Sample records for identify important features
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Designing action games for appealing to buyers.
Hsu, Shang Hwa; Lee, Feng-Liang; Wu, Muh-Cherng
2005-12-01
This study aims to identify design features for action games that would appeal to game-buyers, rather than game-players. Sixteen frequent-buyers of computer games identified 39 design features that appeal to buyers by contrasting different versions of Pacman games. Twenty-eight versions of Pacman were then evaluated in terms of the identified design features by 45 participants (27 male and 18 female college students). Qnet2000 neural network software was used to determine the relative importance of these design features. The results indicated that the top 10 most important design features could account for more than 50% of "perceived fun" among these 39 design features. The feature of avatar is important to game-buyers, yet not revealed in previous player-oriented studies. Moreover, six design factors underlying the 39 features were identified through factor analysis. These factors included "novelty and powerfulness," "appealing presentation," "interactivity," "challenging," "sense of control," and "rewarding," and could account for 54% of total variance. Among these six factors, appealing presentation has not been emphasized by player-oriented research. Implications of the findings were discussed.
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NASA Astrophysics Data System (ADS)
Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan
2017-10-01
This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.
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Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui
2017-08-17
It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.
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Online Patient Education for Chronic Disease Management: Consumer Perspectives.
Win, Khin Than; Hassan, Naffisah Mohd; Oinas-Kukkonen, Harri; Probst, Yasmine
2016-04-01
Patient education plays an important role in chronic disease management. The aim of this study is to identify patients' preferences in regard to the design features of effective online patient education (OPE) and the benefits. A review of the existing literature was conducted in order to identify the benefits of OPE and its essential design features. These design features were empirically tested by conducting survey with patients and caregivers. Reliability analysis, construct validity and regression analysis were performed for data analysis. The results identified patient-tailored information, interactivity, content credibility, clear presentation of content, use of multimedia and interpretability as the essential design features of online patient education websites for chronic disease management.
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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.
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Courseware Components and Features: Preferences of Faculty in the Human Sciences
ERIC Educational Resources Information Center
Causin, Gina Fe G.; Robertson, Lona J.; Ryan, Bill
2008-01-01
This project gathered information on the important components and features of distance education courseware identified by faculty teaching in the Great Plains Interactive Distance Education Alliance. Respondents indicated that they were most interested in features that helped with course management, allowed them to update and post course materials…
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Zheng, Jiaping; Yu, Hong
2016-01-01
Background Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients’ notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. Objective We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. Methods First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians’ agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Results Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen’s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P<.001). Rich learning features contributed to FOCUS’s performance substantially. Conclusions FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care. PMID:27903489
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Chen, Jinying; Zheng, Jiaping; Yu, Hong
2016-11-30
Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients' notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians' agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen's kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P<.001). Rich learning features contributed to FOCUS's performance substantially. FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care. ©Jinying Chen, Jiaping Zheng, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 30.11.2016.
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The Role of Attention for Context-Context Binding of Intrinsic and Extrinsic Features
ERIC Educational Resources Information Center
Boywitt, C. Dennis; Meiser, Thorsten
2012-01-01
There is converging evidence that the feeling of conscious recollection is usually accompanied by the bound retrieval of context features of the encoding episode (e.g., Meiser, Sattler, & Weiber, 2008). Recently, however, important limiting conditions have been identified for the binding between context features in memory. For example, focusing on…
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Boosted Multivariate Trees for Longitudinal Data
Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant
2017-01-01
Machine learning methods provide a powerful approach for analyzing longitudinal data in which repeated measurements are observed for a subject over time. We boost multivariate trees to fit a novel flexible semi-nonparametric marginal model for longitudinal data. In this model, features are assumed to be nonparametric, while feature-time interactions are modeled semi-nonparametrically utilizing P-splines with estimated smoothing parameter. In order to avoid overfitting, we describe a relatively simple in sample cross-validation method which can be used to estimate the optimal boosting iteration and which has the surprising added benefit of stabilizing certain parameter estimates. Our new multivariate tree boosting method is shown to be highly flexible, robust to covariance misspecification and unbalanced designs, and resistant to overfitting in high dimensions. Feature selection can be used to identify important features and feature-time interactions. An application to longitudinal data of forced 1-second lung expiratory volume (FEV1) for lung transplant patients identifies an important feature-time interaction and illustrates the ease with which our method can find complex relationships in longitudinal data. PMID:29249866
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EFS: an ensemble feature selection tool implemented as R-package and web-application.
Neumann, Ursula; Genze, Nikita; Heider, Dominik
2017-01-01
Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.
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A bootstrap based Neyman-Pearson test for identifying variable importance.
Ditzler, Gregory; Polikar, Robi; Rosen, Gail
2015-04-01
Selection of most informative features that leads to a small loss on future data are arguably one of the most important steps in classification, data analysis and model selection. Several feature selection (FS) algorithms are available; however, due to noise present in any data set, FS algorithms are typically accompanied by an appropriate cross-validation scheme. In this brief, we propose a statistical hypothesis test derived from the Neyman-Pearson lemma for determining if a feature is statistically relevant. The proposed approach can be applied as a wrapper to any FS algorithm, regardless of the FS criteria used by that algorithm, to determine whether a feature belongs in the relevant set. Perhaps more importantly, this procedure efficiently determines the number of relevant features given an initial starting point. We provide freely available software implementations of the proposed methodology.
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Designing attractive gamification features for collaborative storytelling websites.
Hsu, Shang Hwa; Chang, Jen-Wei; Lee, Chun-Chia
2013-06-01
Gamification design is considered as the predictor of collaborative storytelling websites' success. Although aforementioned studies have mentioned a broad range of factors that may influence gamification, they neither depicted the actual design features nor relative attractiveness among them. This study aims to identify attractive gamification features for collaborative storytelling websites. We first constructed a hierarchical system structure of gamification design of collaborative storytelling websites and conducted a focus group interview with eighteen frequent users to identify 35gamification features. After that, this study determined the relative attractiveness of these gamification features by administrating an online survey to 6333 collaborative storytelling websites users. The results indicated that the top 10 most attractive gamification features could account for more than 50% of attractiveness among these 35 gamification features. The feature of unpredictable time pressure is important to website users, yet not revealed in previous relevant studies. Implications of the findings were discussed.
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Textural features for radar image analysis
NASA Technical Reports Server (NTRS)
Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.
1981-01-01
Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.
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Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy
2010-04-01
A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.
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How important is vehicle safety for older consumers in the vehicle purchase process?
Koppel, Sjaan; Clark, Belinda; Hoareau, Effie; Charlton, Judith L; Newstead, Stuart V
2013-01-01
This study aimed to investigate the importance of vehicle safety to older consumers in the vehicle purchase process. Older (n = 102), middle-aged (n = 791), and younger (n = 109) participants throughout the eastern Australian states of Victoria, New South Wales, and Queensland who had recently purchased a new or used vehicle completed an online questionnaire about their vehicle purchase process. When asked to list the 3 most important considerations in the vehicle purchase process (in an open-ended format), older consumers were mostly likely to list price as their most important consideration (43%). Similarly, when presented with a list of vehicle factors (such as price, design, Australasian New Car Assessment Program [ANCAP] rating), older consumers were most likely to identify price as the most important vehicle factor (36%). When presented with a list of vehicle features (such as automatic transmission, braking, air bags), older consumers in the current study were most likely to identify an antilock braking system (41%) as the most important vehicle feature, and 50 percent of older consumers identified a safety-related vehicle feature as the highest priority vehicle feature (50%). When asked to list up to 3 factors that make a vehicle safe, older consumers in the current study were most likely to list braking systems (35%), air bags (22%), and the driver's behavior or skill (11%). When asked about the influence of safety in the new vehicle purchase process, one third of older consumers reported that all new vehicles are safe (33%) and almost half of the older consumers rated their vehicle as safer than average (49%). A logistic regression model was developed to predict the profile of older consumers more likely to assign a higher priority to safety features in the vehicle purchasing process. The model predicted that the importance of safety-related features was influenced by several variables, including older consumers' beliefs that they could protect themselves and their family from a crash, their traffic infringement history, and whether they had children. These findings are consistent with previous research that suggests that, though older consumers highlight the importance of safety features (i.e., seat belts, air bags, braking), they often downplay the role of safety in their vehicle purchasing process and are more likely to equate vehicle safety with the presence of specific vehicle safety features or technologies rather than the vehicle's crash safety/test results or crashworthiness. The findings from this study provide a foundation to support further research in this area that can be used by policy makers, manufacturers, and other stakeholders to better target the promotion and publicity of vehicle safety features to particular consumer groups (such as older consumers). Better targeted campaigns may help to emphasize the value of safety features and their role in reducing the risk of injury/death. If older consumers are better informed of the benefits of safety features when purchasing a vehicle, a further reduction in injuries and deaths related to motor vehicle crashes may be realized.
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ERIC Educational Resources Information Center
Patchan, Melissa M.; Schunn, Christian D.; Correnti, Richard J.
2016-01-01
Although feedback is often seen as a critical component of the learning process, many open questions about how specific feedback features contribute to the effectiveness of feedback remain--especially in regards to peer feedback of writing. Nelson and Schunn (2009) identified several important features of peer feedback in their nature of feedback…
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Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics.
Piao, Guanying; Wu, Jiarui
2013-11-01
To analyze the differentially expressed genes and identify featured biomarkers from prostatic carcinoma. The software "Significance Analysis of Microarray" (SAM) was used to identify the differentially coexpressed genes (DCGs). The DCGs existed in two datasets were analyzed by GO (Gene Ontology) functional annotation. A total of 389 DCGs were obtained. By GO analysis, we found these DCGs were closely related with the acinus development, TGF-β receptor and signal transduction pathways. Furthermore, five featured biomarkers were discovered by interaction analysis. These important signal pathways and oncogenes may provide potential therapeutic targets for prostatic carcinoma.
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Standoff Human Identification Using Body Shape
DOE Office of Scientific and Technical Information (OSTI.GOV)
Matzner, Shari; Heredia-Langner, Alejandro; Amidan, Brett G.
2015-09-01
The ability to identify individuals is a key component of maintaining safety and security in public spaces and around critical infrastructure. Monitoring an open space is challenging because individuals must be identified and re-identified from a standoff distance nonintrusively, making methods like fingerprinting and even facial recognition impractical. We propose using body shape features as a means for identification from standoff sensing, either complementing other identifiers or as an alternative. An important challenge in monitoring open spaces is reconstructing identifying features when only a partial observation is available, because of the view-angle limitations and occlusion or subject pose changes. Tomore » address this challenge, we investigated the minimum number of features required for a high probability of correct identification, and we developed models for predicting a key body feature—height—from a limited set of observed features. We found that any set of nine randomly selected body measurements was sufficient to correctly identify an individual in a dataset of 4426 subjects. For predicting height, anthropometric measures were investigated for correlation with height. Their correlation coefficients and associated linear models were reported. These results—a sufficient number of features for identification and height prediction from a single feature—contribute to developing systems for standoff identification when views of a subject are limited.« less
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Miller, Kristen; Mosby, Danielle; Capan, Muge; Kowalski, Rebecca; Ratwani, Raj; Noaiseh, Yaman; Kraft, Rachel; Schwartz, Sanford; Weintraub, William S; Arnold, Ryan
2018-05-01
Provider acceptance and associated patient outcomes are widely discussed in the evaluation of clinical decision support systems (CDSSs), but critical design criteria for tools have generally been overlooked. The objective of this work is to inform electronic health record alert optimization and clinical practice workflow by identifying, compiling, and reporting design recommendations for CDSS to support the efficient, effective, and timely delivery of high-quality care. A narrative review was conducted from 2000 to 2016 in PubMed and The Journal of Human Factors and Ergonomics Society to identify papers that discussed/recommended design features of CDSSs that are associated with the success of these systems. Fourteen papers were included as meeting the criteria and were found to have a total of 42 unique recommendations; 11 were classified as interface features, 10 as information features, and 21 as interaction features. Features are defined and described, providing actionable guidance that can be applied to CDSS development and policy. To our knowledge, no reviews have been completed that discuss/recommend design features of CDSS at this scale, and thus we found that this was important for the body of literature. The recommendations identified in this narrative review will help to optimize design, organization, management, presentation, and utilization of information through presentation, content, and function. The designation of 3 categories (interface, information, and interaction) should be further evaluated to determine the critical importance of the categories. Future work will determine how to prioritize them with limited resources for designers and developers in order to maximize the clinical utility of CDSS. This review will expand the field of knowledge and provide a novel organization structure to identify key recommendations for CDSS.
