of lower histologic grade (P = 0.045). Morphometric analysis showed a larger amount of T-cells and B-cells in MCs with a higher histologic grade and lymphatic invasion, but the T/B ratio did not change. Lymphocyte infiltration was not associated with histologic type or molecular phenotype, as assessed from the immunohistochemical expression of epidermal growth factor receptor 2, estrogen receptor, cytokeratin 14, and p63. Since intense lymphocyte infiltration was associated with aggressive histologic features, lymphocytes may be important for tumor aggressiveness and greater malignant behavior in the tumor microenvironment.

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

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.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Chinese wine classification system based on micrograph using combination of shape and structure features

NASA Astrophysics Data System (ADS)

Wan, Yi

2011-06-01

Chinese wines can be classification or graded by the micrographs. Micrographs of Chinese wines show floccules, stick and granule of variant shape and size. Different wines have variant microstructure and micrographs, we study the classification of Chinese wines based on the micrographs. Shape and structure of wines' particles in microstructure is the most important feature for recognition and classification of wines. So we introduce a feature extraction method which can describe the structure and region shape of micrograph efficiently. First, the micrographs are enhanced using total variation denoising, and segmented using a modified Otsu's method based on the Rayleigh Distribution. Then features are extracted using proposed method in the paper based on area, perimeter and traditional shape feature. Eight kinds total 26 features are selected. Finally, Chinese wine classification system based on micrograph using combination of shape and structure features and BP neural network have been presented. We compare the recognition results for different choices of features (traditional shape features or proposed features). The experimental results show that the better classification rate have been achieved using the combinational features proposed in this paper.

A novel comparator featured with input data characteristic

NASA Astrophysics Data System (ADS)

Jiang, Xiaobo; Ye, Desheng; Xu, Xiangmin; Zheng, Shuai

2016-03-01

Two types of low-power asynchronous comparators featured with input data statistical characteristic are proposed in this article. The asynchronous ripple comparator stops comparing at the first unequal bit but delivers the result to the least significant bit. The pre-stop asynchronous comparator can completely stop comparing and obtain results immediately. The proposed and contrastive comparators were implemented in SMIC 0.18 μm process with different bit widths. Simulation shows that the proposed pre-stop asynchronous comparator features the lowest power consumption, shortest average propagation delay and highest area efficiency among the comparators. Data path of low-density parity check decoder using the proposed pre-stop asynchronous comparators are most power efficient compared with other data paths with synthesised, clock gating and bitwise competition logic comparators.

What Models of Verbal Working Memory Can Learn from Phonological Theory: Decomposing the Phonological Similarity Effect

ERIC Educational Resources Information Center

Schweppe, Judith; Grice, Martine; Rummer, Ralf

2011-01-01

Despite developments in phonology over the last few decades, models of verbal working memory make reference to phoneme-sized phonological units, rather than to the features of which they are composed. This study investigates the influence on short-term retention of such features by comparing the serial recall of lists of syllables with varying…

Quantum correlation exists in any non-product state

PubMed Central

Guo, Yu; Wu, Shengjun

2014-01-01

Simultaneous existence of correlation in complementary bases is a fundamental feature of quantum correlation, and we show that this characteristic is present in any non-product bipartite state. We propose a measure via mutually unbiased bases to study this feature of quantum correlation, and compare it with other measures of quantum correlation for several families of bipartite states. PMID:25434458

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

PubMed

Wu, Chung Kit; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei

2016-05-09

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human's biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

HST PanCET Program: A Cloudy Atmosphere for the Promising JWST Target WASP-101b

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

Wakeford, H. R.; Mandell, A.; Stevenson, K. B.

We present results from the first observations of the Hubble Space Telescope (HST) Panchromatic Comparative Exoplanet Treasury program for WASP-101b, a highly inflated hot Jupiter and one of the community targets proposed for the James Webb Space Telescope ( JWST ) Early Release Science (ERS) program. From a single HST Wide Field Camera 3 observation, we find that the near-infrared transmission spectrum of WASP-101b contains no significant H{sub 2}O absorption features and we rule out a clear atmosphere at 13 σ . Therefore, WASP-101b is not an optimum target for a JWST ERS program aimed at observing strong molecular transmissionmore » features. We compare WASP-101b to the well-studied and nearly identical hot Jupiter WASP-31b. These twin planets show similar temperature–pressure profiles and atmospheric features in the near-infrared. We suggest exoplanets in the same parameter space as WASP-101b and WASP-31b will also exhibit cloudy transmission spectral features. For future HST exoplanet studies, our analysis also suggests that a lower count limit needs to be exceeded per pixel on the detector in order to avoid unwanted instrumental systematics.« less

Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?

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

Fave, Xenia, E-mail: xjfave@mdanderson.org; Fried, David; Mackin, Dennis

Purpose: Increasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone-beam CT (CBCT) images taken using medical Linac onboard-imaging systems in order to track them through treatment. Methods: Test-retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rankmore » correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test-retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features’ interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic-motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test-retest image set. Results: Of the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4–13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturer-protocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6–8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray-level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray-level run emphasis, gray-level nonuniformity, sum-average, information measure correlation, scaled mean, and entropy) from the center-slice measurements were considered reproducible. Conclusions: Some radiomics features are robust to the noise and poor image quality of CBCT images when the imaging protocol is consistent, relative changes in the features are used, and patients are limited to those with less than 1 cm of motion.« less

A Pilot Study of the Attractive Features of Active Videogames Among Chinese Primary School Children.

PubMed

Lau, Patrick W C; Lau, Erica Y; Wang, Jing Jing; Choi, Cheong-Rak; Kim, Chang Gyun

2017-04-01

The present study (1) explored the attractive features that affect Chinese primary school children's preferences of active videogames (AVGs) and (2) contrasted these findings with those in the Western literature. A total of 22 Chinese primary school children were recruited and interviewed. Four AVGs (Wii "Boxing," "Wii Fit™ Plus Obstacle Run"; "EyeToy Knockout", "EyeToy Keep ups") from two commercial consoles (Nintendo® Wii™ and Sony PlayStation ® 2 "EyeToy ® ") were employed. Participants used four selected AVGs for 3 minutes each. After each play period, children (1) described the strengths and weaknesses of each game as well as rated the attractive features of each game based on a 16-item questionnaire and (2) rated up to 5 items that were most influential regarding their AVG preferences. Participants indicated that control was the most significant feature, followed by feedback, goal, and graphics. The top five rated features imply that the perception of competence was the most appealing aspect and expected outcome of Chinese children who play AVGs. Compared with the Western findings regarding attractive AVG features, the present study found certain similarities as well as significant differences among Chinese AVG players. Based on the present study, control, feedback, goal, and graphics are the most significant features that attract Chinese children to play AVGs. Physical exertion, social interaction, competition, and learning outcomes, which are valued according to Western studies, were not mentioned as significant features by Chinese children. These findings demonstrate a need to investigate the effect of cultural background in AVG study design.

The Importance of Prior Knowledge when Comparing Examples: Influences on Conceptual and Procedural Knowledge of Equation Solving

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Star, Jon R.; Durkin, Kelley

2009-01-01

Comparing multiple examples typically supports learning and transfer in laboratory studies and is considered a key feature of high-quality mathematics instruction. This experimental study investigated the importance of prior knowledge in learning from comparison. Seventh- and 8th-grade students (N = 236) learned to solve equations by comparing…

Histological features of the gastrointestinal tract of wild Indonesian shortfin eel, Anguilla bicolor bicolor (McClelland, 1844), captured in Peninsular Malaysia.

PubMed

Nasruddin, Nurrul Shaqinah; Azmai, Mohammad Noor Amal; Ismail, Ahmad; Saad, Mohd Zamri; Daud, Hassan Mohd; Zulkifli, Syaizwan Zahmir

2014-01-01

This study was conducted to record the histological features of the gastrointestinal tract of wild Indonesian shortfin eel, Anguilla bicolor bicolor (McClelland, 1844), captured in Peninsular Malaysia. The gastrointestinal tract was segmented into the oesophagus, stomach, and intestine. Then, the oesophagus was divided into five (first to fifth), the stomach into two (cardiac and pyloric), and the intestine into four segments (anterior, intermediate, posterior, and rectum) for histological examinations. The stomach had significantly taller villi and thicker inner circular muscles compared to the intestine and oesophagus. The lamina propria was thickest in stomach, significantly when compared with oesophagus, but not with the intestine. However, the intestine showed significantly thicker outer longitudinal muscle while gastric glands were observed only in the stomach. The histological features were closely associated with the functions of the different segments of the gastrointestinal tract. In conclusion, the histological features of the gastrointestinal tract of A. b. bicolor are consistent with the feeding habit of a carnivorous fish.

Periosteal ganglia: CT and MR imaging features.

PubMed

Abdelwahab, I F; Kenan, S; Hermann, G; Klein, M J; Lewis, M M

1993-07-01

The imaging features of four cases of periosteal ganglia were studied. Three lesions were located over the proximal shaft of the tibia, in proximity to the pes anserinus. The fourth lesion involved the distal shaft of the ulna. Three lesions had different degrees of external cortical erosion, scalloping, and thick spicules of periosteal bone on plain radiographs. The bone adjacent to the fourth lesion was not involved. Computed tomography (CT) showed these lesions to be sharply defined soft-tissue masses abutting the periosteum. All of the lesions had the same attenuation as fluid. Magnetic resonance (MR) imaging revealed the ganglia to be sharply defined masses that were isointense compared with neighboring muscles on T1-weighted images. There was markedly increased signal intensity compared with that of fat on T2-weighted images. The signal intensity on both types of images was homogeneous. The MR imaging features were consistent with the fluid nature of the lesions. Under the appropriate clinical circumstances, the MR imaging and CT features of periosteal ganglia are diagnostic.

A genomic view of food-related and probiotic Enterococcus strains

PubMed Central

Suárez, Nadia; Hormigo, Ricardo; Fadda, Silvina; Saavedra, Lucila

2017-01-01

Abstract The study of enterococcal genomes has grown considerably in recent years. While special attention is paid to comparative genomic analysis among clinical relevant isolates, in this study we performed an exhaustive comparative analysis of enterococcal genomes of food origin and/or with potential to be used as probiotics. Beyond common genetic features, we especially aimed to identify those that are specific to enterococcal strains isolated from a certain food-related source as well as features present in a species-specific manner. Thus, the genome sequences of 25 Enterococcus strains, from 7 different species, were examined and compared. Their phylogenetic relationship was reconstructed based on orthologous proteins and whole genomes. Likewise, markers associated with a successful colonization (bacteriocin genes and genomic islands) and genome plasticity (phages and clustered regularly interspaced short palindromic repeats) were investigated for lifestyle specific genetic features. At the same time, a search for antibiotic resistance genes was carried out, since they are of big concern in the food industry. Finally, it was possible to locate 1617 FIGfam families as a core proteome universally present among the genera and to determine that most of the accessory genes code for hypothetical proteins, providing reasonable hints to support their functional characterization. PMID:27773878

Comparative study of resist stabilization techniques for metal etch processing

NASA Astrophysics Data System (ADS)

Becker, Gerry; Ross, Matthew F.; Wong, Selmer S.; Minter, Jason P.; Marlowe, Trey; Livesay, William R.

1999-06-01

This study investigates resist stabilization techniques as they are applied to a metal etch application. The techniques that are compared are conventional deep-UV/thermal stabilization, or UV bake, and electron beam stabilization. The electron beam tool use din this study, an ElectronCure system from AlliedSignal Inc., ELectron Vision Group, utilizes a flood electron source and a non-thermal process. These stabilization techniques are compared with respect to a metal etch process. In this study, two types of resist are considered for stabilization and etch: a g/i-line resist, Shipley SPR-3012, and an advanced i-line, Shipley SPR 955- Cm. For each of these resist the effects of stabilization on resist features are evaluated by post-stabilization SEM analysis. Etch selectivity in all cases is evaluated by using a timed metal etch, and measuring resists remaining relative to total metal thickness etched. Etch selectivity is presented as a function of stabilization condition. Analyses of the effects of the type of stabilization on this method of selectivity measurement are also presented. SEM analysis was also performed on the features after a compete etch process, and is detailed as a function of stabilization condition. Post-etch cleaning is also an important factor impacted by pre-etch resist stabilization. Results of post- etch cleaning are presented for both stabilization methods. SEM inspection is also detailed for the metal features after resist removal processing.

Neural connectivity in Internet gaming disorder and alcohol use disorder: A resting-state EEG coherence study.

PubMed

Park, Su Mi; Lee, Ji Yoon; Kim, Yeon Jin; Lee, Jun-Young; Jung, Hee Yeon; Sohn, Bo Kyung; Kim, Dai Jin; Choi, Jung-Seok

2017-05-02

The present study compared neural connectivity and the level of phasic synchronization between neural populations in patients with Internet gaming disorder (IGD), patients with alcohol use disorder (AUD), and healthy controls (HCs) using resting-state electroencephalography (EEG) coherence analyses. For this study, 92 adult males were categorized into three groups: IGD (n = 30), AUD (n = 30), and HC (n = 32). The IGD group exhibited increased intrahemispheric gamma (30-40 Hz) coherence compared to the AUD and HC groups regardless of psychological features (e.g., depression, anxiety, and impulsivity) and right fronto-central gamma coherence positively predicted the scores of the Internet addiction test in all groups. In contrast, the AUD group showed marginal tendency of increased intrahemispheric theta (4-8 Hz) coherence relative to the HC group and this was dependent on the psychological features. The present findings indicate that patients with IGD and AUD exhibit different neurophysiological patterns of brain connectivity and that an increase in the fast phasic synchrony of gamma coherence might be a core neurophysiological feature of IGD.

Differential impairment of social cognition factors in bipolar disorder with and without psychotic features and schizophrenia.

PubMed

Thaler, Nicholas S; Allen, Daniel N; Sutton, Griffin P; Vertinski, Mary; Ringdahl, Erik N

2013-12-01

While it is well-established that patients with schizophrenia and bipolar disorder exhibit deficits in social cognition, few studies have separately examined bipolar disorder with and without psychotic features. The current study addressed this gap by comparing patients with bipolar disorder with (BD+) and without (BD-) psychotic features, patients with schizophrenia (SZ), and healthy controls (NC) across social cognitive measures. Principal factor analysis on five social cognition tasks extracted a two-factor structure comprised of social/emotional processing and theory of mind. Factor scores were compared among the four groups. Results identified differential patterns of impairment between the BD+ and BD- group on the social/emotional processing factor while all clinical groups performed poorer than controls on the theory of mind factor. This provides evidence that a history of psychosis should be taken into account while evaluating social cognition in patients with bipolar disorder and also raises hypotheses about the relationship between social cognition and psychosis. Copyright © 2013 Elsevier Ltd. All rights reserved.

Is recurrence in major depressive disorder related to bipolarity and mixed features? Results from the BRIDGE-II-Mix study.

PubMed

Mazzarini, Lorenzo; Kotzalidis, Georgios D; Piacentino, Daria; Rizzato, Salvatore; Angst, Jules; Azorin, Jean-Michel; Bowden, Charles L; Mosolov, Sergey; Young, Allan H; Vieta, Eduard; Girardi, Paolo; Perugi, Giulio

2018-03-15

Current classifications separate Bipolar (BD) from Major Depressive Disorder (MDD) based on polarity rather than recurrence. We aimed to determine bipolar/mixed feature frequency in a large MDD multinational sample with (High-Rec) and without (Low-Rec) >3 recurrences, comparing the two subsamples. We measured frequency of bipolarity/hypomanic features during current depressive episodes (MDEs) in 2347 MDD patients from the BRIDGE-II-mix database, comparing High-Rec with Low-Rec. We used Bonferroni-corrected Student's t-test for continuous, and chi-squared test, for categorical variables. Logistic regression estimated the size of the association between clinical characteristics and High-Rec MDD. Compared to Low-Rec (n = 1084, 46.2%), High-Rec patients (n = 1263, 53.8%) were older, with earlier depressive onset, had more family history of BD, more atypical features, suicide attempts, hospitalisations, and treatment resistance and (hypo)manic switches when treated with antidepressants, higher comorbidity with borderline personality disorder, and more hypomanic symptoms during current MDE, resulting in higher rates of mixed depression according to both DSM-5 and research-based diagnostic (RBDC) criteria. Logistic regression showed age at first symptoms < 30 years, current MDE duration ≤ 1 month, hypomania/mania among first-degree relatives, past suicide attempts, treatment-resistance, antidepressant-induced swings, and atypical, mixed, or psychotic features during MDE to associate with High-Rec. Number of MDEs for defining recurrence was arbitrary; cross-sectionality did not allow assessment of conversion from MDD to BD. High-Rec MDD differed from Low-Rec group for several clinical/epidemiological variables, including bipolar/mixed features. Bipolarity specifier and RBDC were more sensitive than DSM-5 criteria in detecting bipolar and mixed features in MDD. Copyright © 2017. Published by Elsevier B.V.

Average vs item response theory scores: an illustration using neighbourhood measures in relation to physical activity in adults with arthritis.

PubMed

Mielenz, T J; Callahan, L F; Edwards, M C

2017-01-01

Our study had two main objectives: 1) to determine whether perceived neighbourhood physical features are associated with physical activity levels in adults with arthritis; and 2) to determine whether the conclusions are more precise when item response theory (IRT) scores are used instead of average scores for the perceived neighbourhood physical features scales. Information on health outcomes, neighbourhood characteristics, and physical activity levels were collected using a telephone survey of 937 participants with self-reported arthritis. Neighbourhood walkability and aesthetic features and physical activity levels were measured by self-report. Adjusted proportional odds models were constructed separately for each neighbourhood physical features scale. We found that among adults with arthritis, poorer perceived neighbourhood physical features (both walkability and aesthetics) are associated with decreased physical activity level compared to better perceived neighbourhood features. This association was only observed in our adjusted models when IRT scoring was employed with the neighbourhood physical feature scales (walkability scale: odds ratio [OR] 1.20, 95% confidence interval [CI] 1.02, 1.41; aesthetics scale: OR 1.32, 95% CI 1.09, 1.62), not when average scoring was used (walkability scale: OR 1.14, 95% CI 1.00, 1.30; aesthetics scale: OR 1.16, 95% CI 1.00, 1.36). In adults with arthritis, those reporting poorer walking and aesthetics features were found to have decreased physical activity levels compared to those reporting better features when IRT scores were used, but not when using average scores. This study may inform public health physical environmental interventions implemented to increase physical activity, especially since arthritis prevalence is expected to be close to 20% of the population in 2020. Based on NIH initiatives, future health research will utilize IRT scores. The differences found in this study may be a precursor for research on how past and future treatment effects may vary between these two types of measurement scores. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

USGS Publications Warehouse

Kokaly, Raymond F.; Skidmore, Andrew K

2015-01-01

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic C-H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).

Higher species richness of octocorals in the upper mesophotic zone in Eilat (Gulf of Aqaba) compared to shallower reef zones

NASA Astrophysics Data System (ADS)

Shoham, Erez; Benayahu, Yehuda

2017-03-01

Mesophotic coral-reef ecosystems (MCEs), which comprise the light-dependent communities of corals and other organisms found at depths between 30 and 150 m, have received very little study to date. However, current technological advances, such as remotely operated vehicles and closed-circuit rebreather diving, now enable their thorough investigation. Following the reef-building stony corals, octocorals are the second most common benthic component on many shallow reefs and a major component on deep reefs, the Red Sea included. This study is the first to examine octocoral community features on upper MCEs based on species-level identification and to compare them with the shallower reef zones. The study was carried out at Eilat (Gulf of Aqaba, northern Red Sea), comparing octocoral communities at two mesophotic reefs (30-45 m) and two shallow reef zones (reef flat and upper fore-reef) by belt transects. A total of 30 octocoral species were identified, with higher species richness on the upper MCEs compared to the shallower reefs. Although the MCEs were found to host a higher number of species than the shallower reefs, both featured a similar diversity. Each reef zone revealed a unique octocoral species composition and distinct community structure, with only 16% of the species shared by both the MCEs and the shallower reefs. This study has revealed an almost exclusive dominance of zooxanthellate species at the studied upper MCE reefs, thus indicating an adequate light regime for photosynthesis there. The findings should encourage similar studies on other reefs, aimed at understanding the spatiotemporal features and ecological role of octocorals in reef ecosystems down to the deepest limit of the MCEs.

A comparative review of the pharmacoeconomic guidelines in South Africa.

PubMed

Carapinha, João L

2017-01-01

To compare the pharmacoeconomic guidelines in South Africa (SA) with other middle- and high-income countries. A comparative review of key features of the pharmacoeconomic guidelines in SA was undertaken using the Comparative Table of Pharmacoeconomic Guidelines developed by the International Society of Pharmacoeconomics and Outcomes Research, and published country-level pharmacoeconomics guidelines. A random sample of guidelines in high- and middle-income countries were analyzed if data on all key features were available. Key features of the pharmacoeconomic guidelines in SA were compared with those in other countries, and divergent features were identified and elaborated. Five upper middle-income countries (Brazil, Colombia, Cuba, Malaysia, and Mexico), one lower middle-income country (Egypt), and six high-income countries (Germany, Ireland, Norway, Portugal, Taiwan, and the Netherlands) were analyzed. The pharmacoeconomic guidelines in SA differ in important areas when compared with other countries. In SA, the study perspective and costs are limited to private health-insurance companies, complex modelling is discouraged and models require pre-approval, equity issues are not explicitly stated, a budget impact analysis is not required, and pharmacoeconomic submissions are voluntary. Future updates to the pharmacoeconomic guidelines in SA may include a societal perspective with limitations, incentivize complex and transparent models, and integrate equity issues. The pharmacoeconomic guidelines could be improved by addressing conflicting objectives with policies on National Health Insurance, incentivize private health insurance companies to disclose reimbursement data, and require the inclusion of a budget impact analysis in all pharmacoeconomic submissions. Further research is also needed on the impact of mandatory pharmacoeconomic submissions in middle-income countries.

Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study

PubMed Central

Nankali, Saber; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-01

In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers. PACS numbers: 87.55.km, 87.56.Fc PMID:26894358

Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study.

PubMed

Nankali, Saber; Torshabi, Ahmad Esmaili; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-08

In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers.

Pap-tests with non-hyperchromatic dyskariosis are often associated with squamous intraepithelial lesions of the cervix uteri with eosinophilic features.

PubMed

Bellisano, Giulia; Ambrosini-Spaltro, Andrea; Faa, Gavino; Ravarino, Alberto; Piccin, Andrea; Peer, Irmgard; Kasal, Armin; Vittadello, Fabio; Negri, Giovanni

2016-10-01

Squamous intraepithelial lesions of the cervix uteri with eosinophilic features (eosinophilic dysplasia, ED) are a peculiar type of dysplasia with metaplastic phenotype which was described in histological specimens. The cytological features of these lesions have not been studied yet. Histological samples from 66 women with features of ED and positive p16(INK4a) staining were included in the study. Within the previous year, all women had at least one pap-test, whose features were recorded and compared with 31 control samples with high-grade dysplasia of usual type. The previous pap-test showed high-grade dysplastic cells with non-hyperchromatic nuclei in 56/66 (84.8%) cases and metaplastic features in 60/66 (90.9%) cases. Conversely, the dysplastic cells of the usual lesions showed non-hyperchromatic nuclei in 6/31 (19.4%) and metaplastic features in 4/31 (12.9%) cases. Statistical analysis showed significant differences in distribution of the non-hyperchromatic nuclei (P < 0.001), metaplastic features (P < 0.001), presence of both non-hyperchromatic nuclei and metaplastic features (P < 0.001) and usual dysplastic features (P < 0.001) among the study and control groups. A high-grade squamous intraepithelial lesion with non-hyperchromatic nuclei or metaplastic features is often found in the pap-test previous to the histological diagnosis of ED and may represent the cytologic correlate of this particular type of dysplasia. Diagn. Cytopathol. 2016;44:783-786. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Comparison of online marketing techniques on food and beverage companies' websites in six countries.

PubMed

Bragg, Marie A; Eby, Margaret; Arshonsky, Josh; Bragg, Alex; Ogedegbe, Gbenga

2017-10-26

Food and beverage marketing contributes to poor dietary choices among adults and children. As consumers spend more time on the Internet, food and beverage companies have increased their online marketing efforts. Studies have shown food companies' online promotions use a variety of marketing techniques to promote mostly energy-dense, nutrient-poor products, but no studies have compared the online marketing techniques and nutritional quality of products promoted on food companies' international websites. For this descriptive study, we developed a qualitative codebook to catalogue the marketing themes used on 18 international corporate websites associated with the world's three largest fast food and beverage companies (i.e. Coca-Cola, McDonald's, Kentucky Fried Chicken). Nutritional quality of foods featured on those websites was evaluated based on quantitative Nutrient Profile Index scores and food category (e.g. fried, fresh). Beverages were sorted into categories based on added sugar content. We report descriptive statistics to compare the marketing techniques and nutritional quality of products featured on the company websites for the food and beverage company websites in two high-income countries (HICs), Germany and the United States, two upper-middle-income countries (UMICs), China and Mexico, and two lower-middle-income countries (LMICs), India and the Philippines. Of the 406 screenshots captured from company websites, 67·8% depicted a food or beverage product. HICs' websites promoted diet food or beverage products/healthier alternatives (e.g. baked chicken sandwich) significantly more often on their pages (25%), compared to LMICs (14·5%). Coca-Cola featured diet products significantly more frequently on HIC websites compared to LMIC websites. Charities were featured more often on webpages in LMICs (15·4%) compared to UMICs (2·6%) and HICs (2·3%). This study demonstrates that companies showcase healthier products in wealthier countries and advertise their philanthropic activities in lower income countries, which is concerning given the negative effect of nutrition transition (double burden of overnutrition and undernutrition) on burden of non-communicable diseases and obesity in lower income countries.

Profitability of Cropping Systems Featuring Tillage and Compost

USDA-ARS?s Scientific Manuscript database

Productivity rather than profitability is often used to compare agronomic systems. Increasing energy prices will force producers to scrutinize machinery operation and input costs, which will shift emphasis to profitability. The objective of this study was to compare returns to land and management fo...

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

PubMed Central

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

Rigorous assessment and integration of the sequence and structure based features to predict hot spots

PubMed Central

2011-01-01

Background Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. Results In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. Conclusion Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots. PMID:21798070

The reliability of continuous brain responses during naturalistic listening to music.

PubMed

Burunat, Iballa; Toiviainen, Petri; Alluri, Vinoo; Bogert, Brigitte; Ristaniemi, Tapani; Sams, Mikko; Brattico, Elvira

2016-01-01

Low-level (timbral) and high-level (tonal and rhythmical) musical features during continuous listening to music, studied by functional magnetic resonance imaging (fMRI), have been shown to elicit large-scale responses in cognitive, motor, and limbic brain networks. Using a similar methodological approach and a similar group of participants, we aimed to study the replicability of previous findings. Participants' fMRI responses during continuous listening of a tango Nuevo piece were correlated voxelwise against the time series of a set of perceptually validated musical features computationally extracted from the music. The replicability of previous results and the present study was assessed by two approaches: (a) correlating the respective activation maps, and (b) computing the overlap of active voxels between datasets at variable levels of ranked significance. Activity elicited by timbral features was better replicable than activity elicited by tonal and rhythmical ones. These results indicate more reliable processing mechanisms for low-level musical features as compared to more high-level features. The processing of such high-level features is probably more sensitive to the state and traits of the listeners, as well as of their background in music. Copyright © 2015 Elsevier Inc. All rights reserved.

Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

PubMed Central

Lele, Ramachandra Dattatraya; Joshi, Mukund; Chowdhary, Abhay

2014-01-01

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data. PMID:25332717

Clinical decision support improves quality of telephone triage documentation - an analysis of triage documentation before and after computerized clinical decision support

PubMed Central

2014-01-01

Background Clinical decision support (CDS) has been shown to be effective in improving medical safety and quality but there is little information on how telephone triage benefits from CDS. The aim of our study was to compare triage documentation quality associated with the use of a clinical decision support tool, ExpertRN©. Methods We examined 50 triage documents before and after a CDS tool was used in nursing triage. To control for the effects of CDS training we had an additional control group of triage documents created by nurses who were trained in the CDS tool, but who did not use it in selected notes. The CDS intervention cohort of triage notes was compared to both the pre-CDS notes and the CDS trained (but not using CDS) cohort. Cohorts were compared using the documentation standards of the American Academy of Ambulatory Care Nursing (AAACN). We also compared triage note content (documentation of associated positive and negative features relating to the symptoms, self-care instructions, and warning signs to watch for), and documentation defects pertinent to triage safety. Results Three of five AAACN documentation standards were significantly improved with CDS. There was a mean of 36.7 symptom features documented in triage notes for the CDS group but only 10.7 symptom features in the pre-CDS cohort (p < 0.0001) and 10.2 for the cohort that was CDS-trained but not using CDS (p < 0.0001). The difference between the mean of 10.2 symptom features documented in the pre-CDS and the mean of 10.7 symptom features documented in the CDS-trained but not using was not statistically significant (p = 0.68). Conclusions CDS significantly improves triage note documentation quality. CDS-aided triage notes had significantly more information about symptoms, warning signs and self-care. The changes in triage documentation appeared to be the result of the CDS alone and not due to any CDS training that came with the CDS intervention. Although this study shows that CDS can improve documentation, further study is needed to determine if it results in improved care. PMID:24645674

Clinical decision support improves quality of telephone triage documentation--an analysis of triage documentation before and after computerized clinical decision support.

PubMed

North, Frederick; Richards, Debra D; Bremseth, Kimberly A; Lee, Mary R; Cox, Debra L; Varkey, Prathibha; Stroebel, Robert J

2014-03-20

Clinical decision support (CDS) has been shown to be effective in improving medical safety and quality but there is little information on how telephone triage benefits from CDS. The aim of our study was to compare triage documentation quality associated with the use of a clinical decision support tool, ExpertRN©. We examined 50 triage documents before and after a CDS tool was used in nursing triage. To control for the effects of CDS training we had an additional control group of triage documents created by nurses who were trained in the CDS tool, but who did not use it in selected notes. The CDS intervention cohort of triage notes was compared to both the pre-CDS notes and the CDS trained (but not using CDS) cohort. Cohorts were compared using the documentation standards of the American Academy of Ambulatory Care Nursing (AAACN). We also compared triage note content (documentation of associated positive and negative features relating to the symptoms, self-care instructions, and warning signs to watch for), and documentation defects pertinent to triage safety. Three of five AAACN documentation standards were significantly improved with CDS. There was a mean of 36.7 symptom features documented in triage notes for the CDS group but only 10.7 symptom features in the pre-CDS cohort (p < 0.0001) and 10.2 for the cohort that was CDS-trained but not using CDS (p < 0.0001). The difference between the mean of 10.2 symptom features documented in the pre-CDS and the mean of 10.7 symptom features documented in the CDS-trained but not using was not statistically significant (p = 0.68). CDS significantly improves triage note documentation quality. CDS-aided triage notes had significantly more information about symptoms, warning signs and self-care. The changes in triage documentation appeared to be the result of the CDS alone and not due to any CDS training that came with the CDS intervention. Although this study shows that CDS can improve documentation, further study is needed to determine if it results in improved care.

Artificial bee colony algorithm for single-trial electroencephalogram analysis.

PubMed

Hsu, Wei-Yen; Hu, Ya-Ping

2015-04-01

In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Occlusal and facial features in Amazon indigenous: An insight into the role of genetics and environment in the etiology dental malocclusion.

PubMed

de Souza, Bento Sousa; Bichara, Livia Monteiro; Guerreiro, João Farias; Quintão, Cátia Cardoso Abdo; Normando, David

2015-09-01

Indigenous people of the Xingu river present a similar tooth wear pattern, practise exclusive breast-feeding, no pacifier use, and have a large intertribal genetic distance. To revisit the etiology of dental malocclusion features considering these population characteristics. Occlusion and facial features of five semi-isolated Amazon indigenous populations (n=351) were evaluated and compared to previously published data from urban Amazon people. Malocclusion prevalence ranged from 33.8% to 66.7%. Overall this prevalence is lower when compared to urban people mainly regarding posterior crossbite. A high intertribal diversity was found. The Arara-Laranjal village had a population with a normal face profile (98%) and a high rate of normal occlusion (66.2%), while another group from the same ethnicity presented a high prevalence of malocclusion, the highest occurrence of Class III malocclusion (32.6%) and long face (34.8%). In Pat-Krô village the population had the highest prevalence of Class II malocclusion (43.9%), convex profile (38.6%), increased overjet (36.8%) and deep bite (15.8%). Another village's population, from the same ethnicity, had a high frequency of anterior open bite (22.6%) and anterior crossbite (12.9%). The highest occurrence of bi-protrusion was found in the group with the lowest prevalence of dental crowding, and vice versa. Supported by previous genetic studies and given their similar environmental conditions, the high intertribal diversity of occlusal and facial features suggests that genetic factors contribute substantially to the morphology of occlusal and facial features in the indigenous groups studied. The low prevalence of posterior crossbite in the remote indigenous populations compared with urban populations may relate to prolonged breastfeeding and an absence of pacifiers in the indigenous groups. Copyright © 2015 Elsevier Ltd. All rights reserved.

How have changes in air bag designs affected frontal crash mortality?

PubMed

Braver, Elisa R; Shardell, Michelle; Teoh, Eric R

2010-07-01

To determine whether front air bag changes have affected occupant protection, frontal crash mortality rates were compared among front outboard occupants in vehicles having certified-advanced air bags (latest generation of air bags) or sled-certified air bags with and without advanced features. Poisson marginal structural models were used to calculate standardized mortality rate ratios (MRRs) for front occupants per registered vehicle. Vehicle age-corrected mortality rates were lower for drivers of vehicles having sled-certified air bags with advanced features than for drivers having sled-certified air bags without advanced features (MRR = 0.88; 95% confidence interval [CI]: 0.81-0.95), including unbelted men and drivers younger than 60. The mortality rate was higher, though not statistically significant, for drivers having certified-advanced air bags compared with sled-certified air bags with advanced features (vehicle age-corrected MRR = 1.13; 95% CI: 0.97-1.32) and significantly higher for belted drivers (MRR = 1.21; 95% CI: 1.04-1.39). Advanced air bag features appeared protective for some occupants. However, increased mortality rates among belted drivers of vehicles having certified-advanced air bags relative to those having sled-certified air bags with advanced features suggest that further study is needed to identify any potential problems with requirements for certification. 2010 Elsevier Inc. All rights reserved.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

Quantitative image feature variability amongst CT scanners with a controlled scan protocol

NASA Astrophysics Data System (ADS)

Ger, Rachel B.; Zhou, Shouhao; Chi, Pai-Chun Melinda; Goff, David L.; Zhang, Lifei; Lee, Hannah J.; Fuller, Clifton D.; Howell, Rebecca M.; Li, Heng; Stafford, R. Jason; Court, Laurence E.; Mackin, Dennis S.

2018-02-01

Radiomics studies often analyze patient computed tomography (CT) images acquired from different CT scanners. This may result in differences in imaging parameters, e.g. different manufacturers, different acquisition protocols, etc. However, quantifiable differences in radiomics features can occur based on acquisition parameters. A controlled protocol may allow for minimization of these effects, thus allowing for larger patient cohorts from many different CT scanners. In order to test radiomics feature variability across different CT scanners a radiomics phantom was developed with six different cartridges encased in high density polystyrene. A harmonized protocol was developed to control for tube voltage, tube current, scan type, pitch, CTDIvol, convolution kernel, display field of view, and slice thickness across different manufacturers. The radiomics phantom was imaged on 18 scanners using the control protocol. A linear mixed effects model was created to assess the impact of inter-scanner variability with decomposition of feature variation between scanners and cartridge materials. The inter-scanner variability was compared to the residual variability (the unexplained variability) and to the inter-patient variability using two different patient cohorts. The patient cohorts consisted of 20 non-small cell lung cancer (NSCLC) and 30 head and neck squamous cell carcinoma (HNSCC) patients. The inter-scanner standard deviation was at least half of the residual standard deviation for 36 of 49 quantitative image features. The ratio of inter-scanner to patient coefficient of variation was above 0.2 for 22 and 28 of the 49 features for NSCLC and HNSCC patients, respectively. Inter-scanner variability was a significant factor compared to patient variation in this small study for many of the features. Further analysis with a larger cohort will allow more thorough analysis with additional variables in the model to truly isolate the interscanner difference.

Diagnostic specificity of poor premorbid adjustment: comparison of schizophrenia, schizoaffective disorder, and mood disorder with psychotic features.

PubMed

Tarbox, Sarah I; Brown, Leslie H; Haas, Gretchen L

2012-10-01

Individuals with schizophrenia have significant deficits in premorbid social and academic adjustment compared to individuals with non-psychotic diagnoses. However, it is unclear how severity and developmental trajectory of premorbid maladjustment compare across psychotic disorders. This study examined the association between premorbid functioning (in childhood, early adolescence, and late adolescence) and psychotic disorder diagnosis in a first-episode sample of 105 individuals: schizophrenia (n=68), schizoaffective disorder (n=22), and mood disorder with psychotic features (n=15). Social and academic maladjustment was assessed using the Cannon-Spoor Premorbid Adjustment Scale. Worse social functioning in late adolescence was associated with higher odds of schizophrenia compared to odds of either schizoaffective disorder or mood disorder with psychotic features, independently of child and early adolescent maladjustment. Greater social dysfunction in childhood was associated with higher odds of schizoaffective disorder compared to odds of schizophrenia. Premorbid decline in academic adjustment was observed for all groups, but did not predict diagnosis at any stage of development. Results suggest that social functioning is disrupted in the premorbid phase of both schizophrenia and schizoaffective disorder, but remains fairly stable in mood disorders with psychotic features. Disparities in the onset and time course of social dysfunction suggest important developmental differences between schizophrenia and schizoaffective disorder. Copyright © 2012 Elsevier B.V. All rights reserved.

Mobile Applications to Improve Medication Adherence.

PubMed

Haase, Jamie; Farris, Karen B; Dorsch, Michael P

2017-02-01

Background and Introduction: Mobile applications are useful tools to improve medication adherence. As developers continue to improve the features of existing mobile applications, pharmacists should be aware of the current features that are available to patients. There are limited studies available that discuss which applications have the most desirable features. The aim of this study was to compare available mobile applications and identify ideal application features used to improve medication adherence. As of September 5, 2014, the search terms "medication adherence" and "medication reminder" generated a total of 225 hits. Ideal application features were used to create an Application Score Card to identify applications with the highest number of ideal features. We identified 30 applications that were written in English, medication related, last updated in 2014, and did not meet any exclusion criteria. The top five applications RxNetwork, Mango Health, MyMeds, C3HealthLink, and HuCare are discussed in detail. There are numerous studies looking at medication adherence. However, current literature regarding mobile applications to improve medication adherence is lacking. This article will provide pharmacists with a brief overview of the available mobile applications and features that could be used to improve patient adherence to medications. Existing mobile applications to improve medication adherence have ideal features that could help patients take medication as prescribed. Once further research is performed to establish their efficacy, pharmacists could begin to recommend mobile applications to their patients.

Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

NASA Astrophysics Data System (ADS)

Almerico, Anna Maria; Tutone, Marco; Lauria, Antonino

2008-05-01

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.

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.

Content management systems and E-commerce: a comparative case study

NASA Astrophysics Data System (ADS)

Al Rasheed, Amal A.; El-Masri, Samir D.

2011-12-01

The need for CMS's to create and edit e-commerce websites has increased with the growing importance of e-commerce. In this paper, the various features essential for e-commerce CMS's are explored. The aim of the paper was to find the best CMS solution for e-commerce which includes the best of both CMS and store management. Accordingly, we conducted a study on three popular open source CMS's for e-commerce: VirtueMart from Joomla!, Ubercart from Drupal, and Magento. We took into account features like hosting and installation, performance, support/community, content management, add on modules and functional features. We concluded with improvements that could be made in order to alleviate problems.

Association Between Patient- Centered Medical Home Features and Satisfaction With Family Medicine Residency Training in the US.

PubMed

Carney, Patricia A; Waller, Elaine; Dexter, Eve; Marino, Miguel; Rosener, Stephanie E; Green, Larry A; Jones, Geoffrey; M Keister, J Drew; Dostal, Julie A; Jones, Samuel M; Eiff, M Patrice

2016-11-01

Primary care residencies are undergoing dramatic changes because of changing health care systems and evolving demands for updated training models. We examined the relationships between residents' exposures to patient-centered medical home (PCMH) features in their assigned continuity clinics and their satisfaction with training. Longitudinal surveys were collected annually from residents evaluating satisfaction with training using a 5-point Likert-type scale (1=very unsatisfied to 5=very satisfied) from 2007 through 2011, and the presence or absence of PCMH features were collected from 24 continuity clinics during the same time period. Odds ratios on residents' overall satisfaction were compared according to whether they had no exposure to PCMH features, some exposure (1-2 years), or full exposure (all 3 or more years). Fourteen programs and 690 unique residents provided data to this study. Resident satisfaction with training was highest with full exposure for integrated case management compared to no exposure, which occurred in 2010 (OR=2.85, 95% CI=1.40, 5.80). Resident satisfaction was consistently statistically lower with any or full exposure (versus none) to expanded clinic hours in 2007 and 2009 (eg, OR for some exposure in 2009 was 0.31 95% CI=0.19, 0.51, and OR for full exposure 0.28 95% CI=0.16, 0.49). Resident satisfaction for many electronic health record (EHR)-based features tended to be significantly lower with any exposure (some or full) versus no exposure over the study period. For example, the odds ratio for resident satisfaction was significantly lower with any exposure to electronic health records in continuity practice in 2008, 2009, and 2010 (OR for some exposure in 2008 was 0.36; 95% CI=0.19, 0.70, with comparable results in 2009, 2010). Resident satisfaction with training was inconsistently correlated with exposure to features of PCMH. No correlation between PCMH exposure and resident satisfaction was sustained over time.

Bruxism in craniocervical dystonia: a prospective study.

PubMed

Borie, Laetitia; Langbour, Nicolas; Guehl, Dominique; Burbaud, Pierre; Ella, Bruno

2016-09-01

Bruxism pathophysiology remains unclear, and its occurrence has been poorly investigated in movement disorders. The aim of this study was to compare the frequency of bruxism in patients with craniocervical dystonia vs. normal controls and to determine its associated clinical features. This is a prospective-control study. A total of 114 dystonic subjects (45 facial dystonia, 69 cervical dystonia) and 182 controls were included. Bruxism was diagnosed using a hetero-questionnaire and a clinical examination performed by trained dentists. Occurrence of bruxism was compared between the different study populations. A binomial logistic regression analysis was used to determine which clinical features influenced bruxism occurrence in each population. The frequency of bruxism was significantly higher in the dystonic group than in normal controls but there was no difference between facial and cervical dystonia. It was also higher in women than in men. Bruxism features were similar between normal controls and dystonic patients except for a higher score of temporomandibular jaw pain in the dystonic group. The higher frequency of bruxism in dystonic patients suggests that bruxism is increased in patients with basal ganglia dysfunction but that its nature does not differ from that seen in bruxers from the normal population.

Single- and double-row repair for rotator cuff tears - biology and mechanics.

PubMed

Papalia, Rocco; Franceschi, Francesco; Vasta, Sebastiano; Zampogna, Biagio; Maffulli, Nicola; Denaro, Vincenzo

2012-01-01

We critically review the existing studies comparing the features of single- and double-row repair, and discuss suggestions about the surgical indications for the two repair techniques. All currently available studies comparing the biomechanical, clinical and the biological features of single and double row. Biomechanically, the double-row repair has greater performances in terms of higher initial fixation strength, greater footprint coverage, improved contact area and pressure, decreased gap formation, and higher load to failure. Results of clinical studies demonstrate no significantly better outcomes for double-row compared to single-row repair. Better results are achieved by double-row repair for larger lesions (tear size 2.5-3.5 cm). Considering the lack of statistically significant differences between the two techniques and that the double row is a high cost and a high surgical skill-dependent technique, we suggest using the double-row technique only in strictly selected patients. Copyright © 2012 S. Karger AG, Basel.

Toward Predicting Social Support Needs in Online Health Social Networks.

PubMed

Choi, Min-Je; Kim, Sung-Hee; Lee, Sukwon; Kwon, Bum Chul; Yi, Ji Soo; Choo, Jaegul; Huh, Jina

2017-08-02

While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs. ©Min-Je Choi, Sung-Hee Kim, Sukwon Lee, Bum Chul Kwon, Ji Soo Yi, Jaegul Choo, Jina Huh. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.08.2017.

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.

The impact of feature selection on one and two-class classification performance for plant microRNAs.

PubMed

Khalifa, Waleed; Yousef, Malik; Saçar Demirci, Müşerref Duygu; Allmer, Jens

2016-01-01

MicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18-24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ∼29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ∼13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on par with TCC given the proper set of features.

Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths

NASA Astrophysics Data System (ADS)

Whitney, Heather M.; Drukker, Karen; Edwards, Alexandra; Papaioannou, John; Giger, Maryellen L.

2018-02-01

Radiomics features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As clinical institutions transition from 1.5 T to 3.0 T magnetic resonance imaging (MRI), it is helpful to identify robust features across these field strengths. In this study, dynamic contrast-enhanced MR images were acquired retrospectively under IRB/HIPAA compliance, yielding 738 cases: 241 and 124 benign lesions imaged at 1.5 T and 3.0 T and 231 and 142 luminal A cancers imaged at 1.5 T and 3.0 T, respectively. Lesions were segmented using a fuzzy C-means method. Extracted radiomic values for each group of lesions by cancer status and field strength of acquisition were compared using a Kolmogorov-Smirnov test for the null hypothesis that two groups being compared came from the same distribution, with p-values being corrected for multiple comparisons by the Holm-Bonferroni method. Two shape features, one texture feature, and three enhancement variance kinetics features were found to be potentially robust. All potentially robust features had areas under the receiver operating characteristic curve (AUC) statistically greater than 0.5 in the task of distinguishing between lesion types (range of means 0.57-0.78). The significant difference in voxel size between field strength of acquisition limits the ability to affirm more features as robust or not robust according to field strength alone, and inhomogeneities in static field strength and radiofrequency field could also have affected the assessment of kinetic curve features as robust or not. Vendor-specific image scaling could have also been a factor. These findings will contribute to the development of radiomic signatures that use features identified as robust across field strength.

Prediction of acoustic feature parameters using myoelectric signals.

PubMed

Lee, Ki-Seung

2010-07-01

It is well-known that a clear relationship exists between human voices and myoelectric signals (MESs) from the area of the speaker's mouth. In this study, we utilized this information to implement a speech synthesis scheme in which MES alone was used to predict the parameters characterizing the vocal-tract transfer function of specific speech signals. Several feature parameters derived from MES were investigated to find the optimal feature for maximization of the mutual information between the acoustic and the MES features. After the optimal feature was determined, an estimation rule for the acoustic parameters was proposed, based on a minimum mean square error (MMSE) criterion. In a preliminary study, 60 isolated words were used for both objective and subjective evaluations. The results showed that the average Euclidean distance between the original and predicted acoustic parameters was reduced by about 30% compared with the average Euclidean distance of the original parameters. The intelligibility of the synthesized speech signals using the predicted features was also evaluated. A word-level identification ratio of 65.5% and a syllable-level identification ratio of 73% were obtained through a listening test.

Comparative study of feature selection with ensemble learning using SOM variants

NASA Astrophysics Data System (ADS)

Filali, Ameni; Jlassi, Chiraz; Arous, Najet

2017-03-01

Ensemble learning has succeeded in the growth of stability and clustering accuracy, but their runtime prohibits them from scaling up to real-world applications. This study deals the problem of selecting a subset of the most pertinent features for every cluster from a dataset. The proposed method is another extension of the Random Forests approach using self-organizing maps (SOM) variants to unlabeled data that estimates the out-of-bag feature importance from a set of partitions. Every partition is created using a various bootstrap sample and a random subset of the features. Then, we show that the process internal estimates are used to measure variable pertinence in Random Forests are also applicable to feature selection in unsupervised learning. This approach aims to the dimensionality reduction, visualization and cluster characterization at the same time. Hence, we provide empirical results on nineteen benchmark data sets indicating that RFS can lead to significant improvement in terms of clustering accuracy, over several state-of-the-art unsupervised methods, with a very limited subset of features. The approach proves promise to treat with very broad domains.

Death-associated protein kinase promoter methylation correlates with clinicopathological and prognostic features in nonsmall cell lung cancer patients: A cohort study.

PubMed

Yang, Xiao-Yu; Zhang, Jun; Yu, Xiao-Ling; Zheng, Guo-Feng; Zhao, Fei; Jia, Xiao-Jing

2018-01-01

The objective was to study the correlation between death-associated protein kinase (DAPK) promoter methylation and the clinicopathological and prognostic features in nonsmall cell lung cancer (NSCLC) patients. A total of 117 NSCLC patients were recruited into our study between December 2012 and December 2014. Methylation-specific polymerase chain reaction was employed to detect the methylation status of DAPK in cancer tissues, peficancerous tissues, and serum samples of 117 NSCLC patients. In addition, serum samples of 115 healthy subjects were analyzed as controls. A literature search of English and Chinese databases, based on predefined criteria, identified published studies closely related to this study. Data were extracted, and meta-analysis was performed using STATA 12.0 software (STATA Corporation, College Station, TX, USA). Our study results showed that DAPK promoter methylation frequency was significantly higher in NSCLC tissues compared to peficancerous normal tissues (58.1% vs. 12.8%, χ 2 = 52.45, P < 0.001). When serum samples were compared, DAPK methylation frequency in NSCLC patients was higher than the control group (27.4% vs. 0, χ 2 = 37.07, P < 0.001). Our meta-analysis results demonstrated that DAPK methylation frequency was lower in tumor node metastasis (TNM) stage I-II compared to TNM stage III-IV (relative risk [RR] =0.87, 95% confidence interval [CI] =0.76-0.99, P = 0.041). DAPK promoter methylation frequency in NSCLC patients with lymph node metastasis was significantly higher compared to the patients with no metastases (RR = 1.26, 95% CI = 1.04-1.52, P = 0.020). Finally, the 5-year survival rate was lower in NSCLC patient group with high frequency of DAPK methylation, compared to the patient group with unmethylated DAPK (RR = 0.71, 95% CI = 0.56-0.89, P = 0.004). Our results showed that DAPK promoter methylation is tightly correlated with clinicopathological features of NSCLC and is associated with poor prognosis in patients.

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

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

2014-01-01

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

The association between retinal vascular geometry changes and diabetic retinopathy and their role in prediction of progression – an exploratory study

PubMed Central

2014-01-01

Background The study describes the relationship of retinal vascular geometry (RVG) to severity of diabetic retinopathy (DR), and its predictive role for subsequent development of proliferative diabetic retinopathy (PDR). Methods The research project comprises of two stages. Firstly, a comparative study of diabetic patients with different grades of DR. (No DR: Minimal non-proliferative DR: Severe non-proliferative DR: PDR) (10:10: 12: 19). Analysed RVG features including vascular widths and branching angles were compared between patient cohorts. A preliminary statistical model for determination of the retinopathy grade of patients, using these features, is presented. Secondly, in a longitudinal predictive study, RVG features were analysed for diabetic patients with progressive DR over 7 years. RVG at baseline was examined to determine risk for subsequent PDR development. Results In the comparative study, increased DR severity was associated with gradual vascular dilatation (p = 0.000), and widening of the bifurcating angle (p = 0.000) with increase in smaller-child-vessel branching angle (p = 0.027). Type 2 diabetes and increased diabetes duration were associated with increased vascular width (p = <0.05 In the predictive study, at baseline, reduced small-child vascular width (OR = 0.73 (95% CI 0.58-0.92)), was predictive of future progression to PDR. Conclusions The study findings suggest that RVG alterations can act as novel markers indicative of progression of DR severity and establishment of PDR. RVG may also have a potential predictive role in determining the risk of future retinopathy progression. PMID:25001248

Corrosion casting of the subglottis following endotracheal tube intubation injury: a pilot study in Yorkshire piglets

PubMed Central

2013-01-01

Purpose Subglottic stenosis can result from endotracheal tube injury. The mechanism by which this occurs, however, is not well understood. The purpose of this study was to examine the role of angiogenesis, hypoxia and ischemia in subglottic mucosal injury following endotracheal intubation. Methods Six Yorkshire piglets were randomized to either a control group (N=3, ventilated through laryngeal mask airway for corrosion casting) or accelerated subglottic injury group through intubation and induced hypoxia as per a previously described model (N=3). The vasculature of all animals was injected with liquid methyl methacrylate. After polymerization, the surrounding tissue was corroded with potassium hydroxide. The subglottic region was evaluated using scanning electron microscopy looking for angiogenic and hypoxic or degenerative features and groups were compared using Mann–Whitney tests and Friedman’s 2-way ANOVA. Results Animals in the accelerated subglottic injury group had less overall angiogenic features (P=.002) and more overall hypoxic/degenerative features (P=.000) compared with controls. Amongst angiogenic features, there was decreased budding (P=.000) and a trend toward decreased sprouting (P=.037) in the accelerated subglottic injury group with an increase in intussusception (P=.004), possibly representing early attempts at rapid revascularization. Amongst hypoxic/degenerative features, extravasation was the only feature that was significantly higher in the accelerated subglottic injury group (P=.000). Conclusions Subglottic injury due to intubation and hypoxia may lead to decreased angiogenesis and increased blood vessel damage resulting in extravasation of fluid and a decreased propensity toward wound healing in this animal model. PMID:24401165

Is Posner's "beam" the same as Treisman's "glue"?: On the relation between visual orienting and feature integration theory.

PubMed

Briand, K A; Klein, R M

1987-05-01

In the present study we investigated whether the visually allocated "beam" studied by Posner and others is the same visual attentional resource that performs the role of feature integration in Treisman's model. Subjects were cued to attend to a certain spatial location by a visual cue, and performance at expected and unexpected stimulus locations was compared. Subjects searched for a target letter (R) with distractor letters that either could give rise to illusory conjunctions (PQ) or could not (PB). Results from three separate experiments showed that orienting attention in response to central cues (endogenous orienting) showed similar effects for both conjunction and feature search. However, when attention was oriented with peripheral visual cues (exogenous orienting), conjunction search showed larger effects of attention than did feature search. It is suggested that the attentional systems that are oriented in response to central and peripheral cues may not be the same and that only the latter performs a role in feature integration. Possibilities for future research are discussed.

Evaluating depressive symptoms in mania: a naturalistic study of patients with bipolar disorder.

PubMed

Young, Allan H; Eberhard, Jonas

2015-01-01

This study aimed to evaluate patients with bipolar I disorder (BD-I) who have mania with depressive symptoms and who meet the new "with mixed features" specifier of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5). This prospective, multinational, naturalistic study surveyed psychiatrists and their patients with BD-I from October 2013 to March 2014. Eligible patients had BD-I, had a (current) manic episode, and had experienced onset of a manic episode within the previous 3 months. Psychiatrists provided patient information on depressive symptoms (DSM-5 criteria); symptoms of anxiety, irritability, and agitation; suicide attempts; and physician satisfaction with treatment response. Data were stratified according to whether patients met the criteria for the BD-I "with mixed features" specifier of DSM-5 (≥3 depressive symptoms) or not, and characteristics were compared between the two subgroups. Patients also self-reported on depressive symptoms using the Mini-International Neuropsychiatric Interview module questionnaire. Overall, 34% of 1,035 patients met the criteria for BD-I "with mixed features," exhibiting ≥3 depressive symptoms during their current manic episode. This correlated with the matched patient self-reports of depressive symptoms. During their current manic episode, BD-I patients "with mixed features" had more severe symptoms of anxiety, irritability, and agitation (average composite severity score of 4.1 vs 3.4), a higher incidence of suicide attempts (38% vs 9%), and more physician dissatisfaction with treatment response (22% vs 14%), compared to patients with 0-2 depressive symptoms (all P<0.05). This study found that patients with BD-I "with mixed features" (ie, ≥3 depressive symptoms during a manic episode), suffered, on average, from a greater burden of disease than patients with pure mania. Improved identification of these patients may help to optimize treatment outcomes.

Comparative anatomy, morphology, and molecular phylogenetics of the African genus Satanocrater (Acanthaceae).

PubMed

Tripp, Erin A; Fatimah, Siti

2012-06-01

Anatomical and morphological features of Satanocrater were studied to test hypotheses of xeric adaptations in the genus, which is endemic to arid tropical Africa. These features, together with molecular data, were used to test the phylogenetic placement of Satanocrater within the large plant family Acanthaceae. We undertook a comparative study of four species of Satanocrater. Carbon isotope ratios were generated to test a hypothesis of C(4) photosynthesis. Molecular data from chloroplast (trnG-trnS, trnG-trnR, psbA-trnH) and nuclear (Eif3E) loci were used to test the placement of Satanocrater within Acanthaceae. Anatomical features reflecting xeric adaptations of species of Satanocrater included a thick-walled epidermis, thick cuticle, abundant trichomes and glandular scales, stomata overarched by subsidiary cells, tightly packed mesophyll cells, and well-developed palisade parenchyma on both leaf surfaces. Although two species had enlarged bundle sheath cells, a feature often implicated in C(4) photosynthesis, isotope ratios indicated all species of Satanocrater use the C(3) pathway. Molecular data resolved Satanocrater within tribe Ruellieae with strong support. Within Ruellieae, our data suggest that pollen morphology of Satanocrater may represent an intermediate stage in a transition series. Anatomical and morphological features of Satanocrater reflect adaptation to xeric environments and add new information about the biology of xerophytes. Morphological and molecular data place Satanocrater in the tribe Ruellieae with confidence. This study adds to our capacity to test hypotheses of broad evolutionary and ecological interest in a diverse and important family of flowering plants.

Diagnostic utility of endobronchial ultrasound features in differentiating malignant and benign lymph nodes.

PubMed

Agrawal, Sumita P; Ish, Pranav; Goel, Akhil D; Gupta, Nitesh; Chakrabarti, Shibdas; Bhattacharya, Dipak; Sen, Manas K; Suri, Jagdish C

2018-06-25

Endobronchial ultrasound (EBUS) features have been shown to be useful in predicting etiology of enlarged malignant lymph nodes. However, there is dearth of evidence especially from developing countries. We assessed the EBUS characteristics across various mediastinal and hilar lymphadenopathies. In this prospective study, all patients with mediastinal and hilar lymphadenopathy on CT Chest and who were planned for EBUS-FNA (Fine Needle Aspiration) were included. EBUS features of lymph nodes studied were shape, size, margins, echogenicity, central hilar structure (CHS), coagulation necrosis sign and colour power doppler index (CPDI). These were scored and compared between benign and malignant lymphadenopathies. A total of 86 lymph nodes in 46 patients were prospectively studied of which 23 (26.7%) were malignant, 27 (31.3%) tuberculosis and 36 (41.8%) sarcoidosis. There was significant difference between malignant and benign lymph nodes in terms of CHS [central hilar structutre] (p=0.011), margins (p=0.036) and coagulation necrosis sign (p<0.001). On comparison of features of malignancy and tuberculosis, there were significant differences in margins (p=0.016) and coagulation necrosis sign (p 0.001). However, when malignancy and sarcoidosis was compared, there were differences in echogenicity (p=0.002), CHS (p=0.009) and coagulation necrosis sign (p<0.001). Only coagulation necrosis sign was found to be highly consistent with malignant lymph nodes. The other features cannot be used to distinguish malignant from benign lymph nodes, especially in a developing country like India where tuberculosis is a common cause of mediastinal lymphadenopathy.

The use of semantic- and phonological-based feature approaches to treat naming deficits in aphasia.

PubMed

Hashimoto, Naomi

2012-06-01

The aim of the study was to compare approaches highlighting either semantic or phonological features to treat naming deficits in aphasia. Treatment focused on improving picture naming. An alternating treatments design was used with a multiple baseline design across stimuli to examine effects of both approaches in two participants with varying degrees of anomia. The features approaches were modified in that three, rather than six, features were used. Significant differential effects were found across participants; this appeared to be a function of each participant's strengths or preferences over the course of treatment. Modest generalization effects were obtained for one participant. Naming error analyses revealed patterns suggestive of increased lexical access for both participants. These findings provide evidence that using a modified features-based protocol can improve naming when incorporating both semantic and phonological feature cues. Naming error patterns can provide additional evidence of improved naming during treatment.

Comparing the role of shape and texture on staging hepatic fibrosis from medical imaging

NASA Astrophysics Data System (ADS)

Zhang, Xuejun; Louie, Ryan; Liu, Brent J.; Gao, Xin; Tan, Xiaomin; Qu, Xianghe; Long, Liling

2016-03-01

The purpose of this study is to investigate the role of shape and texture in the classification of hepatic fibrosis by selecting the optimal parameters for a better Computer-aided diagnosis (CAD) system. 10 surface shape features are extracted from a standardized profile of liver; while15 texture features calculated from gray level co-occurrence matrix (GLCM) are extracted within an ROI in liver. Each combination of these input subsets is checked by using support vector machine (SVM) with leave-one-case-out method to differentiate fibrosis into two groups: normal or abnormal. The accurate rate value of all 10/15 types number of features is 66.83% by texture, while 85.74% by shape features, respectively. The irregularity of liver shape can demonstrate fibrotic grade efficiently and texture feature of CT image is not recommended to use with shape feature for interpretation of cirrhosis.

Late electrophysiological modulations of feature-based attention to object shapes.

PubMed

Stojanoski, Bobby Boge; Niemeier, Matthias

2014-03-01

Feature-based attention has been shown to aid object perception. Our previous ERP effects revealed temporally late feature-based modulation in response to objects relative to motion. The aim of the current study was to confirm the timing of feature-based influences on object perception while cueing within the feature dimension of shape. Participants were told to expect either "pillow" or "flower" objects embedded among random white and black lines. Participants more accurately reported the object's main color for valid compared to invalid shapes. ERPs revealed modulation from 252-502 ms, from occipital to frontal electrodes. Our results are consistent with previous findings examining the time course for processing similar stimuli (illusory contours). Our results provide novel insights into how attending to features of higher complexity aids object perception presumably via feed-forward and feedback mechanisms along the visual hierarchy. Copyright © 2014 Society for Psychophysiological Research.

Correcting for batch effects in case-control microbiome studies

PubMed Central

Gibbons, Sean M.; Duvallet, Claire

2018-01-01

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses. PMID:29684016

Quality Perception of the 2012 World Indoor Athletics Championships

PubMed Central

2016-01-01

Abstract The objective of this study was to compare the views of spectators concerning the quality perception of the World Indoor Athletics Championships. The study group consisted of 568 spectators who watched the events. A measurement scale of event quality in spectator sports (SEQSS) developed by Ko et al. (2011) was used as a data collection tool in the study. In order to determine the views of the spectators concerning the quality of the Indoor Athletics Championships, the dimensions constituting the scale were compared according to the demographic features of the sample group. As a consequence, important differences in most of the dimensions of the scale were revealed with respect to the demographic data of the subjects. The most relevant finding of the study is that the dimension of “physical environment quality”, which is one of the dimensions constituting the event quality, differed significantly in all comparisons that were made according to demographic features. PMID:28031769

Longitudinal study of skin aging: from microrelief to wrinkles.

PubMed

Bazin, Roland; Lévêque, Jean Luc

2011-05-01

To study the changes in skin microrelief and periocular wrinkles during the aging process. Replicas of the crow's feet area of volunteers were recorded in 1987 and 2008 and observed comparatively. Characteristic features were quantified by image analysis. Observation shows that some microrelief features disappear and even merge with wrinkles that become more marked. Some primary lines also tend to merge to form thin new wrinkles. Quantitative data support these observations: the size of small and medium objects of skin relief decreases with age while large objects are becoming larger. Over 21 years, in the group studied, the total area of the detected objects remains quite constant. Only the distribution between small and large detected objects (microrelief features and wrinkles, respectively) is modified. © 2011 John Wiley & Sons A/S.

Joint Feature Selection and Classification for Multilabel Learning.

PubMed

Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

2018-03-01

Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

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.

Exploring Chinese Students' Experience of Curriculum Internationalisation: A Comparative Study of Scotland and Australia

ERIC Educational Resources Information Center

Cheng, Ming; Adekola, Olalekan Adeban; Shah, Mahsood; Valyrakis, Manousos

2018-01-01

Increasing enrolment of Chinese students has become a key feature of internationalisation for Western universities, but there is limited research into how curriculum internationalisation affects Chinese students' learning experiences. Using the typologies of curriculum internationalisation as a framework, this paper explores and compares how…

Comparing Team Learning Approaches through the Lens of Activity Theory

ERIC Educational Resources Information Center

Park, Sunyoung; Cho, Yonjoo; Yoon, Seung Won; Han, Heeyoung

2013-01-01

Purpose: The purpose of this study is to examine the distinctive features of three team learning approaches (action learning, problem-based learning, and project-based learning), compare and contrast them, and discuss implications for practice and research. Design/methodology/approach: The authors used Torraco's integrative literature review…

A Comparison of Hyporheic Transport at a Cross-Vane Structure and Natural Riffle.

PubMed

Smidt, Samuel J; Cullin, Joseph A; Ward, Adam S; Robinson, Jesse; Zimmer, Margaret A; Lautz, Laura K; Endreny, Theodore A

2015-01-01

While restoring hyporheic flowpaths has been cited as a benefit to stream restoration structures, little documentation exists confirming that constructed restoration structures induce comparable hyporheic exchange to natural stream features. This study compares a stream restoration structure (cross-vane) to a natural feature (riffle) concurrently in the same stream reach using time-lapsed electrical resistivity (ER) tomography. Using this hydrogeophysical approach, we were able to quantify hyporheic extent and transport beneath the cross-vane structure and the riffle. We interpret from the geophysical data that the cross-vane and the natural riffle induced spatially and temporally unique hyporheic extent and transport, and the cross-vane created both spatially larger and temporally longer hyporheic flowpaths than the natural riffle. Tracer from the 4.67-h injection was detected along flowpaths for 4.6 h at the cross-vane and 4.2 h at the riffle. The spatial extent of the hyporheic zone at the cross-vane was 12% larger than that at the riffle. We compare ER results of this study to vertical fluxes calculated from temperature profiles and conclude significant differences in the interpretation of hyporheic transport from these different field techniques. Results of this study demonstrate a high degree of heterogeneity in transport metrics at both the cross-vane and the riffle and differences between the hyporheic flowpath networks at the two different features. Our results suggest that restoration structures may be capable of creating sufficient exchange flux and timescales of transport to achieve the same ecological functions as natural features, but engineering of the physical and biogeochemical environment may be necessary to realize these benefits. © 2014, National Ground Water Association.

Reinforcement learning algorithms for robotic navigation in dynamic environments.

PubMed

Yen, Gary G; Hickey, Travis W

2004-04-01

The purpose of this study was to examine improvements to reinforcement learning (RL) algorithms in order to successfully interact within dynamic environments. The scope of the research was that of RL algorithms as applied to robotic navigation. Proposed improvements include: addition of a forgetting mechanism, use of feature based state inputs, and hierarchical structuring of an RL agent. Simulations were performed to evaluate the individual merits and flaws of each proposal, to compare proposed methods to prior established methods, and to compare proposed methods to theoretically optimal solutions. Incorporation of a forgetting mechanism did considerably improve the learning times of RL agents in a dynamic environment. However, direct implementation of a feature-based RL agent did not result in any performance enhancements, as pure feature-based navigation results in a lack of positional awareness, and the inability of the agent to determine the location of the goal state. Inclusion of a hierarchical structure in an RL agent resulted in significantly improved performance, specifically when one layer of the hierarchy included a feature-based agent for obstacle avoidance, and a standard RL agent for global navigation. In summary, the inclusion of a forgetting mechanism, and the use of a hierarchically structured RL agent offer substantially increased performance when compared to traditional RL agents navigating in a dynamic environment.

Likelihood ratio-based differentiation of nodular Hashimoto thyroiditis and papillary thyroid carcinoma in patients with sonographically evident diffuse hashimoto thyroiditis: preliminary study.

PubMed

Wang, Liang; Xia, Yu; Jiang, Yu-Xin; Dai, Qing; Li, Xiao-Yi

2012-11-01

To assess the efficacy of sonography for discriminating nodular Hashimoto thyroiditis from papillary thyroid carcinoma in patients with sonographically evident diffuse Hashimoto thyroiditis. This study included 20 patients with 24 surgically confirmed Hashimoto thyroiditis nodules and 40 patients with 40 papillary thyroid carcinoma nodules; all had sonographically evident diffuse Hashimoto thyroiditis. A retrospective review of the sonograms was performed, and significant benign and malignant sonographic features were selected by univariate and multivariate analyses. The combined likelihood ratio was calculated as the product of each feature's likelihood ratio for papillary thyroid carcinoma. We compared the abilities of the original sonographic features and combined likelihood ratios in diagnosing nodular Hashimoto thyroiditis and papillary thyroid carcinoma by their sensitivity, specificity, and Youden index. The diagnostic capabilities of the sonographic features varied greatly, with Youden indices ranging from 0.175 to 0.700. Compared with single features, combinations of features were unable to improve the Youden indices effectively because the sensitivity and specificity usually changed in opposite directions. For combined likelihood ratios, however, the sensitivity improved greatly without an obvious reduction in specificity, which resulted in the maximum Youden index (0.825). With a combined likelihood ratio greater than 7.00 as the diagnostic criterion for papillary thyroid carcinoma, sensitivity reached 82.5%, whereas specificity remained at 100.0%. With a combined likelihood ratio less than 1.00 for nodular Hashimoto thyroiditis, sensitivity and specificity were 90.0% and 92.5%, respectively. Several sonographic features of nodular Hashimoto thyroiditis and papillary thyroid carcinoma in a background of diffuse Hashimoto thyroiditis were significantly different. The combined likelihood ratio may be superior to original sonographic features for discrimination of nodular Hashimoto thyroiditis from papillary thyroid carcinoma; therefore, it is a promising risk index for thyroid nodules and warrants further investigation.

New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems.

PubMed

Thomas, Minta; De Brabanter, Kris; De Moor, Bart

2014-05-10

DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history of almost a decade. Disease class predictors can be designed for known disease cases and provide diagnostic confirmation or clarify abnormal cases. The main input to this class predictors are high dimensional data with many variables and few observations. Dimensionality reduction of these features set significantly speeds up the prediction task. Feature selection and feature transformation methods are well known preprocessing steps in the field of bioinformatics. Several prediction tools are available based on these techniques. Studies show that a well tuned Kernel PCA (KPCA) is an efficient preprocessing step for dimensionality reduction, but the available bandwidth selection method for KPCA was computationally expensive. In this paper, we propose a new data-driven bandwidth selection criterion for KPCA, which is related to least squares cross-validation for kernel density estimation. We propose a new prediction model with a well tuned KPCA and Least Squares Support Vector Machine (LS-SVM). We estimate the accuracy of the newly proposed model based on 9 case studies. Then, we compare its performances (in terms of test set Area Under the ROC Curve (AUC) and computational time) with other well known techniques such as whole data set + LS-SVM, PCA + LS-SVM, t-test + LS-SVM, Prediction Analysis of Microarrays (PAM) and Least Absolute Shrinkage and Selection Operator (Lasso). Finally, we assess the performance of the proposed strategy with an existing KPCA parameter tuning algorithm by means of two additional case studies. We propose, evaluate, and compare several mathematical/statistical techniques, which apply feature transformation/selection for subsequent classification, and consider its application in medical diagnostics. Both feature selection and feature transformation perform well on classification tasks. Due to the dynamic selection property of feature selection, it is hard to define significant features for the classifier, which predicts classes of future samples. Moreover, the proposed strategy enjoys a distinctive advantage with its relatively lesser time complexity.

Segmentation of photospheric magnetic elements corresponding to coronal features to understand the EUV and UV irradiance variability

NASA Astrophysics Data System (ADS)

Zender, J. J.; Kariyappa, R.; Giono, G.; Bergmann, M.; Delouille, V.; Damé, L.; Hochedez, J.-F.; Kumara, S. T.

2017-09-01

Context. The magnetic field plays a dominant role in the solar irradiance variability. Determining the contribution of various magnetic features to this variability is important in the context of heliospheric studies and Sun-Earth connection. Aims: We studied the solar irradiance variability and its association with the underlying magnetic field for a period of five years (January 2011-January 2016). We used observations from the Large Yield Radiometer (LYRA), the Sun Watcher with Active Pixel System detector and Image Processing (SWAP) on board PROBA2, the Atmospheric Imaging Assembly (AIA), and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). Methods: The Spatial Possibilistic Clustering Algorithm (SPoCA) is applied to the extreme ultraviolet (EUV) observations obtained from the AIA to segregate coronal features by creating segmentation maps of active regions (ARs), coronal holes (CHs) and the quiet sun (QS). Further, these maps are applied to the full-disk SWAP intensity images and the full-disk (FD) HMI line-of-sight (LOS) magnetograms to isolate the SWAP coronal features and photospheric magnetic counterparts, respectively. We then computed full-disk and feature-wise averages of EUV intensity and line of sight (LOS) magnetic flux density over ARs/CHs/QS/FD. The variability in these quantities is compared with that of LYRA irradiance values. Results: Variations in the quantities resulting from the segmentation, namely the integrated intensity and the total magnetic flux density of ARs/CHs/QS/FD regions, are compared with the LYRA irradiance variations. We find that the EUV intensity over ARs/CHs/QS/FD is well correlated with the underlying magnetic field. In addition, variations in the full-disk integrated intensity and magnetic flux density values are correlated with the LYRA irradiance variations. Conclusions: Using the segmented coronal features observed in the EUV wavelengths as proxies to isolate the underlying magnetic structures is demonstrated in this study. Sophisticated feature identification and segmentation tools are important in providing more insights into the role of various magnetic features in both the short- and long-term changes in the solar irradiance. The movie associated to Fig. 2 is available at http://www.aanda.org

Automatically measuring the effect of strategy drawing features on pupils' handwriting and gender

NASA Astrophysics Data System (ADS)

Tabatabaey-Mashadi, Narges; Sudirman, Rubita; Guest, Richard M.; Khalid, Puspa Inayat

2013-12-01

Children's dynamic drawing strategies have been recently recognized as indicators of handwriting ability. However the influence of each feature in predicting handwriting is unknown due to lack of a measuring system. An automated measuring algorithm suitable for psychological assessment and non-subjective scoring is presented here. Using the weight vector and classification rate of a machine learning algorithm, an overall feature's effect is calculated which is comparable in different groupings. In this study thirteen previously detected drawing strategy features are measured for their influence on handwriting and gender. Features are extracted from drawing a triangle, Beery VMI and Bender Gestalt tangent patterns. Samples are related to 203 pupils (77 below average writers, and 101 female). The results show that the number of strokes in drawing the triangle pattern plays a major role in both groupings; however Left Tendency flag feature is affected by children's handwriting about 2.5 times greater than their gender. Experiments indicate that different forms of a feature sometimes show different influences.

Classification of speech dysfluencies using LPC based parameterization techniques.

PubMed

Hariharan, M; Chee, Lim Sin; Ai, Ooi Chia; Yaacob, Sazali

2012-06-01

The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

Effects of achievement contexts on the meaning structure of emotion words.

PubMed

Gentsch, Kornelia; Loderer, Kristina; Soriano, Cristina; Fontaine, Johnny R J; Eid, Michael; Pekrun, Reinhard; Scherer, Klaus R

2018-03-01

Little is known about the impact of context on the meaning of emotion words. In the present study, we used a semantic profiling instrument (GRID) to investigate features representing five emotion components (appraisal, bodily reaction, expression, action tendencies, and feeling) of 11 emotion words in situational contexts involving success or failure. We compared these to the data from an earlier study in which participants evaluated the typicality of features out of context. Profile analyses identified features for which typicality changed as a function of context for all emotion words, except contentment, with appraisal features being most frequently affected. Those context effects occurred for both hypothesised basic and non-basic emotion words. Moreover, both data sets revealed a four-dimensional structure. The four dimensions were largely similar (valence, power, arousal, and novelty). The results suggest that context may not change the underlying dimensionality but affects facets of the meaning of emotion words.

Radiation biology of HZE particles

NASA Technical Reports Server (NTRS)

Nelson, Gregory A.

1990-01-01

The biological effects of heavy charged particle (HZE) radiation are of particular interest to travellers and planners for long duration space flights where exposure levels represent a potential health hazard. The unique feature of HZE radiation is the structured pattern of its energy deposition in targets which may be related to charge, velocity, or rate of energy loss. There are many consequences of this feature to biological endpoints when compared to effects of ionizing photons. Dose vs response and dose rate kinetics are modified, DNA and cellular repair systems are altered in their abilities to cope with damage and, the qualitative features of damage are unique for different ions. These features must be incorporated into any risk assessment system for radiation health management. HZE induced mutation, cell inactivation and altered organogenesis will be discussed emphasizing studies with the nematode Caenorhabditis elegans and cultured cells. Observations from radiobiology experiments in space will also be reviewed along with plans for future space-based studies.

Rapid Characterization of Shorelines using a Georeferenced Video Mapping System

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

Anderson, Michael G.; Judd, Chaeli; Marcoe, K.

Increased understanding of shoreline conditions is needed, yet current approaches are limited in ability to characterize remote areas or document features at a finer resolution. Documentation using video mapping may provide a rapid and repeatable method for assessing the current state of the environment and determining changes to the shoreline over time. In this study, we compare two studies using boat-based, georeferenced video mapping in coastal Washington and the Columbia River Estuary to map and characterize coastal stressors and functional data. In both areas, mapping multiple features along the shoreline required approximation of the coastline. However, characterization of vertically orientedmore » features such as shoreline armoring and small features such as pilings and large woody debris was possible. In addition, end users noted that geovideo provides a permanent record to allow a user to examine recorded video anywhere along a transect or at discrete points.« less

The Nature of the Phonological Processing in French Dyslexic Children: Evidence for the Phonological Syllable and Linguistic Features' Role in Silent Reading and Speech Discrimination

ERIC Educational Resources Information Center

Maionchi-Pino, Norbert; Magnan, Annie; Ecalle, Jean

2010-01-01

This study investigated the status of phonological representations in French dyslexic children (DY) compared with reading level- (RL) and chronological age-matched (CA) controls. We focused on the syllable's role and on the impact of French linguistic features. In Experiment 1, we assessed oral discrimination abilities of pairs of syllables that…

A systematic literature review of diabetes self-management education features to improve diabetes education in women of Black African/Caribbean and Hispanic/Latin American ethnicity.

PubMed

Gucciardi, Enza; Chan, Vivian Wing-Sheung; Manuel, Lisa; Sidani, Souraya

2013-08-01

This systematic literature review aims to identify diabetes self-management education (DSME) features to improve diabetes education for Black African/Caribbean and Hispanic/Latin American women with Type 2 diabetes mellitus. We conducted a literature search in six health databases for randomized controlled trials and comparative studies. Success rates of intervention features were calculated based on effectiveness in improving glycosolated hemoglobin (HbA1c), anthropometrics, physical activity, or diet outcomes. Calculations of rate differences assessed whether an intervention feature positively or negatively affected an outcome. From 13 studies included in our analysis, we identified 38 intervention features in relation to their success with an outcome. Five intervention features had positive rate differences across at least three outcomes: hospital-based interventions, group interventions, the use of situational problem-solving, frequent sessions, and incorporating dietitians as interventionists. Six intervention features had high positive rate differences (i.e. ≥50%) on specific outcomes. Different DSME intervention features may influence broad and specific self-management outcomes for women of African/Caribbean and Hispanic/Latin ethnicity. With the emphasis on patient-centered care, patients and care providers can consider options based on DSME intervention features for its broad and specific impact on outcomes to potentially make programming more effective. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Examining End-Of-Chapter Problems Across Editions of an Introductory Calculus-Based Physics Textbook

NASA Astrophysics Data System (ADS)

Xiao, Bin

End-Of-Chapter (EOC) problems have been part of many physics education studies. Typically, only problems "localized" as relevant to a single chapter were used. This work examines how well this type of problem represents all EOC problems and whether EOC problems found in leading textbooks have changed over the past several decades. To investigate whether EOC problems have connections between chapters, I solved all problems of the E&M; chapters of the most recent edition of a popular introductory level calculus-based textbook and coded the equations used to solve each problem. These results were compared to the first edition of the same text. Also, several relevant problem features were coded for those problems and results were compared for sample chapters across all editions. My findings include two parts. The result of equation usage shows that problems in the E&M; chapters do use equations from both other E&M; chapters and non-E&M; chapters. This out-of-chapter usage increased from the first edition to the last edition. Information about the knowledge structure of E&M; chapters was also revealed. The results of the problem feature study show that most EOC problems have common features but there was an increase of diversity in some of the problem features across editions.