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Teacher Explanation of Physics Concepts: A Video Study
ERIC Educational Resources Information Center
Geelan, David
2013-01-01
Video recordings of Year 11 physics lessons were analyzed to identify key features of teacher explanations. Important features of the explanations used included teachers' ability to move between qualitative and quantitative modes of discussion, attention to what students require to succeed in high stakes examinations, thoughtful use of…
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NASA Astrophysics Data System (ADS)
Halim, N. Z. A.; Sulaiman, S. A.; Talib, K.; Ng, E. G.
2018-02-01
This paper explains the process carried out in identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis. The research was initially a part of a larger research exercise to identify the significance of NDCDB from the legal, technical, role and land-based analysis perspectives. The research methodology of applying the Delphi technique is substantially discussed in this paper. A heterogeneous panel of 14 experts was created to determine the importance of NDCDB from the technical relevance standpoint. Three statements describing the relevant features of NDCDB for spatial analysis were established after three rounds of consensus building. It highlighted the NDCDB’s characteristics such as its spatial accuracy, functions, and criteria as a facilitating tool for spatial analysis. By recognising the relevant features of NDCDB for spatial analysis in this study, practical application of NDCDB for various analysis and purpose can be widely implemented.
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Hooper, Paula; Knuiman, Matthew; Foster, Sarah; Giles-Corti, Billie
2015-11-01
Planning policy makers are requesting clearer guidance on the key design features required to build neighbourhoods that promote active living. Using a backwards stepwise elimination procedure (logistic regression with generalised estimating equations adjusting for demographic characteristics, self-selection factors, stage of construction and scale of development) this study identified specific design features (n=16) from an operational planning policy ("Liveable Neighbourhoods") that showed the strongest associations with walking behaviours (measured using the Neighbourhood Physical Activity Questionnaire). The interacting effects of design features on walking behaviours were also investigated. The urban design features identified were grouped into the "building blocks of a Liveable Neighbourhood", reflecting the scale, importance and sequencing of the design and implementation phases required to create walkable, pedestrian friendly developments. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Moradi, Milad; Ghadiri, Nasser
2018-01-01
Automatic text summarization tools help users in the biomedical domain to acquire their intended information from various textual resources more efficiently. Some of biomedical text summarization systems put the basis of their sentence selection approach on the frequency of concepts extracted from the input text. However, it seems that exploring other measures rather than the raw frequency for identifying valuable contents within an input document, or considering correlations existing between concepts, may be more useful for this type of summarization. In this paper, we describe a Bayesian summarization method for biomedical text documents. The Bayesian summarizer initially maps the input text to the Unified Medical Language System (UMLS) concepts; then it selects the important ones to be used as classification features. We introduce six different feature selection approaches to identify the most important concepts of the text and select the most informative contents according to the distribution of these concepts. We show that with the use of an appropriate feature selection approach, the Bayesian summarizer can improve the performance of biomedical summarization. Using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) toolkit, we perform extensive evaluations on a corpus of scientific papers in the biomedical domain. The results show that when the Bayesian summarizer utilizes the feature selection methods that do not use the raw frequency, it can outperform the biomedical summarizers that rely on the frequency of concepts, domain-independent and baseline methods. Copyright © 2017 Elsevier B.V. All rights reserved.
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Connelly, Elizabeth B.; Allen, Craig R.; Hatfield, Kirk; ...
2017-02-20
The National Academy of Sciences (NAS) definition of resilience is used here to organize common concepts and synthesize a set of key features of resilience that can be used across diverse application domains. The features in common include critical functions (services), thresholds, cross-scale (both space and time) interactions, and memory and adaptive management. We propose a framework for linking these features to the planning, absorbing, recovering, and adapting phases identified in the NAS definition. As a result, the proposed delineation of resilience can be important in understanding and communicating resilience concepts.
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Connelly, Elizabeth B.; Allen, Craig R.; Hatfield, Kirk; Palma-Oliveira, José M.; Woods, David D.; Linkov, Igor
2017-01-01
The National Academy of Sciences (NAS) definition of resilience is used here to organize common concepts and synthesize a set of key features of resilience that can be used across diverse application domains. The features in common include critical functions (services), thresholds, cross-scale (both space and time) interactions, and memory and adaptive management. We propose a framework for linking these features to the planning, absorbing, recovering, and adapting phases identified in the NAS definition. The proposed delineation of resilience can be important in understanding and communicating resilience concepts.
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Prediction of interface residue based on the features of residue interaction network.
Jiao, Xiong; Ranganathan, Shoba
2017-11-07
Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Kistler, Christine E; Crutchfield, Trisha M; Sutfin, Erin L; Ranney, Leah M; Berman, Micah L; Zarkin, Gary A; Goldstein, Adam O
2017-06-07
To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS) product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18-25, n = 11; middle-age group aged 26-64, n = 9; and women's group aged 26-64, n = 9). We conducted five individual older adult interviews (aged 68-80). Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1) user experience; (2) social acceptability; (3) cost; (4) health risks/benefits; (5) ease of use; (6) flavors; (7) smoking cessation aid; (8) nicotine content; (9) modifiability; (10) ENDS regulation; (11) bridge between tobacco cigarettes; (12) collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women's group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts.
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Kistler, Christine E.; Crutchfield, Trisha M.; Sutfin, Erin L.; Ranney, Leah M.; Berman, Micah L.; Zarkin, Gary A.; Goldstein, Adam O.
2017-01-01
To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS) product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18–25, n = 11; middle-age group aged 26–64, n = 9; and women’s group aged 26–64, n = 9). We conducted five individual older adult interviews (aged 68–80). Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1) user experience; (2) social acceptability; (3) cost; (4) health risks/benefits; (5) ease of use; (6) flavors; (7) smoking cessation aid; (8) nicotine content; (9) modifiability; (10) ENDS regulation; (11) bridge between tobacco cigarettes; (12) collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women’s group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts. PMID:28590444
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Incorporating Distance Learning into Counselor Education Programs: A Research Study.
ERIC Educational Resources Information Center
Wantz, Richard A.; Tromski, Donna M.; Mortsolf, Christina Joelle; Yoxtheimer, Greggory; Brill, Samantha; Cole, Alison
The purpose of this study is to determine the number of counselor education programs that utilize distance learning, to identify the distance learning software delivery products used, and to identify features of software used. The researchers also attempt to identify faculty perceptions related to and experience with the importance of distance…
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Toward real-time performance benchmarks for Ada
NASA Technical Reports Server (NTRS)
Clapp, Russell M.; Duchesneau, Louis; Volz, Richard A.; Mudge, Trevor N.; Schultze, Timothy
1986-01-01
The issue of real-time performance measurements for the Ada programming language through the use of benchmarks is addressed. First, the Ada notion of time is examined and a set of basic measurement techniques are developed. Then a set of Ada language features believed to be important for real-time performance are presented and specific measurement methods discussed. In addition, other important time related features which are not explicitly part of the language but are part of the run-time related features which are not explicitly part of the language but are part of the run-time system are also identified and measurement techniques developed. The measurement techniques are applied to the language and run-time system features and the results are presented.
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2001-10-25
neural network (ANN) has been adopted for the human chromosome classification. It is important to select optimum features for training neural network...Many studies for computer-based chromosome analysis have shown that it is possible to classify chromosomes into 24 subgroups. In addition, artificial
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ERIC Educational Resources Information Center
Tikunoff, William J.
The Significant Bilingual Instructional Features (SBIF) descriptive study identified, described, and verified important features of bilingual education for instuction of limited English proficient students. Part I involved the study of 58 classrooms and 232 students, grade K-12, at six diverse sites representing a variety of ethnolinguistic…
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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.
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Delirium and refeeding syndrome in anorexia nervosa.
Norris, Mark L; Pinhas, Leora; Nadeau, Pierre-Olivier; Katzman, Debra K
2012-04-01
To review the literature on delirium and refeeding syndrome in patients with anorexia nervosa (AN) and present case examples in an attempt to identify common clinical features and response to therapy. A comprehensive literature review was completed. In addition to the cases identified in the literature, we present two additional cases of our own. We identified a total of 10 cases (all female; mean age 19 years old, range 12-29 years); 2/3 of the cases had similar clinical features predating the delirium and during refeeding. Delirium, albeit rare, can be associated with the refeeding syndrome in low weight patients with AN. During the initial refeeding phase, close monitoring of medical, metabolic, and psychological parameters are important in establishing factors that may elevate risk. Early detection and treatment of delirium using nonpharmacologic and pharmacologic means are also important to help minimize the effects of this potentially deadly condition. Copyright © 2011 Wiley Periodicals, Inc.
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A Typology of Social Workers in Long-Term Care Facilities in Israel.
Lev, Sagit; Ayalon, Liat
2018-04-01
This article explores moral distress among long-term care facility (LTCF) social workers by examining the relationships between moral distress and environmental and personal features. Based on these features, authors identified a typology of LTCF social workers and how they handle moral distress. Such a typology can assist in the identification of social workers who are in a particular need for assistance. Overall, 216 LTCF social workers took part in the study. A two-step cluster analysis was conducted to identify a typology of LTCF social workers based on features such as ethical environment, support in workplace, mastery, and resilience. The variance of the identified clusters and their associations with moral distress were examined, and four clusters of LTCF social workers were identified. The clusters varied from each other in relation to their personal and environmental features and in relation to their experience of moral distress. The article concludes with a discussion of the importance of developing programs for LTCF social workers that provide support and enhancement of personal resources and an adequate and ethical environment for practice.
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ERIC Educational Resources Information Center
Dunn, Julie; Bundy, Penny; Stinson, Madonna
2015-01-01
Emotion is a complex and important aspect of participatory drama experience. This is because drama work of this kind provokes emotional responses to both actual and dramatic worlds. This paper identifies two key features of participatory drama that influence the generation and experience of emotion: commitment and connection. These features are…
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Recovering faces from memory: the distracting influence of external facial features.
Frowd, Charlie D; Skelton, Faye; Atherton, Chris; Pitchford, Melanie; Hepton, Gemma; Holden, Laura; McIntyre, Alex H; Hancock, Peter J B
2012-06-01
Recognition memory for unfamiliar faces is facilitated when contextual cues (e.g., head pose, background environment, hair and clothing) are consistent between study and test. By contrast, inconsistencies in external features, especially hair, promote errors in unfamiliar face-matching tasks. For the construction of facial composites, as carried out by witnesses and victims of crime, the role of external features (hair, ears, and neck) is less clear, although research does suggest their involvement. Here, over three experiments, we investigate the impact of external features for recovering facial memories using a modern, recognition-based composite system, EvoFIT. Participant-constructors inspected an unfamiliar target face and, one day later, repeatedly selected items from arrays of whole faces, with "breeding," to "evolve" a composite with EvoFIT; further participants (evaluators) named the resulting composites. In Experiment 1, the important internal-features (eyes, brows, nose, and mouth) were constructed more identifiably when the visual presence of external features was decreased by Gaussian blur during construction: higher blur yielded more identifiable internal-features. In Experiment 2, increasing the visible extent of external features (to match the target's) in the presented face-arrays also improved internal-features quality, although less so than when external features were masked throughout construction. Experiment 3 demonstrated that masking external-features promoted substantially more identifiable images than using the previous method of blurring external-features. Overall, the research indicates that external features are a distractive rather than a beneficial cue for face construction; the results also provide a much better method to construct composites, one that should dramatically increase identification of offenders.
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Automatic topics segmentation for TV news video
NASA Astrophysics Data System (ADS)
Hmayda, Mounira; Ejbali, Ridha; Zaied, Mourad
2017-03-01
Automatic identification of television programs in the TV stream is an important task for operating archives. This article proposes a new spatio-temporal approach to identify the programs in TV stream into two main steps: First, a reference catalogue for video features visual jingles built. We operate the features that characterize the instances of the same program type to identify the different types of programs in the flow of television. The role of video features is to represent the visual invariants for each visual jingle using appropriate automatic descriptors for each television program. On the other hand, programs in television streams are identified by examining the similarity of the video signal for visual grammars in the catalogue. The main idea of the identification process is to compare the visual similarity of the video signal features in the flow of television to the catalogue. After presenting the proposed approach, the paper overviews encouraging experimental results on several streams extracted from different channels and compounds of several programs.