Identification of critical chemical features for Aurora kinase-B inhibitors using Hip-Hop, virtual screening and molecular docking

NASA Astrophysics Data System (ADS)

Sakkiah, Sugunadevi; Thangapandian, Sundarapandian; John, Shalini; Lee, Keun Woo

2011-01-01

This study was performed to find the selective chemical features for Aurora kinase-B inhibitors using the potent methods like Hip-Hop, virtual screening, homology modeling, molecular dynamics and docking. The best hypothesis, Hypo1 was validated toward a wide range of test set containing the selective inhibitors of Aurora kinase-B. Homology modeling and molecular dynamics studies were carried out to perform the molecular docking studies. The best hypothesis Hypo1 was used as a 3D query to screen the chemical databases. The screened molecules from the databases were sorted based on ADME and drug like properties. The selective hit compounds were docked and the hydrogen bond interactions with the critical amino acids present in Aurora kinase-B were compared with the chemical features present in the Hypo1. Finally, we suggest that the chemical features present in the Hypo1 are vital for a molecule to inhibit the Aurora kinase-B activity.

Clinical decision support provided within physician order entry systems: a systematic review of features effective for changing clinician behavior.

PubMed

Kawamoto, Kensaku; Lobach, David F

2003-01-01

Computerized physician order entry (CPOE) systems represent an important tool for providing clinical decision support. In undertaking this systematic review, our objective was to identify the features of CPOE-based clinical decision support systems (CDSSs) most effective at modifying clinician behavior. For this review, two independent reviewers systematically identified randomized controlled trials that evaluated the effectiveness of CPOE-based CDSSs in changing clinician behavior. Furthermore, each included study was assessed for the presence of 14 CDSS features. We screened 10,023 citations and included 11 studies. Of the 10 studies comparing a CPOE-based CDSS intervention against a non-CDSS control group, 7 reported a significant desired change in professional practice. Moreover, meta-regression analysis revealed that automatic provision of the decision support was strongly associated with improved professional practice (adjusted odds ratio, 23.72; 95% confidence interval, 1.75-infiniti). Thus, we conclude that automatic provision of decision support is a critical feature of successful CPOE-based CDSS interventions.

Categorizing biomedicine images using novel image features and sparse coding representation

PubMed Central

2013-01-01

Background Images embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process. Method We observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features. Results we randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance. Conclusions Compared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study. PMID:24565470

CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data.

PubMed

Minhas, Fayyaz Ul Amir Afsar; Asif, Amina; Arif, Muhammad

2016-12-01

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples. CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

IMMAN: free software for information theory-based chemometric analysis.

PubMed

Urias, Ricardo W Pino; Barigye, Stephen J; Marrero-Ponce, Yovani; García-Jacas, César R; Valdes-Martiní, José R; Perez-Gimenez, Facundo

2015-05-01

The features and theoretical background of a new and free computational program for chemometric analysis denominated IMMAN (acronym for Information theory-based CheMoMetrics ANalysis) are presented. This is multi-platform software developed in the Java programming language, designed with a remarkably user-friendly graphical interface for the computation of a collection of information-theoretic functions adapted for rank-based unsupervised and supervised feature selection tasks. A total of 20 feature selection parameters are presented, with the unsupervised and supervised frameworks represented by 10 approaches in each case. Several information-theoretic parameters traditionally used as molecular descriptors (MDs) are adapted for use as unsupervised rank-based feature selection methods. On the other hand, a generalization scheme for the previously defined differential Shannon's entropy is discussed, as well as the introduction of Jeffreys information measure for supervised feature selection. Moreover, well-known information-theoretic feature selection parameters, such as information gain, gain ratio, and symmetrical uncertainty are incorporated to the IMMAN software ( http://mobiosd-hub.com/imman-soft/ ), following an equal-interval discretization approach. IMMAN offers data pre-processing functionalities, such as missing values processing, dataset partitioning, and browsing. Moreover, single parameter or ensemble (multi-criteria) ranking options are provided. Consequently, this software is suitable for tasks like dimensionality reduction, feature ranking, as well as comparative diversity analysis of data matrices. Simple examples of applications performed with this program are presented. A comparative study between IMMAN and WEKA feature selection tools using the Arcene dataset was performed, demonstrating similar behavior. In addition, it is revealed that the use of IMMAN unsupervised feature selection methods improves the performance of both IMMAN and WEKA supervised algorithms. Graphic representation for Shannon's distribution of MD calculating software.

Fast traffic sign recognition with a rotation invariant binary pattern based feature.

PubMed

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-19

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

PubMed Central

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-01

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. PMID:25608217

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

Post-Mortem Magnetic Resonance Imaging Appearances of Feticide in Perinatal Deaths.

PubMed

Shelmerdine, Susan C; Hickson, Melissa; Sebire, Neil J; Arthurs, Owen J

2018-06-06

The aim of this study was to characterise the imaging features seen in fetuses having undergone feticide by intracardiac potassium chloride injection compared to those of non-terminated fetuses at post-mortem magnetic resonance imaging (PMMRI). A case-control study was performed comparing PMMRI findings between two groups of patients - those having undergone feticide were matched to a control group of miscarried/stillborn fetuses. The groups were matched according to gestational age, weight, and time since death. Two independent readers reviewed the PMMRI for thoracic, abdominal, and musculoskeletal imaging features. The Fishers exact test was conducted for differences between the patient groups. Twenty-six cases of feticide (mean gestation 25 weeks [20-36]) and 75 non-terminated fetuses (mean gestation 26.7 weeks [19-36]) were compared. There was a higher proportion of feticide cases demonstrating pneumothorax (23.1 vs. 1.3%, p = 0.001), haemothorax (42.3 vs. 4%, p = 0.001), pneumopericardium (30.8 vs. 5.3%, p = 0.002), and haemopericardium (34.6 vs. 0%, p = 0.0001). Intracardiac gas and intra-abdominal findings were higher in the feticide group, but the differences were not statistically significant. Characteristic PMMRI features of feticide can help improve reporter confidence in differentiating iatrogenic from physiological/pathological processes. © 2018 S. Karger AG, Basel.

A genomic view of food-related and probiotic Enterococcus strains.

PubMed

Bonacina, Julieta; Suárez, Nadia; Hormigo, Ricardo; Fadda, Silvina; Lechner, Marcus; Saavedra, Lucila

2017-02-01

The study of enterococcal genomes has grown considerably in recent years. While special attention is paid to comparative genomic analysis among clinical relevant isolates, in this study we performed an exhaustive comparative analysis of enterococcal genomes of food origin and/or with potential to be used as probiotics. Beyond common genetic features, we especially aimed to identify those that are specific to enterococcal strains isolated from a certain food-related source as well as features present in a species-specific manner. Thus, the genome sequences of 25 Enterococcus strains, from 7 different species, were examined and compared. Their phylogenetic relationship was reconstructed based on orthologous proteins and whole genomes. Likewise, markers associated with a successful colonization (bacteriocin genes and genomic islands) and genome plasticity (phages and clustered regularly interspaced short palindromic repeats) were investigated for lifestyle specific genetic features. At the same time, a search for antibiotic resistance genes was carried out, since they are of big concern in the food industry. Finally, it was possible to locate 1617 FIGfam families as a core proteome universally present among the genera and to determine that most of the accessory genes code for hypothetical proteins, providing reasonable hints to support their functional characterization. © The Author 2016. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

PubMed

Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

2013-03-01

Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data.

PubMed

Kim, Eunji; Ivanov, Ivan; Hua, Jianping; Lampe, Johanna W; Hullar, Meredith Aj; Chapkin, Robert S; Dougherty, Edward R

2017-01-01

Ranking feature sets for phenotype classification based on gene expression is a challenging issue in cancer bioinformatics. When the number of samples is small, all feature selection algorithms are known to be unreliable, producing significant error, and error estimators suffer from different degrees of imprecision. The problem is compounded by the fact that the accuracy of classification depends on the manner in which the phenomena are transformed into data by the measurement technology. Because next-generation sequencing technologies amount to a nonlinear transformation of the actual gene or RNA concentrations, they can potentially produce less discriminative data relative to the actual gene expression levels. In this study, we compare the performance of ranking feature sets derived from a model of RNA-Seq data with that of a multivariate normal model of gene concentrations using 3 measures: (1) ranking power, (2) length of extensions, and (3) Bayes features. This is the model-based study to examine the effectiveness of reporting lists of small feature sets using RNA-Seq data and the effects of different model parameters and error estimators. The results demonstrate that the general trends of the parameter effects on the ranking power of the underlying gene concentrations are preserved in the RNA-Seq data, whereas the power of finding a good feature set becomes weaker when gene concentrations are transformed by the sequencing machine.

Economic indicators selection for crime rates forecasting using cooperative feature selection

NASA Astrophysics Data System (ADS)

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina

2013-04-01

Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.

An Investigation of Construct Relevant and Irrelevant Features of Mathematics Problem-Solving Questions Using Comparative Judgement and Kelly's Repertory Grid

ERIC Educational Resources Information Center

Holmes, Stephen D.; He, Qingping; Meadows, Michelle

2017-01-01

The relationship between the characteristics of 33 mathematical problem-solving questions answered by 16-year-old students in England and the quality of problem-solving elicited was investigated in two studies. The first study used comparative judgement (CJ) to estimate the quality of the problem-solving elicited by each question, involving 33…

A Standard System to Study Vertebrate Embryos

PubMed Central

Werneburg, Ingmar

2009-01-01

Staged embryonic series are important as reference for different kinds of biological studies. I summarise problems that occur when using ‘staging tables’ of ‘model organisms’. Investigations of developmental processes in a broad scope of taxa are becoming commonplace. Beginning in the 1990s, methods were developed to quantify and analyse developmental events in a phylogenetic framework. The algorithms associated with these methods are still under development, mainly due to difficulties of using non-independent characters. Nevertheless, the principle of comparing clearly defined newly occurring morphological features in development (events) in quantifying analyses was a key innovation for comparative embryonic research. Up to date no standard was set for how to define such events in a comparative approach. As a case study I compared the external development of 23 land vertebrate species with a focus on turtles, mainly based on reference staging tables. I excluded all the characters that are only identical for a particular species or general features that were only analysed in a few species. Based on these comparisons I defined 104 developmental characters that are common either for all vertebrates (61 characters), gnathostomes (26), tetrapods (3), amniotes (7), or only for sauropsids (7). Characters concern the neural tube, somite, ear, eye, limb, maxillary and mandibular process, pharyngeal arch, eyelid or carapace development. I present an illustrated guide listing all the defined events. This guide can be used for describing developmental series of any vertebrate species or for documenting specimen variability of a particular species. The guide incorporates drawings and photographs as well as consideration of species identifying developmental features such as colouration. The simple character-code of the guide is extendable to further characters pertaining to external and internal morphological, physiological, genetic or molecular development, and also for other vertebrate groups not examined here, such as Chondrichthyes or Actinopterygii. An online database to type in developmental events for different stages and species could be a basis for further studies in comparative embryology. By documenting developmental events with the standard code, sequence heterochrony studies (i.e. Parsimov) and studies on variability can use this broad comparative data set. PMID:19521537

Measuring Black Hole Spin

NASA Astrophysics Data System (ADS)

Garmire, Gordon

1999-09-01

WE PROPOSE TO CARRY OUT A SYSTEMATIC STUDY OF EMISSION AND ABSORPTION SPECTRAL FEATURES THAT ARE OFTEN SEEN IN X-RAY SPECTRA OF BLACK HOLE BINARIES. THE EXCELLENT SENSITIVITY AND ENERGY RESOLUTION OF THE ACIS/HETG COMBINATION WILL NOT ONLY HELP RESOLVE AMBIGUITIES IN INTERPRETING THESE FEATURES, BUT MAY ALLOW MODELLING OF THE EMISSION LINE PROFILES IN DETAIL. THE PROFILES MAY CONTAIN INFORMATION ON SUCH FUNDAMENTAL PROPERTIES AS THE SPIN OF BLACK HOLES. THEREFORE, THIS STUDY COULD LEAD TO A MEASUREMENT OF BLACK HOLE SPIN FOR SELECTED SOURCES. THE RESULT CAN THEN BE DIRECTLY COMPARED WITH THOSE FROM PREVIOUS STUDIES BASED ON INDEPENDENT METHODS.

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

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.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Artificially intelligent recognition of Arabic speaker using voice print-based local features

NASA Astrophysics Data System (ADS)

Mahmood, Awais; Alsulaiman, Mansour; Muhammad, Ghulam; Akram, Sheeraz

2016-11-01

Local features for any pattern recognition system are based on the information extracted locally. In this paper, a local feature extraction technique was developed. This feature was extracted in the time-frequency plain by taking the moving average on the diagonal directions of the time-frequency plane. This feature captured the time-frequency events producing a unique pattern for each speaker that can be viewed as a voice print of the speaker. Hence, we referred to this technique as voice print-based local feature. The proposed feature was compared to other features including mel-frequency cepstral coefficient (MFCC) for speaker recognition using two different databases. One of the databases used in the comparison is a subset of an LDC database that consisted of two short sentences uttered by 182 speakers. The proposed feature attained 98.35% recognition rate compared to 96.7% for MFCC using the LDC subset.

[Affective temperaments in the bipolar and unipolar disorders: distinctive profiles and relationship with clinical features].

PubMed

Gassab, L; Mechri, A; Bacha, M; Gaddour, N; Gaha, L

2008-10-01

Recent research postulated that temperaments represent the subclinical foundations of affective disorders, and an early clue for a recurrent, prebipolar disorder. Akiskal et al. operationalized five types of temperaments: depressive, hyperthymic, irritable, cyclothymic and anxious. The aims of this study were to compare the affective temperaments scores in patients with bipolar I, II and recurrent depression disorders and to explore the relation between temperaments scores and clinical features of those affective disorders. This was a comparative cross-sectional study, concerning three groups: patients with bipolar I disorder (BIP I) (n=31, 20 men and 11 women, mean age=42.0+/-10.1 years), patients with bipolar II disorder (BIP II) (n=18, 11 men and seven women, mean age=40.7+/-10.8 years) and patients with recurrent depressive disorder (RDD) (n=66, 28 men and 38 women, mean age=45.0+/-9.3 years). All patients were in remission of a major depressive episode. The affective temperaments were assessed by the Akiskal and Mallya Affective Temperament questionnaires. Hyperthymic temperament mean scores were higher in BIP I (10.8+/-5.4) and BIP II (10.3+/-5.5) groups compared to RDD group (5.5+/-4.0) (p<10(-3)). Depressive temperament mean score was significantly higher in RDD group (10.5+/-4.3), compared to BIP I (7.3+/-4.6) and BIP II (5.4+/-2.9) groups (p<10(-3)). Cyclothymic temperament mean score was higher in BIP II group (4.7+/-5.8) compared to BIP I (3.3+/-3.9) and RDD (2.5+/-3.9) groups, but this difference was not significant (p=0.08). No difference was found between the three groups concerning irritable temperament scores. Negative correlation was found between hyperthymic and depressive temperament scores in BIP I (r=-0.81, p<0.001) and RDD (r=-0.73, p<0.001) groups, but not in BIP II group. Concerning the clinical correlates with affective temperament scores, negative correlation was found between hyperthymic temperament score and number of depressive episodes in BIP II group (r=-0.53, p=0.02). Hyperthymic temperament score was associated with psychotic features in the last depressive episode in BIP I (p=0.01) and BIP II (p=0.008) groups and seasonal features in BIP II group (p=0.02). Moreover, cyclothymic temperament score was associated with psychotic (p=0.009) and seasonal features (p=0.03) in BIP II group. Despite the small sample sizes for our study groups, we can conclude that hyperthymic and cyclothymic temperaments characterized bipolar disorders and are correlated with other markers of bipolarity such as psychotic and seasonal features. Thus, temperament assessment might become a useful tool to predict bipolarity in association with those markers.

Visual word ambiguity.

PubMed

van Gemert, Jan C; Veenman, Cor J; Smeulders, Arnold W M; Geusebroek, Jan-Mark

2010-07-01

This paper studies automatic image classification by modeling soft assignment in the popular codebook model. The codebook model describes an image as a bag of discrete visual words selected from a vocabulary, where the frequency distributions of visual words in an image allow classification. One inherent component of the codebook model is the assignment of discrete visual words to continuous image features. Despite the clear mismatch of this hard assignment with the nature of continuous features, the approach has been successfully applied for some years. In this paper, we investigate four types of soft assignment of visual words to image features. We demonstrate that explicitly modeling visual word assignment ambiguity improves classification performance compared to the hard assignment of the traditional codebook model. The traditional codebook model is compared against our method for five well-known data sets: 15 natural scenes, Caltech-101, Caltech-256, and Pascal VOC 2007/2008. We demonstrate that large codebook vocabulary sizes completely deteriorate the performance of the traditional model, whereas the proposed model performs consistently. Moreover, we show that our method profits in high-dimensional feature spaces and reaps higher benefits when increasing the number of image categories.

Histological Features of the Gastrointestinal Tract of Wild Indonesian Shortfin Eel, Anguilla bicolor bicolor (McClelland, 1844), Captured in Peninsular Malaysia

PubMed Central

Nasruddin, Nurrul Shaqinah; Azmai, Mohammad Noor Amal; Ismail, Ahmad; Saad, Mohd Zamri; Daud, Hassan Mohd; Zulkifli, Syaizwan Zahmir

2014-01-01

This study was conducted to record the histological features of the gastrointestinal tract of wild Indonesian shortfin eel, Anguilla bicolor bicolor (McClelland, 1844), captured in Peninsular Malaysia. The gastrointestinal tract was segmented into the oesophagus, stomach, and intestine. Then, the oesophagus was divided into five (first to fifth), the stomach into two (cardiac and pyloric), and the intestine into four segments (anterior, intermediate, posterior, and rectum) for histological examinations. The stomach had significantly taller villi and thicker inner circular muscles compared to the intestine and oesophagus. The lamina propria was thickest in stomach, significantly when compared with oesophagus, but not with the intestine. However, the intestine showed significantly thicker outer longitudinal muscle while gastric glands were observed only in the stomach. The histological features were closely associated with the functions of the different segments of the gastrointestinal tract. In conclusion, the histological features of the gastrointestinal tract of A. b. bicolor are consistent with the feeding habit of a carnivorous fish. PMID:25587561

Factor Analysis of Drawings: Application to college student models of the greenhouse effect

NASA Astrophysics Data System (ADS)

Libarkin, Julie C.; Thomas, Stephen R.; Ording, Gabriel

2015-09-01

Exploratory factor analysis was used to identify models underlying drawings of the greenhouse effect made by over 200 entering university freshmen. Initial content analysis allowed deconstruction of drawings into salient features, with grouping of these features via factor analysis. A resulting 4-factor solution explains 62% of the data variance, suggesting that 4 archetype models of the greenhouse effect dominate thinking within this population. Factor scores, indicating the extent to which each student's drawing aligned with representative models, were compared to performance on conceptual understanding and attitudes measures, demographics, and non-cognitive features of drawings. Student drawings were also compared to drawings made by scientists to ascertain the extent to which models reflect more sophisticated and accurate models. Results indicate that student and scientist drawings share some similarities, most notably the presence of some features of the most sophisticated non-scientific model held among the study population. Prior knowledge, prior attitudes, gender, and non-cognitive components are also predictive of an individual student's model. This work presents a new technique for analyzing drawings, with general implications for the use of drawings in investigating student conceptions.

Threat as a feature in visual semantic object memory.

PubMed

Calley, Clifford S; Motes, Michael A; Chiang, H-Sheng; Buhl, Virginia; Spence, Jeffrey S; Abdi, Hervé; Anand, Raksha; Maguire, Mandy; Estevez, Leonardo; Briggs, Richard; Freeman, Thomas; Kraut, Michael A; Hart, John

2013-08-01

Threatening stimuli have been found to modulate visual processes related to perception and attention. The present functional magnetic resonance imaging (fMRI) study investigated whether threat modulates visual object recognition of man-made and naturally occurring categories of stimuli. Compared with nonthreatening pictures, threatening pictures of real items elicited larger fMRI BOLD signal changes in medial visual cortices extending inferiorly into the temporo-occipital (TO) "what" pathways. This region elicited greater signal changes for threatening items compared to nonthreatening from both the natural-occurring and man-made stimulus supraordinate categories, demonstrating a featural component to these visual processing areas. Two additional loci of signal changes within more lateral inferior TO areas (bilateral BA18 and 19 as well as the right ventral temporal lobe) were detected for a category-feature interaction, with stronger responses to man-made (category) threatening (feature) stimuli than to natural threats. The findings are discussed in terms of visual recognition of processing efficiently or rapidly groups of items that confer an advantage for survival. Copyright © 2012 Wiley Periodicals, Inc.

On application of kernel PCA for generating stimulus features for fMRI during continuous music listening.

PubMed

Tsatsishvili, Valeri; Burunat, Iballa; Cong, Fengyu; Toiviainen, Petri; Alluri, Vinoo; Ristaniemi, Tapani

2018-06-01

There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing. Copyright © 2018 Elsevier B.V. All rights reserved.

Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

PubMed

Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En

2015-06-01

Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.

Features of polycystic ovary syndrome (PCOS) in women with functional hypothalamic amenorrhea (FHA) may be reversible with recovery of menstrual function.

PubMed

Carmina, Enrico; Fruzzetti, Franca; Lobo, Roger A

2018-04-01

Since features of polycystic ovary syndrome (PCOS) have been found to be prevalent in women with functional hypothalamic amenorrhea (FHA), we wished to determine what happens to these features after recovery of menstrual function in FHA Design: Prospective cohort study. Twenty-eight women with FHA and 30 age-matched ovulatory controls were studied. Twenty-eight women with FHA and 30 age-matched ovulatory controls were studied. We measured serum estradiol, LH, FSH, testosterone, DHEAS, anti-Mullerian hormone (AMH), body mass index, and ovarian morphology on transvaginal ultrasound. At baseline, 12 of the 28 women (43%) had increased AMH (>4.7 ng/mL), and higher testosterone and larger ovaries compared to the other 16 women with normal AMH. One year after recovery of menstrual function, in the 12 women with increased AMH, serum AMH, testosterone and ovarian size decreased, while LH and estradiol increased. At one year, only one of the 12 women in the high AMH group developed clinical features of PCOS. In the majority of women with FHA who have PCOS-like features, these features may be due to the hypothalamic state and appear to be reversible. Few women may develop clinical PCOS after recovery.

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

NASA Astrophysics Data System (ADS)

Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

2012-01-01

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

Ultrastructural hepatocellular features associated with severe hepatic lipidosis in cats.

PubMed

Center, S A; Guida, L; Zanelli, M J; Dougherty, E; Cummings, J; King, J

1993-05-01

In this study, we compared hepatic ultrastructure in healthy cats, in cats with severe hepatic lipidosis, and in cats with experimentally induced, chronic, extrahepatic bile duct occlusion. Ultrastructural features unique to the lipidosis syndrome included an apparent reduction in number of peroxisomes and alteration in their morphologic features. The quantity of endoplasmic reticulum, Golgi complexes, and lysosomes was subjectively reduced, and paucity of cytosolic glycogen was observed. Bile canaliculi appeared collapsed because of cytosolic distention with lipid. Mitochondria were reduced in number and were markedly pleomorphic. Cristae assumed a variety of shapes, lengths, and orientations. Ultrastructural features of bile duct occlusion were similar to those described in other species and differed from those in cats with hepatic lipidosis.

Adult interpersonal features of subtypes of sexual offenders.

PubMed

Sigre-Leirós, Vera; Carvalho, Joana; Nobre, Pedro J

2015-08-01

Although the role of interpersonal factors on sexual offending is already recognized, there is a need for further investigation on the psychosocial correlates of pedophilic behavior. This study aimed to examine the relationship between adult interpersonal features and subtypes of sexual offending. The study involved the participation of a total of 164 male convicted offenders namely 50 rapists, 63 child molesters (20 pedophilic and 43 nonpedophilic), and 51 nonsexual offenders. All participants were assessed using the Adult Attachment Scale, the Interpersonal Behavior Survey, the Brief Symptom Inventory, and the Socially Desirable Response Set Measure. Results from sets of multinomial logistic regression analyses showed that pedophilic offenders were more likely to present anxiety in adult relationships compared to nonsex offenders. Likewise, nonpedophilic child molesters were less likely to be generally aggressive compared to rapists and nonsex offenders, as well as less generally assertive than rapists. Overall, findings indicated that certain interpersonal features characterized subtypes of offenders, thus providing some insight on their particular therapeutic needs. Further replications with larger samples particularly of pedophilic child molesters are required. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Solid particle impingement erosion characteristics of cylindrical surfaces, pre-existing holes and slits

NASA Technical Reports Server (NTRS)

Rao, P. V.; Buckley, D. H.

1983-01-01

The erosion characteristics of aluminum cylinders sand-blasted with both spherical and angular erodent particles were studied and compared with results from previously studied flat surfaces. The cylindrical results are discussed with respect to impact conditions. The relationship between erosion rate and pit morphology (width, depth, and width to depth ratio) is established. The aspects of (1) erosion rate versus time curves on cylindrical surfaces; (2) long-term exposures; and (3) erosion rate versus time curves with spherical and angular particles are presented. The erosion morphology and characteristics of aluminum surfaces with pre-existing circular cylindrical and conical holes of different sizes were examined using weight loss measurements, scanning electron microscopy, a profilometer, and a depth gage. The morphological features (radial and concentric rings) are discussed with reference to flat surfaces, and the erosion features with spherical microglass beads. The similarities and differences of erosion and morphological features are highlighted. The erosion versus time curves of various shapes of holes are discussed and are compared with those of a flat surface. The erosion process at slits is considered.

Comparsion analysis of data mining models applied to clinical research in traditional Chinese medicine.

PubMed

Zhao, Yufeng; Xie, Qi; He, Liyun; Liu, Baoyan; Li, Kun; Zhang, Xiang; Bai, Wenjing; Luo, Lin; Jing, Xianghong; Huo, Ruili

2014-10-01

To help researchers selecting appropriate data mining models to provide better evidence for the clinical practice of Traditional Chinese Medicine (TCM) diagnosis and therapy. Clinical issues based on data mining models were comprehensively summarized from four significant elements of the clinical studies: symptoms, symptom patterns, herbs, and efficacy. Existing problems were further generalized to determine the relevant factors of the performance of data mining models, e.g. data type, samples, parameters, variable labels. Combining these relevant factors, the TCM clinical data features were compared with regards to statistical characters and informatics properties. Data models were compared simultaneously from the view of applied conditions and suitable scopes. The main application problems were the inconsistent data type and the small samples for the used data mining models, which caused the inappropriate results, even the mistake results. These features, i.e. advantages, disadvantages, satisfied data types, tasks of data mining, and the TCM issues, were summarized and compared. By aiming at the special features of different data mining models, the clinical doctors could select the suitable data mining models to resolve the TCM problem.

Some Questions about Feature Re-Assembly

ERIC Educational Resources Information Center

White, Lydia

2009-01-01

In this commentary, differences between feature re-assembly and feature selection are discussed. Lardiere's proposals are compared to existing approaches to grammatical features in second language (L2) acquisition. Questions are raised about the predictive power of the feature re-assembly approach. (Contains 1 footnote.)

Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.

PubMed

Kim, Hyungjin; Park, Chang Min; Lee, Myunghee; Park, Sang Joon; Song, Yong Sub; Lee, Jong Hyuk; Hwang, Eui Jin; Goo, Jin Mo

2016-01-01

To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two readers independently segmented the whole tumor volume. Fifteen radiomic features were extracted and compared among reconstruction algorithms. Intra- and inter-reader variability and inter-reconstruction algorithm variability were calculated using coefficients of variation (CVs) and then compared. Among the 15 features, 5 first-order tumor intensity features and 4 gray level co-occurrence matrix (GLCM)-based features showed significant differences (p<0.05) among reconstruction algorithms. As for the variability, effective diameter, sphericity, entropy, and GLCM entropy were the most robust features (CV≤5%). Inter-reader variability was larger than intra-reader or inter-reconstruction algorithm variability in 9 features. However, for entropy, homogeneity, and 4 GLCM-based features, inter-reconstruction algorithm variability was significantly greater than inter-reader variability (p<0.013). Most of the radiomic features were significantly affected by the reconstruction algorithms. Inter-reconstruction algorithm variability was greater than inter-reader variability for entropy, homogeneity, and GLCM-based features.

Visual cues to geographical orientation during low-level flight

NASA Technical Reports Server (NTRS)

Battiste, Vernol; Delzell, Suzanne

1991-01-01

A field study of an operational Emergency Medical Service (EMS) unit was conducted to investigate the relationships among geographical orientation, pilot decision making, and workload in EMS flights. The map data collected during this study were compared to protocols gathered in the laboratory, where pilots viewed a simulated flight over different types of unfamiliar terrain and verbally identified the features utilized to maintain geographical orientation. The EMS pilot's questionnaire data were compared with data from non-EMS helicopter pilots with comparable flight experience.

Melanocytoma-like melanoma may be the missing link between benign and malignant uveal melanocytic lesions in humans and dogs: a comparative study.

PubMed

Zoroquiain, Pablo; Mayo-Goldberg, Erin; Alghamdi, Sarah; Alhumaid, Sulaiman; Perlmann, Eduardo; Barros, Paulo; Mayo, Nancy; Burnier, Miguel N

2016-12-01

The cutoff presented in the current classification of canine melanocytic lesions by Wilcock and Pfeiffer is based on the clinical outcome rather than morphological concepts. Classification of tumors based on morphology or molecular signatures is the key to identifying new therapies or prognostic factors. Therefore, the aim of this study was to analyze morphological findings in canine melanocytic lesions based on classic malignant morphologic principles of neoplasia and to compare these features with human uveal melanoma (HUM) samples. In total, 64 canine and 111 human morphologically malignant melanocytic lesions were classified into two groups (melanocytoma-like or classic melanoma) based on the presence or absence of M cells, respectively. Histopathological characteristics were compared between the two groups using the χ-test, t-test, and multivariate discriminant analysis. Among the 64 canine tumors, 28 (43.7%) were classic and 36 (56.3%) were melanocytoma-like melanomas. Smaller tumor size, a higher degree of pigmentation, and lower mitotic activity distinguished melanocytoma-like from classic tumors with an accuracy of 100% for melanocytoma-like lesions. From the human series, only one case showed melanocytoma-like features and had a low risk for metastasis characteristics. Canine uveal melanoma showed a morphological spectrum with features similar to the HUM counterpart (classic melanoma) and overlapped features between uveal melanoma and melanocytoma (melanocytoma-like melanoma). Recognition that the subgroup of melanocytoma-like melanoma may represent the missing link between benign and malignant lesions could help explain the progression of uveal melanoma in dogs; these findings can potentially be translated to HUM.

Propulsion Study for Small Transport Aircraft Technology (STAT)

NASA Technical Reports Server (NTRS)

Gill, J. C.; Earle, R. V.; Staton, D. V.; Stolp, P. C.; Huelster, D. S.; Zolezzi, B. A.

1980-01-01

Propulsion requirements were determined for 0.5 and 0.7 Mach aircraft. Sensitivity studies were conducted on both these aircraft to determine parametrically the influence of propulsion characteristics on aircraft size and direct operating cost (DOC). Candidate technology elements and design features were identified and parametric studies conducted to select the STAT advanced engine cycle. Trade off studies were conducted to determine those advanced technologies and design features that would offer a reduction in DOC for operation of the STAT engines. These features were incorporated in the two STAT engines. A benefit assessment was conducted comparing the STAT engines to current technology engines of the same power and to 1985 derivatives of the current technology engines. Research and development programs were recommended as part of an overall technology development plan to ensure that full commercial development of the STAT engines could be initiated in 1988.

Volcanology and morphology

NASA Technical Reports Server (NTRS)

Bryan, W. B.

1976-01-01

Apollo 15 photographs of the southern parts of Serenitatis and Imbrium were used for a study of the morphology and distribution of wrinkle ridges. Volcanic and structural features along the south margin of Serenitatis were also studied, including the Dawes basalt cinder cones. Volcanic and structural features in crater Aitken were investigated as well. Study of crater Goclenius showed a close relationship between morphology of the impact crater and grabens which tend to parallel directions of the lunar grid. Similar trends were observed in the walls of crater Tsiolkovsky and other linear structures. Small craters of possible volcanic origin were also studied. Possible cinder cones were found associated with the Dawes basalt and in the floor of craters Aitken and Goclenius. Small pit craters were observed in the floors of these craters. Attempts were made to obtain contour maps of specific small features and to compare Orbiter and Apollo photographs to determine short term changes associated with other processes.

How much does the amphioxus genome represent the ancestor of chordates?

PubMed

Louis, Alexandra; Roest Crollius, Hugues; Robinson-Rechavi, Marc

2012-03-01

One of the main motivations to study amphioxus is its potential for understanding the last common ancestor of chordates, which notably gave rise to the vertebrates. An important feature in this respect is the slow evolutionary rate that seems to have characterized the cephalochordate lineage, making amphioxus an interesting proxy for the chordate ancestor, as well as a key lineage to include in comparative studies. Whereas slow evolution was first noticed at the phenotypic level, it has also been described at the genomic level. Here, we examine whether the amphioxus genome is indeed a good proxy for the genome of the chordate ancestor, with a focus on protein-coding genes. We investigate genome features, such as synteny, gene duplication and gene loss, and contrast the amphioxus genome with those of other deuterostomes that are used in comparative studies, such as Ciona, Oikopleura and urchin.

Comparative study between ultrasonography and optical coherence tomography in interventional cardiology

NASA Astrophysics Data System (ADS)

Fanjul-Vélez, Félix; de la Torre-Hernández, José María; Ortega-Quijano, Noé; Zueco-Gil, José Javier; Arce-Diego, José Luis

2009-07-01

In this work, we present clinical images of IVUS and OCT in the evaluation of pharmacological stent endothelization. These preliminary imaging results are analyzed and compared in order to determine the ability of these technologies to visualize relevant intravascular features of interest in interventional cardiology. The results enable to compare the performance of both techniques and to evaluate their potential for clinical purposes.

Single molecule imaging of RNA polymerase II using atomic force microscopy

NASA Astrophysics Data System (ADS)

Rhodin, Thor; Fu, Jianhua; Umemura, Kazuo; Gad, Mohammed; Jarvis, Suzi; Ishikawa, Mitsuru

2003-03-01

An atomic force microscopy (AFM) study of the shape, orientation and surface topology of RNA polymerase II supported on silanized freshly cleaved mica was made. The overall aim is to define the molecular topology of RNA polymerase II in appropriate fluids to help clarify the relationship of conformational features to biofunctionality. A Nanoscope III atomic force microscope was used in the tapping mode with oxide-sharpened (8-10 nm) Si 3N 4 probes in aqueous zinc chloride buffer. The main structural features observed by AFM were compared to those derived from electron-density plots based on X-ray crystallographic studies. The conformational features included a bilobal silhouette with an inverted umbrella-shaped crater connected to a reaction site. These studies provide a starting point for constructing a 3D-AFM profiling analysis of proteins such as RNA polymerase complexes.

Disappearance of 19P/Borrelly's Silicate Feature in 2001 Apparition Is Attributed to Increase in Grain Size

NASA Technical Reports Server (NTRS)

Wooden, D. H.; Woodward, C. E.; Harker, D. E.

2002-01-01

We report on observations and analysis of HIFOGS 10 microns spectrophotometry of short period comet 19P/Borrelly on 2003 October 13, 15 UT at the NASA IRTF. 19P/Borrelly is one of two short period comets, comet 4PIFaye being the other, to have a silicate feature detected. During Borrelly s perihelion passage in 1994 December, a silicate feature was present with a flux-to-continuum ratio of 0.25. Two apparitions later in 2003 October, the silicate feature is absent. Thermal emission modeling using amorphous olivine and amorphous carbon shows that a slight increase in grain size accounts for the disappearance of the silicate feature. Analysis of 19P/Borrelly suggests grain size, and not the absence of olivine minerals, may be responsible for the absence of silicate features in most short period comets. 19P/Borrelly is one of the more active short period comets. However, short period comets as a family are less active than long period comets. Short period comets probably originated in the Kuiper Belt and suffered collisions while in residence in the outer solar system. Upon evolution into orbits that take them through the inner solar system, the surfaces of short period comets are exposed to sunlight through their many perihelion passages. This is in contrast to long period comets which probably originated near Jupiter and were expelled to the Oort cloud where they have existed and been exposed to cosmic ray processing. By studying the grain properties in short period comets and comparing to long period comets, we compare the effects on the grain populations of different parent body evolution histories. Upcoming opportunities to study short and long period comets will be advertised.

Expanding the phenotype of Triple X syndrome: A comparison of prenatal versus postnatal diagnosis.

PubMed

Wigby, Kristen; D'Epagnier, Cheryl; Howell, Susan; Reicks, Amy; Wilson, Rebecca; Cordeiro, Lisa; Tartaglia, Nicole

2016-11-01

Triple X syndrome (47, XXX) occurs in approximately 1:1,000 female births and has a variable phenotype of physical and psychological features. Prenatal diagnosis rates of 47, XXX are increasing due to non-invasive prenatal genetic testing. Previous studies suggest that prenatal diagnosed females have better neurodevelopmental outcomes. This cross-sectional study describes diagnosis, physical features, medical problems, and neurodevelopmental features in a large cohort of females with 47, XXX. Evaluation included review of medical and developmental history, physical exam, cognitive, and adaptive testing. Medical and developmental features were compared between the prenatal and postnatal diagnosis groups using rate calculations and Fisher's exact test. Cognitive and adaptive tests scores were compared using t-tests. Seventy-four females age 6 months-24 years (mean 8.3 years) participated. Forty-four (59.5%) females were in the prenatal diagnosis group. Mean age of postnatal diagnosis was 5.9 years; developmental delay was the most common indication for postnatal genetic testing. Common physical features included hypertelorism, epicanthal folds, clinodactyly, and hypotonia. Medical problems included dental disorders (44.4%), seizure disorders (16.2%), genitourinary malformations (12.2%). The prenatal diagnosis group had higher verbal (P < 0.001), general ability index (P = 0.004), and adaptive functioning scores (P < 0.001). Rates of ADHD (52.2% vs. 45.5%, P = 0.77) and learning disabilities (39.1% vs. 36.3%, P = 1.00) were similar between the two groups. These findings expand on the phenotypic features in females with Triple X syndrome and support that prenatally ascertained females have better cognitive and functional outcomes. However, prenatally diagnosed females are still at risk for neurodevelopmental disorders. Genetic counseling and treatment recommendations are summarized. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Combined value of Virtual Touch tissue quantification and conventional sonographic features for differentiating benign and malignant thyroid nodules smaller than 10 mm.

PubMed

Zhang, Huiping; Shi, Qiusheng; Gu, Jiying; Jiang, Luying; Bai, Min; Liu, Long; Wu, Ying; Du, Lianfang

2014-02-01

This study aimed to investigate the value of sonographic features including Virtual Touch tissue quantification (VTQ; Siemens Medical Solutions, Mountain View, CA) for differentiating benign and malignant thyroid nodules smaller than 10 mm. Seventy-one thyroid nodules smaller than 10 mm with pathologic diagnoses were included in this study. The conventional sonographic features and quantitative elasticity features (VTQ) were observed and compared between benign and malignant nodules. There were 39 benign and 32 malignant nodules according to histopathologic examination. When compared with benign nodules, malignant nodules were more frequently taller than wide, poorly defined, and markedly hypoechoic (P < .05). Color Doppler sonographic features were not significantly different between benign and malignant nodules. The VTQ value for malignant nodules (mean ± SD 3.260 ± 0.725 m/s) was significantly higher than that of benign ones (2.108 ± 0.455 m/s; P < .001). The cutoff point for the differential diagnosis was 2.910 m/s, with sensitivity, specificity, a positive predictive value, a negative predictive value, and diagnostic accuracy of 71.9%, 100%, 100%, 81.2%, and 87.3% respectively. Logistic regression analysis showed that a taller-than-wide shape, a poorly defined boundary, marked hypoechogenicity, and a VTQ value greater than 2.910 m/s were independent risk factors for malignancy, with odds ratios of 69.366, 41.864, 5.945, and 64.991. The combination of VTQ with a taller-than-wide shape had the highest sensitivity and specificity of 90.6% and 97.4%. The shape, margin, echogenicity, and VTQ value are useful sonographic criteria for differentiating benign and malignant thyroid nodules smaller than 10 mm. When VTQ was combined with B-mode sonographic features, the sensitivity was improved significantly.

Correlation of SASH1 expression and ultrasonographic features in breast cancer.

PubMed

Gong, Xuchu; Wu, Jinna; Wu, Jian; Liu, Jun; Gu, Hailin; Shen, Hao

2017-01-01

SASH1 is a member of the SH3/SAM adapter molecules family and has been identified as a new tumor suppressor and critical protein in signal transduction. An ectopic expression of SASH1 is associated with decreased cell viability of breast cancer. The aim of this study was to explore the association between SASH1 expression and the ultrasonographic features in breast cancer. A total of 186 patients diagnosed with breast cancer were included in this study. The patients received preoperative ultrasound examination, and the expression of SASH1 was determined using immunohistochemistry methods. Spearman's rank correlation analysis was used to analyze the correlation between SASH1-positive expression and the ultrasonographic features. The positive expression of SASH1 was observed in 63 (33.9%) patients. The positive expression rate of SASH1 was significantly decreased in patients with breast cancer (63/186, 33.9%) compared with controls ( P <0.001). The positive expression rate of SASH1 was significantly decreased in patients with edge burr sign ( P =0.025), lymph node metastasis ( P =0.007), and a blood flow grade of III ( P =0.013) compared with patients without those adverse ultrasonographic features. The expression of SASH1 was negatively correlated with edge burr sign ( P =0.025), lymph node metastasis ( P =0.007), and blood flow grade ( P =0.003) of the patients with breast cancer. The expression of SASH1 was inversely correlated with some critical ultrasonographic features, including edge burr sign, lymph node metastasis, and blood flow grade in breast cancer, and decreased SASH1 expression appears to be associated with adverse clinical and imaging features in breast cancer.

Correlation of SASH1 expression and ultrasonographic features in breast cancer

PubMed Central

Gong, Xuchu; Wu, Jinna; Wu, Jian; Liu, Jun; Gu, Hailin; Shen, Hao

2017-01-01

Objective SASH1 is a member of the SH3/SAM adapter molecules family and has been identified as a new tumor suppressor and critical protein in signal transduction. An ectopic expression of SASH1 is associated with decreased cell viability of breast cancer. The aim of this study was to explore the association between SASH1 expression and the ultrasonographic features in breast cancer. Patients and methods A total of 186 patients diagnosed with breast cancer were included in this study. The patients received preoperative ultrasound examination, and the expression of SASH1 was determined using immunohistochemistry methods. Spearman’s rank correlation analysis was used to analyze the correlation between SASH1-positive expression and the ultrasonographic features. Results The positive expression of SASH1 was observed in 63 (33.9%) patients. The positive expression rate of SASH1 was significantly decreased in patients with breast cancer (63/186, 33.9%) compared with controls (P<0.001). The positive expression rate of SASH1 was significantly decreased in patients with edge burr sign (P=0.025), lymph node metastasis (P=0.007), and a blood flow grade of III (P=0.013) compared with patients without those adverse ultrasonographic features. The expression of SASH1 was negatively correlated with edge burr sign (P=0.025), lymph node metastasis (P=0.007), and blood flow grade (P=0.003) of the patients with breast cancer. Conclusion The expression of SASH1 was inversely correlated with some critical ultrasonographic features, including edge burr sign, lymph node metastasis, and blood flow grade in breast cancer, and decreased SASH1 expression appears to be associated with adverse clinical and imaging features in breast cancer. PMID:28138250

Reflectance confocal microscopy features of thin versus thick melanomas.

PubMed

Kardynal, Agnieszka; Olszewska, Małgorzata; de Carvalho, Nathalie; Walecka, Irena; Pellacani, Giovanni; Rudnicka, Lidia

2018-01-24

In vivo reflectance confocal microscopy (RCM) plays an increasingly important role in differential diagnosis of melanoma. The aim of the study was to assess typical confocal features of thin (≤1mm according to Breslow index) versus thick (>1mm) melanomas. 30 patients with histopathologically confirmed cutaneous melanoma were included in the study. Reflectance confocal microscopy was performed with Vivascope equipment prior to excision. Fifteen melanomas were thin (Breslow thickness ≤ 1mm) and 15 were thick melanomas (Breslow thickness >1mm). In the RCM examination, the following features were more frequently observed in thin compared to thick melanomas: edged papillae (26.7% vs 0%, p=0.032) and areas with honeycomb or cobblestone pattern (33.3% vs 6.7%, p=0.068). Both features are present in benign melanocytic lesions, so in melanoma are good prognostic factors. The group of thick melanomas compared to the group of thin melanomas in the RCM images presented with greater frequency of roundish cells (100% vs 40%, p=0.001), non-edged papillae (100% vs 60%, p=0.006), numerous pagetoid cells (73.3% vs 33.3%, p=0.028), numerous atypical cells at dermal-epidermal junction (53.3% vs 20%, p=0.058) and epidermal disarray (93.3% vs 66.7%, p=0.068). Non-invasive imaging methods helps in deepening of knowledge about the evolution and biology of melanoma. The most characteristic features for thin melanomas in confocal examination are: fragments of cobblestone or honeycomb pattern and edged papillae (as good prognostic factors). The features of thick melanomas in RCM examination are: roundish cells, non-edged papillae, numerous pagetoid cells at dermal-epidermal junction and epidermal disarray.

A Unified Fisher's Ratio Learning Method for Spatial Filter Optimization.

PubMed

Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.

Reference Management Software: A Comparative Analysis of Four Products

ERIC Educational Resources Information Center

Gilmour, Ron; Cobus-Kuo, Laura

2011-01-01

Reference management (RM) software is widely used by researchers in the health and natural sciences. Librarians are often called upon to provide support for these products. The present study compares four prominent RMs: CiteULike, RefWorks, Mendeley, and Zotero, in terms of features offered and the accuracy of the bibliographies that they…

Binge Eating Disorder and Night Eating Syndrome: A Comparative Study of Disordered Eating

ERIC Educational Resources Information Center

Allison, Kelly C.; Grilo, Carlos M.; Masheb, Robin M.; Stunkard, Albert J.

2005-01-01

The authors compared eating patterns, disordered eating, features of eating disorders, and depressive symptoms in persons with binge eating disorder (BED; n = 177), with night eating syndrome (NES; n = 68), and in an overweight comparison group without BED or NES (comparison; n = 45). Participants completed semistructured interviews and several…

A Corpus-Based Comparative Study of "Learn" and "Acquire"

ERIC Educational Resources Information Center

Yang, Bei

2016-01-01

As an important yet intricate linguistic feature in English language, synonymy poses a great challenge for second language learners. Using the 100 million-word British National Corpus (BNC) as data and the software Sketch Engine (SkE) as an analyzing tool, this article compares the usage of "learn" and "acquire" used in natural…

Simulation-based training for cardiac auscultation skills: systematic review and meta-analysis.

PubMed

McKinney, James; Cook, David A; Wood, David; Hatala, Rose

2013-02-01

The current review examines the effectiveness of simulation-based medical education (SBME) for training health professionals in cardiac physical examination and examines the relative effectiveness of key instructional design features. Data sources included a comprehensive, systematic search of MEDLINE, EMBASE, CINAHL, PsychINFO, ERIC, Web of Science, and Scopus through May 2011. Included studies investigated SBME to teach health profession learners cardiac physical examination skills using outcomes of knowledge or skill. We carried out duplicate assessment of study quality and data abstraction and pooled effect sizes using random effects. We identified 18 articles for inclusion. Thirteen compared SBME to no-intervention (either single group pre-post comparisons or SBME added to other instruction common to all learners, such as traditional bedside teaching), three compared SBME to other educational interventions, and two compared two SBME interventions. Meta-analysis of the 13 no-intervention comparison studies demonstrated that simulation-based instruction in cardiac auscultation was effective, with pooled effect sizes of 1.10 (95 % CI 0.49-1.72; p < 0.001; I(2) = 92.4 %) for knowledge outcomes and 0.87 (95 % CI 0.52-1.22; p < 0.001; I(2) = 91.5 %) for skills. In sub-group analysis, hands-on practice with the simulator appeared to be an important teaching technique. Narrative review of the comparative effectiveness studies suggests that SBME may be of similar effectiveness to other active educational interventions, but more studies are required. The quantity of published evidence and the relative lack of comparative effectiveness studies limit this review. SBME is an effective educational strategy for teaching cardiac auscultation. Future studies should focus on comparing key instructional design features and establishing SBME's relative effectiveness compared to other educational interventions.

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

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

Nawrocki, J; Chino, J; Das, S

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Eachmore » clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.« less

Features of effective medical knowledge resources to support point of care learning: a focus group study.

PubMed

Cook, David A; Sorensen, Kristi J; Hersh, William; Berger, Richard A; Wilkinson, John M

2013-01-01

Health care professionals access various information sources to quickly answer questions that arise in clinical practice. The features that favorably influence the selection and use of knowledge resources remain unclear. We sought to better understand how clinicians select among the various knowledge resources available to them, and from this to derive a model for an effective knowledge resource. We conducted 11 focus groups at an academic medical center and outlying community sites. We included a purposive sample of 50 primary care and subspecialist internal medicine and family medicine physicians. We transcribed focus group discussions and analyzed these using a constant comparative approach to inductively identify features that influence the selection of knowledge resources. We identified nine features that influence users' selection of knowledge resources, namely efficiency (with sub-features of comprehensiveness, searchability, and brevity), integration with clinical workflow, credibility, user familiarity, capacity to identify a human expert, reflection of local care processes, optimization for the clinical question (e.g., diagnosis, treatment options, drug side effect), currency, and ability to support patient education. No single existing resource exemplifies all of these features. The influential features identified in this study will inform the development of knowledge resources, and could serve as a framework for future research in this field.

Features of Effective Medical Knowledge Resources to Support Point of Care Learning: A Focus Group Study

PubMed Central

Cook, David A.; Sorensen, Kristi J.; Hersh, William; Berger, Richard A.; Wilkinson, John M.

2013-01-01

Objective Health care professionals access various information sources to quickly answer questions that arise in clinical practice. The features that favorably influence the selection and use of knowledge resources remain unclear. We sought to better understand how clinicians select among the various knowledge resources available to them, and from this to derive a model for an effective knowledge resource. Methods We conducted 11 focus groups at an academic medical center and outlying community sites. We included a purposive sample of 50 primary care and subspecialist internal medicine and family medicine physicians. We transcribed focus group discussions and analyzed these using a constant comparative approach to inductively identify features that influence the selection of knowledge resources. Results We identified nine features that influence users' selection of knowledge resources, namely efficiency (with sub-features of comprehensiveness, searchability, and brevity), integration with clinical workflow, credibility, user familiarity, capacity to identify a human expert, reflection of local care processes, optimization for the clinical question (e.g., diagnosis, treatment options, drug side effect), currency, and ability to support patient education. No single existing resource exemplifies all of these features. Conclusion The influential features identified in this study will inform the development of knowledge resources, and could serve as a framework for future research in this field. PMID:24282535

The future of primordial features with large-scale structure surveys

NASA Astrophysics Data System (ADS)

Chen, Xingang; Dvorkin, Cora; Huang, Zhiqi; Namjoo, Mohammad Hossein; Verde, Licia

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopic and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.

The future of primordial features with large-scale structure surveys

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

Chen, Xingang; Namjoo, Mohammad Hossein; Dvorkin, Cora

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopicmore » and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.« less

Morphological and wavelet features towards sonographic thyroid nodules evaluation.

PubMed

Tsantis, Stavros; Dimitropoulos, Nikos; Cavouras, Dionisis; Nikiforidis, George

2009-03-01

This paper presents a computer-based classification scheme that utilized various morphological and novel wavelet-based features towards malignancy risk evaluation of thyroid nodules in ultrasonography. The study comprised 85 ultrasound images-patients that were cytological confirmed (54 low-risk and 31 high-risk). A set of 20 features (12 based on nodules boundary shape and 8 based on wavelet local maxima located within each nodule) has been generated. Two powerful pattern recognition algorithms (support vector machines and probabilistic neural networks) have been designed and developed in order to quantify the power of differentiation of the introduced features. A comparative study has also been held, in order to estimate the impact speckle had onto the classification procedure. The diagnostic sensitivity and specificity of both classifiers was made by means of receiver operating characteristics (ROC) analysis. In the speckle-free feature set, the area under the ROC curve was 0.96 for the support vector machines classifier whereas for the probabilistic neural networks was 0.91. In the feature set with speckle, the corresponding areas under the ROC curves were 0.88 and 0.86 respectively for the two classifiers. The proposed features can increase the classification accuracy and decrease the rate of missing and misdiagnosis in thyroid cancer control.

Clinicopathological feature and prognosis of primary hepatic gastrointestinal stromal tumor.

PubMed

Liu, Zhen; Tian, Yangzi; Liu, Shushang; Xu, Guanghui; Guo, Man; Lian, Xiao; Fan, Daiming; Zhang, Hongwei; Feng, Fan

2016-09-01

Compared to gastric gastrointestinal stromal tumor (GIST), hepatic GIST is very rare in clinic. Reports on clinicopathological feature and prognosis of this rare disease are limited in literature. The purpose of this study was, therefore, to summarize clinical and pathological features as well as prognosis of the primary hepatic GIST. One case of primary hepatic GIST from our center and 22 cases reported in MEDLINE or China National Knowledge Infrastructure (CNKI) were enrolled into this study. Clinicopathological features as well as survival data of hepatic GIST were analyzed and compared with 297 gastric GISTs and 59 small intestinal GISTs from our center. Majority of the 22 cases (95.7%) of hepatic GIST was larger than 5 cm in size, and 75.0% of the tumors were over 5/50 HPF in mitotic index. Most of the hepatic GISTs (85.7%) displayed spindle cell shape in morphology. All of the hepatic GIST (100%) enrolled in this study were classified as high-risk category by the National Institute of Health (NIH) risk classification. The 5-year median disease-free survival (DFS) time was 24.0 months and 5-year disease-specific survival (DSS) rate was 33.3%, respectively. Distribution of clinicopathological features was significantly different among hepatic, gastric, and small intestinal GIST. The DFS and DSS of hepatic GIST were significantly lower than those of the other two groups. Majority of the hepatic GIST is large in size and highly malignant. Prognosis of the primary hepatic GIST is worse than that of gastric GIST and small intestinal GIST. © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Plasma amino acid and urine organic acid profiles of Filipino patients with maple syrup urine disease (MSUD) and correlation with their neurologic features.

PubMed

Chiong, Mary Anne D; Tan, Marilyn A; Cordero, Cynthia P; Fodra, Esphie Grace D; Manliguis, Judy S; Lopez, Cristine P; Dalmacio, Leslie Michelle M

2016-12-01

Maple syrup urine disease (MSUD) is the most common inborn error of metabolism in the country. The cause of the neuropathology is still not well established although accumulation of branched chain amino acids (BCAA) and alteration in large neutral amino acids (LNAA) as well as energy deprivation are suggested. It is therefore the aim of this study to determine the plasma amino acid and urine organic acid profiles of patients with MSUD and correlate the findings with their neurologic features. Twenty six Filipino patients with MSUD were studied in terms of their plasma amino acid and urine organic acid profiles. Their results were compared with 26 age and sex matched controls. The neurologic features were correlated with the results of the plasma amino acids and urine organic acids. Majority of the patients with MSUD had developmental delay/intellectual disability (88%), speech delay (69%), and seizures (65%). Their amino acid profiles revealed low glutamine and alanine with high levels of leucine, isoleucine, phenylalanine, threonine and alloisoleucine compared to controls (p < 0.05). The urine organic acids showed significantly elevated excretion of the branched chain ketoacids and succinate (p < 0.05). However there were no biochemical markers that correlated significantly with the neurologic features. The findings suggest that there could still be altered LNAA metabolism among patients with MSUD when the BCAAs are elevated. Although the biochemical findings were not significantly correlated with the neurologic features, the study showed that prevention and avoidance of neurologic disturbances may still rely primarily on early diagnosis and prompt institution of treatment, along with strict compliance with the dietary regimen and maintenance of good metabolic control over time.

Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.

PubMed

Memarian, Negar; Kim, Sally; Dewar, Sandra; Engel, Jerome; Staba, Richard J

2015-09-01

This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE. Published by Elsevier Ltd.

Is PiSS Alpha-1 Antitrypsin Deficiency Associated with Disease?

PubMed

McGee, Dawn; Schwarz, Laura; McClure, Rebecca; Peterka, Lauren; Rouhani, Farshid; Brantly, Mark; Strange, Charlie

2010-01-01

Background. Alpha-1 antitrypsin deficiency (AAT) is an inherited condition that predisposes to lung and/or liver disease. Objective. The current study examined the clinical features of the PiSS genotype. Methods. Nineteen study participants (PiSS) and 29 matched control participants (PiMM) were telephone interviewed using a standardized questionnaire. Demographic features, cigarette smoking, vocation, medication history, and clinical diagnoses were compared. Statistical analysis was performed. Finally, a comprehensive literature review was performed by two investigators. Results. 12/19 (63.2%) study participants reported the presence of lung and/or liver disease compared to 12/29 (41.4%) control participants. There trended toward having a higher frequency of medication allergies in the study population (42.11% versus 20.69%). Conclusions. The PiSS genotype was associated with a similar incidence of obstructive lung disease to controls. Selective bias intrinsic in testing for AAT deficiency and the rarity of the PiSS genotype will make future study of this association dependent on population-based tests.

Is PiSS Alpha-1 Antitrypsin Deficiency Associated with Disease?

PubMed Central

McGee, Dawn; Schwarz, Laura; McClure, Rebecca; Peterka, Lauren; Rouhani, Farshid; Brantly, Mark; Strange, Charlie

2010-01-01

Background. Alpha-1 antitrypsin deficiency (AAT) is an inherited condition that predisposes to lung and/or liver disease. Objective. The current study examined the clinical features of the PiSS genotype. Methods. Nineteen study participants (PiSS) and 29 matched control participants (PiMM) were telephone interviewed using a standardized questionnaire. Demographic features, cigarette smoking, vocation, medication history, and clinical diagnoses were compared. Statistical analysis was performed. Finally, a comprehensive literature review was performed by two investigators. Results. 12/19 (63.2%) study participants reported the presence of lung and/or liver disease compared to 12/29 (41.4%) control participants. There trended toward having a higher frequency of medication allergies in the study population (42.11% versus 20.69%). Conclusions. The PiSS genotype was associated with a similar incidence of obstructive lung disease to controls. Selective bias intrinsic in testing for AAT deficiency and the rarity of the PiSS genotype will make future study of this association dependent on population-based tests. PMID:21687342

A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study.

PubMed

Joshuva, A; Sugumaran, V

2017-03-01

Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Atypical depression is more common than melancholic in fibromyalgia: an observational cohort study.

PubMed

Ross, Rebecca L; Jones, Kim D; Ward, Rachel L; Wood, Lisa J; Bennett, Robert M

2010-06-14

It has been postulated that atypical and melancholic depression subtypes exist in depressed fibromyalgia (FM) patients, yet no study has empirically tested this hypothesis. The purpose of this study is to determine whether major depressive disorder (MDD) with atypical features and MDD with melancholic features occurs in a FM sample and to describe their demographic, clinical and diagnostic characteristics. An observational cohort study using a descriptive cross-sectional design recruited a convenience sample of 76 outpatients with FM from an academic rheumatology clinic and a community mental health practice. Diagnoses of FM were confirmed using the 1990 ACR classification guidelines. Diagnoses of MDD and diagnostic subtypes were determined using the DSM-IV-TR criteria. Clinical characteristics were measured using the Fibromyalgia Impact Questionnaire, Structured Interview Guide for the Hamilton Depression Rating Scale with Atypical Depression Supplement and other standardized instruments. Odds ratios were computed on subtype-specific diagnostic criteria. Correlations assessed associations between subtype diagnoses and diagnostic criteria. Of the 76 subjects with FM, 11.8% (n = 9) were euthymic, 52.6% (n = 40) met diagnostic criteria for MDD with atypical features and 35.6% (n = 27) for MDD with melancholic features. Groups did not differ on demographic characteristics except for gender (p = 0.01). The non-depressed and atypical groups trended toward having a longer duration of FM symptoms (18.05 yrs. +/- 12.83; 20.36 yrs. +/- 15.07) compared to the melancholic group (14.11 yrs. +/- 8.82; p = 0.09). The two depressed groups experienced greater severity on all clinical features compared to the non-depressed group. The atypical group did not differ clinically from the melancholic group except the latter experienced greater depression severity (p = 0.001). The atypical group demonstrated the highest prevalence and correlations with atypical-specific diagnostic criteria: (e.g., weight gain/ increased appetite: OR = 3.5, p = 0.02), as did the melancholic group for melancholic-specific criteria: (e.g., anhedonia: OR = 20, p < 0.001). Depressed fibromyalgia patients commonly experience both atypical and melancholic depressive features; however, in this study, atypical depression was 1.5 times more common than melancholic depression. This finding may have significant research and clinical implications.

Software for objective comparison of vocal acoustic features over weeks of audio recording: KLFromRecordingDays

NASA Astrophysics Data System (ADS)

Soderstrom, Ken; Alalawi, Ali

KLFromRecordingDays allows measurement of Kullback-Leibler (KL) distances between 2D probability distributions of vocal acoustic features. Greater KL distance measures reflect increased phonological divergence across the vocalizations compared. The software has been used to compare *.wav file recordings made by Sound Analysis Recorder 2011 of songbird vocalizations pre- and post-drug and surgical manipulations. Recordings from individual animals in *.wav format are first organized into subdirectories by recording day and then segmented into individual syllables uttered and acoustic features of these syllables using Sound Analysis Pro 2011 (SAP). KLFromRecordingDays uses syllable acoustic feature data output by SAP to a MySQL table to generate and compare "template" (typically pre-treatment) and "target" (typically post-treatment) probability distributions. These distributions are a series of virtual 2D plots of the duration of each syllable (as x-axis) to each of 13 other acoustic features measured by SAP for that syllable (as y-axes). Differences between "template" and "target" probability distributions for each acoustic feature are determined by calculating KL distance, a measure of divergence of the target 2D distribution pattern from that of the template. KL distances and the mean KL distance across all acoustic features are calculated for each recording day and output to an Excel spreadsheet. Resulting data for individual subjects may then be pooled across treatment groups and graphically summarized and used for statistical comparisons. Because SAP-generated MySQL files are accessed directly, data limits associated with spreadsheet output are avoided, and the totality of vocal output over weeks may be objectively analyzed all at once. The software has been useful for measuring drug effects on songbird vocalizations and assessing recovery from damage to regions of vocal motor cortex. It may be useful in studies employing other species, and as part of speech therapies tracking progress in producing distinct speech sounds in isolation.

A global optimization approach to multi-polarity sentiment analysis.

PubMed

Li, Xinmiao; Li, Jing; Wu, Yukeng

2015-01-01

Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From the results of this comparison, we found that PSOGO-Senti is more suitable for improving a difficult multi-polarity sentiment analysis problem.

A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Tseng, Tzu-Liang B.; Zheng, Bin; Zhang, Jianying; Qian, Wei

2015-03-01

A novel breast cancer risk analysis approach is proposed for enhancing performance of computerized breast cancer risk analysis using bilateral mammograms. Based on the intensity of breast area, five different sub-regions were acquired from one mammogram, and bilateral features were extracted from every sub-region. Our dataset includes 180 bilateral mammograms from 180 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including sub-region segmentation, bilateral feature extraction, feature selection, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under the curve (AUC) is 0.763 ± 0.021 when applying the multiple sub-region features to our testing dataset. The positive predictive value and the negative predictive value were 0.60 and 0.73, respectively. The study demonstrates that (1) features extracted from multiple sub-regions can improve the performance of our scheme compared to using features from whole breast area only; (2) a classifier using asymmetry bilateral features can effectively predict breast cancer risk; (3) incorporating texture and morphological features with density features can boost the classification accuracy.

Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

NASA Astrophysics Data System (ADS)

Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

2017-03-01

This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor.

PubMed

Saravanan, Vijayakumar; Gautham, Namasivayam

2015-10-01

Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. However, reliable prediction of linear B-cell epitope remains a formidable challenge. Earlier studies have reported discrepancy in amino acid composition between the epitopes and non-epitopes. Hence, this study proposed and developed a novel amino acid composition-based feature descriptor, Dipeptide Deviation from Expected Mean (DDE), to distinguish the linear B-cell epitopes from non-epitopes effectively. In this study, for the first time, only exact linear B-cell epitopes and non-epitopes have been utilized for developing the prediction method, unlike the use of epitope-containing regions in earlier reports. To evaluate the performance of the DDE feature vector, models have been developed with two widely used machine-learning techniques Support Vector Machine and AdaBoost-Random Forest. Five-fold cross-validation performance of the proposed method with error-free dataset and dataset from other studies achieved an overall accuracy between nearly 61% and 73%, with balance between sensitivity and specificity metrics. Performance of the DDE feature vector was better (with accuracy difference of about 2% to 12%), in comparison to other amino acid-derived features on different datasets. This study reflects the efficiency of the DDE feature vector in enhancing the linear B-cell epitope prediction performance, compared to other feature representations. The proposed method is made as a stand-alone tool available freely for researchers, particularly for those interested in vaccine design and novel molecular target development for systems therapeutics and diagnostics: https://github.com/brsaran/LBEEP.

Prominent feature extraction for review analysis: an empirical study

NASA Astrophysics Data System (ADS)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Contributions of emotional state and attention to the processing of syntactic agreement errors: evidence from P600

PubMed Central

Verhees, Martine W. F. T.; Chwilla, Dorothee J.; Tromp, Johanne; Vissers, Constance T. W. M.

2015-01-01

The classic account of language is that language processing occurs in isolation from other cognitive systems, like perception, motor action, and emotion. The central theme of this paper is the relationship between a participant’s emotional state and language comprehension. Does emotional context affect how we process neutral words? Recent studies showed that processing of word meaning – traditionally conceived as an automatic process – is affected by emotional state. The influence of emotional state on syntactic processing is less clear. One study reported a mood-related P600 modulation, while another study did not observe an effect of mood on syntactic processing. The goals of this study were: First, to clarify whether and if so how mood affects syntactic processing. Second, to shed light on the underlying mechanisms by separating possible effects of mood from those of attention on syntactic processing. Event-related potentials (ERPs) were recorded while participants read syntactically correct or incorrect sentences. Mood (happy vs. sad) was manipulated by presenting film clips. Attention was manipulated by directing attention to syntactic features vs. physical features. The mood induction was effective. Interactions between mood, attention and syntactic correctness were obtained, showing that mood and attention modulated P600. The mood manipulation led to a reduction in P600 for sad as compared to happy mood when attention was directed at syntactic features. The attention manipulation led to a reduction in P600 when attention was directed at physical features compared to syntactic features for happy mood. From this we draw two conclusions: First, emotional state does affect syntactic processing. We propose mood-related differences in the reliance on heuristics as the underlying mechanism. Second, attention can contribute to emotion-related ERP effects in syntactic language processing. Therefore, future studies on the relation between language and emotion will have to control for effects of attention. PMID:25914660

Contributions of emotional state and attention to the processing of syntactic agreement errors: evidence from P600.

PubMed

Verhees, Martine W F T; Chwilla, Dorothee J; Tromp, Johanne; Vissers, Constance T W M

2015-01-01

The classic account of language is that language processing occurs in isolation from other cognitive systems, like perception, motor action, and emotion. The central theme of this paper is the relationship between a participant's emotional state and language comprehension. Does emotional context affect how we process neutral words? Recent studies showed that processing of word meaning - traditionally conceived as an automatic process - is affected by emotional state. The influence of emotional state on syntactic processing is less clear. One study reported a mood-related P600 modulation, while another study did not observe an effect of mood on syntactic processing. The goals of this study were: First, to clarify whether and if so how mood affects syntactic processing. Second, to shed light on the underlying mechanisms by separating possible effects of mood from those of attention on syntactic processing. Event-related potentials (ERPs) were recorded while participants read syntactically correct or incorrect sentences. Mood (happy vs. sad) was manipulated by presenting film clips. Attention was manipulated by directing attention to syntactic features vs. physical features. The mood induction was effective. Interactions between mood, attention and syntactic correctness were obtained, showing that mood and attention modulated P600. The mood manipulation led to a reduction in P600 for sad as compared to happy mood when attention was directed at syntactic features. The attention manipulation led to a reduction in P600 when attention was directed at physical features compared to syntactic features for happy mood. From this we draw two conclusions: First, emotional state does affect syntactic processing. We propose mood-related differences in the reliance on heuristics as the underlying mechanism. Second, attention can contribute to emotion-related ERP effects in syntactic language processing. Therefore, future studies on the relation between language and emotion will have to control for effects of attention.

A Comparison of Raman Spectral Features of Frozen and Deparaffinized Tissues in Neuroblastoma and Ganglioneuroma

NASA Astrophysics Data System (ADS)

Devpura, Suneetha; Thakur, Jagdish S.; Poulik, Janet M.; Rabah, Raja; Naik, Vaman M.; Naik, Ratna

2012-02-01

We have investigated the cellular regions in neuroblastoma and ganglioneuroma using Raman spectroscopy and compared their spectral characteristics with those of normal adrenal gland. Thin sections from both frozen and deparaffinized tissues, obtained from the same tissue specimen, were studied in conjunction with the pathological examination of the tissues. We found a significant difference in the spectral features of frozen sections of normal adrenal gland, neuroblastoma, and ganglioneuroma when compared to deparaffinized tissues. The quantitative analysis of the Raman data using chemometric methods of principal component analysis and discriminant function analysis obtained from the frozen tissues show a sensitivity and specificity of 100% each. The biochemical identification based on the spectral differences shows that the normal adrenal gland tissues have higher levels of carotenoids, lipids, and cholesterol compared to the neuroblastoma and ganglioneuroma frozen tissues. However, deparaffinized tissues show complete removal of these biochemicals in adrenal tissues. This study demonstrates that Raman spectroscopy combined with chemometric methods can successfully distinguish neuroblastoma and ganglioneuroma at cellular level.

Using qualitative comparative analysis in a systematic review of a complex intervention.

PubMed

Kahwati, Leila; Jacobs, Sara; Kane, Heather; Lewis, Megan; Viswanathan, Meera; Golin, Carol E

2016-05-04

Systematic reviews evaluating complex interventions often encounter substantial clinical heterogeneity in intervention components and implementation features making synthesis challenging. Qualitative comparative analysis (QCA) is a non-probabilistic method that uses mathematical set theory to study complex phenomena; it has been proposed as a potential method to complement traditional evidence synthesis in reviews of complex interventions to identify key intervention components or implementation features that might explain effectiveness or ineffectiveness. The objective of this study was to describe our approach in detail and examine the suitability of using QCA within the context of a systematic review. We used data from a completed systematic review of behavioral interventions to improve medication adherence to conduct two substantive analyses using QCA. The first analysis sought to identify combinations of nine behavior change techniques/components (BCTs) found among effective interventions, and the second analysis sought to identify combinations of five implementation features (e.g., agent, target, mode, time span, exposure) found among effective interventions. For each substantive analysis, we reframed the review's research questions to be designed for use with QCA, calibrated sets (i.e., transformed raw data into data used in analysis), and identified the necessary and/or sufficient combinations of BCTs and implementation features found in effective interventions. Our application of QCA for each substantive analysis is described in detail. We extended the original review findings by identifying seven combinations of BCTs and four combinations of implementation features that were sufficient for improving adherence. We found reasonable alignment between several systematic review steps and processes used in QCA except that typical approaches to study abstraction for some intervention components and features did not support a robust calibration for QCA. QCA was suitable for use within a systematic review of medication adherence interventions and offered insights beyond the single dimension stratifications used in the original completed review. Future prospective use of QCA during a review is needed to determine the optimal way to efficiently integrate QCA into existing approaches to evidence synthesis of complex interventions.

Do Teachers Make All Their Students Play the Same Learning Games? A Comparative Study of Learning Games in Biology and English as a Second Language

ERIC Educational Resources Information Center

Gruson, Brigitte; Marlot, Corinne

2016-01-01

This article, based upon the field of comparative didactics, seeks to contribute to the identification of generic and specific features in the teaching and learning process. More particularly, its aim was to examine, through the study of two different school subjects: biology and English as a second language, how "passive didactic…

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Nanometer polymer surface features: the influence on surface energy, protein adsorption and endothelial cell adhesion

NASA Astrophysics Data System (ADS)

Carpenter, Joseph; Khang, Dongwoo; Webster, Thomas J.

2008-12-01

Current small diameter (<5 mm) synthetic vascular graft materials exhibit poor long-term patency due to thrombosis and intimal hyperplasia. Tissue engineered solutions have yielded functional vascular tissue, but some require an eight-week in vitro culture period prior to implantation—too long for immediate clinical bedside applications. Previous in vitro studies have shown that nanostructured poly(lactic-co-glycolic acid) (PLGA) surfaces elevated endothelial cell adhesion, proliferation, and extracellular matrix synthesis when compared to nanosmooth surfaces. Nonetheless, these studies failed to address the importance of lateral and vertical surface feature dimensionality coupled with surface free energy; nor did such studies elicit an optimum specific surface feature size for promoting endothelial cell adhesion. In this study, a series of highly ordered nanometer to submicron structured PLGA surfaces of identical chemistry were created using a technique employing polystyrene nanobeads and poly(dimethylsiloxane) (PDMS) molds. Results demonstrated increased endothelial cell adhesion on PLGA surfaces with vertical surface features of size less than 18.87 nm but greater than 0 nm due to increased surface energy and subsequently protein (fibronectin and collagen type IV) adsorption. Furthermore, this study provided evidence that the vertical dimension of nanometer surface features, rather than the lateral dimension, is largely responsible for these increases. In this manner, this study provides key design parameters that may promote vascular graft efficacy.

Comparative study on the microbiological features of angular cheilitis in HIV seropositive and HIV seronegative patients from South India

PubMed Central

Krishnan, P Anitha; Kannan, Ranganathan

2013-01-01

Objective: This study was designed to compare the microbiological features of angular cheilitis (AC) in human immunodeficiency virus (HIV) seropositive and HIV seronegative individuals, in a group of south Indians. Materials and Methods: Swabs from oral commissures of 46 patients were obtained and inoculated on to Sabouraud's dextrose agar (SDA) supplemented with chloramphenicol, blood agar (BA) and MacConkey's agar (MCA) plates and cultured. α-hemolytic Streptococci, Staphylococcus albus, Staphylococcus aureus, Candida species, Klebsiella species and Pseudomonas species were cultured. Candidal colonies were further speciated by the conventional biotyping technique. Results: In AC of HIV seropositive patients Candida albicans and Staphylococcus aureus were more prevalent than that in HIV seronegative patients. Incidentally in patients with CD4 cell count less than 200 there was an increase in the incidence of Candidal and Staphylococcus aureus colonization when compared to patients with CD4 cell count higher than 200. Conclusion: The present study suggests a definite difference in the microbial flora of AC in HIV seropositive patients than that of HIV seronegative population. PMID:24574650

Lung texture in serial thoracic CT scans: Assessment of change introduced by image registration1

PubMed Central

Cunliffe, Alexandra R.; Al-Hallaq, Hania A.; Labby, Zacariah E.; Pelizzari, Charles A.; Straus, Christopher; Sensakovic, William F.; Ludwig, Michelle; Armato, Samuel G.

2012-01-01

Purpose: The aim of this study was to quantify the effect of four image registration methods on lung texture features extracted from serial computed tomography (CT) scans obtained from healthy human subjects. Methods: Two chest CT scans acquired at different time points were collected retrospectively for each of 27 patients. Following automated lung segmentation, each follow-up CT scan was registered to the baseline scan using four algorithms: (1) rigid, (2) affine, (3) B-splines deformable, and (4) demons deformable. The registration accuracy for each scan pair was evaluated by measuring the Euclidean distance between 150 identified landmarks. On average, 1432 spatially matched 32 × 32-pixel region-of-interest (ROI) pairs were automatically extracted from each scan pair. First-order, fractal, Fourier, Laws’ filter, and gray-level co-occurrence matrix texture features were calculated in each ROI, for a total of 140 features. Agreement between baseline and follow-up scan ROI feature values was assessed by Bland–Altman analysis for each feature; the range spanned by the 95% limits of agreement of feature value differences was calculated and normalized by the average feature value to obtain the normalized range of agreement (nRoA). Features with small nRoA were considered “registration-stable.” The normalized bias for each feature was calculated from the feature value differences between baseline and follow-up scans averaged across all ROIs in every patient. Because patients had “normal” chest CT scans, minimal change in texture feature values between scan pairs was anticipated, with the expectation of small bias and narrow limits of agreement. Results: Registration with demons reduced the Euclidean distance between landmarks such that only 9% of landmarks were separated by ≥1 mm, compared with rigid (98%), affine (95%), and B-splines (90%). Ninety-nine of the 140 (71%) features analyzed yielded nRoA > 50% for all registration methods, indicating that the majority of feature values were perturbed following registration. Nineteen of the features (14%) had nRoA < 15% following demons registration, indicating relative feature value stability. Student's t-tests showed that the nRoA of these 19 features was significantly larger when rigid, affine, or B-splines registration methods were used compared with demons registration. Demons registration yielded greater normalized bias in feature value change than B-splines registration, though this difference was not significant (p = 0.15). Conclusions: Demons registration provided higher spatial accuracy between matched anatomic landmarks in serial CT scans than rigid, affine, or B-splines algorithms. Texture feature changes calculated in healthy lung tissue from serial CT scans were smaller following demons registration compared with all other algorithms. Though registration altered the values of the majority of texture features, 19 features remained relatively stable after demons registration, indicating their potential for detecting pathologic change in serial CT scans. Combined use of accurate deformable registration using demons and texture analysis may allow for quantitative evaluation of local changes in lung tissue due to disease progression or treatment response. PMID:22894392

Comparing ground-penetrating radar (GPR) techniques in 18th-century yard spaces

NASA Astrophysics Data System (ADS)

Carducci, Christiane M.

Yards surrounding historical homesteads are the liminal space between private houses and public space, and contain artifactural and structural remains that help us understand how the residents interfaced with the world. Comparing different yards means collecting reliable evidence, and what is missing is just as important as what is found. Excavations can rely on randomly placed 50-cm shovel test pits to locate features, but this can miss important features. Shallow geophysics, in particular ground-penetrating radar (GPR), can be used to identify features and reliably and efficiently collect evidence. GPR is becoming more integrated into archaeological investigations due to the potential to quickly and nondestructively identify archaeological features and to recent advancements in processing software that make these methods more user-friendly. The most efficacious GPR surveys must take into consideration what is expected to be below the surface, what features look like in GPR outputs, the best methods for detecting features, and the limitations of GPR surveys. Man-made landscape features are expected to have existed within yard spaces, and the alteration of these features shows how the domestic economy of the residence changed through time. This study creates an inventory of these features. By producing a standardized sampling method for GPR in yard spaces, archaeologists can quickly map subsurface features and carry out broad comparisons between yards. To determine the most effective sampling method, several GPR surveys were conducted at the 18th-century Durant-Kenrick House in Newton, Massachusetts, using varied line spacing, line direction, and bin size. Examples of the GPR signatures of features, obtained using GPR-Slice software, from the Durant-Kenrick House and similar sites were analyzed. The efficacy of each method was determined based on the number of features distinguished, clarity of the results, and the time involved. The survey at Newton showed that ground surface conditions are extremely important when using GPR. Furthermore, GPR and archaeological excavations together provide the most complete interpretation because GPR has the ability to detect large-scale features that might be missed with test units, while excavation provides more detailed information, finds small-scale objects, and can be used to test false negatives seen in GPR surveys.

Sex differences and within-family associations in the broad autism phenotype

PubMed Central

Klusek, Jessica; Losh, Molly; Martin, Gary E

2013-01-01

While there is a strong sex bias in the presentation of autism, it is unknown whether this bias is also present in subclinical manifestations of autism among relatives, or the broad autism phenotype (BAP). This study examined this question, and investigated patterns of co-occurrence of BAP traits within families of individuals with autism. Pragmatic language and personality features of the BAP were studied in 42 fathers and 50 mothers of individuals with autism using direct assessment tools used in prior family studies of the BAP. Higher rates of aloof personality style were detected among fathers, while no sex differences were detected for other BAP traits. Within individuals, pragmatic language features were associated with the social personality styles of the BAP in mothers but not fathers. A number of BAP features were correlated within spousal pairs. Finally, associations were detected between paternal BAP characteristics and the severity of children’s autism symptoms in all three domains (social, communication, and repetitive behaviors). Mother-child correlations were detected for aspects of communication only. Together, findings suggest that most features of the BAP express comparably in males and females, and raise some specific questions about how such features might inform studies of the genetic basis of autism. PMID:23188882

The role of experiential avoidance, psychopathology, and borderline personality features in experiencing positive emotions: a path analysis.