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NASA Astrophysics Data System (ADS)
de Araujo, Zandra; Orrill, Chandra Hawley; Jacobson, Erik
2018-04-01
While there is considerable scholarship describing principles for effective professional development, there have been few attempts to examine these principles in practice. In this paper, we identify and examine the particular design features of a mathematics professional development experience provided for middle grades teachers over 14 weeks. The professional development was grounded in a set of mathematical tasks that each had one right answer, but multiple solution paths. The facilitator engaged participants in problem solving and encouraged participants to work collaboratively to explore different solution paths. Through analysis of this collaborative learning environment, we identified five design features for supporting teacher learning of important mathematics and pedagogy in a problem-solving setting. We discuss these design features in depth and illustrate them by presenting an elaborated example from the professional development. This study extends the existing guidance for the design of professional development by examining and operationalizing the relationships among research-based features of effective professional development and the enacted features of a particular design.
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Feature selection and classification model construction on type 2 diabetic patients' data.
Huang, Yue; McCullagh, Paul; Black, Norman; Harper, Roy
2007-11-01
Diabetes affects between 2% and 4% of the global population (up to 10% in the over 65 age group), and its avoidance and effective treatment are undoubtedly crucial public health and health economics issues in the 21st century. The aim of this research was to identify significant factors influencing diabetes control, by applying feature selection to a working patient management system to assist with ranking, classification and knowledge discovery. The classification models can be used to determine individuals in the population with poor diabetes control status based on physiological and examination factors. The diabetic patients' information was collected by Ulster Community and Hospitals Trust (UCHT) from year 2000 to 2004 as part of clinical management. In order to discover key predictors and latent knowledge, data mining techniques were applied. To improve computational efficiency, a feature selection technique, feature selection via supervised model construction (FSSMC), an optimisation of ReliefF, was used to rank the important attributes affecting diabetic control. After selecting suitable features, three complementary classification techniques (Naïve Bayes, IB1 and C4.5) were applied to the data to predict how well the patients' condition was controlled. FSSMC identified patients' 'age', 'diagnosis duration', the need for 'insulin treatment', 'random blood glucose' measurement and 'diet treatment' as the most important factors influencing blood glucose control. Using the reduced features, a best predictive accuracy of 95% and sensitivity of 98% was achieved. The influence of factors, such as 'type of care' delivered, the use of 'home monitoring', and the importance of 'smoking' on outcome can contribute to domain knowledge in diabetes control. In the care of patients with diabetes, the more important factors identified: patients' 'age', 'diagnosis duration' and 'family history', are beyond the control of physicians. Treatment methods such as 'insulin', 'diet' and 'tablets' (a variety of oral medicines) may be controlled. However lifestyle indicators such as 'body mass index' and 'smoking status' are also important and may be controlled by the patient. This further underlines the need for public health education to aid awareness and prevention. More subtle data interactions need to be better understood and data mining can contribute to the clinical evidence base. The research confirms and to a lesser extent challenges current thinking. Whilst fully appreciating the requirement for clinical verification and interpretation, this work supports the use of data mining as an exploratory tool, particularly as the domain is suffering from a data explosion due to enhanced monitoring and the (potential) storage of this data in the electronic health record. FSSMC has proved a useful feature estimator for large data sets, where processing efficiency is an important factor.
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The American University as a Civic Institution.
ERIC Educational Resources Information Center
Stanley, Manfred; And Others
1989-01-01
This Civic Arts Review issue contains an essay by Stanley Manfred titled "The American University as a Civic Institution" which identifies some of the features which have made the contemporary American university a civic institution with important civic functions, and identifies some of the societal contradictions which impede recognition and…
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Mapping genomic features to functional traits through microbial whole genome sequences.
Zhang, Wei; Zeng, Erliang; Liu, Dan; Jones, Stuart E; Emrich, Scott
2014-01-01
Recently, the utility of trait-based approaches for microbial communities has been identified. Increasing availability of whole genome sequences provide the opportunity to explore the genetic foundations of a variety of functional traits. We proposed a machine learning framework to quantitatively link the genomic features with functional traits. Genes from bacteria genomes belonging to different functional traits were grouped to Cluster of Orthologs (COGs), and were used as features. Then, TF-IDF technique from the text mining domain was applied to transform the data to accommodate the abundance and importance of each COG. After TF-IDF processing, COGs were ranked using feature selection methods to identify their relevance to the functional trait of interest. Extensive experimental results demonstrated that functional trait related genes can be detected using our method. Further, the method has the potential to provide novel biological insights.
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A Reduced Set of Features for Chronic Kidney Disease Prediction
Misir, Rajesh; Mitra, Malay; Samanta, Ranjit Kumar
2017-01-01
Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs. PMID:28706750
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Mutations in epilepsy and intellectual disability genes in patients with features of Rett syndrome.
Olson, Heather E; Tambunan, Dimira; LaCoursiere, Christopher; Goldenberg, Marti; Pinsky, Rebecca; Martin, Emilie; Ho, Eugenia; Khwaja, Omar; Kaufmann, Walter E; Poduri, Annapurna
2015-09-01
Rett syndrome and neurodevelopmental disorders with features overlapping this syndrome frequently remain unexplained in patients without clinically identified MECP2 mutations. We recruited a cohort of 11 patients with features of Rett syndrome and negative initial clinical testing for mutations in MECP2. We analyzed their phenotypes to determine whether patients met formal criteria for Rett syndrome, reviewed repeat clinical genetic testing, and performed exome sequencing of the probands. Using 2010 diagnostic criteria, three patients had classical Rett syndrome, including two for whom repeat MECP2 gene testing had identified mutations. In a patient with neonatal onset epilepsy with atypical Rett syndrome, we identified a frameshift deletion in STXBP1. Among seven patients with features of Rett syndrome not fulfilling formal diagnostic criteria, four had suspected pathogenic mutations, one each in MECP2, FOXG1, SCN8A, and IQSEC2. MECP2 mutations are highly correlated with classical Rett syndrome. Genes associated with atypical Rett syndrome, epilepsy, or intellectual disability should be considered in patients with features overlapping with Rett syndrome and negative MECP2 testing. While most of the identified mutations were apparently de novo, the SCN8A variant was inherited from an unaffected parent mosaic for the mutation, which is important to note for counseling regarding recurrence risks. © 2015 Wiley Periodicals, Inc.
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Mutations in Epilepsy and Intellectual Disability Genes in Patients with Features of Rett Syndrome
Olson, Heather E.; Tambunan, Dimira; LaCoursiere, Christopher; Goldenberg, Marti; Pinsky, Rebecca; Martin, Emilie; Ho, Eugenia; Khwaja, Omar; Kaufmann, Walter E.; Poduri, Annapurna
2017-01-01
Rett syndrome and neurodevelopmental disorders with features overlapping this syndrome frequently remain unexplained in patients without clinically identified MECP2 mutations. We recruited a cohort of 11 patients with features of Rett syndrome and negative initial clinical testing for mutations in MECP2. We analyzed their phenotypes to determine whether patients met formal criteria for Rett syndrome, reviewed repeat clinical genetic testing, and performed exome sequencing of the probands. Using 2010 diagnostic criteria, three patients had classical Rett syndrome, including two for whom repeat MECP2 gene testing had identified mutations. In a patient with neonatal onset epilepsy with atypical Rett syndrome, we identified a frameshift deletion in STXBP1. Among seven patients with features of Rett syndrome not fulfilling formal diagnostic criteria, four had suspected pathogenic mutations, one each in MECP2, FOXG1, SCN8A, and IQSEC2. MECP2 mutations are highly correlated with classical Rett syndrome. Genes associated with atypical Rett syndrome, epilepsy, or intellectual disability should be considered in patients with features overlapping with Rett syndrome and negative MECP2 testing. While most of the identified mutations were apparently de novo, the SCN8A variant was inherited from an unaffected parent mosaic for the mutation, which is important to note for counseling regarding recurrence risks. PMID:25914188
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Scombroid fish poisoning syndrome.
Dickinson, G
1982-09-01
Two patients presented to the emergency department with features of a histamine-like reaction and were subsequently determined to have scombroid fish poisoning. It is important to recognize the syndrome as an intoxication rather than an allergic reaction in order that the source of the toxin can be identified and further cases prevented. A review of the features and pathogenesis of the syndrome is presented.
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ERIC Educational Resources Information Center
Bandini, Andrea; Green, Jordan R.; Wang, Jun; Campbell, Thomas F.; Zinman, Lorne; Yunusova, Yana
2018-01-01
Purpose: The goals of this study were to (a) classify speech movements of patients with amyotrophic lateral sclerosis (ALS) in presymptomatic and symptomatic phases of bulbar function decline relying solely on kinematic features of lips and jaw and (b) identify the most important measures that detect the transition between early and late bulbar…
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ERIC Educational Resources Information Center
Van De Bogart, Kevin L.; Dounas-Frazer, Dimitri R.; Lewandowski, H. J.; Stetzer, MacKenzie R.
2017-01-01
Developing students' ability to troubleshoot is an important learning outcome for many undergraduate physics lab courses, especially electronics courses. In other work, metacognition has been identified as an important feature of troubleshooting. However, that work has focused primarily on "individual" students' metacognitive processes…
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Discriminative Features Mining for Offline Handwritten Signature Verification
NASA Astrophysics Data System (ADS)
Neamah, Karrar; Mohamad, Dzulkifli; Saba, Tanzila; Rehman, Amjad
2014-03-01
Signature verification is an active research area in the field of pattern recognition. It is employed to identify the particular person with the help of his/her signature's characteristics such as pen pressure, loops shape, speed of writing and up down motion of pen, writing speed, pen pressure, shape of loops, etc. in order to identify that person. However, in the entire process, features extraction and selection stage is of prime importance. Since several signatures have similar strokes, characteristics and sizes. Accordingly, this paper presents combination of orientation of the skeleton and gravity centre point to extract accurate pattern features of signature data in offline signature verification system. Promising results have proved the success of the integration of the two methods.
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A survey of stakeholder perspectives on exoskeleton technology.
Wolff, Jamie; Parker, Claire; Borisoff, Jaimie; Mortenson, W Ben; Mattie, Johanne
2014-12-19
Exoskeleton technology has potential benefits for wheelchair users' health and mobility. However, there are practical barriers to their everyday use as a mobility device. To further understand potential exoskeleton use, and facilitate the development of new technologies, a study was undertaken to explore perspectives of wheelchair users and healthcare professionals on reasons for use of exoskeleton technology, and the importance of a variety of device characteristics. An online survey with quantitative and qualitative components was conducted with wheelchair users and healthcare professionals working directly with individuals with mobility impairments. Respondents rated whether they would use or recommend an exoskeleton for four potential reasons. Seventeen design features were rated and compared in terms of their importance. An exploratory factor analysis was conducted to categorize the 17 design features into meaningful groupings. Content analysis was used to identify themes for the open ended questions regarding reasons for use of an exoskeleton. 481 survey responses were analyzed, 354 from wheelchair users and 127 from healthcare professionals. The most highly rated reason for potential use or recommendation of an exoskeleton was health benefits. Of the design features, 4 had a median rating of very important: minimization of falls risk, comfort, putting on/taking off the device, and purchase cost. Factor analysis identified two main categories of design features: Functional Activities and Technology Characteristics. Qualitative findings indicated that health and physical benefits, use for activity and access reasons, and psychosocial benefits were important considerations in whether to use or recommend an exoskeleton. This study emphasizes the importance of developing future exoskeletons that are comfortable, affordable, minimize fall risk, and enable functional activities. Findings from this study can be utilized to inform the priorities for future development of this technology.
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Identifying sports videos using replay, text, and camera motion features
NASA Astrophysics Data System (ADS)
Kobla, Vikrant; DeMenthon, Daniel; Doermann, David S.
1999-12-01
Automated classification of digital video is emerging as an important piece of the puzzle in the design of content management systems for digital libraries. The ability to classify videos into various classes such as sports, news, movies, or documentaries, increases the efficiency of indexing, browsing, and retrieval of video in large databases. In this paper, we discuss the extraction of features that enable identification of sports videos directly from the compressed domain of MPEG video. These features include detecting the presence of action replays, determining the amount of scene text in vide, and calculating various statistics on camera and/or object motion. The features are derived from the macroblock, motion,and bit-rate information that is readily accessible from MPEG video with very minimal decoding, leading to substantial gains in processing speeds. Full-decoding of selective frames is required only for text analysis. A decision tree classifier built using these features is able to identify sports clips with an accuracy of about 93 percent.
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Print Advertisements for Alzheimer’s Disease Drugs: Informational and Transformational Features
Gooblar, Jonathan; Carpenter, Brian D.
2014-01-01
Purpose We examined print advertisements for Alzheimer’s disease drugs published in journals and magazines between January 2008 and February 2012, using an informational versus transformational theoretical framework to identify objective and persuasive features. Methods In 29 unique advertisements, we used qualitative methods to code and interpret identifying information, charts, benefit and side effect language, and persuasive appeals embedded in graphics and narratives. Results Most elements contained a mixture of informational and transformational features. Charts were used infrequently, but when they did appear the accompanying text often exaggerated the data. Benefit statements covered an array of symptoms, drug properties, and caregiver issues. Side effect statements often used positive persuasive appeals. Graphics and narrative features emphasized positive emotions and outcomes. Implications We found subtle and sophisticated attempts both to educate and to persuade readers. It is important for consumers and prescribing physicians to read print advertisements critically so that they can make informed treatment choices. PMID:23687184
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Print advertisements for Alzheimer's disease drugs: informational and transformational features.
Gooblar, Jonathan; Carpenter, Brian D
2013-06-01
We examined print advertisements for Alzheimer's disease drugs published in journals and magazines between January 2008 and February 2012, using an informational versus transformational theoretical framework to identify objective and persuasive features. In 29 unique advertisements, we used qualitative methods to code and interpret identifying information, charts, benefit and side effect language, and persuasive appeals embedded in graphics and narratives. Most elements contained a mixture of informational and transformational features. Charts were used infrequently, but when they did appear the accompanying text often exaggerated the data. Benefit statements covered an array of symptoms, drug properties, and caregiver issues. Side effect statements often used positive persuasive appeals. Graphics and narrative features emphasized positive emotions and outcomes. We found subtle and sophisticated attempts both to educate and to persuade readers. It is important for consumers and prescribing physicians to read print advertisements critically so that they can make informed treatment choices.
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NASA Astrophysics Data System (ADS)
Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu
2018-05-01
The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.
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Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text
Xin, Yu; Hochberg, Ephraim; Joshi, Rohit; Uzuner, Ozlem; Szolovits, Peter
2015-01-01
Objective Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability. Methods The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria. Results and Conclusion SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features. PMID:25862765
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Wang, ShaoPeng; Zhang, Yu-Hang; Lu, Jing; Cui, Weiren; Hu, Jerry; Cai, Yu-Dong
2016-01-01
The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request. PMID:26955638
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Wang, ShaoPeng; Zhang, Yu-Hang; Lu, Jing; Cui, Weiren; Hu, Jerry; Cai, Yu-Dong
2016-01-01
The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.
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Salgado, Teresa M; Fedrigon, Alexa; Riccio Omichinski, Donna; Meade, Michelle A
2018-01-01
Background Smartphone apps can be a tool to facilitate independent medication management among persons with developmental disabilities. At present, multiple medication management apps exist in the market, but only 1 has been specifically designed for persons with developmental disabilities. Before initiating further app development targeting this population, input from stakeholders including persons with developmental disabilities, caregivers, and professionals regarding the most preferred features should be obtained. Objective The aim of this study was to identify medication management app features that are suitable to promote independence in the medication management process by young adults with developmental disabilities using a Delphi consensus method. Methods A compilation of medication management app features was performed by searching the iTunes App Store, United States, in February 2016, using the following terms: adherence, medication, medication management, medication list, and medication reminder. After identifying features within the retrieved apps, a final list of 42 features grouped into 4 modules (medication list, medication reminder, medication administration record, and additional features) was included in a questionnaire for expert consensus rating. A total of 52 experts in developmental disabilities, including persons with developmental disabilities, caregivers, and professionals, were invited to participate in a 3-round Delphi technique. The purpose was to obtain consensus on features that are preferred and suitable to promote independence in the medication management process among persons with developmental disabilities. Consensus for the first, second, and third rounds was defined as ≥90%, ≥80%, and ≥75% agreement, respectively. Results A total of 75 responses were received over the 3 Delphi rounds—30 in the first round, 24 in the second round, and 21 in the third round. At the end of the third round, cumulative consensus was achieved for 60% (12/20) items in the medication list module, 100% (3/3) in the medication reminder module, 67% (2/3) in the medication administration record module, and 63% (10/16) in the additional features module. In addition to the medication list, medication reminder, and medication administration record features, experts selected the following top 3 most important additional features: automatic refills through pharmacies; ability to share medication information from the app with providers; and ability to share medication information from the app with family, friends, and caregivers. The top 3 least important features included a link to an official drug information source, privacy settings and password protection, and prescription refill reminders. Conclusions Although several mobile apps for medication management exist, few are specifically designed to support persons with developmental disabilities in the complex medication management process. Of the 42 different features assessed, 64% (27/42) achieved consensus for inclusion in a future medication management app. This study provides information on the features of a medication management app that are most important to persons with developmental disabilities, caregivers, and professionals. PMID:29792292
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NASA Technical Reports Server (NTRS)
Ellis, Stephen R.; Liston, Dorion B.
2011-01-01
Visual motion and other visual cues are used by tower controllers to provide important support for their control tasks at and near airports. These cues are particularly important for anticipated separation. Some of them, which we call visual features, have been identified from structured interviews and discussions with 24 active air traffic controllers or supervisors. The visual information that these features provide has been analyzed with respect to possible ways it could be presented at a remote tower that does not allow a direct view of the airport. Two types of remote towers are possible. One could be based on a plan-view, map-like computer-generated display of the airport and its immediate surroundings. An alternative would present a composite perspective view of the airport and its surroundings, possibly provided by an array of radially mounted cameras positioned at the airport in lieu of a tower. An initial more detailed analyses of one of the specific landing cues identified by the controllers, landing deceleration, is provided as a basis for evaluating how controllers might detect and use it. Understanding other such cues will help identify the information that may be degraded or lost in a remote or virtual tower not located at the airport. Some initial suggestions how some of the lost visual information may be presented in displays are mentioned. Many of the cues considered involve visual motion, though some important static cues are also discussed.
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Empowering out of School Youth through Non-Formal Education in Kenya
ERIC Educational Resources Information Center
Mualuko, Ndiku Judah
2008-01-01
Non-formal education, defined as any organized educational activity outside the established formal system whether operating separately or as an important feature of some broader activity that is intended to serve identifiable learning clienteles and learning objective is of great importance to society. It emerged out of the feeling that formal…
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Exploring the limits of identifying sub-pixel thermal features using ASTER TIR data
Vaughan, R.G.; Keszthelyi, L.P.; Davies, A.G.; Schneider, D.J.; Jaworowski, C.; Heasler, H.
2010-01-01
Understanding the characteristics of volcanic thermal emissions and how they change with time is important for forecasting and monitoring volcanic activity and potential hazards. Satellite instruments view volcanic thermal features across the globe at various temporal and spatial resolutions. Thermal features that may be a precursor to a major eruption, or indicative of important changes in an on-going eruption can be subtle, making them challenging to reliably identify with satellite instruments. The goal of this study was to explore the limits of the types and magnitudes of thermal anomalies that could be detected using satellite thermal infrared (TIR) data. Specifically, the characterization of sub-pixel thermal features with a wide range of temperatures is considered using ASTER multispectral TIR data. First, theoretical calculations were made to define a "thermal mixing detection threshold" for ASTER, which quantifies the limits of ASTER's ability to resolve sub-pixel thermal mixing over a range of hot target temperatures and % pixel areas. Then, ASTER TIR data were used to model sub-pixel thermal features at the Yellowstone National Park geothermal area (hot spring pools with temperatures from 40 to 90 ??C) and at Mount Erebus Volcano, Antarctica (an active lava lake with temperatures from 200 to 800 ??C). Finally, various sources of uncertainty in sub-pixel thermal calculations were quantified for these empirical measurements, including pixel resampling, atmospheric correction, and background temperature and emissivity assumptions.
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NASA Astrophysics Data System (ADS)
Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong
2012-05-01
Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.
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Applications of alignment-free methods in epigenomics.
Pinello, Luca; Lo Bosco, Giosuè; Yuan, Guo-Cheng
2014-05-01
Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.
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Intrinsic and contextual features in object recognition.
Schlangen, Derrick; Barenholtz, Elan
2015-01-28
The context in which an object is found can facilitate its recognition. Yet, it is not known how effective this contextual information is relative to the object's intrinsic visual features, such as color and shape. To address this, we performed four experiments using rendered scenes with novel objects. In each experiment, participants first performed a visual search task, searching for a uniquely shaped target object whose color and location within the scene was experimentally manipulated. We then tested participants' tendency to use their knowledge of the location and color information in an identification task when the objects' images were degraded due to blurring, thus eliminating the shape information. In Experiment 1, we found that, in the absence of any diagnostic intrinsic features, participants identified objects based purely on their locations within the scene. In Experiment 2, we found that participants combined an intrinsic feature, color, with contextual location in order to uniquely specify an object. In Experiment 3, we found that when an object's color and location information were in conflict, participants identified the object using both sources of information equally. Finally, in Experiment 4, we found that participants used whichever source of information-either color or location-was more statistically reliable in order to identify the target object. Overall, these experiments show that the context in which objects are found can play as important a role as intrinsic features in identifying the objects. © 2015 ARVO.
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Hexagonal wavelet processing of digital mammography
NASA Astrophysics Data System (ADS)
Laine, Andrew F.; Schuler, Sergio; Huda, Walter; Honeyman-Buck, Janice C.; Steinbach, Barbara G.
1993-09-01
This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms and used to enhance features of importance to mammography within a continuum of scale-space. We present a method of contrast enhancement based on an overcomplete, non-separable multiscale representation: the hexagonal wavelet transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by local and global non-linear operators. Multiscale edges identified within distinct levels of transform space provide local support for enhancement. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification.
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ERIC Educational Resources Information Center
Blackburn, Greg
2011-01-01
Factors which influence students' selection of a Master of Business Administration programme are identified and the variation in their relative importance across the student population investigated. This research also identifies the features of a university which attracts students, as well as examining the students' perceptions of the management…
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ERIC Educational Resources Information Center
Bodycott, Peter
2009-01-01
Mainland China is one of the largest sources of undergraduate and postgraduate students. Previous research has identified the push-pull factors and features that influence a student choice of study abroad destination. This article extends understanding by identifying and examining what 251 mainland Chinese parents and 100 students rated as most…
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Patient-centred care in general dental practice - a systematic review of the literature
2014-01-01
Background Delivering improvements in quality is a key objective within most healthcare systems, and a view which has been widely embraced within the NHS in the United Kingdom. Within the NHS, quality is evaluated across three key dimensions: clinical effectiveness, safety and patient experience, with the latter modelled on the Picker Principles of Patient-Centred Care (PCC). Quality improvement is an important feature of the current dental contract reforms in England, with “patient experience” likely to have a central role in the evaluation of quality. An understanding and appreciation of the evidence underpinning PCC within dentistry is highly relevant if we are to use this as a measure of quality in general dental practice. Methods A systematic review of the literature was undertaken to identify the features of PCC relevant to dentistry and ascertain the current research evidence base underpinning its use as a measure of quality within general dental practice. Results Three papers were identified which met the inclusion criteria and demonstrated the use of primary research to provide an understanding of the key features of PCC within dentistry. None of the papers identified were based in general dental practice and none of the three studies sought the views of patients. Some distinct differences were noted between the key features of PCC reported within the dental literature and those developed within the NHS Patient Experience Framework. Conclusions This systematic review reveals a lack of understanding of PCC within dentistry, and in particular general dental practice. There is currently a poor evidence base to support the use of the current patient reported outcome measures as indicators of patient-centredness. Further research is necessary to understand the important features of PCC in dentistry and patients’ views should be central to this research. PMID:24902842
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Features of Inner Structure of Placer Gold of the North-Eastern Part Siberian Platform
NASA Astrophysics Data System (ADS)
Gerasimov, Boris; Zhuravlev, Anatolii; Ivanov, Alexey
2017-12-01
Mineral and raw material base of placer and ore gold is based on prognosis evaluation, which allows to define promising areas regarding gold-bearing deposit prospecting. But there are some difficulties in gold primary source predicting and prospecting at the North-east Siberian platform, because the studied area is overlapped by thick cover of the Cenozoic deposits, where traditional methods of gold deposit prospecting are ineffective. In this connection, detailed study of typomorphic features of placer gold is important, because it contains key genetic information, necessary for development of mineralogical criteria of prognosis evaluation of ore gold content. Authors studied mineralogical-geochemical features of placer gold of the Anabar placer area for 15 years, with a view to identify indicators of gold, typical for different formation types of primary sources. This article presents results of these works. In placer regions, where primary sources of gold are not identified, there is need to study typomorphic features of placer gold, because it contains important genetic information, necessary for the development of mineralogical criteria of prognosis evaluation of ore gold content. Inner structures of gold from the Anabar placer region are studied, as one of the diagnostic typomorphic criteria as described in prominent method, developed by N.V. Petrovskaya [1980]. Etching of gold was carried out using reagent: HCl + HNO3 + FeCl3 × 6H2O + CrO3 +thioureat + water. Identified inner structures wer studied in details by means of scanning electron microscope JEOL JSM-6480LV. Two types of gold are identified according to the features of inner structure of placer gold of the Anabar region. First type - medium-high karat fine, well processed gold with significantly changed inner structure. This gold is allochthonous, which was redeposited many times from ancient intermediate reservoirs to younger deposits. Second type - low-medium karat, poorly rounded gold with unchanged inner structure. Poor roundness of gold particles and preservation of their primary inner structures indicate close proximity of primary source.