PubMed

Jacob, Gitta A; Ower, Nicole; Buchholz, Angela

2013-03-01

Experiential avoidance (EA) is an important factor in maintaining different forms of psychopathology including borderline personality pathology (BPD). So far little is known about the functions of EA, BPD features and general psychopathology for positive emotions. In this study we investigated three different anticipated pathways of their influence on positive emotions. A total of 334 subjects varying in general psychopathology &/or BPD features completed an online survey including self-ratings of BPD features, psychopathology, negative and positive emotions, and EA. Measures of positive emotions included both a general self-rating (PANAS) and emotional changes induced by two positive movie clips. Data were analyzed by means of path analysis. In comparing the three path models, one model was found clearly superior: In this model, EA acts as a mediator of the influence of psychopathology, BPD features, and negative emotions in the prediction of both measures of positive emotions. EA plays a central role in maintaining lack of positive emotions. Therapeutic implications and study limitations are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Perception Of "Features" And "Objects": Applications To The Design Of Instrument Panel Displays

NASA Astrophysics Data System (ADS)

Poynter, Douglas; Czarnomski, Alan J.

1988-10-01

An experiment was conducted to determine whether socalled feature displays allow for faster and more accurate processing compared to object displays. Previous psychological studies indicate that features can be processed in parallel across the visual field, whereas objects must be processed one at a time with the aid of attentional focus. Numbers and letters are examples of objects; line orientation and color are examples of features. In this experiment, subjects were asked to search displays composed of up to 16 elements for the presence of specific elements. The ability to detect, localize, and identify targets was influenced by display format. Digital errors increased with the number of elements, the number of targets, and the distance of the target from the fixation point. Line orientation errors increased only with the number of targets. Several other display types were evaluated, and each produced a pattern of errors similar to either digital or line orientation format. Results of the study were discussed in terms of Feature Integration Theory, which distinguishes between elements that are processed with parallel versus serial mechanisms.

Fizzy: feature subset selection for metagenomics.

PubMed

Ditzler, Gregory; Morrison, J Calvin; Lan, Yemin; Rosen, Gail L

2015-11-04

Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α- & β-diversity. Feature subset selection--a sub-field of machine learning--can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate between age groups in the human gut microbiome. We have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets. We have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.

Fizzy. Feature subset selection for metagenomics

DOE PAGES

Ditzler, Gregory; Morrison, J. Calvin; Lan, Yemin; ...

2015-11-04

Background: Some of the current software tools for comparative metagenomics provide ecologists with the ability to investigate and explore bacterial communities using α– & β–diversity. Feature subset selection – a sub-field of machine learning – can also provide a unique insight into the differences between metagenomic or 16S phenotypes. In particular, feature subset selection methods can obtain the operational taxonomic units (OTUs), or functional features, that have a high-level of influence on the condition being studied. For example, in a previous study we have used information-theoretic feature selection to understand the differences between protein family abundances that best discriminate betweenmore » age groups in the human gut microbiome. Results: We have developed a new Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. We demonstrate the software tools capabilities on publicly available datasets. Conclusions: We have made the software implementation of Fizzy available to the public under the GNU GPL license. The standalone implementation can be found at http://github.com/EESI/Fizzy.« less

Feature-based registration of historical aerial images by Area Minimization

NASA Astrophysics Data System (ADS)

Nagarajan, Sudhagar; Schenk, Toni

2016-06-01

The registration of historical images plays a significant role in assessing changes in land topography over time. By comparing historical aerial images with recent data, geometric changes that have taken place over the years can be quantified. However, the lack of ground control information and precise camera parameters has limited scientists' ability to reliably incorporate historical images into change detection studies. Other limitations include the methods of determining identical points between recent and historical images, which has proven to be a cumbersome task due to continuous land cover changes. Our research demonstrates a method of registering historical images using Time Invariant Line (TIL) features. TIL features are different representations of the same line features in multi-temporal data without explicit point-to-point or straight line-to-straight line correspondence. We successfully determined the exterior orientation of historical images by minimizing the area formed between corresponding TIL features in recent and historical images. We then tested the feasibility of the approach with synthetic and real data and analyzed the results. Based on our analysis, this method shows promise for long-term 3D change detection studies.

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

Aging, selective attention, and feature integration.

PubMed

Plude, D J; Doussard-Roosevelt, J A

1989-03-01

This study used feature-integration theory as a means of determining the point in processing at which selective attention deficits originate. The theory posits an initial stage of processing in which features are registered in parallel and then a serial process in which features are conjoined to form complex stimuli. Performance of young and older adults on feature versus conjunction search is compared. Analyses of reaction times and error rates suggest that elderly adults in addition to young adults, can capitalize on the early parallel processing stage of visual information processing, and that age decrements in visual search arise as a result of the later, serial stage of processing. Analyses of a third, unconfounded, conjunction search condition reveal qualitatively similar modes of conjunction search in young and older adults. The contribution of age-related data limitations is found to be secondary to the contribution of age decrements in selective attention.

Eye movement identification based on accumulated time feature

NASA Astrophysics Data System (ADS)

Guo, Baobao; Wu, Qiang; Sun, Jiande; Yan, Hua

2017-06-01

Eye movement is a new kind of feature for biometrical recognition, it has many advantages compared with other features such as fingerprint, face, and iris. It is not only a sort of static characteristics, but also a combination of brain activity and muscle behavior, which makes it effective to prevent spoofing attack. In addition, eye movements can be incorporated with faces, iris and other features recorded from the face region into multimode systems. In this paper, we do an exploring study on eye movement identification based on the eye movement datasets provided by Komogortsev et al. in 2011 with different classification methods. The time of saccade and fixation are extracted from the eye movement data as the eye movement features. Furthermore, the performance analysis was conducted on different classification methods such as the BP, RBF, ELMAN and SVM in order to provide a reference to the future research in this field.

Accuracy of computed tomographic features in differentiating intestinal tuberculosis from Crohn's disease: a systematic review with meta-analysis.

PubMed

Kedia, Saurabh; Sharma, Raju; Sreenivas, Vishnubhatla; Madhusudhan, Kumble Seetharama; Sharma, Vishal; Bopanna, Sawan; Pratap Mouli, Venigalla; Dhingra, Rajan; Yadav, Dawesh Prakash; Makharia, Govind; Ahuja, Vineet

2017-04-01

Abdominal computed tomography (CT) can noninvasively image the entire gastrointestinal tract and assess extraintestinal features that are important in differentiating Crohn's disease (CD) and intestinal tuberculosis (ITB). The present meta-analysis pooled the results of all studies on the role of CT abdomen in differentiating between CD and ITB. We searched PubMed and Embase for all publications in English that analyzed the features differentiating between CD and ITB on abdominal CT. The features included comb sign, necrotic lymph nodes, asymmetric bowel wall thickening, skip lesions, fibrofatty proliferation, mural stratification, ileocaecal area, long segment, and left colonic involvements. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated for all the features. Symmetric receiver operating characteristic curve was plotted for features present in >3 studies. Heterogeneity and publication bias was assessed and sensitivity analysis was performed by excluding studies that compared features on conventional abdominal CT instead of CT enterography (CTE). We included 6 studies (4 CTE, 1 conventional abdominal CT, and 1 CTE+conventional abdominal CT) involving 417 and 195 patients with CD and ITB, respectively. Necrotic lymph nodes had the highest diagnostic accuracy (sensitivity, 23%; specificity, 100%; DOR, 30.2) for ITB diagnosis, and comb sign (sensitivity, 82%; specificity, 81%; DOR, 21.5) followed by skip lesions (sensitivity, 86%; specificity, 74%; DOR, 16.5) had the highest diagnostic accuracy for CD diagnosis. On sensitivity analysis, the diagnostic accuracy of other features excluding asymmetric bowel wall thickening remained similar. Necrotic lymph nodes and comb sign on abdominal CT had the best diagnostic accuracy in differentiating CD and ITB.

Comparative analysis of smoking cessation smartphone applications available in 2012 versus 2014

PubMed Central

Ubhi, Harveen Kaur; Kotz, Daniel; Michie, Susan; van Schayck, Onno C.P.; Sheard, David; Selladurai, Abiram; West, Robert

2016-01-01

Background and aims Smartphone applications (apps) offer a potentially cost-effective and a wide-reach aid to smoking cessation. In 2012, a content analysis of smoking cessation apps suggested that most apps did not adopt behaviour change techniques (BCTs), which according to previous research had suggested would promote higher success rates in quitting smoking. This study examined whether or not, this situation had changed by 2014 for free smoking cessation apps available in the Apple App Store. It also compared the use of engagement and ease-of-use features between the two time points. Methods 137 free apps available in the Apple App Sore in 2014 were coded using an established framework for the presence or absence of evidence-based BCTs, and engagement and ease-of-use features. The results from the 2014 data were compared with a similar exercise conducted on 83 free apps available in 2012. Results BCTs supporting identity change, rewarding abstinence and advising on changing routines were less prevalent in 2014 as compared with 2012 (14.6% vs. 42.2%, 18.2% vs. 48.2%, and 17.5% vs. 24.1%, respectively). Advice on coping with cravings and advice on the use of stop-smoking medication were more prevalent in 2014 as compared with 2012 (27.7% vs. 20.5% and 14.6% vs 3.6%, respectively). The use of recognised engagement features was less common in 2014 than in 2012 (45.3% vs. 69.6%) while ease-of-use features remained very high (94.5% vs. 82.6%). Conclusion There was little evidence of improvement in the use of evidence-based BCTs in free smoking cessation iPhone-based apps between 2012 and 2014. PMID:26950256

Martian Buried Basins and Implications for Characteristics of the Burial Layer and Underlying Surface

NASA Technical Reports Server (NTRS)

Sarid, A. R.; Frey, H. V.; Roark, J. H.

2003-01-01

Deciphering the cratering record on Mars has been challenging because it may reflect the changes in both the population of impactors and in the resurfacing processes on Mars. However, it is possible to determine the breadth of impactors captured in the cratering record. Extensive areas of resurfacing are of particular interest because they likely contain material from various ages in Martian history. By deducing the impact populations in both surface and underlying layers of terrain, it is possible to determine the age of the layers and constrain theories on the development of the Martian surface. However, to do so requires a method of seeing impact features which are no longer visible. Topographic data of Mars, taken by the Mars Orbiter Laser Altimeter (MOLA), has revealed impact features buried by resurfacing processes. These features are often indistinguishable on Viking images of the Martian surface. In this study, gridded MOLA data was analyzed in order to locate buried impact features, also called buried basins, in Syria, Solis, and Sinai Planum just south of Valles Marineris. The population statistics of buried features can be compared to those of visible features in order to determine the age of the underlying material and characteristics of the surface cover. Specifically, if the buried population in the Hesperian terrain is similar to the population of visible features in the Noachian, it would suggest that the underlying terrain is Noachian in age. The buried craters can then be compared to visible Noachian craters to reveal the amount of deterioration of the buried features. These comparisons allow us to explore the morphology of the terrain in the Hesperian region to determine if topographic variations are due to differences in the thickness of the overlying material or are a characteristic of the underlying terrain.

The perceptual saliency of fearful eyes and smiles: A signal detection study

PubMed Central

Saban, Muhammet Ikbal; Rotshtein, Pia

2017-01-01

Facial features differ in the amount of expressive information they convey. Specifically, eyes are argued to be essential for fear recognition, while smiles are crucial for recognising happy expressions. In three experiments, we tested whether expression modulates the perceptual saliency of diagnostic facial features and whether the feature’s saliency depends on the face configuration. Participants were presented with masked facial features or noise at perceptual conscious threshold. The task was to indicate whether eyes (experiments 1-3A) or a mouth (experiment 3B) was present. The expression of the face and its configuration (i.e. spatial arrangement of the features) were manipulated. Experiment 1 compared fearful with neutral expressions, experiments 2 and 3 compared fearful versus happy expressions. The detection accuracy data was analysed using Signal Detection Theory (SDT), to examine the effects of expression and configuration on perceptual precision (d’) and response bias (c), separately. Across all three experiments, fearful eyes were detected better (higher d’) than neutral and happy eyes. Eyes were more precisely detected than mouths, whereas smiles were detected better than fearful mouths. The configuration of the features had no consistent effects across the experiments on the ability to detect expressive features. But facial configuration affected consistently the response bias. Participants used a more liberal criterion for detecting the eyes in canonical configuration and fearful expression. Finally, the power in low spatial frequency of a feature predicted its discriminability index. The results suggest that expressive features are perceptually more salient with a higher d’ due to changes at the low-level visual properties, with emotions and configuration affecting perception through top-down processes, as reflected by the response bias. PMID:28267761

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

2017-04-01

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects.

Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images.

PubMed

Chaddad, Ahmad; Daniel, Paul; Niazi, Tamim

2018-01-01

Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention. This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively. 12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of p  < 0.01 after correction. Combining multiscale texture features lead to a better predictive capacity compared to prediction models based on individual scale features with an average (±SD) classification accuracy of 93.33 (±3.52)%, sensitivity of 88.33 (± 4.12)%, and specificity of 96.89 (± 3.88)%. Entropy was found to be the best classifier feature across all the PT grades with an average of the area under the curve (AUC) value of 91.17, 94.21, 97.70, 100% for ST, BH, IN, and CA, respectively. Our results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade.

Using neuronal populations to study the mechanisms underlying spatial and feature attention

PubMed Central

Cohen, Marlene R.; Maunsell, John H.R.

2012-01-01

Summary Visual attention affects both perception and neuronal responses. Whether the same neuronal mechanisms mediate spatial attention, which improves perception of attended locations, and non-spatial forms of attention has been a subject of considerable debate. Spatial and feature attention have similar effects on individual neurons. Because visual cortex is retinotopically organized, however, spatial attention can co-modulate local neuronal populations, while feature attention generally requires more selective modulation. We compared the effects of feature and spatial attention on local and spatially separated populations by recording simultaneously from dozens of neurons in both hemispheres of V4. Feature and spatial attention affect the activity of local populations similarly, modulating both firing rates and correlations between pairs of nearby neurons. However, while spatial attention appears to act on local populations, feature attention is coordinated across hemispheres. Our results are consistent with a unified attentional mechanism that can modulate the responses of arbitrary subgroups of neurons. PMID:21689604

Graphemes Sharing Phonetic Features Tend to Induce Similar Synesthetic Colors.

PubMed

Kang, Mi-Jeong; Kim, Yeseul; Shin, Ji-Young; Kim, Chai-Youn

2017-01-01

Individuals with grapheme-color synesthesia experience idiosyncratic colors when viewing achromatic letters or digits. Despite large individual differences in grapheme-color association, synesthetes tend to associate graphemes sharing a perceptual feature with similar synesthetic colors. Sound has been suggested as one such feature. In the present study, we investigated whether graphemes of which representative phonemes have similar phonetic features tend to be associated with analogous synesthetic colors. We tested five Korean multilingual synesthetes on a color-matching task using graphemes from Korean, English, and Japanese orthography. We then compared the similarity of synesthetic colors induced by those characters sharing a phonetic feature. Results showed that graphemes associated with the same phonetic feature tend to induce synesthetic color in both within- and cross-script analyses. Moreover, this tendency was consistent for graphemes that are not transliterable into each other as well as graphemes that are. These results suggest that it is the perceptual-i.e., phonetic-properties associated with graphemes, not just conceptual associations such as transliteration, that determine synesthetic color.

Graphemes Sharing Phonetic Features Tend to Induce Similar Synesthetic Colors

PubMed Central

Kang, Mi-Jeong; Kim, Yeseul; Shin, Ji-Young; Kim, Chai-Youn

2017-01-01

Individuals with grapheme-color synesthesia experience idiosyncratic colors when viewing achromatic letters or digits. Despite large individual differences in grapheme-color association, synesthetes tend to associate graphemes sharing a perceptual feature with similar synesthetic colors. Sound has been suggested as one such feature. In the present study, we investigated whether graphemes of which representative phonemes have similar phonetic features tend to be associated with analogous synesthetic colors. We tested five Korean multilingual synesthetes on a color-matching task using graphemes from Korean, English, and Japanese orthography. We then compared the similarity of synesthetic colors induced by those characters sharing a phonetic feature. Results showed that graphemes associated with the same phonetic feature tend to induce synesthetic color in both within- and cross-script analyses. Moreover, this tendency was consistent for graphemes that are not transliterable into each other as well as graphemes that are. These results suggest that it is the perceptual—i.e., phonetic—properties associated with graphemes, not just conceptual associations such as transliteration, that determine synesthetic color. PMID:28348537

The first case series of Chinese patients in Hong Kong with familial Alzheimer's disease compared with those with biomarker-confirmed sporadic late-onset Alzheimer's disease.

PubMed

Shea, Y F; Chu, L W; Lee, S C; Chan, A Ok

2017-12-01

Patients with familial Alzheimer's disease are being increasingly reported in Hong Kong. The objectives of this study were to report the clinical features of these patients, and to compare them with those with biomarker-confirmed sporadic late-onset Alzheimer's disease. All symptomatic Chinese patients with familial Alzheimer's disease who attended Queen Mary Hospital, Memory Clinic between January 1998 and December 2016 were included. Information about clinical features, baseline Mini-Mental State Examination score, and presenting cognitive symptoms or atypical clinical features were collected. Their clinical features were compared with those of 12 patients with sporadic late-onset Alzheimer's disease with cerebrospinal fluid biomarker evidence of Alzheimer's disease and 14 patients with late-onset Alzheimer's disease and positive amyloid loading on Pittsburgh compound B imaging. There were three families with familial Alzheimer's disease among whom eight family members were affected. The mean (± standard deviation) age of onset and the Mini-Mental State Examination score were 48.4 ± 7.7 years and 7.9 ± 9.2, respectively. Compared with the sporadic late-onset Alzheimer's disease patients, those with familial Alzheimer's disease had an earlier age of onset and presentation (both P<0.001) and received the correct diagnosis later (median [interquartile range], 7.5 [5.3-14.5] vs 2 [1.0-3.3] years; P<0.001). Patients with familial disease had a lower Mini-Mental State Examination score at presentation than those having late-onset Alzheimer's disease (mean, 7.9 ± 9.2 vs 17.6 ± 7.2; P=0.01). They also had fewer delusions, and less dysphoria and irritability (0% vs 41.7%, 0% vs 50% and 0% vs 54.2%; P=0.04, 0.01 and 0.01, respectively). There was a trend of less frequent amnesia among patients with familial Alzheimer's disease compared with those having late-onset Alzheimer's disease (75% vs 100%; P=0.05). Clinical features differ for patients with familial Alzheimer's disease compared with those with late-onset Alzheimer's disease. There is a delay in diagnosis. Promotion of public awareness of familial Alzheimer's disease is much needed.

Design of combinatorial libraries for the exploration of virtual hits from fragment space searches with LoFT.

PubMed

Lessel, Uta; Wellenzohn, Bernd; Fischer, J Robert; Rarey, Matthias

2012-02-27

A case study is presented illustrating the design of a focused CDK2 library. The scaffold of the library was detected by a feature trees search in a fragment space based on reactions from combinatorial chemistry. For the design the software LoFT (Library optimizer using Feature Trees) was used. The special feature called FTMatch was applied to restrict the parts of the queries where the reagents are permitted to match. This way a 3D scoring function could be simulated. Results were compared with alternative designs by GOLD docking and ROCS 3D alignments.

Ambient response of a unique performance-based design building with dynamic response modification features

USGS Publications Warehouse

Çelebi, Mehmet; Huang, Moh; Shakal, Antony; Hooper, John; Klemencic, Ron

2012-01-01

A 64-story, performance-based design building with reinforced concrete core shear-walls and unique dynamic response modification features (tuned liquid sloshing dampers and buckling-restrained braces) has been instrumented with a monitoring array of 72 channels of accelerometers. Ambient vibration data recorded are analyzed to identify modes and associated frequencies and damping. The low-amplitude dynamic characteristics are considerably different than those computed from design analyses, but serve as a baseline against which to compare with future strong shaking responses. Such studies help to improve our understanding of the effectiveness of the added features to the building and help improve designs in the future.

Combustion of Gaseous Fuels with High Temperature Air in Normal- and Micro-gravity Conditions

NASA Technical Reports Server (NTRS)

Wang, Y.; Gupta, A. K.

2001-01-01

The objective of this study is determine the effect of air preheat temperature on flame characteristics in normal and microgravity conditions. We have obtained qualitative (global flame features) and some quantitative information on the features of flames using high temperature combustion air under normal gravity conditions with propane and methane as the fuels. This data will be compared with the data under microgravity conditions. The specific focus under normal gravity conditions has been on determining the global flame features as well as the spatial distribution of OH, CH, and C2 from flames using high temperature combustion air at different equivalence ratio.

Internet marketing directed at children on food and restaurant websites in two policy environments.

PubMed

Kent, M Potvin; Dubois, L; Kent, E A; Wanless, A J

2013-04-01

Food and beverage marketing has been associated with childhood obesity yet little research has examined the influence of advertising policy on children's exposure to food/beverage marketing on the Internet. The purpose of this study was to assess the influence of Quebec's Consumer Protection Act and the self-regulatory Canadian Children's Food and Beverage Advertising Initiative (CAI) on food manufacturer and restaurant websites in Canada. A content analysis of 147 French and English language food and restaurant websites was undertaken. The presence of child-directed content was assessed and an analysis of marketing features, games and activities, child protection features, and the promotion of healthy lifestyle messages was then examined on those sites with child-directed content. There were statistically no fewer French language websites (n = 22) with child-directed content compared to English language websites (n = 27). There were no statistically significant differences in the number of the various marketing features, or in the average number of marketing features between the English and French websites. There were no fewer CAI websites (n = 14) with child-directed content compared to non-CAI websites (n = 13). The CAI sites had more healthy lifestyle messages and child protection features compared to the non-CAI sites. Systematic surveillance of the Consumer Protection Act in Quebec is recommended. In the rest of Canada, the CAI needs to be significantly expanded or replaced by regulatory measures to adequately protect children from the marketing of foods/beverages high in fat, sugar, and sodium on the Internet. Copyright © 2012 The Obesity Society.

Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers

PubMed Central

Racette, Lyne; Chiou, Christine Y.; Hao, Jiucang; Bowd, Christopher; Goldbaum, Michael H.; Zangwill, Linda M.; Lee, Te-Won; Weinreb, Robert N.; Sample, Pamela A.

2009-01-01

Purpose To investigate whether combining optic disc topography and short-wavelength automated perimetry (SWAP) data improves the diagnostic accuracy of relevance vector machine (RVM) classifiers for detecting glaucomatous eyes compared to using each test alone. Methods One eye of 144 glaucoma patients and 68 healthy controls from the Diagnostic Innovations in Glaucoma Study were included. RVM were trained and tested with cross-validation on optimized (backward elimination) SWAP features (thresholds plus age; pattern deviation (PD); total deviation (TD)) and on Heidelberg Retina Tomograph II (HRT) optic disc topography features, independently and in combination. RVM performance was also compared to two HRT linear discriminant functions (LDF) and to SWAP mean deviation (MD) and pattern standard deviation (PSD). Classifier performance was measured by the area under the receiver operating characteristic curves (AUROCs) generated for each feature set and by the sensitivities at set specificities of 75%, 90% and 96%. Results RVM trained on combined HRT and SWAP thresholds plus age had significantly higher AUROC (0.93) than RVM trained on HRT (0.88) and SWAP (0.76) alone. AUROCs for the SWAP global indices (MD: 0.68; PSD: 0.72) offered no advantage over SWAP thresholds plus age, while the LDF AUROCs were significantly lower than RVM trained on the combined SWAP and HRT feature set and on HRT alone feature set. Conclusions Training RVM on combined optimized HRT and SWAP data improved diagnostic accuracy compared to training on SWAP and HRT parameters alone. Future research may identify other combinations of tests and classifiers that can also improve diagnostic accuracy. PMID:19528827

An empirical assessment of which inland floods can be managed

USGS Publications Warehouse

Mogollón, Beatriz; Frimpong, Emmanuel A.; Hoegh, Andrew B.; Angermeier, Paul

2016-01-01

Riverine flooding is a significant global issue. Although it is well documented that the influence of landscape structure on floods decreases as flood size increases, studies that define a threshold flood-return period, above which landscape features such as topography, land cover and impoundments can curtail floods, are lacking. Further, the relative influences of natural versus built features on floods is poorly understood. Assumptions about the types of floods that can be managed have considerable implications for the cost-effectiveness of decisions to invest in transforming land cover (e.g., reforestation) and in constructing structures (e.g., storm-water ponds) to control floods. This study defines parameters of floods for which changes in landscape structure can have an impact. We compare nine flood-return periods across 31 watersheds with widely varying topography and land cover in the southeastern United States, using long-term hydrologic records (≥20 years). We also assess the effects of built flow-regulating features (best management practices and artificial water bodies) on selected flood metrics across urban watersheds. We show that landscape features affect magnitude and duration of only those floods with return periods ≤10 years, which suggests that larger floods cannot be managed effectively by manipulating landscape structure. Overall, urban watersheds exhibited larger (270 m3/s) but quicker (0.41 days) floods than non-urban watersheds (50 m3/s and 1.5 days). However, urban watersheds with more flow-regulating features had lower flood magnitudes (154 m3/s), but similar flood durations (0.55 days), compared to urban watersheds with fewer flow-regulating features (360 m3/s and 0.23 days). Our analysis provides insight into the magnitude, duration and count of floods that can be curtailed by landscape structure and its management. Our findings are relevant to other areas with similar climate, topography, and land use, and can help ensure that investments in flood management are made wisely after considering the limitations of landscape features to regulate floods.

Staff Nurse Perceptions of Open-Pod and Single Family Room NICU Designs on Work Environment and Patient Care.

PubMed

Winner-Stoltz, Regina; Lengerich, Alexander; Hench, Anna Jeanine; OʼMalley, Janet; Kjelland, Kimberly; Teal, Melissa

2018-06-01

Neonatal intensive care units have historically been constructed as open units or multiple-bed bays, but since the 1990s, the trend has been toward single family room (SFR) units. The SFR design has been found to promote family-centered care and to improve patient outcomes and safety. The impact of the SFR design NICU on staff, however, has been mixed. The purposes of this study were to compare staff nurse perceptions of their work environments in an open-pod versus an SFR NICU and to compare staff nurse perceptions of the impact of 2 NICU designs on the care they provide for patients/families. A prospective cohort study was conducted. Questionnaires were completed at 6 months premove and again at 3, 9, and 15 months postmove. A series of 1-way analyses of variance were conducted to compare each group in each of the 8 domains. Open-ended questions were evaluated using thematic analysis. The SFR design is favorable in relation to environmental quality and control of primary workspace, privacy and interruption, unit features supporting individual work, and unit features supporting teamwork; the open-pod design is preferable in relation to walking. Incorporating design features that decrease staff isolation and walking and ensuring both patient and staff safety and security are important considerations. Further study is needed on unit design at a microlevel including headwall design and human milk mixing areas, as well as on workflow processes.

Question analysis for Indonesian comparative question

NASA Astrophysics Data System (ADS)

Saelan, A.; Purwarianti, A.; Widyantoro, D. H.

2017-01-01

Information seeking is one of human needs today. Comparing things using search engine surely take more times than search only one thing. In this paper, we analyzed comparative questions for comparative question answering system. Comparative question is a question that comparing two or more entities. We grouped comparative questions into 5 types: selection between mentioned entities, selection between unmentioned entities, selection between any entity, comparison, and yes or no question. Then we extracted 4 types of information from comparative questions: entity, aspect, comparison, and constraint. We built classifiers for classification task and information extraction task. Features used for classification task are bag of words, whether for information extraction, we used lexical, 2 previous and following words lexical, and previous label as features. We tried 2 scenarios: classification first and extraction first. For classification first, we used classification result as a feature for extraction. Otherwise, for extraction first, we used extraction result as features for classification. We found that the result would be better if we do extraction first before classification. For the extraction task, classification using SMO gave the best result (88.78%), while for classification, it is better to use naïve bayes (82.35%).

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

Comparing experts and novices in Martian surface feature change detection and identification

NASA Astrophysics Data System (ADS)

Wardlaw, Jessica; Sprinks, James; Houghton, Robert; Muller, Jan-Peter; Sidiropoulos, Panagiotis; Bamford, Steven; Marsh, Stuart

2018-02-01

Change detection in satellite images is a key concern of the Earth Observation field for environmental and climate change monitoring. Satellite images also provide important clues to both the past and present surface conditions of other planets, which cannot be validated on the ground. With the volume of satellite imagery continuing to grow, the inadequacy of computerised solutions to manage and process imagery to the required professional standard is of critical concern. Whilst studies find the crowd sourcing approach suitable for the counting of impact craters in single images, images of higher resolution contain a much wider range of features, and the performance of novices in identifying more complex features and detecting change, remains unknown. This paper presents a first step towards understanding whether novices can identify and annotate changes in different geomorphological features. A website was developed to enable visitors to flick between two images of the same location on Mars taken at different times and classify 1) if a surface feature changed and if so, 2) what feature had changed from a pre-defined list of six. Planetary scientists provided ;expert; data against which classifications made by novices could be compared when the project subsequently went public. Whilst no significant difference was found in images identified with surface changes by expert and novices, results exhibited differences in consensus within and between experts and novices when asked to classify the type of change. Experts demonstrated higher levels of agreement in classification of changes as dust devil tracks, slope streaks and impact craters than other features, whilst the consensus of novices was consistent across feature types; furthermore, the level of consensus amongst regardless of feature type. These trends are secondary to the low levels of consensus found, regardless of feature type or classifier expertise. These findings demand the attention of researchers who want to use crowd-sourcing for similar scientific purposes, particularly for the supervised training of computer algorithms, and inform the scope and design of future projects.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

Applying a new unequally weighted feature fusion method to improve CAD performance of classifying breast lesions

NASA Astrophysics Data System (ADS)

Zargari Khuzani, Abolfazl; Danala, Gopichandh; Heidari, Morteza; Du, Yue; Mashhadi, Najmeh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Higher recall rates are a major challenge in mammography screening. Thus, developing computer-aided diagnosis (CAD) scheme to classify between malignant and benign breast lesions can play an important role to improve efficacy of mammography screening. Objective of this study is to develop and test a unique image feature fusion framework to improve performance in classifying suspicious mass-like breast lesions depicting on mammograms. The image dataset consists of 302 suspicious masses detected on both craniocaudal and mediolateral-oblique view images. Amongst them, 151 were malignant and 151 were benign. The study consists of following 3 image processing and feature analysis steps. First, an adaptive region growing segmentation algorithm was used to automatically segment mass regions. Second, a set of 70 image features related to spatial and frequency characteristics of mass regions were initially computed. Third, a generalized linear regression model (GLM) based machine learning classifier combined with a bat optimization algorithm was used to optimally fuse the selected image features based on predefined assessment performance index. An area under ROC curve (AUC) with was used as a performance assessment index. Applying CAD scheme to the testing dataset, AUC was 0.75+/-0.04, which was significantly higher than using a single best feature (AUC=0.69+/-0.05) or the classifier with equally weighted features (AUC=0.73+/-0.05). This study demonstrated that comparing to the conventional equal-weighted approach, using an unequal-weighted feature fusion approach had potential to significantly improve accuracy in classifying between malignant and benign breast masses.

Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.

PubMed

Larue, Ruben T H M; Defraene, Gilles; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

2017-02-01

Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

PubMed

Sun, Wenqing; Zheng, Bin; Qian, Wei

2017-10-01

This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

Identification of Location Specific Feature Points in a Cardiac Cycle Using a Novel Seismocardiogram Spectrum System.

PubMed

Lin, Wen-Yen; Chou, Wen-Cheng; Chang, Po-Cheng; Chou, Chung-Chuan; Wen, Ming-Shien; Ho, Ming-Yun; Lee, Wen-Chen; Hsieh, Ming-Jer; Lin, Chung-Chih; Tsai, Tsai-Hsuan; Lee, Ming-Yih

2018-03-01

Seismocardiogram (SCG) or mechanocardiography is a noninvasive cardiac diagnostic method; however, previous studies used only a single sensor to detect cardiac mechanical activities that will not be able to identify location-specific feature points in a cardiac cycle corresponding to the four valvular auscultation locations. In this study, a multichannel SCG spectrum measurement system was proposed and examined for cardiac activity monitoring to overcome problems like, position dependency, time delay, and signal attenuation, occurring in traditional single-channel SCG systems. ECG and multichannel SCG signals were simultaneously recorded in 25 healthy subjects. Cardiac echocardiography was conducted at the same time. SCG traces were analyzed and compared with echocardiographic images for feature point identification. Fifteen feature points were identified in the corresponding SCG traces. Among them, six feature points, including left ventricular lateral wall contraction peak velocity, septal wall contraction peak velocity, transaortic peak flow, transpulmonary peak flow, transmitral ventricular relaxation flow, and transmitral atrial contraction flow were identified. These new feature points were not observed in previous studies because the single-channel SCG could not detect the location-specific signals from other locations due to time delay and signal attenuation. As the results, the multichannel SCG spectrum measurement system can record the corresponding cardiac mechanical activities with location-specific SCG signals and six new feature points were identified with the system. This new modality may help clinical diagnoses of valvular heart diseases and heart failure in the future.

Spatial-temporal features of thermal images for Carpal Tunnel Syndrome detection

NASA Astrophysics Data System (ADS)

Estupinan Roldan, Kevin; Ortega Piedrahita, Marco A.; Benitez, Hernan D.

2014-02-01

Disorders associated with repeated trauma account for about 60% of all occupational illnesses, Carpal Tunnel Syndrome (CTS) being the most consulted today. Infrared Thermography (IT) has come to play an important role in the field of medicine. IT is non-invasive and detects diseases based on measuring temperature variations. IT represents a possible alternative to prevalent methods for diagnosis of CTS (i.e. nerve conduction studies and electromiography). This work presents a set of spatial-temporal features extracted from thermal images taken in healthy and ill patients. Support Vector Machine (SVM) classifiers test this feature space with Leave One Out (LOO) validation error. The results of the proposed approach show linear separability and lower validation errors when compared to features used in previous works that do not account for temperature spatial variability.

Benefits and Pitfalls of Multimedia and Interactive Features in Technology-Enhanced Storybooks

PubMed Central

Takacs, Zsofia K.; Swart, Elise K.; Bus, Adriana G.

2015-01-01

A meta-analysis was conducted on the effects of technology-enhanced stories for young children’s literacy development when compared to listening to stories in more traditional settings like storybook reading. A small but significant additional benefit of technology was found for story comprehension (g+ = 0.17) and expressive vocabulary (g+ = 0.20), based on data from 2,147 children in 43 studies. When investigating the different characteristics of technology-enhanced stories, multimedia features like animated pictures, music, and sound effects were found beneficial. In contrast, interactive elements like hotspots, games, and dictionaries were found to be distracting. Especially for children disadvantaged because of less stimulating family environments, multimedia features were helpful and interactive features were detrimental. Findings are discussed from the perspective of cognitive processing theories. PMID:26640299

Benefits and Pitfalls of Multimedia and Interactive Features in Technology-Enhanced Storybooks: A Meta-Analysis.

PubMed

Takacs, Zsofia K; Swart, Elise K; Bus, Adriana G

2015-12-01

A meta-analysis was conducted on the effects of technology-enhanced stories for young children's literacy development when compared to listening to stories in more traditional settings like storybook reading. A small but significant additional benefit of technology was found for story comprehension (g+ = 0.17) and expressive vocabulary (g+ = 0.20), based on data from 2,147 children in 43 studies. When investigating the different characteristics of technology-enhanced stories, multimedia features like animated pictures, music, and sound effects were found beneficial. In contrast, interactive elements like hotspots, games, and dictionaries were found to be distracting. Especially for children disadvantaged because of less stimulating family environments, multimedia features were helpful and interactive features were detrimental. Findings are discussed from the perspective of cognitive processing theories.

Emotion computing using Word Mover's Distance features based on Ren_CECps.

PubMed

Ren, Fuji; Liu, Ning

2018-01-01

In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover's Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.

Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions.

PubMed

Yu, Kaixin; Wang, Xuetong; Li, Qiongling; Zhang, Xiaohui; Li, Xinwei; Li, Shuyu

2018-01-01

Morphological brain network plays a key role in investigating abnormalities in neurological diseases such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, most of the morphological brain network construction methods only considered a single morphological feature. Each type of morphological feature has specific neurological and genetic underpinnings. A combination of morphological features has been proven to have better diagnostic performance compared with a single feature, which suggests that an individual morphological brain network based on multiple morphological features would be beneficial in disease diagnosis. Here, we proposed a novel method to construct individual morphological brain networks for two datasets by calculating the exponential function of multivariate Euclidean distance as the evaluation of similarity between two regions. The first dataset included 24 healthy subjects who were scanned twice within a 3-month period. The topological properties of these brain networks were analyzed and compared with previous studies that used different methods and modalities. Small world property was observed in all of the subjects, and the high reproducibility indicated the robustness of our method. The second dataset included 170 patients with MCI (86 stable MCI and 84 progressive MCI cases) and 169 normal controls (NC). The edge features extracted from the individual morphological brain networks were used to distinguish MCI from NC and separate MCI subgroups (progressive vs. stable) through the support vector machine in order to validate our method. The results showed that our method achieved an accuracy of 79.65% (MCI vs. NC) and 70.59% (stable MCI vs. progressive MCI) in a one-dimension situation. In a multiple-dimension situation, our method improved the classification performance with an accuracy of 80.53% (MCI vs. NC) and 77.06% (stable MCI vs. progressive MCI) compared with the method using a single feature. The results indicated that our method could effectively construct an individual morphological brain network based on multiple morphological features and could accurately discriminate MCI from NC and stable MCI from progressive MCI, and may provide a valuable tool for the investigation of individual morphological brain networks.

Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest.

PubMed

Du, Xiuquan; Hu, Changlin; Yao, Yu; Sun, Shiwei; Zhang, Yanping

2017-12-12

In bioinformatics, exon skipping (ES) event prediction is an essential part of alternative splicing (AS) event analysis. Although many methods have been developed to predict ES events, a solution has yet to be found. In this study, given the limitations of machine learning algorithms with RNA-Seq data or genome sequences, a new feature, called RS (RNA-seq and sequence) features, was constructed. These features include RNA-Seq features derived from the RNA-Seq data and sequence features derived from genome sequences. We propose a novel Rotation Forest classifier to predict ES events with the RS features (RotaF-RSES). To validate the efficacy of RotaF-RSES, a dataset from two human tissues was used, and RotaF-RSES achieved an accuracy of 98.4%, a specificity of 99.2%, a sensitivity of 94.1%, and an area under the curve (AUC) of 98.6%. When compared to the other available methods, the results indicate that RotaF-RSES is efficient and can predict ES events with RS features.

Comparison expert and novice scan behavior for using e-learning

NASA Astrophysics Data System (ADS)

Novita Sari, Felisia; Insap Santosa, Paulus; Wibirama, Sunu

2017-06-01

E-Learning is an important media that an educational institution must have. Successful information design for e-learning depends on its user's characteristics. This study explores differences between novice and expert users' eye movement data. This differences between expert and novice users were compared and identified based on gaze features. Each participant must do three main tasks of e-learning. This paper gives the result that there are differences between gaze features of experts and novices.

Design and Synthesis of Mannich bases as Benzimidazole Derivatives as Analgesic Agents.

PubMed

Datar, Prasanna A; Limaye, Saleel A

2015-01-01

Mannich bases were selected for 2D QSAR study to derive meaningful relationship between the structural features and analgesic activity. Using the knowledge of important features a novel series was designed to obtain improved analgesic activity. A series of novel Mannich bases 1-(N-substituted amino)methyl]-2-substituted benzimidazole derivatives were synthesized and were screened for analgesic activity. Some of these compounds showed promising analgesic activity when compared with the standard drug diclofenac sodium.

The Precedence of Global Features in the Perception of Map Symbols

DTIC Science & Technology

1988-06-01

be continually updated. The present study evaluated the feasibility of a serial model of visual processing. By comparing performance between a symbol...symbols, is based on a " filter - ing" procedure, consisting of a series of passive-to-active or global- to-local stages. Navon (1977, 1981a) has proposed a...packages or segments. This advances the earlier, static feature aggregation ap- proaches to comprise a "figure." According to the global precedence model

Study of the isotopic features of Swan bands in comets

NASA Technical Reports Server (NTRS)

Krishna Swamy, K. S.

1987-01-01

It is shown from a detailed statistical equilibrium calculation of the (C-12)(C-13) molecule that the interpretation of the observed intensities of Swan bands of the normal and the isotopic molecule of C2 in terms of the abundance ratio of C-12 and C-13 is a reasonable one. The synthetic profile of some isotopic features in the (0.0) Swan band is compared with the observed profiles for comet West.

Prescribing patterns of psychotropic medications and clinical features in patients with major depressive disorder with and without comorbid dysthymia in China.