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A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes
Liu, Jian; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Wang, Xue-Song; Wang, Dong
2015-01-01
In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data. PMID:26201006
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A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.
Liu, Jian; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Wang, Xue-Song; Wang, Dong
2015-01-01
In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Wang, J; Chuong, M; Choi, W
Purpose: To identify PET/CT based imaging predictors of anal cancer recurrence and evaluate baseline vs. mid-treatment vs. post-treatment PET/CT scans in the tumor recurrence prediction. Methods: FDG-PET/CT scans were obtained at baseline, during chemoradiotherapy (CRT, midtreatment), and after CRT (post-treatment) in 17 patients of anal cancer. Four patients had tumor recurrence. For each patient, the mid-treatment and post-treatment scans were respectively aligned to the baseline scan by a rigid registration followed by a deformable registration. PET/CT image features were computed within the manually delineated tumor volume of each scan to characterize the intensity histogram, spatial patterns (texture), and shape ofmore » the tumors, as well as the changes of these features resulting from CRT. A total of 335 image features were extracted. An Exact Logistic Regression model was employed to analyze these PET/CT image features in order to identify potential predictors for tumor recurrence. Results: Eleven potential predictors of cancer recurrence were identified with p < 0.10, including five shape features, five statistical texture features, and one CT intensity histogram feature. Six features were indentified from posttreatment scans, 3 from mid-treatment scans, and 2 from baseline scans. These features indicated that there were differences in shape, intensity, and spatial pattern between tumors with and without recurrence. Recurrent tumors tended to have more compact shape (higher roundness and lower elongation) and larger intensity difference between baseline and follow-up scans, compared to non-recurrent tumors. Conclusion: PET/CT based anal cancer recurrence predictors were identified. The post-CRT PET/CT is the most important scan for the prediction of cancer recurrence. The baseline and mid-CRT PET/CT also showed value in the prediction and would be more useful for the predication of tumor recurrence in early stage of CRT. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less
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Liu, Wei; Li, Dong; Zhang, Jiyang; Zhu, Yunping; He, Fuchu
2006-11-27
Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.
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Bennett, Robert M; Russell, Jon; Cappelleri, Joseph C; Bushmakin, Andrew G; Zlateva, Gergana; Sadosky, Alesia
2010-06-28
The purpose of this study was to determine whether some of the clinical features of fibromyalgia (FM) that patients would like to see improved aggregate into definable clusters. Seven hundred and eighty-eight patients with clinically confirmed FM and baseline pain > or =40 mm on a 100 mm visual analogue scale ranked 5 FM clinical features that the subjects would most like to see improved after treatment (one for each priority quintile) from a list of 20 developed during focus groups. For each subject, clinical features were transformed into vectors with rankings assigned values 1-5 (lowest to highest ranking). Logistic analysis was used to create a distance matrix and hierarchical cluster analysis was applied to identify cluster structure. The frequency of cluster selection was determined, and cluster importance was ranked using cluster scores derived from rankings of the clinical features. Multidimensional scaling was used to visualize and conceptualize cluster relationships. Six clinical features clusters were identified and named based on their key characteristics. In order of selection frequency, the clusters were Pain (90%; 4 clinical features), Fatigue (89%; 4 clinical features), Domestic (42%; 4 clinical features), Impairment (29%; 3 functions), Affective (21%; 3 clinical features), and Social (9%; 2 functional). The "Pain Cluster" was ranked of greatest importance by 54% of subjects, followed by Fatigue, which was given the highest ranking by 28% of subjects. Multidimensional scaling mapped these clusters to two dimensions: Status (bounded by Physical and Emotional domains), and Setting (bounded by Individual and Group interactions). Common clinical features of FM could be grouped into 6 clusters (Pain, Fatigue, Domestic, Impairment, Affective, and Social) based on patient perception of relevance to treatment. Furthermore, these 6 clusters could be charted in the 2 dimensions of Status and Setting, thus providing a unique perspective for interpretation of FM symptomatology.
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Prediction of lysine ubiquitylation with ensemble classifier and feature selection.
Zhao, Xiaowei; Li, Xiangtao; Ma, Zhiqiang; Yin, Minghao
2011-01-01
Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the proposed method, 468 ubiquitylation sites from 323 proteins retrieved from the Swiss-Prot database were encoded into feature vectors by using four kinds of protein sequences information. An effective feature selection method was then applied to extract informative feature subsets. After different feature subsets were obtained by setting different starting points in the search procedure, they were used to train multiple random forests classifiers and then aggregated into a consensus classifier by majority voting. Evaluated by jackknife tests and independent tests respectively, the accuracy of the proposed predictor reached 76.82% for the training dataset and 79.16% for the test dataset, indicating that this predictor is a useful tool to predict lysine ubiquitylation sites. Furthermore, site-specific feature analysis was performed and it was shown that ubiquitylation is intimately correlated with the features of its surrounding sites in addition to features derived from the lysine site itself. The feature selection method is available upon request.
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Tropical Timber Identification using Backpropagation Neural Network
NASA Astrophysics Data System (ADS)
Siregar, B.; Andayani, U.; Fatihah, N.; Hakim, L.; Fahmi, F.
2017-01-01
Each and every type of wood has different characteristics. Identifying the type of wood properly is important, especially for industries that need to know the type of timber specifically. However, it requires expertise in identifying the type of wood and only limited experts available. In addition, the manual identification even by experts is rather inefficient because it requires a lot of time and possibility of human errors. To overcome these problems, a digital image based method to identify the type of timber automatically is needed. In this study, backpropagation neural network is used as artificial intelligence component. Several stages were developed: a microscope image acquisition, pre-processing, feature extraction using gray level co-occurrence matrix and normalization of data extraction using decimal scaling features. The results showed that the proposed method was able to identify the timber with an accuracy of 94%.
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Features of Coping with Disease in Iranian Multiple Sclerosis Patients: a Qualitative Study.
Dehghani, Ali; Dehghan Nayeri, Nahid; Ebadi, Abbas
2018-03-01
Introduction: Coping with disease is of the main components improving the quality of life in multiple sclerosis patients. Identifying the characteristics of this concept is based on the experiences of patients. Using qualitative research is essential to improve the quality of life. This study was conducted to explore the features of coping with the disease in patients with multiple sclerosis. Method: In this conventional content analysis study, eleven multiple sclerosis patients from Iran MS Society in Tehran (Iran) participated. Purposive sampling was used to select participants. Data were gathered using semi structured interviews. To analyze data, a conventional content analysis approach was used to identify meaning units and to make codes and categories. Results: Results showed that features of coping with disease in multiple sclerosis patients consists of (a) accepting the current situation, (b) maintenance and development of human interactions, (c) self-regulation and (d) self-efficacy. Each of these categories is composed of sub-categories and codes that showed the perception and experience of patients about the coping with disease. Conclusion: Accordingly, a unique set of features regarding features of coping with the disease were identified among the patients with multiple sclerosis. Therefore, working to ensure the emergence of, and subsequent reinforcement of these features in MS patients can be an important step in improving the adjustment and quality of their lives.
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Kate, Rohit J.; Swartz, Ann M.; Welch, Whitney A.; Strath, Scott J.
2016-01-01
Wearable accelerometers can be used to objectively assess physical activity. However, the accuracy of this assessment depends on the underlying method used to process the time series data obtained from accelerometers. Several methods have been proposed that use this data to identify the type of physical activity and estimate its energy cost. Most of the newer methods employ some machine learning technique along with suitable features to represent the time series data. This paper experimentally compares several of these techniques and features on a large dataset of 146 subjects doing eight different physical activities wearing an accelerometer on the hip. Besides features based on statistics, distance based features and simple discrete features straight from the time series were also evaluated. On the physical activity type identification task, the results show that using more features significantly improve results. Choice of machine learning technique was also found to be important. However, on the energy cost estimation task, choice of features and machine learning technique were found to be less influential. On that task, separate energy cost estimation models trained specifically for each type of physical activity were found to be more accurate than a single model trained for all types of physical activities. PMID:26862679
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Easy Ergonomics: A Guide to Selecting Non-Powered Hand Tools
... identifying the presence or absence of basic ergonomic design features (Dababneh et al.*). The right tool will ... Cal/OSHA). Both agencies recognize the importance of design and selection of hand tools in strategies to ...
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Rutgers University Subcontract B611610 Final Report
DOE Office of Scientific and Technical Information (OSTI.GOV)
Soundarajan, Sucheta; Eliassi-Rad, Tina; Gallagher, Brian
Given an incomplete (i.e., partially-observed) network, which nodes should we actively probe in order to achieve the highest accuracy for a given network feature? For example, consider a cyber-network administrator who observes only a portion of the network at time t and wants to accurately identify the most important (e.g., highest PageRank) nodes in the complete network. She has a limited budget for probing the network. Of all the nodes she has observed, which should she probe in order to most accurately identify the important nodes? We propose a novel and scalable algorithm, MaxOutProbe, and evaluate it w.r.t. four networkmore » features (largest connected component, PageRank, core-periphery, and community detection), five network sampling strategies, and seven network datasets from different domains. Across a range of conditions, MaxOutProbe demonstrates consistently high performance relative to several baseline strategies« less
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Li, Shelly-Anne; Jeffs, Lianne; Barwick, Melanie; Stevens, Bonnie
2018-05-05
Organizational contextual features have been recognized as important determinants for implementing evidence-based practices across healthcare settings for over a decade. However, implementation scientists have not reached consensus on which features are most important for implementing evidence-based practices. The aims of this review were to identify the most commonly reported organizational contextual features that influence the implementation of evidence-based practices across healthcare settings, and to describe how these features affect implementation. An integrative review was undertaken following literature searches in CINAHL, MEDLINE, PsycINFO, EMBASE, Web of Science, and Cochrane databases from January 2005 to June 2017. English language, peer-reviewed empirical studies exploring organizational context in at least one implementation initiative within a healthcare setting were included. Quality appraisal of the included studies was performed using the Mixed Methods Appraisal Tool. Inductive content analysis informed data extraction and reduction. The search generated 5152 citations. After removing duplicates and applying eligibility criteria, 36 journal articles were included. The majority (n = 20) of the study designs were qualitative, 11 were quantitative, and 5 used a mixed methods approach. Six main organizational contextual features (organizational culture; leadership; networks and communication; resources; evaluation, monitoring and feedback; and champions) were most commonly reported to influence implementation outcomes in the selected studies across a wide range of healthcare settings. We identified six organizational contextual features that appear to be interrelated and work synergistically to influence the implementation of evidence-based practices within an organization. Organizational contextual features did not influence implementation efforts independently from other features. Rather, features were interrelated and often influenced each other in complex, dynamic ways to effect change. These features corresponded to the constructs in the Consolidated Framework for Implementation Research (CFIR), which supports the use of CFIR as a guiding framework for studies that explore the relationship between organizational context and implementation. Organizational culture was most commonly reported to affect implementation. Leadership exerted influence on the five other features, indicating it may be a moderator or mediator that enhances or impedes the implementation of evidence-based practices. Future research should focus on how organizational features interact to influence implementation effectiveness.
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Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.
Mourad, Raphaël; Cuvier, Olivier
2016-05-01
Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1.
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Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation
Mourad, Raphaël; Cuvier, Olivier
2016-01-01
Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1. PMID:27203237
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Manavalan, Balachandran; Shin, Tae Hwan; Lee, Gwang
2018-01-05
DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html.
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Manavalan, Balachandran; Shin, Tae Hwan; Lee, Gwang
2018-01-01
DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html PMID:29416743
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Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre (OPDC) Discovery Cohort.