PubMed

Feng, Yuan; Sha, Sha; Hu, Chen; Wang, Gang; Ungvari, Gabor S; Chiu, Helen F K; Ng, Chee H; Si, Tian-Mei; Chen, Da-Fang; Fang, Yi-Ru; Lu, Zheng; Yang, Hai-Chen; Hu, Jian; Chen, Zhi-Yu; Huang, Yi; Sun, Jing; Wang, Xiao-Ping; Li, Hui-Chun; Zhang, Jin-Bei; Xiang, Yu-Tao

2017-03-01

Little has been reported about the demographic and clinical features of major depressive disorder (MDD) with comorbid dysthymia in Chinese patients. This study examined the frequency of comorbid dysthymia in Chinese MDD patients together with the demographic and clinical correlates and prescribing patterns of psychotropic drugs. Consecutively collected sample of 1178 patients with MDD were examined in 13 major psychiatric hospitals in China. Patients' demographic and clinical characteristics and psychotropic drugs prescriptions were recorded using a standardized protocol and data collection procedure. The diagnosis of dysthymia was established using the Mini International Neuropsychiatric Interview. Medications ascertained included antidepressants, antipsychotics, benzodiazepines, and mood stabilizers. One hundred and three (8.7%) patients fulfilled criteria for dysthymia. In multiple logistic regression analyses, compared to non-dysthymia counterparts, MDD patients with dysthymia had more depressive episodes with atypical features including increased appetite, sleep, and weight gain, more frequent lifetime depressive episodes, and less likelihood of family history of psychiatric disorders. There was no significant difference in the pattern of psychotropic prescription between the 2 groups. There are important differences in the demographic and clinical features of comorbid dysthymia in Chinese MDD patients compared with previous reports. The clinical profile found in this study has implications for treatment decisions. © 2016 John Wiley & Sons Australia, Ltd.

McTwo: a two-step feature selection algorithm based on maximal information coefficient.

PubMed

Ge, Ruiquan; Zhou, Manli; Luo, Youxi; Meng, Qinghan; Mai, Guoqin; Ma, Dongli; Wang, Guoqing; Zhou, Fengfeng

2016-03-23

High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets. This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature. McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.

Assessing the performance of quantitative image features on early stage prediction of treatment effectiveness for ovary cancer patients: a preliminary investigation

NASA Astrophysics Data System (ADS)

Zargari, Abolfazl; Du, Yue; Thai, Theresa C.; Gunderson, Camille C.; Moore, Kathleen; Mannel, Robert S.; Liu, Hong; Zheng, Bin; Qiu, Yuchen

2018-02-01

The objective of this study is to investigate the performance of global and local features to better estimate the characteristics of highly heterogeneous metastatic tumours, for accurately predicting the treatment effectiveness of the advanced stage ovarian cancer patients. In order to achieve this , a quantitative image analysis scheme was developed to estimate a total of 103 features from three different groups including shape and density, Wavelet, and Gray Level Difference Method (GLDM) features. Shape and density features are global features, which are directly applied on the entire target image; wavelet and GLDM features are local features, which are applied on the divided blocks of the target image. To assess the performance, the new scheme was applied on a retrospective dataset containing 120 recurrent and high grade ovary cancer patients. The results indicate that the three best performed features are skewness, root-mean-square (rms) and mean of local GLDM texture, indicating the importance of integrating local features. In addition, the averaged predicting performance are comparable among the three different categories. This investigation concluded that the local features contains at least as copious tumour heterogeneity information as the global features, which may be meaningful on improving the predicting performance of the quantitative image markers for the diagnosis and prognosis of ovary cancer patients.

Improved parallel image reconstruction using feature refinement.

PubMed

Cheng, Jing; Jia, Sen; Ying, Leslie; Liu, Yuanyuan; Wang, Shanshan; Zhu, Yanjie; Li, Ye; Zou, Chao; Liu, Xin; Liang, Dong

2018-07-01

The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS-pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. A simple and effective way to preserve more useful detailed information in CS-pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211-223, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

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.

Discovery and Analysis of 21 micrometer Feature Sources in the Magellanic Clouds (Postprint)

DTIC Science & Technology

2011-07-10

either definitely or may show the 21 μm feature have distinct dust shell properties compared to the Galactic 21 μm objects—the 21 μm features are weaker...13 objects that either definitely or may show the 21μm feature have distinct dust shell properties compared to the Galactic 21μm objects—the 21μm...SMC object J004441 than it is to the spectra of any of the other LMC objects. The optical counterpart, while definitely detected in the MCPS (V ∼ 18.4

Exploring Learner Language through Corpora: Comparing and Interpreting Corpus Frequency Information

ERIC Educational Resources Information Center

Gablasova, Dana; Brezina, Vaclav; McEnery, Tony

2017-01-01

This article contributes to the debate about the appropriate use of corpus data in language learning research. It focuses on frequencies of linguistic features in language use and their comparison across corpora. The majority of corpus-based second language acquisition studies employ a comparative design in which either one or more second language…

What Can Be Learned from a Comparison of Two Agricultural Knowledge Systems? The Case of the Netherlands and Israel.

ERIC Educational Resources Information Center

Blum, Abraham

1991-01-01

Compared the agricultural knowledge systems (AKS) of the Netherlands and Israel; analyzed the features that made the systems effective and applicable to other countries. The analysis discovered eight elements that explain the success of these AKSs and demonstrated the value of comparative case studies. (JOW)

Comparative experimental pharmacokinetics of benzimidazole derivatives.

PubMed

Sergeeva, S A; Gulyaeva, I L

2008-12-01

Comparative study of experimental kinetics of distribution of benzimidazole derivatives (bemithyl, etomerzole, and thietazole) in organs and tissues was carried out after single and course treatment. The drugs intensely passed into organs and tissues from the blood after treatment by all protocols. Specific features of drug distribution were detected; for example, splenic tissue selectively accumulated thietazole during course treatment.

International Practice and Comparative Legal Studies.

ERIC Educational Resources Information Center

Cummins, Richard J.

1985-01-01

The lack of knowledge of and sensitivity to the basic features of foreign legal systems on the part of lawyers doing international work is related to a general lack of legal scholarship. The methodology and subject matter of comparative law must be renewed and revived at a time when barriers between legal systems seem to be increasing. (MSE)

Medial Demons Registration Localizes The Degree of Genetic Influence Over Subcortical Shape Variability: An N= 1480 Meta-Analysis

PubMed Central

Gutman, Boris A.; Jahanshad, Neda; Ching, Christopher R.K.; Wang, Yalin; Kochunov, Peter V.; Nichols, Thomas E.; Thompson, Paul M.

2015-01-01

We present a multi-cohort shape heritability study, extending the fast spherical demons registration to subcortical shapes via medial modeling. A multi-channel demons registration based on vector spherical harmonics is applied to medial and curvature features, while controlling for metric distortion. We registered and compared seven subcortical structures of 1480 twins and siblings from the Queensland Twin Imaging Study and Human Connectome Project: Thalamus, Caudate, Putamen, Pallidum, Hippocampus, Amygdala, and Nucleus Accumbens. Radial distance and tensor-based morphometry (TBM) features were found to be highly heritable throughout the entire basal ganglia and limbic system. Surface maps reveal subtle variation in heritability across functionally distinct parts of each structure. Medial Demons reveals more significantly heritable regions than two previously described surface registration methods. This approach may help to prioritize features and measures for genome-wide association studies. PMID:26413211

Medial Demons Registration Localizes The Degree of Genetic Influence Over Subcortical Shape Variability: An N= 1480 Meta-Analysis.

PubMed

Gutman, Boris A; Jahanshad, Neda; Ching, Christopher R K; Wang, Yalin; Kochunov, Peter V; Nichols, Thomas E; Thompson, Paul M

2015-04-01

We present a multi-cohort shape heritability study, extending the fast spherical demons registration to subcortical shapes via medial modeling. A multi-channel demons registration based on vector spherical harmonics is applied to medial and curvature features, while controlling for metric distortion. We registered and compared seven subcortical structures of 1480 twins and siblings from the Queensland Twin Imaging Study and Human Connectome Project: Thalamus, Caudate, Putamen, Pallidum, Hippocampus, Amygdala, and Nucleus Accumbens . Radial distance and tensor-based morphometry (TBM) features were found to be highly heritable throughout the entire basal ganglia and limbic system. Surface maps reveal subtle variation in heritability across functionally distinct parts of each structure. Medial Demons reveals more significantly heritable regions than two previously described surface registration methods. This approach may help to prioritize features and measures for genome-wide association studies.

A retrospective clinico-pathological study comparing lichen planus pigmentosus with ashy dermatosis.

PubMed

Cheng, Hui Mei; Chuah, Sai Yee; Gan, Emily Yiping; Jhingan, Anjali; Thng, Steven Tien Guan

2018-04-10

Controversy persists as to whether lichen planus pigmentosus and ashy dermatosis are separate clinical entities. This study was conducted to examine the clinicopathological features and treatment outcome of the two conditions. A retrospective medical chart review of all patients who were diagnosed with lichen planus pigmentosus or ashy dermatosis was conducted. The information collected included the participants' age at onset, site of onset, duration of disease, presence of precipitating factors, distribution of disease, pigmentation and presence of symptoms. In patients from whom a biopsy was taken the histopathological reports were included. Altogether 26 patients with ashy dermatosis and 29 with lichen planus pigmentosus were included in the study. Compared with ashy dermatosis, lichen planus pigmentosus had a more localised distribution with a preponderance for facial involvement, compared with the truncal preponderance in ashy dermatosis. Ashy dermatosis tended to have a more stable clinical course than lichen planus pigmentosus, which was more likely to wax and wane. The utility of histopathology in differentiating between the two conditions is low. Ashy dermatosis and lichen planus pigmentosus, as defined in this study, appear to be two separate clinical entities with distinguishable clinical features and natural histories. © 2018 The Australasian College of Dermatologists.

To Evaluate & Compare Retention of Complete Cast Crown in Natural Teeth Using Different Auxiliary Retentive Features with Two Different Crown Heights - An In Vitro Study.

PubMed

Vinaya, Kundapur; Rakshith, Hegde; Prasad D, Krishna; Manoj, Shetty; Sunil, Mankar; Naresh, Shetty

2015-06-01

To evaluate the retention of complete cast crowns in teeth with adequate and inadequate crown height and to evaluate the effects of auxiliary retentive features on retention form complete cast crowns. Sixty freshly extracted human premolars. They were divided into 2 major groups depending upon the height of the teeth after the preparation. Group1 (H1): prepared teeth with constant height of 3.5 mm and Group 2 (H2): prepared teeth with constant height of 2.5 mm. Each group is further subdivided into 3 subgroups, depending upon the retentive features incorporated. First sub group were prepared conventionally, second sub group with proximal grooves and third subgroups with proximal boxes preparation. Castings produced in Nickel chromium alloy were cemented with glass ionomer cement and the cemented castings were subjected to tensional forces required to dislodge each cemented casting from its preparation and used for comparison of retentive quality. The data obtained were statistically analyzed using Oneway ANOVA test. The results showed there was statistically significant difference between adequate (H1) and inadequate (H2) group and increase in retention when there was incorporation of retentive features compared to conventional preparations. Incorporation of retentive grooves was statistically significant compared to retention obtained by boxes. Results also showed there was no statistically significant difference between long conventional and short groove. Complete cast crowns on teeth with adequate crown height exhibited greater retention than with inadequate crown height. Proximal grooves provided greater amount of retention when compared with proximal boxes.

SU-F-R-52: A Comparison of the Performance of Radiomic Features From Free Breathing and 4DCT Scans in Predicting Disease Recurrence in Lung Cancer SBRT Patients

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

Huynh, E; Coroller, T; Narayan, V

Purpose: There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non-invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lungmore » cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence. Methods: FB and AIP images of 113 Stage I-II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p-values were corrected for multiple testing using the false discovery rate. Results: All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65–0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60–0.61) were significant but all AIP conventional features were (range CI=0.64–0.66). Conclusion: Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments. R.M. had consulting interest with Amgen (ended in 2015).« less

Neuropsychological assessment of decision making in alcohol-dependent commercial pilots.

PubMed

Georgemiller, Randy; Machizawa, Sayaka; Young, Kathleen M; Martin, Cynthia N

2013-09-01

The aim of this exploratory archival study was to discern the utility of the Iowa Gambling Task (IGT) in identifying adaptive decision-making capacities among pilots with a history of alcohol dependence both with and without Cluster B personality features. Participants included 18 male airmen at the rank of captain with a history of receiving alcohol dependence treatment and subsequent referral for a fitness-for-duty evaluation. Data from prior comprehensive neuropsychological evaluations conducted in a private practice setting at the mandate of the FAA utilizing criteria outlined in the HIMS program was used. ANOVA was conducted to compare pilots with (N = 4) and without Cluster B personality features (N = 14) on measures of decisionmaking capacities, intelligence, and executive functioning. Pilots with Cluster B personality features were found to have a significantly lower Total Net T-Score on IGT (M = 35.00, SD = 9.27) than pilots without features of Cluster B (M = 56.36, SD = 9.55). Furthermore, with the exception of the first 20 cards (i.e., Net 1); the groups significantly differed in their Net scores. No statistically significant difference was found on airmen's intelligence and executive functioning. The present study found that alcohol-dependent airmen with Cluster B personality features evidenced significantly poorer decisionmaking capacities as measured by the ICT in comparison to alcohol dependent airman without Cluster B personality features. Implications and limitations of the study are discussed.

Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder.

PubMed

Musil, Richard; Seemüller, Florian; Meyer, Sebastian; Spellmann, Ilja; Adli, Mazda; Bauer, Michael; Kronmüller, Klaus-Thomas; Brieger, Peter; Laux, Gerd; Bender, Wolfram; Heuser, Isabella; Fisher, Robert; Gaebel, Wolfgang; Schennach, Rebecca; Möller, Hans-Jürgen; Riedel, Michael

2018-03-01

Subtyping depression is important in order to further delineate biological causes of depressive syndromes. The aim of this study was to evaluate clinical and outcome characteristics of distinct subtypes of depression and to assess proportion and features of patients fulfilling criteria for more than one subtype. Melancholic, atypical and anxious subtypes of depression were assessed in a naturalistic sample of 833 inpatients using DSM-IV specifiers based on operationalized criteria. Baseline characteristics and outcome criteria at discharge were compared between distinct subtypes and their overlap. A substantial proportion of patients (16%) were classified with more than one subtype of depression, 28% were of the distinct anxious, 7% of the distinct atypical and 5% of the distinct melancholic subtype. Distinct melancholic patients had shortest duration of episode, highest baseline depression severity, but were more often early improvers; distinct anxious patients had higher NEO-Five Factor Inventory (NEO-FFI) neuroticism scores compared with patients with unspecific subtype. Melancholic patients with overlap of anxious features had worse treatment outcome compared to distinct melancholic and distinct anxious subtype. Distinct subtypes differed in only few variables and patients with overlap of depression subtypes may have independent clinical and outcome characteristics. Studies investigating biological causes of subtypes of depression should take influence of features of other subtypes into account. Copyright © 2017 John Wiley & Sons, Ltd.

Intraspecific differences in bacterial responses to modelled reduced gravity

NASA Technical Reports Server (NTRS)

Baker, P. W.; Leff, L. G.

2005-01-01

AIMS: Bacteria are important residents of water systems, including those of space stations which feature specific environmental conditions, such as lowered effects of gravity. The purpose of this study was to compare responses with modelled reduced gravity of space station, water system bacterial isolates with other isolates of the same species. METHODS AND RESULTS: Bacterial isolates, Stenotrophomonas paucimobilis and Acinetobacter radioresistens, originally recovered from the water supply aboard the International Space Station (ISS) were grown in nutrient broth under modelled reduced gravity. Their growth was compared with type strains S. paucimobilis ATCC 10829 and A. radioresistens ATCC 49000. Acinetobacter radioresistens ATCC 49000 and the two ISS isolates showed similar growth profiles under modelled reduced gravity compared with normal gravity, whereas S. paucimobilis ATCC 10829 was negatively affected by modelled reduced gravity. CONCLUSIONS: These results suggest that microgravity might have selected for bacteria that were able to thrive under this unusual condition. These responses, coupled with impacts of other features (such as radiation resistance and ability to persist under very oligotrophic conditions), may contribute to the success of these water system bacteria. SIGNIFICANCE AND IMPACT OF THE STUDY: Water quality is a significant factor in many environments including the ISS. Efforts to remove microbial contaminants are likely to be complicated by the features of these bacteria which allow them to persist under the extreme conditions of the systems.

A 4-Week Model of House Dust Mite (HDM) Induced Allergic Airways Inflammation with Airway Remodeling.

PubMed

Woo, L N; Guo, W Y; Wang, X; Young, A; Salehi, S; Hin, A; Zhang, Y; Scott, J A; Chow, C W

2018-05-02

Animal models of allergic airways inflammation are useful tools in studying the pathogenesis of asthma and potential therapeutic interventions. The different allergic airways inflammation models available to date employ varying doses, frequency, duration and types of allergen, which lead to the development of different features of asthma; showing varying degrees of airways inflammation and hyper-responsiveness (AHR) and airways remodeling. Models that also exhibit airway remodeling, a key feature of asthma, in addition to AHR and airway inflammation typically require 5-12 weeks to develop. In this report, we describe a 4-week mouse model of house dust mite (HDM)-induced allergic airways inflammation, and compare the phenotypic features of two different doses of HDM exposures (10 µg and 25 µg) for 5 days/week with a well-characterized 8-week chronic HDM model. We found that 4 weeks of intranasal HDM (25 µg in 35 µl saline; 5 days/week) resulted in AHR, airway inflammation and airway remodeling that were comparable to the 8-week model. We conclude that this new 4-week HDM model is another useful tool in studies of human asthma that offers advantages of shorter duration for development and decreased costs when compared to other models that require longer durations of exposure (5-12 weeks) to develop.

Accuracy of MRI for the diagnosis of metastatic cervical lymphadenopathy in patients with thyroid cancer.

PubMed

Chen, Qinghua; Raghavan, Prashant; Mukherjee, Sugoto; Jameson, Mark J; Patrie, James; Xin, Wenjun; Xian, Junfang; Wang, Zhenchang; Levine, Paul A; Wintermark, Max

2015-10-01

The aim of this study was to systematically compare a comprehensive array of magnetic resonance (MR) imaging features in terms of their sensitivity and specificity to diagnose cervical lymph node metastases in patients with thyroid cancer. The study included 41 patients with thyroid malignancy who underwent surgical excision of cervical lymph nodes and had preoperative MR imaging ≤4weeks prior to surgery. Three head and neck neuroradiologists independently evaluated all the MR images. Using the pathology results as reference, the sensitivity, specificity and interobserver agreement of each MR imaging characteristic were calculated. On multivariate analysis, no single imaging feature was significantly correlated with metastasis. In general, imaging features demonstrated high specificity, but poor sensitivity and moderate interobserver agreement at best. Commonly used MR imaging features have limited sensitivity at correctly identifying cervical lymph node metastases in patients with thyroid cancer. A negative neck MR scan should not dissuade a surgeon from performing a neck dissection in patients with thyroid carcinomas.

Hippocampus and medial striatum dissociation during goal navigation by geometry or features in the domestic chick: An immediate early gene study.

PubMed

Mayer, Uwe; Pecchia, Tommaso; Bingman, Verner Peter; Flore, Michele; Vallortigara, Giorgio

2016-01-01

We employed a standard reference memory task to study the involvement of the hippocampal formation (HF) of domestic chicks that used the boundary geometry of a test environment to orient to and locate a reward. Using the immediate early gene product c-Fos as a neuronal activity marker, we found enhanced HF activation in chicks that learned to locate rewarded corners using the shape of a rectangular arena compared to chicks trained to solve the task by discriminating local features in a square-shaped arena. We also analyzed neuronal activity in the medial part of the medial striatum (mMSt). Surprisingly, in mMSt we observed a reverse pattern, with higher activity in the chicks that were trained to locate the goal by local features. Our results identify two seemingly parallel, memory systems in chicks, with HF central to the processing of spatial-geometrical information and mMSt important in supporting local feature discrimination. © 2015 Wiley Periodicals, Inc.

The effects of age on symbol comprehension in central rail hubs in Taiwan.

PubMed

Liu, Yung-Ching; Ho, Chin-Heng

2012-11-01

The purpose of this study was to investigate the effects of age and symbol design features on passengers' comprehension of symbols and the performance of these symbols with regard to route guidance. In the first experiment, 30 young participants and 30 elderly participants interpreted the meanings and rated the features of 39 symbols. Researchers collected data on each subject's comprehension time, comprehension score, and feature ratings for each symbol. In the second experiment, this study used a series of photos to simulate scenarios in which passengers follow symbols to arrive at their destinations. The length of time each participant required to follow his/her route and his/her errors were recorded. Older adults experienced greater difficulty in understanding particular symbols as compared to younger adults. Familiarity was the feature most highly correlated with comprehension of symbols and accuracy of semantic depiction was the best predictor of behavior in following routes. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

The Role of Surface, Semantic and Grammatical Features on Simplification of Spanish Medical Texts: A User Study

PubMed Central

Mukherjee, Partha; Leroy, Gondy; Kauchak, David; Navarrete, Brianda Armenta; Diaz, Damian Y.; Colina, Sonia

2017-01-01

Simplifying medical texts facilitates readability and comprehension. While most simplification work focuses on English, we investigate whether features important for simplifying English text are similarly helpful for simplifying Spanish text. We conducted a user study on 15 Spanish medical texts using Amazon Mechanical Turk and measured perceived and actual difficulty. Using the median of the difficulty scores, we split the texts into easy and difficult groups and extracted 10 surface, 2 semantic and 4 grammatical features. Using t-tests, we identified those features that significantly distinguish easy text from difficult text in Spanish and compare with prior work in English. We found that easy Spanish texts use more repeated words and adverbs, less negations and more familiar words, similar to English. Also like English, difficult Spanish texts use more nouns and adjectives. However in contrast to English, easier Spanish texts contained longer sentences and used grammatical structures that were more varied. PMID:29854201

Wavelet Packet Feature Assessment for High-Density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation.

PubMed

Wang, Dongqing; Zhang, Xu; Gao, Xiaoping; Chen, Xiang; Zhou, Ping

2016-01-01

This study presents wavelet packet feature assessment of neural control information in paretic upper limb muscles of stroke survivors for myoelectric pattern recognition, taking advantage of high-resolution time-frequency representations of surface electromyogram (EMG) signals. On this basis, a novel channel selection method was developed by combining the Fisher's class separability index and the sequential feedforward selection analyses, in order to determine a small number of appropriate EMG channels from original high-density EMG electrode array. The advantages of the wavelet packet features and the channel selection analyses were further illustrated by comparing with previous conventional approaches, in terms of classification performance when identifying 20 functional arm/hand movements implemented by 12 stroke survivors. This study offers a practical approach including paretic EMG feature extraction and channel selection that enables active myoelectric control of multiple degrees of freedom with paretic muscles. All these efforts will facilitate upper limb dexterity restoration and improved stroke rehabilitation.

Comparison of two feature selection methods for the separability analysis of intertidal sediments with spectrometric datasets in the German Wadden Sea

NASA Astrophysics Data System (ADS)

Jung, Richard; Ehlers, Manfred

2016-10-01

The spectral features of intertidal sediments are all influenced by the same biophysical properties, such as water, salinity, grain size or vegetation and therefore they are hard to separate by using only multispectral sensors. This could be shown by a previous study of Jung et al. (2015). A more detailed analysis of their characteristic spectral feature has to be carried out to understand the differences and similarities. Spectrometry data (i.e., hyperspectral sensors), for instance, have the opportunity to measure the reflection of the landscape as a continuous spectral pattern for each pixel of an image built from dozen to hundreds of narrow spectral bands. This reveals a high potential to measure unique spectral responses of different ecological conditions (Hennig et al., 2007). In this context, this study uses spectrometric datasets to distinguish between 14 different sediment classes obtained from a study area in the German Wadden Sea. A new feature selection method is proposed (Jeffries-Matusita distance bases feature selection; JMDFS), which uses the Euclidean distance to eliminate the wavelengths with the most similar reflectance values in an iterative process. Subsequent to each iteration, the separation capability is estimated by the Jeffries-Matusita distance (JMD). Two classes can be separated if the JMD is greater than 1.9 and if less than four wavelengths remain, no separation can be assumed. The results of the JMDFS are compared with a state-of-the-art feature selection method called ReliefF. Both methods showed the ability to improve the separation by achieving overall accuracies greater than 82%. The accuracies are 4%-13% better than the results with all wavelengths applied. The number of remaining wavelengths is very diverse and ranges from 14 to 213 of 703. The advantage of JMDFS compared with ReliefF is clearly the processing time. ReliefF needs 30 min for one temporary result. It is necessary to repeat the process several times and to average all temporary results to achieve a final result. In this study 50 iterations were carried out, which makes four days of processing. In contrast, JMDFS needs only 30 min for a final result.

Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?

PubMed

Yang, Fan; Xu, Ying-Ying; Shen, Hong-Bin

2014-01-01

Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

PubMed

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

Integrating gender and number information in Spanish word pairs: an ERP study.

PubMed

Barber, Horacio; Carreiras, Manuel

2003-06-01

The aim of the current study was to explore the integration processes of gender and number morphological features, since it has been proposed that grammatical gender and number features might be associated with different strength with the word stem in lexical representation. Event related potentials (ERPs) were recorded using a 128-channel sensor net while twenty-four volunteers read Spanish word pairs and performed a syntactic judgment task. The word pairs which could agree or disagree in gender or number or in gender and number at the same time, were formed by a noun and an adjective (e.g. faro-alto [lighthouse-high]). A negativity around 400 msec with posterior distribution, which has been related to lexical integration processes, was found in response to both gender and number violations. No differences were found between gender disagreement, number disagreement and the double disagreement. Therefore, ERPs suggest that integration of gender and number features may not be different, and that the detection of disagreement may work under a binary state, since the double disagreement condition did not differ from the others. In addition, a subsequent component (identified as P3) showed delayed latencies in the gender disagreement condition as compared to the number disagreement condition, while the double disagreement conditions showed a shorter peak latency than the other two disagreement conditions and similar to the agreement condition. The variations in the latency of the P3 component, which has been related to categorization processes, suggest that these processes are quickly triggered by the accumulation of two incongruent as compared to one disagreement features, and that reanalysis is costlier in the case of gender disagreement as compared to the number disagreement.

Circulating D-dimer level correlates with disease characteristics in hepatoblastoma patients

PubMed Central

Zhang, BinBin; Liu, GongBao; Liu, XiangQi; Zheng, Shan; Dong, Kuiran; Dong, Rui

2017-01-01

Abstract Objectives: Hepatoblastoma (HB) is the most common pediatric liver malignancy, typically affecting children within the first 4 years of life. However, only a few validated blood biomarkers are used in clinical evaluation. The current study explored the clinical application and significance of D-dimer levels in patients with HB. Method: Forty-four patients with HB were included in this retrospective study. D-dimer and plasma fibrinogen levels were examined, and their correlation with other clinical features was analyzed. D-dimer and plasma fibrinogen levels were examined between HB and other benign hepatic tumors. Results: D-dimer levels were significantly associated with high-risk HB features, such as advanced stage and high α-fetoprotein (AFP) levels. Higher D-dimer levels were observed in stage 4 patients compared with stage 1/2/3 patients (P = .028). Higher D-dimer levels were also observed in patients with higher AFP levels before chemotherapy compared with patients with lower AFP levels after chemotherapy (P< 0.001). In addition, higher D-dimer levels were observed in HB compared with other benign hepatic tumors such as hepatic hemangioma and hepatocellular adenoma (P = .012). No significant difference in D-dimer levels was found between sex (P = .503) and age (≥12 vs <12 months, P = .424). There was no significant difference in plasma fibrinogen levels between sex or age and high-risk HB features, such as advanced stage and high AFP levels. Conclusions: Elevated D-dimer levels could be a useful determinant biomarker for high-risk features in patients with HB. This finding also supports the clinical application of D-dimer in HB. PMID:29381980

Renal cell carcinoma with rhabdoid-like features lack intracytoplasmic inclusion bodies and show aggressive behavior.

PubMed

Sugimoto, Masaaki; Kohashi, Kenichi; Kuroiwa, Kentaro; Abe, Tatsuro; Yamada, Yuichi; Shiota, Masaki; Imada, Kenjiro; Naito, Seiji; Oda, Yoshinao

2016-03-01

In renal cell carcinoma (RCC), tumor cells with rhabdoid features are characterized by eccentric nuclei, prominent nucleoli, and eosinophilic cytoplasm with intracytoplasmic inclusion bodies. In RCC, tumor cells have also been observed resembling rhabdomyoblasts or rhabdoid but without intracytoplasmic inclusion bodies, and here, we defined these rhabdoid-like features of these cells. To this end, we studied a series of clear cell RCC (ccRCC) with rhabdoid features and compared them with a series of ccRCC with rhabdoid-like features to clarify the differences in the immunohistochemical profile and biological behavior. From 695 cases of ccRCC (80.8 % of all RCCs), 18 cases with rhabdoid features (2.1 % of all RCCs) and 25 cases with rhabdoid-like features (2.9 % of all RCCs) were investigated. The 5-year survival rate for ccRCC with rhabdoid features was 44.7 % and for ccRCC with rhabdoid-like features 30.3 %. Although ccRCC with rhabdoid features showed immunohistochemical co-expression of epithelial markers and vimentin as seen in malignant rhabdoid tumors, ccRCC with rhabdoid-like features showed no such co-expression. Multivariate analyses of cancer-specific survival revealed that perinephric tissues invasion was an independent prognostic factor in ccRCC with rhabdoid features (p = 0.0253) but not in ccRCC with rhabdoid-like features. In summary, although their prognosis is similar, the marker profile and pattern of extension of ccRCC with rhabdoid-like is different from that of ccRCC with rhabdoid features. Therefore, ccRCC with rhabdoid-like features should be distinguished from ccRCC with rhabdoid features.

Clinical Named Entity Recognition Using Deep Learning Models.

PubMed

Wu, Yonghui; Jiang, Min; Xu, Jun; Zhi, Degui; Xu, Hua

2017-01-01

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. This study examined two popular deep learning architectures, the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), to extract concepts from clinical texts. We compared the two deep neural network architectures with three baseline Conditional Random Fields (CRFs) models and two state-of-the-art clinical NER systems using the i2b2 2010 clinical concept extraction corpus. The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER.

A comparison study of body dysmorphic disorder versus social phobia

PubMed Central

Kelly, Megan M.; Dalrymple, Kristy; Zimmerman, Mark; Phillips, Katharine A.

2012-01-01

Body dysmorphic disorder (BDD) shares many characteristics with social phobia (SP), including high levels of social anxiety and avoidance, but to our knowledge no studies have directly compared these disorders’ demographic and clinical features. Demographic and clinical features were compared in individuals with BDD (n=172), SP (n=644), and comorbid BDD/SP (n=125). SP participants had a significantly earlier age of onset and lower educational attainment than BDD participants. BDD participants were significantly less likely to ever be married than SP participants, had a greater likelihood of ever being psychiatrically hospitalized, and had significantly lower mean GAF scores than SP participants. The two groups had different comorbidity patterns, which included a greater likelihood for BDD participants to have comorbid obsessive-compulsive disorder (OCD) or an eating disorder, versus a greater likelihood for SP participants to have a comorbid non-OCD anxiety disorder. The comorbid BDD/SP group had significantly greater morbidity across several domains than the SP only group, but not the BDD only group. In summary, although BDD and SP were similar across many demographic and clinical features, they had important differences. Future studies are needed to confirm these findings and address similarities and differences between these disorders across a broader range of variables. PMID:22999105

Clinical Named Entity Recognition Using Deep Learning Models

PubMed Central

Wu, Yonghui; Jiang, Min; Xu, Jun; Zhi, Degui; Xu, Hua

2017-01-01

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. This study examined two popular deep learning architectures, the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), to extract concepts from clinical texts. We compared the two deep neural network architectures with three baseline Conditional Random Fields (CRFs) models and two state-of-the-art clinical NER systems using the i2b2 2010 clinical concept extraction corpus. The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER. PMID:29854252

Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.

PubMed

Zhou, Yongxia; Yu, Fang; Duong, Timothy

2014-01-01

This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

PubMed

Lee, David; Park, Sang-Hoon; Lee, Sang-Goog

2017-10-07

In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.

MCORES: a system for noun phrase coreference resolution for clinical records.

PubMed

Bodnari, Andreea; Szolovits, Peter; Uzuner, Özlem

2012-01-01

Narratives of electronic medical records contain information that can be useful for clinical practice and multi-purpose research. This information needs to be put into a structured form before it can be used by automated systems. Coreference resolution is a step in the transformation of narratives into a structured form. This study presents a medical coreference resolution system (MCORES) for noun phrases in four frequently used clinical semantic categories: persons, problems, treatments, and tests. MCORES treats coreference resolution as a binary classification task. Given a pair of concepts from a semantic category, it determines coreferent pairs and clusters them into chains. MCORES uses an enhanced set of lexical, syntactic, and semantic features. Some MCORES features measure the distance between various representations of the concepts in a pair and can be asymmetric. MCORES was compared with an in-house baseline that uses only single-perspective 'token overlap' and 'number agreement' features. MCORES was shown to outperform the baseline; its enhanced features contribute significantly to performance. In addition to the baseline, MCORES was compared against two available third-party, open-domain systems, RECONCILE(ACL09) and the Beautiful Anaphora Resolution Toolkit (BART). MCORES was shown to outperform both of these systems on clinical records.

Vibration isolation analysis of new design OEM damper for malaysia vehicle suspension system featuring MR fluid

NASA Astrophysics Data System (ADS)

Unuh, M. H.; Muhamad, P.; Norfazrina, H. M. Y.; Ismail, M. A.; Tanasta, Z.

2018-01-01

The applications of semi-active damper employing magnetorheological (MR) fluids keep increasing in fulfilling the demand to control undesired vibration effect. The aim of this study is to introduce the new design of damper for Malaysian vehicle model as well to evaluate its effectiveness in promoting comfort. The vibration isolation performance of the OEM damper featuring MR fluid was analysed physically under real road profile excitation experimentally. An experiment using quarter car rig suspension and LMS SCADAS Mobile was conducted to demonstrate the influence of current in controlling the characteristics of MR fluid in alter the damping behaviour under 5 cm bump impact. Subsequently, the displacement values were measured with respect to time. The new design OEM damper featuring MR fluid was validated by comparing the data with original equipment manufacturer (OEM) passive damper results under the same approach of testing. Comparison of numerical data of the new design OEM damper shown that it can reduce the excitation amplitude up to 40% compared to those obtained by OEM passive damper. Finally, the new design OEM damper featuring MR fluid has effectively isolated the disturbance from the road profile and control the output force.

A comparative study of sequence- and structure-based features of small RNAs and other RNAs of bacteria.

PubMed

Barik, Amita; Das, Santasabuj

2018-01-02

Small RNAs (sRNAs) in bacteria have emerged as key players in transcriptional and post-transcriptional regulation of gene expression. Here, we present a statistical analysis of different sequence- and structure-related features of bacterial sRNAs to identify the descriptors that could discriminate sRNAs from other bacterial RNAs. We investigated a comprehensive and heterogeneous collection of 816 sRNAs, identified by northern blotting across 33 bacterial species and compared their various features with other classes of bacterial RNAs, such as tRNAs, rRNAs and mRNAs. We observed that sRNAs differed significantly from the rest with respect to G+C composition, normalized minimum free energy of folding, motif frequency and several RNA-folding parameters like base-pairing propensity, Shannon entropy and base-pair distance. Based on the selected features, we developed a predictive model using Random Forests (RF) method to classify the above four classes of RNAs. Our model displayed an overall predictive accuracy of 89.5%. These findings would help to differentiate bacterial sRNAs from other RNAs and further promote prediction of novel sRNAs in different bacterial species.

Distinct DNA methylation alterations are associated with cribriform architecture and intraductal carcinoma in Gleason pattern 4 prostate tumors.

PubMed

Olkhov-Mitsel, Ekaterina; Siadat, Farshid; Kron, Ken; Liu, Liyang; Savio, Andrea J; Trachtenberg, John; Fleshner, Neil; van der Kwast, Theodorus; Bapat, Bharati

2017-07-01

The aim of the present study was to explore DNA methylation aberrations in association with cribriform architecture and intraductal carcinoma (IDC) of the prostate, as there is robust evidence that these morphological features are associated with aggressive disease and have significant clinical implications. Herein, the associations of a panel of seven known prognostic DNA methylation biomarkers with cribriform and IDC features were examined in a series of 91 Gleason pattern (GP) 4 tumors derived from Gleason score 7 radical prostatectomies. Gene specific DNA methylation was compared between cribriform and/or IDC positive vs. negative cases, and in association with clinicopathological features, using Chi square and Mann-Whitney U tests. DNA methylation of the adenomatous polyposis coli, Ras association domain family member 1 and T-box 15 genes was significantly elevated in GP4 tumors with cribriform and/or IDC features compared with negative cases (P=0.045, P=0.007 and P=0.013, respectively). To the best of our knowledge, this provides the first evidence for an association between cribriform and/or IDC and methylation biomarkers, and warrants further investigation of additional DNA methylation events in association with various architectural patterns in prostate cancer.

Kinematic measurements of the vocal-fold displacement waveform in typical children and adult populations: quantification of high-speed endoscopic videos.

PubMed

Patel, Rita; Donohue, Kevin D; Unnikrishnan, Harikrishnan; Kryscio, Richard J

2015-04-01

This article presents a quantitative method for assessing instantaneous and average lateral vocal-fold motion from high-speed digital imaging, with a focus on developmental changes in vocal-fold kinematics during childhood. Vocal-fold vibrations were analyzed for 28 children (aged 5-11 years) and 28 adults (aged 21-45 years) without voice disorders. The following kinematic features were analyzed from the vocal-fold displacement waveforms: relative velocity-based features (normalized average and peak opening and closing velocities), relative acceleration-based features (normalized peak opening and closing accelerations), speed quotient, and normalized peak displacement. Children exhibited significantly larger normalized peak displacements, normalized average and peak opening velocities, normalized average and peak closing velocities, peak opening and closing accelerations, and speed quotient compared to adult women. Values of normalized average closing velocity and speed quotient were higher in children compared to adult men. When compared to adult men, developing children typically have higher estimates of kinematic features related to normalized displacement and its derivatives. In most cases, the kinematic features of children are closer to those of adult men than adult women. Even though boys experience greater changes in glottal length and pitch as they mature, results indicate that girls experience greater changes in kinematic features compared to boys.

Automatic topic identification of health-related messages in online health community using text classification.

PubMed

Lu, Yingjie

2013-01-01

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

Comparative cytological study of four species in the genera Holomastigotes and Uteronympha n. comb. (Holomastigotidae, Parabasalia), symbiotic flagellates of termites.

PubMed

Brugerolle, Guy

2006-01-01

Cytological features observed using light, immunofluorescence, and electron microscopy of the type species Holomastigotes elongatum were compared with Holomastigotes lanceolata and to Holomastigotes flexuosum n. sp. The comparison was extended to Spirotrichonymphella pudibunda and to Uteronympha africana n. gen. n. sp., in order to present the common features of the Holomastigotidae (Spirotrichonymphida). All these species have anterior basal bodies bearing microfibrillar or striated rootlets that are reduced or absent posterior to the nucleus. An axostylar trunk is present in Holomastigotes elongatum and Holomastigotes lanceolata, whereas the axostylar microtubules do not extend posterior to the nucleus in Holomastigotes flexuosum, Spirotrichonymphella, and Uteronympha. Uteronympha africana has specific features, such as a transverse plaque inside the columella from which arise microtubules capping the nucleus, and as in Spirotrichonympha the striated lamina is present all along the flagellar lines. Uteronympha africana has ability to endocytose wood particles in addition to the osmotrophic feeding that occurs in all the Holomastigotidae.

Comparative analysis of public opinion research in the U.S. and Canada

NASA Astrophysics Data System (ADS)

Setlakwe, Linda; DiNunzio, Lisa A.

2004-06-01

Bank note producers are working to thwart the threat of counterfeit notes created using high resolution, digital image processing software and color output devices such as inkjet printers, color copiers, and scanners. Genuine notes must incorporate better overt and machine-readable security features that will reduce the chance of counterfeit notes being passed. Recently, Canada and the United States introduced newly designed bank notes that are intended to enable the general public to more easily distinguish genuine notes from counterfeits. The Bank of Canada (BoC) and the U.S. Department of Treasury"s Bureau of Engraving and Printing (BEP) have conducted similar market research projects to explore target audiences' perceptions and attitudes towards currency design and security features. This paper will present a comparative analysis of the two research projects, both of which were conducted using similar methodology. The results of these research studies assist in the selection of security features for future generations of bank notes.

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.

PubMed

Chah, E; Hok, V; Della-Chiesa, A; Miller, J J H; O'Mara, S M; Reilly, R B

2011-02-01

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Unsupervised spike sorting based on discriminative subspace learning.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-01-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

1996-01-01

A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.

Black Studies. Courses of Study: Prejudices; Afro-American Studies.

ERIC Educational Resources Information Center

Gill, Tom; And Others

The African-American curriculum guide for secondary students endeavors to bridge the gap of misunderstanding between blacks and whites and, further, to enhance the esteem of black people. The prefacing unit on prejudice provides a unique feature compared to most guides in that it encourages students toward self examination of their personalities…

Incidence, clinical features, laboratory findings and outcome of patients with multiple myeloma presenting with extramedullary relapse.

PubMed

Papanikolaou, Xenofon; Repousis, Panagiotis; Tzenou, Tatiana; Maltezas, Dimitris; Kotsopoulou, Maria; Megalakaki, Katerina; Angelopoulou, Maria; Dimitrakoloulou, Elektra; Koulieris, Efstathios; Bartzis, Vassiliki; Pangalis, Gerasimos; Panayotidis, Panagiotis; Kyrtsonis, Marie-Christine

2013-07-01

Extramedullary plasmacytomas constitute a rare and not well studied subset of multiple myeloma (MM) relapses. We report the incidence, clinical-laboratory features and outcome of patients with MM and extramedullary relapse (ExMeR). A total of 303 patients with symptomatic MM were recorded in a 13-year period in two institutions. Twenty-eight cases of ExMeR (9%) were recorded. There was an increased frequency of elevated lactate dehydrogenase (LDH) (p = 0.026), bone plasmacytomas (p = 0.001) and fractures (p = 0.002) at diagnosis, in patients with ExMeR compared to the others. ExMeR was associated with an ominous outcome, high LDH, constitutional symptoms and a statistically significant decrease of monoclonal paraprotein compared to levels at diagnosis (p = 0.009). Prior treatment with bortezomib was associated with a decreased hazard of ExMeR (p = 0.041). Overall survival (OS) was decreased in patients with ExMeR compared to the others (38 vs. 59 months, p = 0.006). Patients with MM with ExMeR have a lower OS and their clinical and laboratory features differ from those without.

Use of sourdough made with quinoa (Chenopodium quinoa) flour and autochthonous selected lactic acid bacteria for enhancing the nutritional, textural and sensory features of white bread.

PubMed

Rizzello, Carlo Giuseppe; Lorusso, Anna; Montemurro, Marco; Gobbetti, Marco

2016-06-01

Lactic acid bacteria were isolated and identified from quinoa flour, spontaneously fermented quinoa dough, and type I quinoa sourdough. Strains were further selected based on acidification and proteolytic activities. Selected Lactobacillus plantarum T6B10 and Lactobacillus rossiae T0A16 were used as mixed starter to get quinoa sourdough. Compared to non-fermented flour, organic acids, free amino acids, soluble fibers, total phenols, phytase and antioxidant activities, and in vitro protein digestibility markedly increased during fermentation. A wheat bread was made using 20% (w/w) of quinoa sourdough, and compared to baker's yeast wheat breads manufactured with or without quinoa flour. The use of quinoa sourdough improved the chemical, textural, and sensory features of wheat bread, showing better performances compared to the use of quinoa flour. Protein digestibility and quality, and the rate of starch hydrolysis were also nutritional features that markedly improved using quinoa sourdough as an ingredient. This study exploited the potential of quinoa flour through sourdough fermentation. A number of advantages encouraged the manufacture of novel and healthy leavened baked goods. Copyright © 2015 Elsevier Ltd. All rights reserved.

Impaired distractor inhibition on a selective attention task in unmedicated, depressed subjects.

PubMed

MacQueen, G M; Tipper, S P; Young, L T; Joffe, R T; Levitt, A J

2000-05-01

Impaired distractor inhibition may contribute to the selective attention deficits observed in depressed patients, but studies to date have not tested the distractor inhibition theory against the possibility that processes such as transient memory review processes may account for the observed deficits. A negative priming paradigm can dissociate inhibition from such a potentially confounding process called object review. The negative priming task also isolates features of the distractor such as colour and location for independent examination. A computerized negative priming task was used in which colour, identification and location features of a stimulus and distractor were systematically manipulated across successive prime and probe trials. Thirty-two unmedicated subjects with DSM-IV diagnoses of non-psychotic unipolar depression were compared with 32 age, sex and IQ matched controls. Depressed subjects had reduced levels of negative priming for conditions where the colour feature of the stimulus was repeated across prime and probe trials but not when identity or location was the repeated feature. When both the colour and location feature were the repeated feature across trials, facilitation in response was apparent. The pattern of results supports studies that found reduced distractor inhibition in depressed subjects, and suggests that object review is intact in these subjects. Greater impairment in negative priming for colour versus location suggests that subjects may have greater impairment in the visual stream associated with processing colour features.

Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices.

PubMed

Padma, A; Sukanesh, R

2013-01-01

A computer software system is designed for the segmentation and classification of benign from malignant tumour slices in brain computed tomography (CT) images. This paper presents a method to find and select both the dominant run length and co-occurrence texture features of region of interest (ROI) of the tumour region of each slice to be segmented by Fuzzy c means clustering (FCM) and evaluate the performance of support vector machine (SVM)-based classifiers in classifying benign and malignant tumour slices. Two hundred and six tumour confirmed CT slices are considered in this study. A total of 17 texture features are extracted by a feature extraction procedure, and six features are selected using Principal Component Analysis (PCA). This study constructed the SVM-based classifier with the selected features and by comparing the segmentation results with the experienced radiologist labelled ground truth (target). Quantitative analysis between ground truth and segmented tumour is presented in terms of segmentation accuracy, segmentation error and overlap similarity measures such as the Jaccard index. The classification performance of the SVM-based classifier with the same selected features is also evaluated using a 10-fold cross-validation method. The proposed system provides some newly found texture features have an important contribution in classifying benign and malignant tumour slices efficiently and accurately with less computational time. The experimental results showed that the proposed system is able to achieve the highest segmentation and classification accuracy effectiveness as measured by jaccard index and sensitivity and specificity.

Reduced multimodal integration of memory features following continuous theta burst stimulation of angular gyrus.

PubMed

Yazar, Yasemin; Bergström, Zara M; Simons, Jon S

Lesions of the angular gyrus (AnG) region of human parietal cortex do not cause amnesia, but appear to be associated with reduction in the ability to consciously experience the reliving of previous events. We used continuous theta burst stimulation to test the hypothesis that the cognitive mechanism implicated in this memory deficit might be the integration of retrieved sensory event features into a coherent multimodal memory representation. Healthy volunteers received stimulation to AnG or a vertex control site after studying stimuli that each comprised a visual object embedded in a scene, with the name of the object presented auditorily. Participants were then asked to make memory judgments about the studied stimuli that involved recollection of single event features (visual or auditory), or required integration of event features within the same modality, or across modalities. Participants' ability to retrieve context features from across multiple modalities was significantly reduced after AnG stimulation compared to stimulation of the vertex. This effect was observed only for the integration of cross-modal context features but not for integration of features within the same modality, and could not be accounted for by task difficulty as performance was matched across integration conditions following vertex stimulation. These results support the hypothesis that AnG is necessary for the multimodal integration of distributed cortical episodic features into a unified conscious representation that enables the experience of remembering. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

PubMed

Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

2017-06-09

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Elucidating high-dimensional cancer hallmark annotation via enriched ontology.

PubMed

Yan, Shankai; Wong, Ka-Chun

2017-09-01

Cancer hallmark annotation is a promising technique that could discover novel knowledge about cancer from the biomedical literature. The automated annotation of cancer hallmarks could reveal relevant cancer transformation processes in the literature or extract the articles that correspond to the cancer hallmark of interest. It acts as a complementary approach that can retrieve knowledge from massive text information, advancing numerous focused studies in cancer research. Nonetheless, the high-dimensional nature of cancer hallmark annotation imposes a unique challenge. To address the curse of dimensionality, we compared multiple cancer hallmark annotation methods on 1580 PubMed abstracts. Based on the insights, a novel approach, UDT-RF, which makes use of ontological features is proposed. It expands the feature space via the Medical Subject Headings (MeSH) ontology graph and utilizes novel feature selections for elucidating the high-dimensional cancer hallmark annotation space. To demonstrate its effectiveness, state-of-the-art methods are compared and evaluated by a multitude of performance metrics, revealing the full performance spectrum on the full set of cancer hallmarks. Several case studies are conducted, demonstrating how the proposed approach could reveal novel insights into cancers. https://github.com/cskyan/chmannot. Copyright © 2017 Elsevier Inc. All rights reserved.

Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.

PubMed

Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui

2012-11-07

RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.

Biological Response of Human Bone Marrow-Derived Mesenchymal Stem Cells to Commercial Tantalum Coatings with Microscale and Nanoscale Surface Topographies

NASA Astrophysics Data System (ADS)

Skoog, Shelby A.; Kumar, Girish; Goering, Peter L.; Williams, Brian; Stiglich, Jack; Narayan, Roger J.

2016-06-01

Tantalum is a promising orthopaedic implant coating material due to its robust mechanical properties, corrosion resistance, and excellent biocompatibility. Previous studies have demonstrated improved biocompatibility and tissue integration of surface-treated tantalum coatings compared to untreated tantalum. Surface modification of tantalum coatings with biologically inspired microscale and nanoscale features may be used to evoke optimal tissue responses. The goal of this study was to evaluate commercial tantalum coatings with nanoscale, sub-microscale, and microscale surface topographies for orthopaedic and dental applications using human bone marrow-derived mesenchymal stem cells (hBMSCs). Tantalum coatings with different microscale and nanoscale surface topographies were fabricated using a diffusion process or chemical vapor deposition. Biological evaluation of the tantalum coatings using hBMSCs showed that tantalum coatings promote cellular adhesion and growth. Furthermore, hBMSC adhesion to the tantalum coatings was dependent on surface feature characteristics, with enhanced cell adhesion on sub-micrometer- and micrometer-sized surface topographies compared to hybrid nano-/microstructures. Nanostructured and microstructured tantalum coatings should be further evaluated to optimize the surface coating features to promote osteogenesis and enhance osseointegration of tantalum-based orthopaedic implants.

Visual search for feature conjunctions: an fMRI study comparing alcohol-related neurodevelopmental disorder (ARND) to ADHD.

PubMed

O'Conaill, Carrie R; Malisza, Krisztina L; Buss, Joan L; Bolster, R Bruce; Clancy, Christine; de Gervai, Patricia Dreessen; Chudley, Albert E; Longstaffe, Sally

2015-01-01

Alcohol-related neurodevelopmental disorder (ARND) falls under the umbrella of fetal alcohol spectrum disorder (FASD). Diagnosis of ARND is difficult because individuals do not demonstrate the characteristic facial features associated with fetal alcohol syndrome (FAS). While attentional problems in ARND are similar to those found in attention-deficit/hyperactivity disorder (ADHD), the underlying impairment in attention pathways may be different. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) was conducted at 3 T. Sixty-three children aged 10 to 14 years diagnosed with ARND, ADHD, and typically developing (TD) controls performed a single-feature and a feature-conjunction visual search task. Dorsal and ventral attention pathways were activated during both attention tasks in all groups. Significantly greater activation was observed in ARND subjects during a single-feature search as compared to TD and ADHD groups, suggesting ARND subjects require greater neural recruitment to perform this simple task. ARND subjects appear unable to effectively use the very efficient automatic perceptual 'pop-out' mechanism employed by TD and ADHD groups during presentation of the disjunction array. By comparison, activation was lower in ARND compared to TD and ADHD subjects during the more difficult conjunction search task as compared to the single-feature search. Analysis of DTI data using tract-based spatial statistics (TBSS) showed areas of significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD) in the right inferior longitudinal fasciculus (ILF) in ARND compared to TD subjects. Damage to the white matter of the ILF may compromise the ventral attention pathway and may require subjects to use the dorsal attention pathway, which is associated with effortful top-down processing, for tasks that should be automatic. Decreased functional activity in the right temporoparietal junction (TPJ) of ARND subjects may be due to a reduction in the white matter tract's ability to efficiently convey information critical to performance of the attention tasks. Limited activation patterns in ARND suggest problems in information processing along the ventral frontoparietal attention pathway. Poor integrity of the ILF, which connects the functional components of the ventral attention network, in ARND subjects may contribute to the attention deficits characteristic of the disorder.

Clinical phenotype of ASD-associated DYRK1A haploinsufficiency.

PubMed

Earl, Rachel K; Turner, Tychele N; Mefford, Heather C; Hudac, Caitlin M; Gerdts, Jennifer; Eichler, Evan E; Bernier, Raphael A

2017-01-01

DYRK1A is a gene recurrently disrupted in 0.1-0.5% of the ASD population. A growing number of case reports with DYRK1A haploinsufficiency exhibit common phenotypic features including microcephaly, intellectual disability, speech delay, and facial dysmorphisms. Phenotypic information from previously published DYRK1A cases ( n  = 51) and participants in an ongoing study at the University of Washington (UW, n  = 10) were compiled. Frequencies of recurrent phenotypic features in this population were compared to features observed in a large sample with idiopathic ASD from the Simons Simplex Collection ( n  = 1981). UW DYRK1A cases were further characterized quantitatively and compared to a randomly subsampled set of idiopathic ASD cases matched on age and gender ( n  = 10) and to cases with an ASD-associated disruptive mutation to CHD8 ( n  = 12). Contribution of familial genetic background to clinical heterogeneity was assessed by comparing head circumference, IQ, and ASD-related symptoms of UW DYRK1A cases to their unaffected parents. DYRK1A haploinsufficiency results in a common phenotypic profile including intellectual disability, speech and motor difficulties, microcephaly, feeding difficulties, and vision abnormalities. Eighty-nine percent of DYRK1A cases ascertained for ASD presented with a constellation of five or more of these symptoms. When compared quantitatively, DYRK1A cases presented with significantly lower IQ and adaptive functioning compared to idiopathic cases and significantly smaller head size compared to both idiopathic and CHD8 cases. Phenotypic variability in parental head circumference, IQ, and ASD-related symptoms corresponded to observed variability in affected child phenotype. Results confirm a core clinical phenotype for DYRK1A disruptions, with a combination of features that is distinct from idiopathic ASD. Cases with DYRK1A mutations are also distinguishable from disruptive mutations to CHD8 by head size. Measurable, quantitative characterization of DYRK1A haploinsufficiency illuminates clinical variability, which may be, in part, due to familial genetic background.

Extracting and identifying concrete structural defects in GPR images

NASA Astrophysics Data System (ADS)

Ye, Qiling; Jiao, Liangbao; Liu, Chuanxin; Cao, Xuehong; Huston, Dryver; Xia, Tian

2018-03-01

Traditionally most GPR data interpretations are performed manually. With the advancement of computing technologies, how to automate GPR data interpretation to achieve high efficiency and accuracy has become an active research subject. In this paper, analytical characterizations of major defects in concrete structures, including delamination, air void and moisture in GPR images, are performed. In the study, the image features of different defects are compared. Algorithms are developed for defect feature extraction and identification. For validations, both simulation results and field test data are utilized.

Planetary size comparisons: A photographic study

NASA Technical Reports Server (NTRS)

Meszaros, S. P.

1983-01-01

Over the past two decades NASA spacecraft missions obtained photographs permitting accurate size measurements of the planets and moons, and their surface features. Planetary global views are displayed at the same scale, in each picture to allow visual size comparisons. Additionally, special geographical features on some of the planets are compared with selected Earth areas, again at the same scale. Artist renderings and estimated sizes are used for worlds not yet reached by spacecraft. Included with each picture is number designation for use in ordering copies of the photos.

A retrospective study comparing histopathological and immunopathological features of nasal planum dermatitis in 20 dogs with discoid lupus erythematosus or leishmaniosis.

PubMed

De Lucia, Michela; Mezzalira, Giorgia; Bardagí, Mar; Fondevila, Dolors M; Fabbri, Elisabetta; Fondati, Alessandra

2017-04-01

In areas endemic for leishmaniosis, discoid lupus erythematosus (DLE) and canine leishmaniosis (CanL) are the most common differential diagnoses for nasal planum erosive-ulcerative dermatitis in dogs. To compare histopathological and immunopathological features of canine nasal planum erosive-ulcerative dermatitis with depigmentation due to DLE or CanL. Nasal planum biopsies from dogs with nasal planum loss of architecture, depigmentation, swelling, erosions or ulcerations due to DLE (n = 14) or CanL (n = 6). Sections of paraffin-embedded samples, stained with haematoxylin and eosin were reviewed. Samples were examined using antibodies targeting T cells (CD3), B cells (CD20), macrophages (Mac387) and class II major histocompatibility complex (MHC II). Histopathological and immunophenotypical findings were compared between DLE and CanL cases. Lichenoid and interface dermatitis were observed in both DLE and CanL cases. A nodular-to-diffuse, superficial and/or deep dermatitis with macrophages, lymphocytes and plasma cells was present only in CanL samples. CD20-positive cells predominated over CD3- and Mac387-positive cells in the two conditions. The percentage of dermal Mac387-positive cells was higher in CanL compared to DLE samples and the difference was statistically significant (P = 0.025). In this study, similar histopathological and immunopathological findings were observed in dogs with nasal planum lesions due to DLE or CanL. Therefore, in areas endemic for leishmaniosis, the presence of the parasite should be investigated in canine nasal planum dermatitis showing clinical and histopathological features suggestive of DLE. © 2017 ESVD and ACVD.

Magnetic Resonance Imaging-Based Assessment of Carotid Atheroma: a Comparative Study of Patients with and without Coronary Artery Disease.

PubMed

Usman, Ammara; Sadat, Umar; Teng, Zhongzhao; Graves, Martin J; Boyle, Jonathan R; Varty, Kevin; Hayes, Paul D; Gillard, Jonathan H

2017-02-01

Functional magnetic resonance (MR) imaging of atheroma using contrast media enables assessment of the systemic severity of atherosclerosis in different arterial beds. Whether black-blood imaging has similar ability remains widely unexplored. In this study, we evaluate whether black-blood imaging can differentiate carotid plaques of patients with and without coronary artery disease (CAD) in terms of morphological and biomechanical features of plaque vulnerability, thereby allowing assessment of the systemic severity nature of atherosclerosis in different arterial beds. Forty-one patients with CAD and 59 patients without CAD underwent carotid black-blood MR imaging. Plaque components were segmented to identify large lipid core (LC), ruptured fibrous cap (FC), and plaque hemorrhage (PH). These segmented contours of plaque components were used to quantify maximum structural biomechanical stress. Patients with CAD and without CAD had comparable demographics and comorbidities. Both groups had comparable prevalence of morphological features of plaque vulnerability (FC rupture, 44% versus 41%, P = .90; PH, 58% versus 47%, P = .78; large LC, 32% versus 47%, P = .17), respectively. The maximum biomechanical stress was not significantly different for both groups (241versus 278 kPa, P = .14) respectively. Black-blood imaging does not appear to have the ability to differentiate between the morphological and biomechanical features of plaque vulnerability when comparing patients with and without symptomatic atherosclerotic disease in a distant arterial territory such as coronary artery. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

The myositis autoantibody phenotypes of the juvenile idiopathic inflammatory myopathies.

PubMed

Rider, Lisa G; Shah, Mona; Mamyrova, Gulnara; Huber, Adam M; Rice, Madeline Murguia; Targoff, Ira N; Miller, Frederick W

2013-07-01

The juvenile idiopathic inflammatory myopathies (JIIM) are systemic autoimmune diseases characterized by skeletal muscle weakness, characteristic rashes, and other systemic features. In follow-up to our study defining the major clinical subgroup phenotypes of JIIM, we compared demographics, clinical features, laboratory measures, and outcomes among myositis-specific autoantibody (MSA) subgroups, as well as with published data on adult idiopathic inflammatory myopathy patients enrolled in a separate natural history study. In the present study, of 430 patients enrolled in a nationwide registry study who had serum tested for myositis autoantibodies, 374 had either a single specific MSA (n = 253) or no identified MSA (n = 121) and were the subject of the present report. Following univariate analysis, we used random forest classification and exact logistic regression modeling to compare autoantibody subgroups. Anti-p155/140 autoantibodies were the most frequent subgroup, present in 32% of patients with juvenile dermatomyositis (JDM) or overlap myositis with JDM, followed by anti-MJ autoantibodies, which were seen in 20% of JIIM patients, primarily in JDM. Other MSAs, including anti-synthetase, anti-signal recognition particle (SRP), and anti-Mi-2, were present in only 10% of JIIM patients. Features that characterized the anti-p155/140 autoantibody subgroup included Gottron papules, malar rash, "shawl-sign" rash, photosensitivity, cuticular overgrowth, lowest creatine kinase (CK) levels, and a predominantly chronic illness course. The features that differed for patients with anti-MJ antibodies included muscle cramps, dysphonia, intermediate CK levels, a high frequency of hospitalization, and a monocyclic disease course. Patients with anti-synthetase antibodies had higher frequencies of interstitial lung disease, arthralgia, and "mechanic's hands," and had an older age at diagnosis. The anti-SRP group, which had exclusively juvenile polymyositis, was characterized by high frequencies of black race, severe onset, distal weakness, falling episodes, Raynaud phenomenon, cardiac involvement, high CK levels, chronic disease course, frequent hospitalization, and wheelchair use. Characteristic features of the anti-Mi-2 subgroup included Hispanic ethnicity, classic dermatomyositis and malar rashes, high CK levels, and very low mortality. Finally, the most common features of patients without any currently defined MSA or myositis-associated autoantibodies included linear extensor erythema, arthralgia, and a monocyclic disease course. Several demographic and clinical features were shared between juvenile and adult idiopathic inflammatory myopathy subgroups, but with several important differences. We conclude that juvenile myositis is a heterogeneous group of illnesses with distinct autoantibody phenotypes defined by varying clinical and demographic characteristics, laboratory features, and outcomes.

The Myositis Autoantibody Phenotypes of the Juvenile Idiopathic Inflammatory Myopathies

PubMed Central

Shah, Mona; Mamyrova, Gulnara; Huber, Adam M.; Rice, Madeline Murguia; Targoff, Ira N.; Miller, Frederick W.

2013-01-01

Abstract The juvenile idiopathic inflammatory myopathies (JIIM) are systemic autoimmune diseases characterized by skeletal muscle weakness, characteristic rashes, and other systemic features. In follow-up to our study defining the major clinical subgroup phenotypes of JIIM, we compared demographics, clinical features, laboratory measures, and outcomes among myositis-specific autoantibody (MSA) subgroups, as well as with published data on adult idiopathic inflammatory myopathy patients enrolled in a separate natural history study. In the present study, of 430 patients enrolled in a nationwide registry study who had serum tested for myositis autoantibodies, 374 had either a single specific MSA (n = 253) or no identified MSA (n = 121) and were the subject of the present report. Following univariate analysis, we used random forest classification and exact logistic regression modeling to compare autoantibody subgroups. Anti-p155/140 autoantibodies were the most frequent subgroup, present in 32% of patients with juvenile dermatomyositis (JDM) or overlap myositis with JDM, followed by anti-MJ autoantibodies, which were seen in 20% of JIIM patients, primarily in JDM. Other MSAs, including anti-synthetase, anti-signal recognition particle (SRP), and anti-Mi-2, were present in only 10% of JIIM patients. Features that characterized the anti-p155/140 autoantibody subgroup included Gottron papules, malar rash, “shawl-sign” rash, photosensitivity, cuticular overgrowth, lowest creatine kinase (CK) levels, and a predominantly chronic illness course. The features that differed for patients with anti-MJ antibodies included muscle cramps, dysphonia, intermediate CK levels, a high frequency of hospitalization, and a monocyclic disease course. Patients with anti-synthetase antibodies had higher frequencies of interstitial lung disease, arthralgia, and “mechanic’s hands,” and had an older age at diagnosis. The anti-SRP group, which had exclusively juvenile polymyositis, was characterized by high frequencies of black race, severe onset, distal weakness, falling episodes, Raynaud phenomenon, cardiac involvement, high CK levels, chronic disease course, frequent hospitalization, and wheelchair use. Characteristic features of the anti-Mi-2 subgroup included Hispanic ethnicity, classic dermatomyositis and malar rashes, high CK levels, and very low mortality. Finally, the most common features of patients without any currently defined MSA or myositis-associated autoantibodies included linear extensor erythema, arthralgia, and a monocyclic disease course. Several demographic and clinical features were shared between juvenile and adult idiopathic inflammatory myopathy subgroups, but with several important differences. We conclude that juvenile myositis is a heterogeneous group of illnesses with distinct autoantibody phenotypes defined by varying clinical and demographic characteristics, laboratory features, and outcomes. PMID:23877355

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

Ballering, Nicholas P.; Rieke, George H.; Gáspár, András, E-mail: ballerin@email.arizona.edu

Observations of debris disks allow for the study of planetary systems, even where planets have not been detected. However, debris disks are often only characterized by unresolved infrared excesses that resemble featureless blackbodies, and the location of the emitting dust is uncertain due to a degeneracy with the dust grain properties. Here, we characterize the Spitzer Infrared Spectrograph spectra of 22 debris disks exhibiting 10 μm silicate emission features. Such features arise from small warm dust grains, and their presence can significantly constrain the orbital location of the emitting debris. We find that these features can be explained by themore » presence of an additional dust component in the terrestrial zones of the planetary systems, i.e., an exozodiacal belt. Aside from possessing exozodiacal dust, these debris disks are not particularly unique; their minimum grain sizes are consistent with the blowout sizes of their systems, and their brightnesses are comparable to those of featureless warm debris disks. These disks are in systems of a range of ages, though the older systems with features are found only around A-type stars. The features in young systems may be signatures of terrestrial planet formation. Analyzing the spectra of unresolved debris disks with emission features may be one of the simplest and most accessible ways to study the terrestrial regions of planetary systems.« less

An investigation of sex differences in acoustic features in black-capped chickadee (Poecile atricapillus) chick-a-dee calls.

PubMed

Campbell, Kimberley A; Hahn, Allison H; Congdon, Jenna V; Sturdy, Christopher B

2016-09-01

Sex differences have been identified in a number of black-capped chickadee vocalizations and in the chick-a-dee calls of other chickadee species [i.e., Carolina chickadees (Poecile carolinensis)]. In the current study, 12 acoustic features in black-capped chickadee chick-a-dee calls were investigated, including both frequency and duration measurements. Using permuted discriminant function analyses, these features were examined to determine if any features could be used to identify the sex of the caller. Only one note type (A notes) classified male and female calls at levels approaching significance. In particular, a permuted discriminant function analysis revealed that the start frequency of A notes best allowed for categorization between the sexes compared to any other acoustic parameter. This finding is consistent with previous research on Carolina chickadee chick-a-dee calls that found that the starting frequency differed between male- and female-produced A notes [Freeberg, Lucas, and Clucas (2003). J. Acoust. Soc. Am. 113, 2127-2136]. Taken together, these results and the results of studies with other chickadee species suggest that sex differences likely exist in the chick-a-dee call, specifically acoustic features in A notes, but that more complex features than those addressed here may be associated with the sex of the caller.

Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis.

PubMed

Yeung, Wing-Fai; Chung, Ka-Fai; Zhang, Nevin Lian-Wen; Zhang, Shi Ping; Yung, Kam-Ping; Chen, Pei-Xian; Ho, Yan-Yee

2016-01-01

Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes. One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes. The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into fire, Liver fire flaming upward, Stomach disharmony, Hyperactivity of fire due to yin deficiency, Heart-kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified. Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical feature co-occurrence patterns, four mutual-exclusion patterns, and one pattern with a single clinical feature variable.

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study.

PubMed

A Santos, Jose C; Nassif, Houssam; Page, David; Muggleton, Stephen H; E Sternberg, Michael J

2012-07-11

There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

The role of macrophages and eosinophils in reactive lesions of the oral cavity.

PubMed

Aghbali, Amir Ala; Akbarzadeh, Ayshin; Kouhsoltani, Maryam

2018-01-01

Many studies have reported that macrophages and eosinophils are involved in the pathogenesis of several diseases. To the best of our knowledge, this is the first study comparing macrophages and eosinophils in oral reactive lesions. In this study, we aimed to determine the contribution of macrophages and eosinophils to the pathogenesis of oral reactive lesions and the relationships between these biomarkers and the diverse histopathologic features. Seventy-five paraffin-embedded tissue samples were assessed in this study. Five categories (15 cases for each group), including peripheral ossifying fibroma, pyogenic granuloma, fibroma, inflammatory fibrous hyperplasia, and peripheral giant-cell granuloma, were considered. Anti-CD68 immunohistochemical and hematoxylin-eosin staining was carried out. We found that macrophages, but not eosinophils, were a significant internal component of oral reactive lesions. Macrophages were observed in high densities in all studied groups and diffusely distributed or clustered throughout these lesions. The number of macrophages was increased in peripheral giant-cell granuloma compared with other groups. Our findings suggest that macrophages are involved in the pathogenesis and the variation of microscopic features of oral reactive lesions. However, further clinical studies should be conducted to identify the biological process behind macrophages and the molecular interactions of these cells, with the ultimate aim of suggesting a new potential therapeutic target for these lesions. We found that eosinophils were not involved in the fibrotic process and the variation of microscopic features in oral reactive lesions. Our results showed that peripheral giant-cell granulomas highly demonstrated histiocytic characteristics.

Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their unaffected siblings.

PubMed

Wang, Jicai; Cao, Hongbao; Liao, Yanhui; Liu, Weiqing; Tan, Liwen; Tang, Yanqing; Chen, Jindong; Xu, Xiufeng; Li, Haijun; Luo, Chunrong; Liu, Chunyu; Ries Merikangas, Kathleen; Calhoun, Vince; Tang, Jinsong; Shugart, Yin Yao; Chen, Xiaogang

2015-01-01

Among individuals diagnosed with schizophrenia, approximately 20%-33% are recognized as treatment-resistant schizophrenia (TRS) patients. These TRS patients suffer more severely from the disease but struggle to benefit from existing antipsychotic treatments. A few recent studies suggested that schizophrenia may be caused by impaired synaptic plasticity that manifests as functional dysconnectivity in the brain, however, few of those studies focused on the functional connectivity changes in the brains of TRS groups. In this study, we compared the whole brain connectivity variations in TRS patients, their unaffected siblings, and healthy controls. Connectivity network features between and within the 116 automated anatomical labeling (AAL) brain regions were calculated and compared using maps created with three contrasts: patient vs. control, patient vs. sibling, and sibling vs. To evaluate the predictive power of the selected features, we performed a multivariate classification approach. We also evaluated the influence of six important clinical measures (e.g. age, education level) on the connectivity features. This study identified abnormal significant connectivity changes of three patterns in TRS patients and their unaffected siblings: 1) 69 patient-specific connectivity (PCN); 2) 102 shared connectivity (SCN); and 3) 457 unshared connectivity (UCN). While the first two patterns were widely reported by previous non-TRS specific studies, we were among the first to report widespread significant connectivity differences between TRS patient groups and their healthy sibling groups. Observations of this study may provide new insights for the understanding of the neurophysiological mechanisms of TRS.

Stream specificity and asymmetries in feature binding and content-addressable access in visual encoding and memory.

PubMed

Huynh, Duong L; Tripathy, Srimant P; Bedell, Harold E; Ögmen, Haluk

2015-01-01

Human memory is content addressable-i.e., contents of the memory can be accessed using partial information about the bound features of a stored item. In this study, we used a cross-feature cuing technique to examine how the human visual system encodes, binds, and retains information about multiple stimulus features within a set of moving objects. We sought to characterize the roles of three different features (position, color, and direction of motion, the latter two of which are processed preferentially within the ventral and dorsal visual streams, respectively) in the construction and maintenance of object representations. We investigated the extent to which these features are bound together across the following processing stages: during stimulus encoding, sensory (iconic) memory, and visual short-term memory. Whereas all features examined here can serve as cues for addressing content, their effectiveness shows asymmetries and varies according to cue-report pairings and the stage of information processing and storage. Position-based indexing theories predict that position should be more effective as a cue compared to other features. While we found a privileged role for position as a cue at the stimulus-encoding stage, position was not the privileged cue at the sensory and visual short-term memory stages. Instead, the pattern that emerged from our findings is one that mirrors the parallel processing streams in the visual system. This stream-specific binding and cuing effectiveness manifests itself in all three stages of information processing examined here. Finally, we find that the Leaky Flask model proposed in our previous study is applicable to all three features.

Effects of spatial cues on color-change detection in humans

PubMed Central

Herman, James P.; Bogadhi, Amarender R.; Krauzlis, Richard J.

2015-01-01

Studies of covert spatial attention have largely used motion, orientation, and contrast stimuli as these features are fundamental components of vision. The feature dimension of color is also fundamental to visual perception, particularly for catarrhine primates, and yet very little is known about the effects of spatial attention on color perception. Here we present results using novel dynamic color stimuli in both discrimination and color-change detection tasks. We find that our stimuli yield comparable discrimination thresholds to those obtained with static stimuli. Further, we find that an informative spatial cue improves performance and speeds response time in a color-change detection task compared with an uncued condition, similar to what has been demonstrated for motion, orientation, and contrast stimuli. Our results demonstrate the use of dynamic color stimuli for an established psychophysical task and show that color stimuli are well suited to the study of spatial attention. PMID:26047359

Single-accelerometer-based daily physical activity classification.

PubMed

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

2009-01-01

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

A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques

NASA Astrophysics Data System (ADS)

Uswatun Khasanah, Annisa; Harwati

2017-06-01

Student’s performance prediction is essential to be conducted for a university to prevent student fail. Number of student drop out is one of parameter that can be used to measure student performance and one important point that must be evaluated in Indonesia university accreditation. Data Mining has been widely used to predict student’s performance, and data mining that applied in this field usually called as Educational Data Mining. This study conducted Feature Selection to select high influence attributes with student performance in Department of Industrial Engineering Universitas Islam Indonesia. Then, two popular classification algorithm, Bayesian Network and Decision Tree, were implemented and compared to know the best prediction result. The outcome showed that student’s attendance and GPA in the first semester were in the top rank from all Feature Selection methods, and Bayesian Network is outperforming Decision Tree since it has higher accuracy rate.

Clinical Features, Etiology, and Outcomes of Community-Acquired Pneumonia in Patients With Diabetes Mellitus

PubMed Central

Di Yacovo, Silvana; Garcia-Vidal, Carolina; Viasus, Diego; Adamuz, Jordi; Oriol, Isabel; Gili, Francesca; Vilarrasa, Núria; García-Somoza, M. Dolors; Dorca, Jordi; Carratalà, Jordi

2013-01-01

Abstract We performed an observational analysis of a prospective cohort of immunocompetent hospitalized adults with community-acquired pneumonia (CAP) to determine the epidemiology, clinical features, and outcomes of pneumonia in patients with diabetes mellitus (DM). We also analyzed the risk factors for mortality and the impact of statins and other cardiovascular drugs on outcomes. Of 2407 CAP episodes, 516 (21.4%) occurred in patients with DM; 483 (97%) had type 2 diabetes, 197 (40%) were on insulin treatment, and 119 (23.9%) had end-organ damage related to DM. Patients with DM had different clinical features compared to the other patients. They were less likely to have acute onset, cough, purulent sputum, and pleural chest pain. No differences in etiology were found between study groups. Patients with DM had more inhospital acute metabolic complications, although the case-fatality rate was similar between the groups. Independent risk factors for mortality in patients with DM were advanced age, bacteremia, septic shock, and gram-negative pneumonia. Patients with end-organ damage related to DM had more inhospital cardiac events and a higher early case-fatality rate than did the overall population. The use of statins and other cardiovascular drugs was not associated with better CAP outcomes in patients with DM. In conclusion, CAP in patients with DM presents different clinical features compared to the features of patients without DM. PMID:23263718

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

Near-death experiences in non-life-threatening events and coma of different etiologies

PubMed Central

Charland-Verville, Vanessa; Jourdan, Jean-Pierre; Thonnard, Marie; Ledoux, Didier; Donneau, Anne-Francoise; Quertemont, Etienne; Laureys, Steven

2014-01-01

Background: Near death experiences (NDEs) are increasingly being reported as a clearly identifiable physiological and psychological reality of clinical significance. However, the definition and causes of the phenomenon as well as the identification of NDE experiencers is still a matter of debate. To date, the most widely used standardized tool to identify and characterize NDEs in research is the Greyson NDE scale. Using this scale, retrospective and prospective studies have been trying to estimate their incidence in various populations but few studies have attempted to associate the experiences' intensity and content to etiology. Methods: This retrospective investigation assessed the intensity and the most frequently recounted features of self-reported NDEs after a non-life-threatening event (i.e., “NDE-like” experience) or after a pathological coma (i.e., “real NDE”) and according to the etiology of the acute brain insult. We also compared our retrospectively acquired data in anoxic coma with historical data from the published literature on prospective post-anoxic studies using the Greyson NDE scale. Results: From our 190 reports who met the criteria for NDE (i.e., Greyson NDE scale total score >7/32), intensity (i.e., Greyson NDE scale total score) and content (i.e., Greyson NDE scale features) did not differ between “NDE-like” (n = 50) and “real NDE” (n = 140) groups, nor within the “real NDE” group depending on the cause of coma (anoxic/traumatic/other). The most frequently reported feature was peacefulness (89–93%). Only 2 patients (1%) recounted a negative experience. The overall NDE core features' frequencies were higher in our retrospective anoxic cohort when compared to historical published prospective data. Conclusions: It appears that “real NDEs” after coma of different etiologies are similar to “NDE-like” experiences occurring after non-life threatening events. Subjects reporting NDEs retrospectively tend to have experienced a different content compared to the prospective experiencers. PMID:24904345

Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.

PubMed

Patel, Bhavika K; Ranjbar, Sara; Wu, Teresa; Pockaj, Barbara A; Li, Jing; Zhang, Nan; Lobbes, Mark; Zhang, Bin; Mitchell, J Ross

2018-01-01

To evaluate whether the use of a computer-aided diagnosis-contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists. This IRB-approved retrospective study analyzed 50 lesions described on CESM from August 2014 to December 2015. Histopathologic analyses, used as the criterion standard, revealed 24 benign and 26 malignant lesions. An expert breast radiologist manually outlined lesion boundaries on the different views. A set of morphologic and textural features were then extracted from the low-energy and recombined images. Machine-learning algorithms with feature selection were used along with statistical analysis to reduce, select, and combine features. Selected features were then used to construct a predictive model using a support vector machine (SVM) classification method in a leave-one-out-cross-validation approach. The classification performance was compared against the diagnostic predictions of 2 breast radiologists with access to the same CESM cases. Based on the SVM classification, CAD-CESM correctly identified 45 of 50 lesions in the cohort, resulting in an overall accuracy of 90%. The detection rate for the malignant group was 88% (3 false-negative cases) and 92% for the benign group (2 false-positive cases). Compared with the model, radiologist 1 had an overall accuracy of 78% and a detection rate of 92% (2 false-negative cases) for the malignant group and 62% (10 false-positive cases) for the benign group. Radiologist 2 had an overall accuracy of 86% and a detection rate of 100% for the malignant group and 71% (8 false-positive cases) for the benign group. The results of our feasibility study suggest that a CAD-CESM tool can provide complementary information to radiologists, mainly by reducing the number of false-positive findings. Copyright © 2017 Elsevier B.V. All rights reserved.

Near-death experiences in non-life-threatening events and coma of different etiologies.