Lawton, Michael; Baig, Fahd; Rolinski, Michal; Ruffman, Claudio; Nithi, Kannan; May, Margaret T; Ben-Shlomo, Yoav; Hu, Michele T M
2015-01-01
Within Parkinson's there is a spectrum of clinical features at presentation which may represent sub-types of the disease. However there is no widely accepted consensus of how best to group patients. Use a data-driven approach to unravel any heterogeneity in the Parkinson's phenotype in a well-characterised, population-based incidence cohort. 769 consecutive patients, with mean disease duration of 1.3 years, were assessed using a broad range of motor, cognitive and non-motor metrics. Multiple imputation was carried out using the chained equations approach to deal with missing data. We used an exploratory and then a confirmatory factor analysis to determine suitable domains to include within our cluster analysis. K-means cluster analysis of the factor scores and all the variables not loading into a factor was used to determine phenotypic subgroups. Our factor analysis found three important factors that were characterised by: psychological well-being features; non-tremor motor features, such as posture and rigidity; and cognitive features. Our subsequent five cluster model identified groups characterised by (1) mild motor and non-motor disease (25.4%), (2) poor posture and cognition (23.3%), (3) severe tremor (20.8%), (4) poor psychological well-being, RBD and sleep (18.9%), and (5) severe motor and non-motor disease with poor psychological well-being (11.7%). Our approach identified several Parkinson's phenotypic sub-groups driven by largely dopaminergic-resistant features (RBD, impaired cognition and posture, poor psychological well-being) that, in addition to dopaminergic-responsive motor features may be important for studying the aetiology, progression, and medication response of early Parkinson's.
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Analysis of A Drug Target-based Classification System using Molecular Descriptors.
Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin
2016-01-01
Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.
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Feminization and marginalization? Women Ayurvedic doctors and modernizing health care in Nepal.
Cameron, Mary
2010-03-01
The important diversity of indigenous medical systems around the world suggests that gender issues, well understood for Western science, may differ in significant ways for non-Western science practices and are an important component in understanding how social dimensions of women's health care are being transformed by global biomedicine. Based on ethnographic research conducted with formally trained women Ayurvedic doctors in Nepal, I identify important features of medical knowledge and practice beneficial to women patients, and I discuss these features as potentially transformed by modernizing health care development. The article explores the indirect link between Ayurveda's feminization and its marginalization, in relation to modern biomedicine, which may evolve to become more direct and consequential for women's health in the country.
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Crabtree, Nathaniel M; Moore, Jason H; Bowyer, John F; George, Nysia I
2017-01-01
A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes. These characteristics give CES an advantage over other classification and feature selection algorithms, particularly when the goal is to identify a small number of highly relevant, non-redundant biomarkers. Previously, CESs have been developed only for binary class datasets. In this study, we developed a multi-class CES. The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF). The algorithms were evaluated on three distinct multi-class RNA sequencing datasets. The comparison criteria were run-time, classification accuracy, number of selected features, and stability of selected feature set (as measured by the Tanimoto distance). The performance of each algorithm was data-dependent. CES performed best on the dataset with the smallest sample size, indicating that CES has a unique advantage since the accuracy of most classification methods suffer when sample size is small. The multi-class extension of CES increases the appeal of its application to complex, multi-class datasets in order to identify important biomarkers and features.
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Schulkind, M D
1999-09-01
In three experiments, long-term memory for temporal structure was examined by having participants identify both well-known (e.g., "I've Been Working on the Railroad") and novel songs. The target songs were subjected to a number of rhythmic alterations, to assess the importance of four critical features of identification performance. The four critical features were meter, phrasing, rhythmic contour (ordinal scaling of note durations), and the ratio of successive durations. In contrast with previous work, the unaltered version of each song was identified significantly better than any altered version. This indicates that rhythm is stored in long-term memory. Furthermore, the results demonstrated that all four critical features play a role in the identification of songs. These results held for both well-known and novel tunes.
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Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.
Ju, Zhe; He, Jian-Jun
2018-06-01
Lysine glutarylation is new type of protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of glutarylation, it is important to identify glutarylated substrates and their corresponding glutarylation sites accurately. In this study, a novel bioinformatics tool named GlutPred is developed to predict glutarylation sites by using multiple feature extraction and maximum relevance minimum redundancy feature selection. On the one hand, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs features are incorporated to encode glutarylation sites. And the maximum relevance minimum redundancy method and the incremental feature selection algorithm are adopted to remove the redundant features. On the other hand, a biased support vector machine algorithm is used to handle the imbalanced problem in glutarylation sites training dataset. As illustrated by 10-fold cross-validation, the performance of GlutPred achieves a satisfactory performance with a Sensitivity of 64.80%, a Specificity of 76.60%, an Accuracy of 74.90% and a Matthew's correlation coefficient of 0.3194. Feature analysis shows that some k-spaced amino acid pair features play the most important roles in the prediction of glutarylation sites. The conclusions derived from this study might provide some clues for understanding the molecular mechanisms of glutarylation. Copyright © 2018 Elsevier Inc. All rights reserved.
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Cutaneous Squamous Cell Carcinoma.
Parekh, Vishwas; Seykora, John T
2017-09-01
Cutaneous squamous cell carcinoma (cSCC) is a malignant neoplasm of the skin characterized by an aberrant proliferation of keratinocytes. Cutaneous SCC is the second most common malignancy globally, and usually arises in the chronically sun-damaged skin of elderly white individuals. From a pathologist's perspective, it is important to differentiate cSCC from the benign and reactive squamoproliferative lesions and identify the high-risk features associated with aggressive tumor behavior. In this article, we provide an up-to-date overview of cSCC along with its precursor lesions and important histologic variants, with a particular emphasis on the histopathologic features and molecular pathogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.
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What really matters to healthcare consumers.
Jennings, Bonnie Mowinski; Heiner, Stacy L; Loan, Lori A; Hemman, Eileen A; Swanson, Kristen M
2005-04-01
Consumer satisfaction with healthcare is an important quality and outcome indicator. Satisfaction may be at the crux of survival for healthcare delivery systems because it creates the competitive edge in healthcare. To better understand patient satisfaction by examining consumer healthcare experiences and expectations, a study was conducted. An important concept identified in the data, MY CARE, refers to a constellation of quality healthcare features that were wished for by all participants and realized by only some of them. The features of MY CARE offer lessons for all healthcare leaders to use when making improvements in care delivery systems-improvements that could create a more patient-centered healthcare system and boost patient satisfaction.
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ERIC Educational Resources Information Center
World Wildlife Fund, Washington, DC.
This document features a lesson plan in which genetic traits are identified and classified using a genetic wheel by playing several different games that introduce genetic diversity and highlight why it is important within populations. Samples of instruction and assessment are included. (KHR)
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Special places in the Lake Calumet area.
Herbert W. Schroeder
2004-01-01
An open-ended, qualitative survey was conducted to identify special places in the Lake Calumet area of northeastern Illinois and northwestern Indiana, and to learn what kinds of experiences and environmental features make these places memorable and important to people.
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The Scanning Process: Getting Started.
ERIC Educational Resources Information Center
Renfro, William L.; Morrison, James L.
1983-01-01
Scanning the external environment will become more essential to colleges in the coming decade. Developing an environmental scanning system can identify important emerging issues that may constitute either threats or opportunities. The organizational features of a mature scanning process are described. (MLW)
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Guo, Song; Liu, Chunhua; Zhou, Peng; Li, Yanling
2016-01-01
Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields.
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Liu, Chunhua; Zhou, Peng; Li, Yanling
2016-01-01
Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields. PMID:27034949
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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.
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Wood, Benjamin A; LeBoit, Philip E
2013-08-01
To study the clinical and pathological features of cases of apparent solar purpura, with attention to the recently described phenomenon of inflammatory changes within otherwise typical lesions. We studied 95 cases diagnosed as solar purpura and identified 10 cases (10.5%) in which significant neutrophilic inflammation was present, potentially simulating a leukocytoclastic vasculitis or neutrophilic dermatosis. An additional three cases were identified in subsequent routine practice. The clinical features, including follow-up for subsequent development of vasculitis and histological features were studied. In all cases the histological features were typical of solar purpura, with the exception of inflammatory changes, typically associated with clefting of elastotic stroma. Clinical follow-up information was available for all patients and none developed subsequent evidence of a cutaneous or systemic vasculitis or neutrophilic dermatosis. Inflammatory changes appear to be more frequent in solar purpura than is generally recognised. Awareness of this histological variation and correlation with the clinical findings and evolution is important in avoiding misdiagnosis.
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Wallar, Lauren E; Sargeant, Jan M; McEwen, Scott A; Mercer, Nicola J; Papadopoulos, Andrew
Environmental public health practitioners rely on information technology (IT) to maintain and improve environmental health. However, current systems have limited capacity. A better understanding of the importance of IT features is needed to enhance data and information capacity. (1) Rank IT features according to the percentage of respondents who rated them as essential to an information management system and (2) quantify the relative importance of a subset of these features using best-worst scaling. Information technology features were initially identified from a previously published systematic review of software evaluation criteria and a list of software options from a private corporation specializing in inspection software. Duplicates and features unrelated to environmental public health were removed. The condensed list was refined by a working group of environmental public health management to a final list of 57 IT features. The essentialness of features was electronically rated by environmental public health managers. Features where 50% to 80% of respondents rated them as essential (n = 26) were subsequently evaluated using best-worst scaling. Ontario, Canada. Environmental public health professionals in local public health. Importance scores of IT features. The majority of IT features (47/57) were considered essential to an information management system by at least half of the respondents (n = 52). The highest-rated features were delivery to printer, software encryption capability, and software maintenance services. Of the 26 features evaluated in the best-worst scaling exercise, the most important features were orientation to all practice areas, off-line capability, and ability to view past inspection reports and results. The development of a single, unified environmental public health information management system that fulfills the reporting and functionality needs of system users is recommended. This system should be implemented by all public health units to support data and information capacity in local environmental public health. This study can be used to guide vendor evaluation, negotiation, and selection in local environmental public health, and provides an example of academia-practice partnerships and the use of best-worst scaling in public health research.
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Revenäs, Åsa; Opava, Christina H; Åsenlöf, Pernilla
2014-03-22
Despite the growing evidence of the benefits of physical activity (PA) in individuals with rheumatoid arthritis (RA), the majority is not physically active enough. An innovative strategy is to engage lead users in the development of PA interventions provided over the internet. The aim was to explore lead users' ideas and prioritization of core features in a future internet service targeting adoption and maintenance of healthy PA in people with RA. Six focus group interviews were performed with a purposively selected sample of 26 individuals with RA. Data were analyzed with qualitative content analysis and quantification of participants' prioritization of most important content. Six categories were identified as core features for a future internet service: up-to-date and evidence-based information and instructions, self-regulation tools, social interaction, personalized set-up, attractive design and content, and access to the internet service. The categories represented four themes, or core aspects, important to consider in the design of the future service: (1) content, (2) customized options, (3) user interface and (4) access and implementation. This is, to the best of our knowledge, the first study involving people with RA in the development of an internet service to support the adoption and maintenance of PA.Participants helped identifying core features and aspects important to consider and further explore during the next phase of development. We hypothesize that involvement of lead users will make transfer from theory to service more adequate and user-friendly and therefore will be an effective mean to facilitate PA behavior change.
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Textural signatures for wetland vegetation
NASA Technical Reports Server (NTRS)
Whitman, R. I.; Marcellus, K. L.
1973-01-01
This investigation indicates that unique textural signatures do exist for specific wetland communities at certain times in the growing season. When photographs with the proper resolution are obtained, the textural features can identify the spectral features of the vegetation community seen with lower resolution mapping data. The development of a matrix of optimum textural signatures is the goal of this research. Seasonal variations of spectral and textural features are particularly important when performing a vegetations analysis of fresh water marshes. This matrix will aid in flight planning, since expected seasonal variations and resolution requirements can be established prior to a given flight mission.
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Explosive Cyclogenesis Over the Eastern United States.
NASA Astrophysics Data System (ADS)
MacDonald, Bruce Calvin
Cases of explosive cyclogenesis occurring over the east central United States are identified and analyzed. Other selected cases of weak or nonintensifying cyclones over the same area are identified and studied for comparative purposes. Signatures of explosively deepening cyclones (bombs) are derived from the analyses, including vertical profiles of vorticity, divergence, and latent heating, and also the relative importance of terms in the vorticity tendency equation and the relative importance of convective and stable latent heating. Composite analyses for the differing phases of bomb development and for regular cyclones are presented. Analyses of individual cases reveal the importance of a low-level jet streak, low-level moisture content, and moisture gradients in the lower troposphere. A numerical model is used to further examine the important processes in explosive cyclogenesis. A mesoscale feature is introduced to improve the prediction of sea -level pressure. This feature is based on the tendency of the large scale height field and vorticity field to adjust concurrently at each time step. The model is also used to provide air parcel trajectories to indicate the importance of parcels with high vorticity and moisture content as an ingredient in explosive cyclogenesis. Sensitivity studies are carried out with the model in order to determine the effect of changes in the initial vorticity and moisture field on cyclogenesis.
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Understanding the signature of rock coatings in laser-induced breakdown spectroscopy data
Lanza, Nina L.; Ollila, Ann M.; Cousin, Agnes; Wiens, Roger C.; Clegg, Samuel M.; Mangold, Nicolas; Bridges, Nathan; Cooper, Daniel; Schmidt, Mariek E.; Berger, Jeffrey; Arvidson, Raymond E.; Melikechi, Noureddine; Newsom, Horton E.; Tokar, Robert; Hardgrove, Craig; Mezzacappa, Alissa; Jackson, Ryan S.; Clark, Benton C.; Forni, Olivier; Maurice, Sylvestre; Nachon, Marion; Anderson, Ryan B.; Blank, Jennifer; Deans, Matthew; Delapp, Dorothea; Léveillé, Richard; McInroy, Rhonda; Martinez, Ronald; Meslin, Pierre-Yves; Pinet, Patrick
2015-01-01
Surface compositional features on rocks such as coatings and weathering rinds provide important information about past aqueous environments and water–rock interactions. The search for these features represents an important aspect of the Curiosity rover mission. With its unique ability to do fine-scale chemical depth profiling, the ChemCam laser-induced breakdown spectroscopy instrument (LIBS) onboard Curiosity can be used to both identify and analyze rock surface alteration features. In this study we analyze a terrestrial manganese-rich rock varnish coating on a basalt rock in the laboratory with the ChemCam engineering model to determine the LIBS signature of a natural rock coating. Results show that there is a systematic decrease in peak heights for elements such as Mn that are abundant in the coating but not the rock. There is significant spatial variation in the relative abundance of coating elements detected by LIBS depending on where on the rock surface sampled; this is due to the variability in thickness and spatial discontinuities in the coating. Similar trends have been identified in some martian rock targets in ChemCam data, suggesting that these rocks may have coatings or weathering rinds on their surfaces.
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NASA Astrophysics Data System (ADS)
Kasyidi, Fatan; Puji Lestari, Dessi
2018-03-01
One of the important aspects in human to human communication is to understand emotion of each party. Recently, interactions between human and computer continues to develop, especially affective interaction where emotion recognition is one of its important components. This paper presents our extended works on emotion recognition of Indonesian spoken language to identify four main class of emotions: Happy, Sad, Angry, and Contentment using combination of acoustic/prosodic features and lexical features. We construct emotion speech corpus from Indonesia television talk show where the situations are as close as possible to the natural situation. After constructing the emotion speech corpus, the acoustic/prosodic and lexical features are extracted to train the emotion model. We employ some machine learning algorithms such as Support Vector Machine (SVM), Naive Bayes, and Random Forest to get the best model. The experiment result of testing data shows that the best model has an F-measure score of 0.447 by using only the acoustic/prosodic feature and F-measure score of 0.488 by using both acoustic/prosodic and lexical features to recognize four class emotion using the SVM RBF Kernel.
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Paulsson, Kajsa; Cazier, Jean-Baptiste; MacDougall, Finlay; Stevens, Jane; Stasevich, Irina; Vrcelj, Nikoletta; Chaplin, Tracy; Lillington, Debra M.; Lister, T. Andrew; Young, Bryan D.
2008-01-01
We present here a genome-wide map of abnormalities found in diagnostic samples from 45 adults and adolescents with acute lymphoblastic leukemia (ALL). A 500K SNP array analysis uncovered frequent genetic abnormalities, with cryptic deletions constituting half of the detected changes, implying that microdeletions are a characteristic feature of this malignancy. Importantly, the pattern of deletions resembled that recently reported in pediatric ALL, suggesting that adult, adolescent, and childhood cases may be more similar on the genetic level than previously thought. Thus, 70% of the cases displayed deletion of one or more of the CDKN2A, PAX5, IKZF1, ETV6, RB1, and EBF1 genes. Furthermore, several genes not previously implicated in the pathogenesis of ALL were identified as possible recurrent targets of deletion. In total, the SNP array analysis identified 367 genetic abnormalities not corresponding to known copy number polymorphisms, with all but two cases (96%) displaying at least one cryptic change. The resolution level of this SNP array study is the highest used to date to investigate a malignant hematologic disorder. Our findings provide insights into the leukemogenic process and may be clinically important in adult and adolescent ALL. Most importantly, we report that microdeletions of key genes appear to be a common, characteristic feature of ALL that is shared among different clinical, morphological, and cytogenetic subgroups. PMID:18458336
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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.
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Collective feature selection to identify crucial epistatic variants.
Verma, Shefali S; Lucas, Anastasia; Zhang, Xinyuan; Veturi, Yogasudha; Dudek, Scott; Li, Binglan; Li, Ruowang; Urbanowicz, Ryan; Moore, Jason H; Kim, Dokyoon; Ritchie, Marylyn D
2018-01-01
Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration). In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.
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Wang, ShaoPeng; Zhang, Yu-Hang; Huang, GuoHua; Chen, Lei; Cai, Yu-Dong
2017-01-01
Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Identification of a motor to auditory pathway important for vocal learning
Roberts, Todd F.; Hisey, Erin; Tanaka, Masashi; Kearney, Matthew; Chattree, Gaurav; Yang, Cindy F.; Shah, Nirao M.; Mooney, Richard
2017-01-01
Summary Learning to vocalize depends on the ability to adaptively modify the temporal and spectral features of vocal elements. Neurons that convey motor-related signals to the auditory system are theorized to facilitate vocal learning, but the identity and function of such neurons remain unknown. Here we identify a previously unknown neuron type in the songbird brain that transmits vocal motor signals to the auditory cortex. Genetically ablating these neurons in juveniles disrupted their ability to imitate features of an adult tutor’s song. Ablating these neurons in adults had little effect on previously learned songs, but interfered with their ability to adaptively modify the duration of vocal elements and largely prevented the degradation of song’s temporal features normally caused by deafening. These findings identify a motor to auditory circuit essential to vocal imitation and to the adaptive modification of vocal timing. PMID:28504672
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BagReg: Protein inference through machine learning.
Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou
2015-08-01
Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Kolchinsky, A; Lourenço, A; Li, L; Rocha, L M
2013-01-01
Drug-drug interaction (DDI) is a major cause of morbidity and mortality. DDI research includes the study of different aspects of drug interactions, from in vitro pharmacology, which deals with drug interaction mechanisms, to pharmaco-epidemiology, which investigates the effects of DDI on drug efficacy and adverse drug reactions. Biomedical literature mining can aid both kinds of approaches by extracting relevant DDI signals from either the published literature or large clinical databases. However, though drug interaction is an ideal area for translational research, the inclusion of literature mining methodologies in DDI workflows is still very preliminary. One area that can benefit from literature mining is the automatic identification of a large number of potential DDIs, whose pharmacological mechanisms and clinical significance can then be studied via in vitro pharmacology and in populo pharmaco-epidemiology. We implemented a set of classifiers for identifying published articles relevant to experimental pharmacokinetic DDI evidence. These documents are important for identifying causal mechanisms behind putative drug-drug interactions, an important step in the extraction of large numbers of potential DDIs. We evaluate performance of several linear classifiers on PubMed abstracts, under different feature transformation and dimensionality reduction methods. In addition, we investigate the performance benefits of including various publicly-available named entity recognition features, as well as a set of internally-developed pharmacokinetic dictionaries. We found that several classifiers performed well in distinguishing relevant and irrelevant abstracts. We found that the combination of unigram and bigram textual features gave better performance than unigram features alone, and also that normalization transforms that adjusted for feature frequency and document length improved classification. For some classifiers, such as linear discriminant analysis (LDA), proper dimensionality reduction had a large impact on performance. Finally, the inclusion of NER features and dictionaries was found not to help classification.
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Global screening for Critical Habitat in the terrestrial realm.
Brauneder, Kerstin M; Montes, Chloe; Blyth, Simon; Bennun, Leon; Butchart, Stuart H M; Hoffmann, Michael; Burgess, Neil D; Cuttelod, Annabelle; Jones, Matt I; Kapos, Val; Pilgrim, John; Tolley, Melissa J; Underwood, Emma C; Weatherdon, Lauren V; Brooks, Sharon E
2018-01-01
Critical Habitat has become an increasingly important concept used by the finance sector and businesses to identify areas of high biodiversity value. The International Finance Corporation (IFC) defines Critical Habitat in their highly influential Performance Standard 6 (PS6), requiring projects in Critical Habitat to achieve a net gain of biodiversity. Here we present a global screening layer of Critical Habitat in the terrestrial realm, derived from global spatial datasets covering the distributions of 12 biodiversity features aligned with guidance provided by the IFC. Each biodiversity feature is categorised as 'likely' or 'potential' Critical Habitat based on: 1. Alignment between the biodiversity feature and the IFC Critical Habitat definition; and 2. Suitability of the spatial resolution for indicating a feature's presence on the ground. Following the initial screening process, Critical Habitat must then be assessed in-situ by a qualified assessor. This analysis indicates that a total of 10% and 5% of the global terrestrial environment can be considered as likely and potential Critical Habitat, respectively, while the remaining 85% did not overlap with any of the biodiversity features assessed and was classified as 'unknown'. Likely Critical Habitat was determined principally by the occurrence of Key Biodiversity Areas and Protected Areas. Potential Critical Habitat was predominantly characterised by data representing highly threatened and unique ecosystems such as ever-wet tropical forests and tropical dry forests. The areas we identified as likely or potential Critical Habitat are based on the best available global-scale data for the terrestrial realm that is aligned with IFC's Critical Habitat definition. Our results can help businesses screen potential development sites at the early project stage based on a range of biodiversity features. However, the study also demonstrates several important data gaps and highlights the need to incorporate new and improved global spatial datasets as they become available.
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Triana Hoyos, Ana Maria; Alakörkkö, Tuomas; Kaski, Kimmo; Saramäki, Jari; Isometsä, Erkki; Darst, Richard K
2017-01-01
Background Mental and behavioral disorders are the main cause of disability worldwide. However, their diagnosis is challenging due to a lack of reliable biomarkers; current detection is based on structured clinical interviews which can be biased by the patient’s recall ability, affective state, changing in temporal frames, etc. While digital platforms have been introduced as a possible solution to this complex problem, there is little evidence on the extent of usability and usefulness of these platforms. Therefore, more studies where digital data is collected in larger scales are needed to collect scientific evidence on the capacities of these platforms. Most of the existing platforms for digital psychiatry studies are designed as monolithic systems for a certain type of study; publications from these studies focus on their results, rather than the design features of the data collection platform. Inevitably, more tools and platforms will emerge in the near future to fulfill the need for digital data collection for psychiatry. Currently little knowledge is available from existing digital platforms for future data collection platforms to build upon. Objective The objective of this work was to identify the most important features for designing a digital platform for data collection for mental health studies, and to demonstrate a prototype platform that we built based on these design features. Methods We worked closely in a multidisciplinary collaboration with psychiatrists, software developers, and data scientists and identified the key features which could guarantee short-term and long-term stability and usefulness of the platform from the designing stage to data collection and analysis of collected data. Results The key design features that we identified were flexibility of access control, flexibility of data sources, and first-order privacy protection. We also designed the prototype platform Non-Intrusive Individual Monitoring Architecture (Niima), where we implemented these key design features. We described why each of these features are important for digital data collection for psychiatry, gave examples of projects where Niima was used or is going to be used in the future, and demonstrated how incorporating these design principles opens new possibilities for studies. Conclusions The new methods of digital psychiatry are still immature and need further research. The design features we suggested are a first step to design platforms which can adapt to the upcoming requirements of digital psychiatry. PMID:28600276
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Bahl, Manisha; Barzilay, Regina; Yedidia, Adam B; Locascio, Nicholas J; Yu, Lili; Lehman, Constance D
2018-03-01
Purpose To develop a machine learning model that allows high-risk breast lesions (HRLs) diagnosed with image-guided needle biopsy that require surgical excision to be distinguished from HRLs that are at low risk for upgrade to cancer at surgery and thus could be surveilled. Materials and Methods Consecutive patients with biopsy-proven HRLs who underwent surgery or at least 2 years of imaging follow-up from June 2006 to April 2015 were identified. A random forest machine learning model was developed to identify HRLs at low risk for upgrade to cancer. Traditional features such as age and HRL histologic results were used in the model, as were text features from the biopsy pathologic report. Results One thousand six HRLs were identified, with a cancer upgrade rate of 11.4% (115 of 1006). A machine learning random forest model was developed with 671 HRLs and tested with an independent set of 335 HRLs. Among the most important traditional features were age and HRL histologic results (eg, atypical ductal hyperplasia). An important text feature from the pathologic reports was "severely atypical." Instead of surgical excision of all HRLs, if those categorized with the model to be at low risk for upgrade were surveilled and the remainder were excised, then 97.4% (37 of 38) of malignancies would have been diagnosed at surgery, and 30.6% (91 of 297) of surgeries of benign lesions could have been avoided. Conclusion This study provides proof of concept that a machine learning model can be applied to predict the risk of upgrade of HRLs to cancer. Use of this model could decrease unnecessary surgery by nearly one-third and could help guide clinical decision making with regard to surveillance versus surgical excision of HRLs. © RSNA, 2017.