PubMed

Charland-Verville, Vanessa; Jourdan, Jean-Pierre; Thonnard, Marie; Ledoux, Didier; Donneau, Anne-Francoise; Quertemont, Etienne; Laureys, Steven

2014-01-01

Near death experiences (NDEs) are increasingly being reported as a clearly identifiable physiological and psychological reality of clinical significance. However, the definition and causes of the phenomenon as well as the identification of NDE experiencers is still a matter of debate. To date, the most widely used standardized tool to identify and characterize NDEs in research is the Greyson NDE scale. Using this scale, retrospective and prospective studies have been trying to estimate their incidence in various populations but few studies have attempted to associate the experiences' intensity and content to etiology. This retrospective investigation assessed the intensity and the most frequently recounted features of self-reported NDEs after a non-life-threatening event (i.e., "NDE-like" experience) or after a pathological coma (i.e., "real NDE") and according to the etiology of the acute brain insult. We also compared our retrospectively acquired data in anoxic coma with historical data from the published literature on prospective post-anoxic studies using the Greyson NDE scale. From our 190 reports who met the criteria for NDE (i.e., Greyson NDE scale total score >7/32), intensity (i.e., Greyson NDE scale total score) and content (i.e., Greyson NDE scale features) did not differ between "NDE-like" (n = 50) and "real NDE" (n = 140) groups, nor within the "real NDE" group depending on the cause of coma (anoxic/traumatic/other). The most frequently reported feature was peacefulness (89-93%). Only 2 patients (1%) recounted a negative experience. The overall NDE core features' frequencies were higher in our retrospective anoxic cohort when compared to historical published prospective data. It appears that "real NDEs" after coma of different etiologies are similar to "NDE-like" experiences occurring after non-life threatening events. Subjects reporting NDEs retrospectively tend to have experienced a different content compared to the prospective experiencers.

Psychosis in autism: comparison of the features of both conditions in a dually affected cohort†

PubMed Central

Larson, Felicity V.; Wagner, Adam P.; Jones, Peter B.; Tantam, Digby; Lai, Meng-Chuan; Baron-Cohen, Simon; Holland, Anthony J.

2017-01-01

Background There is limited information on the presentation and characteristics of psychotic illness experienced by people with autism spectrum disorder (ASD). Aims To describe autistic and psychotic phenomenology in a group of individuals with comorbid ASD and psychosis (ASD–P) and compare this group with populations affected by either, alone. Method We studied 116 individuals with ASD–P. We compared features of their ASD with people with ASD and no comorbid psychosis (ASD–NP), and clinical characteristics of psychosis in ASD–P with people with psychosis only. Results Individuals with ASD–P had more diagnoses of atypical psychosis and fewer of schizophrenia compared with individuals with psychosis only. People with ASD–P had fewer stereotyped interests/behaviours compared with those with ASD–NP. Conclusions Our data show there may be a specific subtype of ASD linked to comorbid psychosis. The results support findings that psychosis in people with ASD is often atypical, particularly regarding affective disturbance. PMID:27979819

Accuracy and variability of texture-based radiomics features of lung lesions across CT imaging conditions

NASA Astrophysics Data System (ADS)

Zheng, Yuese; Solomon, Justin; Choudhury, Kingshuk; Marin, Daniele; Samei, Ehsan

2017-03-01

Texture analysis for lung lesions is sensitive to changing imaging conditions but these effects are not well understood, in part, due to a lack of ground-truth phantoms with realistic textures. The purpose of this study was to explore the accuracy and variability of texture features across imaging conditions by comparing imaged texture features to voxel-based 3D printed textured lesions for which the true values are known. The seven features of interest were based on the Grey Level Co-Occurrence Matrix (GLCM). The lesion phantoms were designed with three shapes (spherical, lobulated, and spiculated), two textures (homogenous and heterogeneous), and two sizes (diameter < 1.5 cm and 1.5 cm < diameter < 3 cm), resulting in 24 lesions (with a second replica of each). The lesions were inserted into an anthropomorphic thorax phantom (Multipurpose Chest Phantom N1, Kyoto Kagaku) and imaged using a commercial CT system (GE Revolution) at three CTDI levels (0.67, 1.42, and 5.80 mGy), three reconstruction algorithms (FBP, IR-2, IR-4), four reconstruction kernel types (standard, soft, edge), and two slice thicknesses (0.6 mm and 5 mm). Another repeat scan was performed. Texture features from these images were extracted and compared to the ground truth feature values by percent relative error. The variability across imaging conditions was calculated by standard deviation across a certain imaging condition for all heterogeneous lesions. The results indicated that the acquisition method has a significant influence on the accuracy and variability of extracted features and as such, feature quantities are highly susceptible to imaging parameter choices. The most influential parameters were slice thickness and reconstruction kernels. Thin slice thickness and edge reconstruction kernel overall produced more accurate and more repeatable results. Some features (e.g., Contrast) were more accurately quantified under conditions that render higher spatial frequencies (e.g., thinner slice thickness and sharp kernels), while others (e.g., Homogeneity) showed more accurate quantification under conditions that render smoother images (e.g., higher dose and smoother kernels). Care should be exercised is relating texture features between cases of varied acquisition protocols, with need to cross calibration dependent on the feature of interest.

Impairment and Coping in Children and Adolescents with Chronic Fatigue Syndrome: A Comparative Study with Other Paediatric Disorders

ERIC Educational Resources Information Center

Garralda, M. Elena; Rangel, Luiza

2004-01-01

Background: Functional impairment is a key feature of chronic fatigue syndrome (CFS) of childhood. Aim: To compare impairment, illness attitudes and coping mechanisms in childhood CFS and in other paediatric disorders. Method: Participants were 28 children and adolescents with CFS, 30 with juvenile idiopathic arthritis (JIA) and 27 with emotional…

A Comparative Study on the Governance of Education for Older People in Japan and Korea

ERIC Educational Resources Information Center

Choi, Ilseon; Hori, Shigeo

2016-01-01

This paper compares the governance of education for older people in Japan and Korea. The findings revealed that the overall mechanisms of governance for the education of older people shared a number of similar features such as the structure of relevant laws, ministries, and policies. However, differences were also found regarding independence of…

Funding Systems for Higher Education and Their Impacts on Institutional Strategies and Academia: A Comparative Perspective

ERIC Educational Resources Information Center

Frolich, Nicoline; Kalpazidou Schmidt, Evanthia; Rosa, Maria J.

2010-01-01

Purpose: The purpose of this paper is to discuss how funding systems influence higher education institutions and their strategies and core tasks. Design/methodology/approach: Taking the results of a comparative study between Denmark, Norway and Portugal as a point of departure, the paper identifies and analyses the main features of these state…

Comparative Analysis of Western and Domestic Practice of Interactive Method Application in Teaching Social and Political Disciplines at the Universities

ERIC Educational Resources Information Center

Hladka, Halyna

2014-01-01

The comparative analysis of western and domestic practice of introducing active and interactive methods of studies in the process of teaching social science disciplines has been carried out. Features, realities, prospects and limitations in application of interactive methods of teaching in the process of implementing social-political science…

[THE FEATURES OF PHARMACOKINETICS ANTIBIOTIC CEFTRIAXONE WITH INTRAVENOUS WAY THAT ARE DEPOSITED IN AUTOLOGOUS ERYTHROCYTES AND LEUKOCYTES OF RABBIT].

PubMed

Yussifov, Z; Lokhvitskii, S; Gulyaev, A

2016-11-01

In the experiment on 18 rabbits Сeftriaxone pharmacokinetics after intravenous injection of the medication deposited in autologous erythrocytes and leukocytes were studied. The features of the pharmacokinetics when administered Сeftriaxone in erythrocytes ghost and leukocytes as compared to traditional intravenous drug administration have been determined.It is discussed the possibility of antibiotics transport in the surgical site of infection via cellular carriers in the article. We do the comparative analysis of the main pharmacokinetic parameters of Ceftriaxone in experimental conditions of leukocyte, erythrocyte transport and intravenous way. Based on these results the authors come to the conclusion about the benefits of leukocyte antibiotic transport to the site of surgical infection.

A Narrative Evaluation of Mandarin-Speaking Children With Language Impairment.

PubMed

Hao, Ying; Sheng, Li; Zhang, Yiwen; Jiang, Fan; de Villiers, Jill; Lee, Wendy; Liu, Xueman Lucy

2018-02-15

We aimed to study narrative skills in Mandarin-speaking children with language impairment (LI) to compare with children with LI speaking Indo-European languages. Eighteen Mandarin-speaking children with LI (mean age 6;2 [years;months]) and 18 typically developing (TD) age controls told 3 stories elicited using the Mandarin Expressive Narrative Test (de Villiers & Liu, 2014). We compared macrostructure-evaluating descriptions of characters, settings, initiating events, internal responses,plans, actions, and consequences. We also studied general microstructure, including productivity, lexical diversity, syntactic complexity, and grammaticality. In addition, we compared the use of 6 fine-grained microstructure elements that evaluate particular Mandarin linguistic features. Children with LI exhibited weaknesses in 5 macrostructure elements, lexical diversity, syntactic complexity, and 3 Mandarin-specific, fine-grained microstructure elements. Children with LI and TD controls demonstrated comparable performance on 2 macrostructure elements, productivity, grammaticality, and the remaining 3 fine-grained microstructure features. Similarities and differences are noted in narrative profiles of children with LI who speak Mandarin versus those who speak Indo-European languages. The results are consistent with the view that profiles of linguistic deficits are shaped by the ambient language. Clinical implications are discussed.

Student Use of Scaffolding Software: Relationships with Motivation and Conceptual Understanding

NASA Astrophysics Data System (ADS)

Butler, Kyle A.; Lumpe, Andrew

2008-10-01

This study was designed to theoretically articulate and empirically assess the role of computer scaffolds. In this project, several examples of educational software were developed to scaffold the learning of students performing high level cognitive activities. The software used in this study, Artemis, focused on scaffolding the learning of students as they performed information seeking activities. As 5th grade students traveled through a project-based science unit on photosynthesis, researchers used a pre-post design to test for both student motivation and student conceptual understanding of photosynthesis. To measure both variables, a motivation survey and three methods of concept map analysis were used. The student use of the scaffolding features was determined using a database that tracked students' movement between scaffolding tools. The gain scores of each dependent variable was then correlated to the students' feature use (time and hits) embedded in the Artemis Interface. This provided the researchers with significant relationships between the scaffolding features represented in the software and student motivation and conceptual understanding of photosynthesis. There were a total of three significant correlations in comparing the scaffolding use by hits (clicked on) with the dependent variables and only one significant correlation when comparing the scaffold use in time. The first significant correlation ( r = .499, p < .05) was between the saving/viewing features hits and the students' task value. This correlation supports the assumption that there is a positive relationship between the student use of the saving/viewing features and the students' perception of how interesting, how important, and how useful the task is. The second significant correlation ( r = 0.553, p < 0.01) was between the searching features hits and the students' self-efficacy for learning and performance. This correlation supports the assumption that there is a positive relationship between the student use of the searching features and the students' perception of their ability to accomplish a task as well as their confidence in their skills to perform that task. The third significant correlation ( r = 0.519, p < 0.05) was between the collaborative features hits and the students' essay performance scores. This correlation supports the assumption that there is a positive relationship between the student use of the collaborative features and the students' ability to perform high cognitive tasks. Finally, the last significant correlation ( r = 0.576, p < 0.01) was between the maintenance features time and the qualitative analysis of the concept maps. This correlation supports the assumption that there is a positive relationship between the student use of the maintenance features and student conceptual understanding of photosynthesis.

Appropriateness guidelines and predictive rules to select patients for upper endoscopy: a nationwide multicenter study.

PubMed

Buri, Luigi; Hassan, Cesare; Bersani, Gianluca; Anti, Marcello; Bianco, Maria Antonietta; Cipolletta, Livio; Di Giulio, Emilio; Di Matteo, Giovanni; Familiari, Luigi; Ficano, Leonardo; Loriga, Pietro; Morini, Sergio; Pietropaolo, Vincenzo; Zambelli, Alessandro; Grossi, Enzo; Intraligi, Marco; Buscema, Massimo

2010-06-01

Selecting patients appropriately for upper endoscopy (EGD) is crucial for efficient use of endoscopy. The objective of this study was to compare different clinical strategies and statistical methods to select patients for EGD, namely appropriateness guidelines, age and/or alarm features, and multivariate and artificial neural network (ANN) models. A nationwide, multicenter, prospective study was undertaken in which consecutive patients referred for EGD during a 1-month period were enrolled. Before EGD, the endoscopist assessed referral appropriateness according to the American Society for Gastrointestinal Endoscopy (ASGE) guidelines, also collecting clinical and demographic variables. Outcomes of the study were detection of relevant findings and new diagnosis of malignancy at EGD. The accuracy of the following clinical strategies and predictive rules was compared: (i) ASGE appropriateness guidelines (indicated vs. not indicated), (ii) simplified rule (>or=45 years or alarm features vs. <45 years without alarm features), (iii) logistic regression model, and (iv) ANN models. A total of 8,252 patients were enrolled in 57 centers. Overall, 3,803 (46%) relevant findings and 132 (1.6%) new malignancies were detected. Sensitivity, specificity, and area under the receiver-operating characteristic curve (AUC) of the simplified rule were similar to that of the ASGE guidelines for both relevant findings (82%/26%/0.55 vs. 88%/27%/0.52) and cancer (97%/22%/0.58 vs. 98%/20%/0.58). Both logistic regression and ANN models seemed to be substantially more accurate in predicting new cases of malignancy, with an AUC of 0.82 and 0.87, respectively. A simple predictive rule based on age and alarm features is similarly effective to the more complex ASGE guidelines in selecting patients for EGD. Regression and ANN models may be useful in identifying a relatively small subgroup of patients at higher risk of cancer.

Early Pleistocene hominin deciduous teeth from the Homo antecessor Gran Dolina-TD6 bearing level (Sierra de Atapuerca, Spain).

PubMed

Bermúdez de Castro, José María; Martinón-Torres, María; Martín-Francés, Laura; Martínez de Pinillos, Marina; Modesto-Mata, Mario; García-Campos, Cecilia; Wu, Xiujie; Xing, Song; Liu, Wu

2017-07-01

During the last 13 years, the late Early Pleistocene Gran Dolina-TD6-2 level (Sierra de Atapuerca, northern Spain) has yielded an additional sample of 26 dental specimens attributed to Homo antecessor. In this report, we present a descriptive and comparative study of the six deciduous teeth. We provide external and internal morphological descriptions following classical terminology, as well as the mesiodistal and buccolingual measurements of the teeth. The internal morphology was described by means of micro-CT technique. The TD6 deciduous teeth preserve primitive features regarding the Homo clade, such as the presence of styles in lower and upper canines and developed anterior and posterior foveae in the dm 2 . However, other features related to the complexity of the crown morphology (e.g., cingulum) are not present in this sample. Furthermore, the great reduction of the talonid of the dm 1 s is also noteworthy. Despite the limited comparative evidence, the presence of a remarkably well-developed tuberculum molare in the dm 1 and dm 1 s from TD6 can be also considered a derived feature in the genus Homo. The TD6 hominins exhibit dental dimensions similar to those of other Pleistocene hominins. The dm 1 s are buccolingually elongated and the buccolingual diameter of ATD6-93 is the largest recorded so far in the Homo fossil record. This study expands the list of plesiomorphic features of H. antecessor, and provides some information on the evolutionary status of this species. However, the identification of some advanced traits evinces a step towards the derived morphology of European Pleistocene teeth. The study of the deciduous dentition confirms the mosaic pattern of H. antecessor morphology revealed in previous studies of this hominin sample. © 2017 Wiley Periodicals, Inc.

A survey of Autism knowledge and attitudes among the healthcare professionals in Lahore, Pakistan

PubMed Central

2011-01-01

Background The diagnosis and treatment of Autism in Pakistan occurs in multiple settings and is provided by variety of health professionals. Unfortunately, knowledge and awareness about Autism is low among Pakistani healthcare professionals & the presence of inaccurate and outdated beliefs regarding this disorder may compromise early detection and timely referral for interventions. The study assessed the baseline knowledge and misconceptions regarding autism among healthcare professionals in Pakistan which can impact future awareness campaigns. Methods Physicians (psychiatrists, pediatricians, neurologists and family physicians) and non-physicians (psychologists and speech therapists) participated in this study. Knowledge of DSM-IV TR criteria for Autistic Disorder, beliefs about social, emotional, cognitive, treatment and prognosis of the disorder were assessed. Demographic information regarding the participants of the survey was also gathered. Results Two hundred and forty seven respondents (154 Physicians & 93 Non-physicians) participated in the study. Mean age of respondents was 33.2 years (S.D 11.63) with 53% being females. Reasonably accurate familiarity with the DSM IV-TR diagnostic criteria of Autistic Disorder was observed. However, within the professional groups, differences were found regarding the utilization of the DSM-IV-TR criteria when diagnosing Autistic Disorder. Non-Physicians were comparatively more likely to correctly identify diagnostic features of autism compared with Physicians (P-value <0.001). Significant misunderstandings of some of the salient features of autism were present in both professional groups. Conclusion Results suggests that current professionals in the field have an unbalanced understanding of autism due to presence of several misconceptions regarding many of the salient features of autism including developmental, cognitive and emotional features. The study has clinical implications and calls for continued education for healthcare professionals across disciplines with regards to Autism in Pakistan. PMID:22107951

A survey of Autism knowledge and attitudes among the healthcare professionals in Lahore, Pakistan.

PubMed

Imran, Nazish; Chaudry, Mansoor R; Azeem, Muhammad W; Bhatti, Muhammad R; Choudhary, Zaidan I; Cheema, Mohsin A

2011-11-22

The diagnosis and treatment of Autism in Pakistan occurs in multiple settings and is provided by variety of health professionals. Unfortunately, knowledge and awareness about Autism is low among Pakistani healthcare professionals & the presence of inaccurate and outdated beliefs regarding this disorder may compromise early detection and timely referral for interventions. The study assessed the baseline knowledge and misconceptions regarding autism among healthcare professionals in Pakistan which can impact future awareness campaigns. Physicians (psychiatrists, pediatricians, neurologists and family physicians) and non-physicians (psychologists and speech therapists) participated in this study. Knowledge of DSM-IV TR criteria for Autistic Disorder, beliefs about social, emotional, cognitive, treatment and prognosis of the disorder were assessed. Demographic information regarding the participants of the survey was also gathered. Two hundred and forty seven respondents (154 Physicians & 93 Non-physicians) participated in the study. Mean age of respondents was 33.2 years (S.D 11.63) with 53% being females. Reasonably accurate familiarity with the DSM IV-TR diagnostic criteria of Autistic Disorder was observed. However, within the professional groups, differences were found regarding the utilization of the DSM-IV-TR criteria when diagnosing Autistic Disorder. Non-Physicians were comparatively more likely to correctly identify diagnostic features of autism compared with Physicians (P-value<0.001). Significant misunderstandings of some of the salient features of autism were present in both professional groups. Results suggests that current professionals in the field have an unbalanced understanding of autism due to presence of several misconceptions regarding many of the salient features of autism including developmental, cognitive and emotional features. The study has clinical implications and calls for continued education for healthcare professionals across disciplines with regards to Autism in Pakistan.

Numerical Study of Periodic Traveling Wave Solutions for the Predator-Prey Model with Landscape Features

NASA Astrophysics Data System (ADS)

Yun, Ana; Shin, Jaemin; Li, Yibao; Lee, Seunggyu; Kim, Junseok

We numerically investigate periodic traveling wave solutions for a diffusive predator-prey system with landscape features. The landscape features are modeled through the homogeneous Dirichlet boundary condition which is imposed at the edge of the obstacle domain. To effectively treat the Dirichlet boundary condition, we employ a robust and accurate numerical technique by using a boundary control function. We also propose a robust algorithm for calculating the numerical periodicity of the traveling wave solution. In numerical experiments, we show that periodic traveling waves which move out and away from the obstacle are effectively generated. We explain the formation of the traveling waves by comparing the wavelengths. The spatial asynchrony has been shown in quantitative detail for various obstacles. Furthermore, we apply our numerical technique to the complicated real landscape features.

Content based image retrieval using local binary pattern operator and data mining techniques.

PubMed

Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

2015-01-01

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

High-performance execution of psychophysical tasks with complex visual stimuli in MATLAB

PubMed Central

Asaad, Wael F.; Santhanam, Navaneethan; McClellan, Steven

2013-01-01

Behavioral, psychological, and physiological experiments often require the ability to present sensory stimuli, monitor and record subjects' responses, interface with a wide range of devices, and precisely control the timing of events within a behavioral task. Here, we describe our recent progress developing an accessible and full-featured software system for controlling such studies using the MATLAB environment. Compared with earlier reports on this software, key new features have been implemented to allow the presentation of more complex visual stimuli, increase temporal precision, and enhance user interaction. These features greatly improve the performance of the system and broaden its applicability to a wider range of possible experiments. This report describes these new features and improvements, current limitations, and quantifies the performance of the system in a real-world experimental setting. PMID:23034363

Customer Churn Prediction for Broadband Internet Services

NASA Astrophysics Data System (ADS)

Huang, B. Q.; Kechadi, M.-T.; Buckley, B.

Although churn prediction has been an area of research in the voice branch of telecommunications services, more focused studies on the huge growth area of Broadband Internet services are limited. Therefore, this paper presents a new set of features for broadband Internet customer churn prediction, based on Henley segments, the broadband usage, dial types, the spend of dial-up, line-information, bill and payment information, account information. Then the four prediction techniques (Logistic Regressions, Decision Trees, Multilayer Perceptron Neural Networks and Support Vector Machines) are applied in customer churn, based on the new features. Finally, the evaluation of new features and a comparative analysis of the predictors are made for broadband customer churn prediction. The experimental results show that the new features with these four modelling techniques are efficient for customer churn prediction in the broadband service field.

Sex differences and within-family associations in the broad autism phenotype.

PubMed

Klusek, Jessica; Losh, Molly; Martin, Gary E

2014-02-01

While there is a strong sex bias in the presentation of autism, it is unknown whether this bias is also present in subclinical manifestations of autism among relatives, or the broad autism phenotype. This study examined this question and investigated patterns of co-occurrence of broad autism phenotype traits within families of individuals with autism. Pragmatic language and personality features of the broad autism phenotype were studied in 42 fathers and 50 mothers of individuals with autism using direct assessment tools used in prior family studies of the broad autism phenotype. Higher rates of aloof personality style were detected among fathers, while no sex differences were detected for other broad autism phenotype traits. Within individuals, pragmatic language features were associated with the social personality styles of the broad autism phenotype in mothers but not in fathers. A number of broad autism phenotype features were correlated within spousal pairs. Finally, the associations were detected between paternal broad autism phenotype characteristics and the severity of children's autism symptoms in all three domains (social, communication, and repetitive behaviors). Mother-child correlations were detected for aspects of communication only. Together, the findings suggest that most features of the broad autism phenotype express comparably in males and females and raise some specific questions about how such features might inform studies of the genetic basis of autism.

Inter- and Intra-Dimensional Dependencies in Implicit Phonotactic Learning

ERIC Educational Resources Information Center

Moreton, Elliott

2012-01-01

Is phonological learning subject to the same inductive biases as learning in other domains? Previous studies of non-linguistic learning found that intra-dimensional dependencies (between two instances of the same feature) were learned more easily than inter-dimensional ones. This study compares implicit learning of intra- and inter-dimensional…

Education, Training and Contexts: Studies and Essays.

ERIC Educational Resources Information Center

Lauglo, Jon

This volume provides an overview of some of the outstanding features of the work of the Norwegian sociologist and comparative educationist, Jon Lauglo. After an introduction, "'It Ain't Necessarily So!': Theories and Observations in Jon Lauglo's World of Education and Training" (Se-Yung Lim and Klaus Schaack), essays and studies are…

Studies in matter antimatter separation and in the origin of lunar magnetism

NASA Technical Reports Server (NTRS)

Barker, W. A.; Greeley, R.; Parkin, C.; Aggarwal, H.; Schultz, P.

1975-01-01

A progress report, covering lunar and planetary research is introduced. Data cover lunar ionospheric models, lunar and planetary geology, and lunar magnetism. Wind tunnel simulations of Mars aeolian problems and a comparative study of basaltic analogs of Lunar and Martial volcanic features was discussed.

Fast detection of covert visuospatial attention using hybrid N2pc and SSVEP features

NASA Astrophysics Data System (ADS)

Xu, Minpeng; Wang, Yijun; Nakanishi, Masaki; Wang, Yu-Te; Qi, Hongzhi; Jung, Tzyy-Ping; Ming, Dong

2016-12-01

Objective. Detecting the shift of covert visuospatial attention (CVSA) is vital for gaze-independent brain-computer interfaces (BCIs), which might be the only communication approach for severely disabled patients who cannot move their eyes. Although previous studies had demonstrated that it is feasible to use CVSA-related electroencephalography (EEG) features to control a BCI system, the communication speed remains very low. This study aims to improve the speed and accuracy of CVSA detection by fusing EEG features of N2pc and steady-state visual evoked potential (SSVEP). Approach. A new paradigm was designed to code the left and right CVSA with the N2pc and SSVEP features, which were then decoded by a classification strategy based on canonical correlation analysis. Eleven subjects were recruited to perform an offline experiment in this study. Temporal waves, amplitudes, and topographies for brain responses related to N2pc and SSVEP were analyzed. The classification accuracy derived from the hybrid EEG features (SSVEP and N2pc) was compared with those using the single EEG features (SSVEP or N2pc). Main results. The N2pc could be significantly enhanced under certain conditions of SSVEP modulations. The hybrid EEG features achieved significantly higher accuracy than the single features. It obtained an average accuracy of 72.9% by using a data length of 400 ms after the attention shift. Moreover, the average accuracy reached ˜80% (peak values above 90%) when using 2 s long data. Significance. The results indicate that the combination of N2pc and SSVEP is effective for fast detection of CVSA. The proposed method could be a promising approach for implementing a gaze-independent BCI.

Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

NASA Astrophysics Data System (ADS)

Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

2016-03-01

Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

Odyssey's end: lay conceptions of nostalgia reflect its original Homeric meaning.

PubMed

Hepper, Erica G; Ritchie, Timothy D; Sedikides, Constantine; Wildschut, Tim

2012-02-01

Nostalgia fulfills pivotal functions for individuals, but lacks an empirically derived and comprehensive definition. We examined lay conceptions of nostalgia using a prototype approach. In Study 1, participants generated open-ended features of nostalgia, which were coded into categories. In Study 2, participants rated the centrality of these categories, which were subsequently classified as central (e.g., memories, relationships, happiness) or peripheral (e.g., daydreaming, regret, loneliness). Central (as compared with peripheral) features were more often recalled and falsely recognized (Study 3), were classified more quickly (Study 4), were judged to reflect more nostalgia in a vignette (Study 5), better characterized participants' own nostalgic (vs. ordinary) experiences (Study 6), and prompted higher levels of actual nostalgia and its intrapersonal benefits when used to trigger a personal memory, regardless of age (Study 7). These findings highlight that lay people view nostalgia as a self-relevant and social blended emotional and cognitive state, featuring a mixture of happiness and loss. The findings also aid understanding of nostalgia's functions and identify new methods for future research. PsycINFO Database Record (c) 2012 APA, all rights reserved

Enhanced lines and box-shaped features in the gamma-ray spectrum from annihilating dark matter in the NMSSM

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

Cerdeño, D.G.; Peiró, M.; Robles, S., E-mail: davidg.cerdeno@gmail.com, E-mail: miguel.peiro@uam.es, E-mail: sandra.robles@uam.es

2016-04-01

We study spectral features in the gamma-ray emission from dark matter (DM) annihilation in the Next-to-Minimal Supersymmetric Standard Model (NMSSM), with either neutralino or right-handed (RH) sneutrino DM . We perform a series of scans over the NMSSM parameter space, compute the DM annihilation cross section into two photons and the contribution of box-shaped features, and compare them with the limits derived from the Fermi-LAT search for gamma-ray lines using the latest Pass 8 data. We implement the LHC bounds on the Higgs sector and on the masses of supersymmetric particles as well as the constraints on low-energy observables. Wemore » also consider the recent upper limits from the Fermi-LAT satellite on the continuum gamma-ray emission from dwarf spheroidal galaxies (dSphs). We show that in the case of the RH sneutrino the constraint on gamma-ray spectral features can be more stringent than the dSph bounds. This is due to the Breit-Wigner enhancement near the ubiquitous resonances with a CP even Higgs and the contribution of scalar and pseudoscalar Higgs final states to box-shaped features. By contrast, for neutralino DM, the di-photon final state is only enhanced in the resonance with a Z boson and box-shaped features are even more suppressed. Therefore, the observation of spectral features could constitute a discriminating factor between both models. In addition, we compare our results with direct DM searches, including the SuperCDMS and LUX limits on the elastic DM-nucleus scattering cross section and show that some of these scenarios would be accessible to next generation experiments. Thus, our findings strengthen the idea of complementarity among distinct DM search strategies.« less

Comparison of naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

NASA Astrophysics Data System (ADS)

Cary, Theodore W.; Cwanger, Alyssa; Venkatesh, Santosh S.; Conant, Emily F.; Sehgal, Chandra M.

2012-03-01

This study compares the performance of two proven but very different machine learners, Naïve Bayes and logistic regression, for differentiating malignant and benign breast masses using ultrasound imaging. Ultrasound images of 266 masses were analyzed quantitatively for shape, echogenicity, margin characteristics, and texture features. These features along with patient age, race, and mammographic BI-RADS category were used to train Naïve Bayes and logistic regression classifiers to diagnose lesions as malignant or benign. ROC analysis was performed using all of the features and using only a subset that maximized information gain. Performance was determined by the area under the ROC curve, Az, obtained from leave-one-out cross validation. Naïve Bayes showed significant variation (Az 0.733 +/- 0.035 to 0.840 +/- 0.029, P < 0.002) with the choice of features, but the performance of logistic regression was relatively unchanged under feature selection (Az 0.839 +/- 0.029 to 0.859 +/- 0.028, P = 0.605). Out of 34 features, a subset of 6 gave the highest information gain: brightness difference, margin sharpness, depth-to-width, mammographic BI-RADs, age, and race. The probabilities of malignancy determined by Naïve Bayes and logistic regression after feature selection showed significant correlation (R2= 0.87, P < 0.0001). The diagnostic performance of Naïve Bayes and logistic regression can be comparable, but logistic regression is more robust. Since probability of malignancy cannot be measured directly, high correlation between the probabilities derived from two basic but dissimilar models increases confidence in the predictive power of machine learning models for characterizing solid breast masses on ultrasound.

Cytopathology of non-invasive follicular thyroid neoplasm with papillary-like nuclear features: A comparative study with similar patterned papillary thyroid carcinoma variants.

PubMed

Mahajan, S; Agarwal, S; Kocheri, N; Jain, D; Mathur, S R; Iyer, V K

2018-06-01

Noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is a recently described, indolent thyroid tumor, with well-defined histopathological diagnostic criteria. Cytology features are not well documented. We reviewed cytology of histologically proven cases of NIFTP and some of its common differentials to look for salient diagnostic features. Cases reported on histopathology as follicular variant of papillary thyroid carcinoma (FVPTC), or NIFTP between July 2015 and April 2017 having available cytology smears were retrieved and reclassified as NIFTP, FVPTC, and classical papillary thyroid carcinoma with predominant follicular pattern (PTC-FP). Cytological features were assessed, classified as per The Bethesda System for Reporting Cytopathology and compared. There were 23 NIFTP cases, 18 FVPTC and 8 PTC-FP. A microfollicle-predominant pattern was seen in all. Nuclear score was 2 in most NIFTP cases (61%). Pseudoinclusions were absent. NIFTP showed features of atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) (III) in 61%, follicular neoplasm/suspicious for a follicular neoplasm (FN/SFN) (IV) in 35% and suspicious for malignancy (SFM) (V) in 4%. Most of the FVPTCs were also called FN/SFN (IV) (56%) or AUS/FLUS (III) (22%). Nuclear features did not statistically differ from NIFTP. PTC-FP showed high-grade cytology in 75%, and higher nuclear score (3 in 75%) in contrast to NIFTP (P = .003). NIFTP and FVPTC show a similar distribution among the Bethesda categories hence precluding conclusive distinction on cytology. PTC-FP, in contrast, was found to have a statistically significant higher nuclear score and more commonly showed malignant cytology. © 2018 John Wiley & Sons Ltd.

Individual MRI and radiographic features of knee OA in subjects with unilateral knee pain: Health ABC study

PubMed Central

Javaid, MK; Kiran, A; Guermazi, A; Kwoh, K; Zaim, S; Carbone, L; Harris, T.; McCulloch, C.E.; Arden, NK; Lane, NE; Felson, D; Nevitt, M

2012-01-01

Strong associations between radiographic features of knee OA and pain have been demonstrated in persons with unilateral knee symptoms. Our objectives were to compare radiographic with MRI features of knee OA and assess the discrimination between painful and non-painful knees in persons with unilateral symptoms. 283 individuals with unilateral knee pain aged 71 to 80 years from Health ABC, a study of weight-related diseases and mobility, had bilateral knee radiographs, read for KL grade and individual radiographic features, and 1.5T MRIs, read using WORMS. The association of structural features with pain was assessed using a within-person case/control design and conditional logistic regression. Receiver operator characteristics (ROC) were then used to test the discriminatory performance of structural features. In conditional logistic analyses, knee pain was significantly associated with both radiographic (any JSN grade >=1: OR 3.20 (1.79 – 5.71) and MRI (any cartilage defect:>=2: OR 3.67 (1.49 – 9.04)) features. However, most subjects had MR detected osteophytes, cartilage and bone marrow lesions in both knees and no individual structural feature discriminated well between painful and non-painful knees using ROC. The best performing MRI feature (synovitis/effusion) was not significantly more informative than KL grade >=2 (p=0.42). In persons with unilateral knee pain, MR and radiographic features were associated with knee pain confirming an important role in the etiology of pain. However, no single MRI or radiographic finding performed well in discriminating painful from non-painful knees. Further work is needed to examine how structural and non-structural factors influence knee pain. PMID:22736267

Human papillomavirus-related carcinoma with adenoid cystic-like features: a series of five cases expanding the pathological spectrum.

PubMed

Hang, Jen-Fan; Hsieh, Min-Shu; Li, Wing-Yin; Chen, Jo-Yu; Lin, Shih-Yao; Liu, Shih-Hao; Pan, Chin-Chen; Kuo, Ying-Ju

2017-12-01

Human papillomavirus (HPV)-related carcinoma with adenoid cystic-like features is a newly described entity of the sinonasal tract. In this study, we evaluated histomorphology, immunophenotype and molecular testing to identify potentially helpful features in distinguishing it from classic adenoid cystic carcinoma (AdCC). We retrospectively collected five HPV-related carcinomas with adenoid cystic-like features and 14 AdCCs of the sinonasal tract. All histological slides were retrieved for morphological evaluation. As comparing with AdCC, HPV-related carcinomas with adenoid cystic-like features were associated with squamous dysplasia of surface epithelium (80% versus 0%, P < 0.01) and the presence of a solid growth pattern (100% versus 29%, P = 0.01), but less densely hyalinized tumour stroma (20% versus 86%, P = 0.02). Squamous differentiation in the invasive tumour was seen in three HPV-related carcinomas with adenoid cystic-like features, two of them showing abrupt keratinization and one with scattered non-keratinizing squamous nests. Diffuse p16 staining in ≥75% of tumour cells was noted in all HPV-related carcinomas with adenoid cystic-like features but in only one AdCC (100% versus 7%, P < 0.01). High-risk HPV testing gave positive results in all HPV-related carcinomas with adenoid cystic-like features (four associated with type 33 and one associated with type 16) but not in AdCCs. MYB rearrangement was tested in four HPV-related carcinomas with adenoid cystic-like features, and all were negative. This study has further clarified the histological spectrum of this tumour type, and reports the first HPV type 16-related case. Diffuse p16 staining followed by HPV molecular testing is useful in distinguishing HPV-related carcinomas with adenoid cystic features from classic AdCCs. © 2017 John Wiley & Sons Ltd.

Features of childhood Sjögren's syndrome in comparison to adult Sjögren's syndrome: considerations in establishing child-specific diagnostic criteria.

PubMed

Yokogawa, Naoto; Lieberman, Scott M; Sherry, David D; Vivino, Frederick B

2016-01-01

To describe the clinical features of childhood Sjögren's syndrome (SS) in comparison to adult SS and to evaluate possible child-specific modifications to existing adult criteria for use in diagnosing childhood SS. We retrospectively identified children (age <18 years) with SS and compared the clinical, laboratory, and histopathological features of these children based on presence or absence of parotitis. We compared these features to adults with SS and evaluated the applicability of existing classification criteria in diagnosing childhood SS. Child-specific modifications to existing criteria were evaluated. Twenty-six children were included in our childhood SS group. Sixteen children had parotitis at or before presentation. Absence of parotitis was associated with greater degree of organ damage based on SS disease damage index. Compared to 413 adult SS patients, childhood SS was more commonly associated with parotitis, positive serologies, neurologic and nephrologic manifestations, and non-specific features (fever, lymphadenopathy) but less commonly associated with dry mouth and dry eyes. Only a minority of these children met previously established criteria for adult SS. Inclusion of child-specific features such as parotitis and the presence of any focal lymphocytic sialadenitis on minor salivary gland biopsy increased the proportion of children meeting these criteria. Childhood SS features may be different than adult SS features necessitating child-specific criteria for better diagnosis of childhood SS, a key step towards better understanding the features, prognosis, and outcomes in this disease.

Using Public Data for Comparative Proteome Analysis in Precision Medicine Programs.

PubMed

Hughes, Christopher S; Morin, Gregg B

2018-03-01

Maximizing the clinical utility of information obtained in longitudinal precision medicine programs would benefit from robust comparative analyses to known information to assess biological features of patient material toward identifying the underlying features driving their disease phenotype. Herein, the potential for utilizing publically deposited mass-spectrometry-based proteomics data to perform inter-study comparisons of cell-line or tumor-tissue materials is investigated. To investigate the robustness of comparison between MS-based proteomics studies carried out with different methodologies, deposited data representative of label-free (MS1) and isobaric tagging (MS2 and MS3 quantification) are utilized. In-depth quantitative proteomics data acquired from analysis of ovarian cancer cell lines revealed the robust recapitulation of observable gene expression dynamics between individual studies carried out using significantly different methodologies. The observed signatures enable robust inter-study clustering of cell line samples. In addition, the ability to classify and cluster tumor samples based on observed gene expression trends when using a single patient sample is established. With this analysis, relevant gene expression dynamics are obtained from a single patient tumor, in the context of a precision medicine analysis, by leveraging a large cohort of repository data as a comparator. Together, these data establish the potential for state-of-the-art MS-based proteomics data to serve as resources for robust comparative analyses in precision medicine applications. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

NASA Astrophysics Data System (ADS)

Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

2017-04-01

Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

PubMed

Yu, Sheng; Liao, Katherine P; Shaw, Stanley Y; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Cai, Tianxi

2015-09-01

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Patterns of presentation and clinical features of toxicity after reported use of ([2-aminopropyl]-2,3-dihydrobenzofurans), the 'benzofuran' compounds. A report from the United Kingdom National Poisons Information Service.

PubMed

Kamour, Ashraf; James, David; Lupton, David J; Cooper, Gillian; Eddleston, Micheal; Vale, Allister; Thompson, John P; Thanacoody, Ruben; Hill, Simon L; Thomas, Simon H L

2014-12-01

To characterise the patterns of presentation and clinical features of toxicity following reported recreational use of benzofuran compounds ((2-aminopropyl)-2,3-dihydrobenzofurans) in the UK, as reported to the National Poisons Information Service (NPIS), and to compare clinical features of toxicity with those after reported mephedrone use. NPIS patient-specific telephone enquiries and user sessions for TOXBASE(®), the NPIS online information database, related to (2-aminopropyl)-2,3-dihydrobenzofurans and associated synonyms were reviewed from March 2009 to August 2013. These data were compared with those of mephedrone, the recreational substance most frequently reported to NPIS, collected over the same period. There were 63 telephone enquiries concerning 66 patients and 806 TOXBASE(®) user sessions regarding benzofuran compounds during the period of study. The first telephone enquiry was made in July 2010 and the highest numbers of enquiries were received in August 2010 (33 calls, 112 TOXBASE(®) sessions). Patients were predominantly male (82%) with a median age of 29 years; 9 reported co-ingestion of other substances. Comparing the 57 patients who reported ingesting benzofuran compounds alone with 315 patients ingesting mephedrone alone, benzofurans were more often associated with stimulant features, including tachycardia, hypertension, mydriasis, palpitation, fever, increased sweating, and tremor, (72% vs. 38%, odds ratio [OR] 4.2, 95% confidence interval [CI] 2.27-7.85, P < 0.0001) and mental health disturbances (58% vs. 38%, OR 2.3, 95% CI 1.29-4.07, P = 0.006). Other features reported after benzofuran compound ingestion included gastrointestinal symptoms (16%), reduced level of consciousness (9%), chest pain (7%), and creatinine kinase elevation (5%). Reported ingestion of benzofuran compounds is associated with similar toxic effects to those of amphetamines and cathinones. Mental health disturbances and stimulant features were reported more frequently following reported ingestion of benzofuran compounds than after ingestion of mephedrone.