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Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S; Keck, K; Erdogmus, D; Chiang, M F
2015-01-01
Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure. The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image. Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.
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Optimizing data collection for public health decisions: a data mining approach
2014-01-01
Background Collecting data can be cumbersome and expensive. Lack of relevant, accurate and timely data for research to inform policy may negatively impact public health. The aim of this study was to test if the careful removal of items from two community nutrition surveys guided by a data mining technique called feature selection, can (a) identify a reduced dataset, while (b) not damaging the signal inside that data. Methods The Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed on 885 retail food outlets in two counties in West Virginia between May and November of 2011. A reduced dataset was identified for each outlet type using feature selection. Coefficients from linear regression modeling were used to weight items in the reduced datasets. Weighted item values were summed with the error term to compute reduced item survey scores. Scores produced by the full survey were compared to the reduced item scores using a Wilcoxon rank-sum test. Results Feature selection identified 9 store and 16 restaurant survey items as significant predictors of the score produced from the full survey. The linear regression models built from the reduced feature sets had R2 values of 92% and 94% for restaurant and grocery store data, respectively. Conclusions While there are many potentially important variables in any domain, the most useful set may only be a small subset. The use of feature selection in the initial phase of data collection to identify the most influential variables may be a useful tool to greatly reduce the amount of data needed thereby reducing cost. PMID:24919484
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Optimizing data collection for public health decisions: a data mining approach.
Partington, Susan N; Papakroni, Vasil; Menzies, Tim
2014-06-12
Collecting data can be cumbersome and expensive. Lack of relevant, accurate and timely data for research to inform policy may negatively impact public health. The aim of this study was to test if the careful removal of items from two community nutrition surveys guided by a data mining technique called feature selection, can (a) identify a reduced dataset, while (b) not damaging the signal inside that data. The Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed on 885 retail food outlets in two counties in West Virginia between May and November of 2011. A reduced dataset was identified for each outlet type using feature selection. Coefficients from linear regression modeling were used to weight items in the reduced datasets. Weighted item values were summed with the error term to compute reduced item survey scores. Scores produced by the full survey were compared to the reduced item scores using a Wilcoxon rank-sum test. Feature selection identified 9 store and 16 restaurant survey items as significant predictors of the score produced from the full survey. The linear regression models built from the reduced feature sets had R2 values of 92% and 94% for restaurant and grocery store data, respectively. While there are many potentially important variables in any domain, the most useful set may only be a small subset. The use of feature selection in the initial phase of data collection to identify the most influential variables may be a useful tool to greatly reduce the amount of data needed thereby reducing cost.
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Classification of radiolarian images with hand-crafted and deep features
NASA Astrophysics Data System (ADS)
Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen
2017-12-01
Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.
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Effective method for detecting regions of given colors and the features of the region surfaces
NASA Astrophysics Data System (ADS)
Gong, Yihong; Zhang, HongJiang
1994-03-01
Color can be used as a very important cue for image recognition. In industrial and commercial areas, color is widely used as a trademark or identifying feature in objects, such as packaged goods, advertising signs, etc. In image database systems, one may retrieve an image of interest by specifying prominent colors and their locations in the image (image retrieval by contents). These facts enable us to detect or identify a target object using colors. However, this task depends mainly on how effectively we can identify a color and detect regions of the given color under possibly non-uniform illumination conditions such as shade, highlight, and strong contrast. In this paper, we present an effective method to detect regions matching given colors, along with the features of the region surfaces. We adopt the HVC color coordinates in the method because of its ability of completely separating the luminant and chromatic components of colors. Three basis functions functionally serving as the low-pass, high-pass, and band-pass filters, respectively, are introduced.
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Classification and Recognition of Tomb Information in Hyperspectral Image
NASA Astrophysics Data System (ADS)
Gu, M.; Lyu, S.; Hou, M.; Ma, S.; Gao, Z.; Bai, S.; Zhou, P.
2018-04-01
There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.
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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.
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Crowding with conjunctions of simple features.
Põder, Endel; Wagemans, Johan
2007-11-20
Several recent studies have related crowding with the feature integration stage in visual processing. In order to understand the mechanisms involved in this stage, it is important to use stimuli that have several features to integrate, and these features should be clearly defined and measurable. In this study, Gabor patches were used as target and distractor stimuli. The stimuli differed in three dimensions: spatial frequency, orientation, and color. A group of 3, 5, or 7 objects was presented briefly at 4 deg eccentricity of the visual field. The observers' task was to identify the object located in the center of the group. A strong effect of the number of distractors was observed, consistent with various spatial pooling models. The analysis of incorrect responses revealed that these were a mix of feature errors and mislocalizations of the target object. Feature errors were not purely random, but biased by the features of distractors. We propose a simple feature integration model that predicts most of the observed regularities.
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Vegetation-terrain feature relationships in southeast Arizona
NASA Technical Reports Server (NTRS)
Schrumpf, B. J. (Principal Investigator); Mouat, D. A.
1972-01-01
There are no author-identified significant results in this report. Studies of relationships of vegetation distribution to geomorphic characteristics of the landscape and of plant phenological patterns to vegetation identification of satellite imagery indicate that there exists positive relationships between certain plant species and certain terrain features. Not all species were found to exhibit positive relationships with all terrain feature variables, but enough positive relationships seem to exist to indicate that terrain feature variable-vegetation relationship studies have a definite place in plant ecological investigations. Even more importantly, the vegetation groups examined appeared to be successfully discriminated by the terrain feature variables. This would seem to indicate that spatial interpretations of vegetation groups may be possible. While vegetational distributions aren't determined by terrain feature differences, terrain features do mirror factors which directly influence vegetational response and hence distribution. As a result, those environmental features which can be readily and rapidly ascertained on relatively small-scale imagery may prove to be valuable indicators of vegetation distribution.
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Stopover habitats of spring migrating surf scoters in southeast Alaska
Lok, E.K.; Esler, Daniel N.; Takekawa, John Y.; De La Cruz, S.W.; Sean, Boyd W.; Nysewander, D.R.; Evenson, J.R.; Ward, D.H.
2011-01-01
Habitat conditions and nutrient reserve levels during spring migration have been suggested as important factors affecting population declines in waterfowl, emphasizing the need to identify key sites used during spring and understand habitat features and resource availability at stopover sites. We used satellite telemetry to identify stopover sites used by surf scoters migrating through southeast Alaska during spring. We then contrasted habitat features of these sites to those of random sites to determine habitat attributes corresponding to use by migrating scoters. We identified 14 stopover sites based on use by satellite tagged surf scoters from several wintering sites. We identified Lynn Canal as a particularly important stopover site for surf scoters originating throughout the Pacific winter range; approximately half of tagged coastally migrating surf scoters used this site, many for extended periods. Stopover sites were farther from the mainland coast and closer to herring spawn sites than random sites, whereas physical shoreline habitat attributes were generally poor predictors of site use. The geography and resource availability within southeast Alaska provides unique and potentially critical stopover habitat for spring migrating surf scoters. Our work identifies specific sites and habitat resources that deserve conservation and management consideration. Aggregations of birds are vulnerable to human activity impacts such as contaminant spills and resource management decisions. This information is of value to agencies and organizations responsible for emergency response planning, herring fisheries management, and bird and ecosystem conservation.
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Priority River Metrics for Urban Residents of the Santa Cruz River Watershed
Indicator selection is a persistent question in river and stream assessment and management. We employ qualitative research techniques to identify features of rivers and streams important to urban residents recruited from the general public in the Santa Cruz watershed. Interviews ...
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Priority River Metrics for Residents of an Urbanized Arid Watershed
What indicators to use is a persistent question in river and stream assessment and management. We employ qualitative research techniques to identify features of rivers and streams important to the general public in an urbanized watershed of the Southwestern U.S. Transcriptions an...
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Robust extrema features for time-series data analysis.
Vemulapalli, Pramod K; Monga, Vishal; Brennan, Sean N
2013-06-01
The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.
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2014-01-01
Background Despite the growing evidence of the benefits of physical activity (PA) in individuals with rheumatoid arthritis (RA), the majority is not physically active enough. An innovative strategy is to engage lead users in the development of PA interventions provided over the internet. The aim was to explore lead users’ ideas and prioritization of core features in a future internet service targeting adoption and maintenance of healthy PA in people with RA. Methods Six focus group interviews were performed with a purposively selected sample of 26 individuals with RA. Data were analyzed with qualitative content analysis and quantification of participants’ prioritization of most important content. Results Six categories were identified as core features for a future internet service: up-to-date and evidence-based information and instructions, self-regulation tools, social interaction, personalized set-up, attractive design and content, and access to the internet service. The categories represented four themes, or core aspects, important to consider in the design of the future service: (1) content, (2) customized options, (3) user interface and (4) access and implementation. Conclusions This is, to the best of our knowledge, the first study involving people with RA in the development of an internet service to support the adoption and maintenance of PA. Participants helped identifying core features and aspects important to consider and further explore during the next phase of development. We hypothesize that involvement of lead users will make transfer from theory to service more adequate and user-friendly and therefore will be an effective mean to facilitate PA behavior change. PMID:24655757
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MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins
Li, Hui; Wang, Rong; Gan, Yong
2017-01-01
Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area. PMID:28744305
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Defining immunological dysfunction in sepsis: A requisite tool for precision medicine.
Bermejo-Martin, Jesús F; Andaluz-Ojeda, David; Almansa, Raquel; Gandía, Francisco; Gómez-Herreras, Jose Ignacio; Gomez-Sanchez, Esther; Heredia-Rodríguez, María; Eiros, Jose Maria; Kelvin, David J; Tamayo, Eduardo
2016-05-01
Immunological dysregulation is now recognised as a major pathogenic event in sepsis. Stimulation of immune response and immuno-modulation are emerging approaches for the treatment of this disease. Defining the underlying immunological alterations in sepsis is important for the design of future therapies with immuno-modulatory drugs. Clinical studies evaluating the immunological response in adult patients with Sepsis and published in PubMed were reviewed to identify features of immunological dysfunction. For this study we used key words related with innate and adaptive immunity. Ten major features of immunological dysfunction (FID) were identified involving quantitative and qualitative alterations of [antigen presentation](FID1), [T and B lymphocytes] (FID2), [natural killer cells] (FID3), [relative increase in T regulatory cells] (FID4), [increased expression of PD-1 and PD-ligand1](FID5), [low levels of immunoglobulins](FID6), [low circulating counts of neutrophils and/or increased immature forms in non survivors](FID7), [hyper-cytokinemia] (FID8), [complement consumption] (FID9), [defective bacterial killing by neutrophil extracellular traps](FID10). This review article identified ten major features associated with immunosuppression and immunological dysregulation in sepsis. Assessment of these features could help in utilizing precision medicine for the treatment of sepsis with immuno-modulatory drugs. Copyright © 2016 The British Infection Association. Published by Elsevier Ltd. All rights reserved.
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MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.
Wang, Xiao; Li, Hui; Wang, Rong; Zhang, Qiuwen; Zhang, Weiwei; Gan, Yong
2017-01-01
Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.
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The impact of jackpots on EGM gambling behavior: a review.
Rockloff, Matthew J; Hing, Nerilee
2013-12-01
This paper reviews literature on how jackpots influence Electronic Gaming Machine (EGM) gambling behaviour. Most of the available evidence addresses the motivational effect of the mere presence of EGM jackpots on play, as actual wins are relatively rare for individual gamblers. The review identifies a distinction between rational, biased and irrational motivations that attract people to EGM jackpots. The evidence suggests that EGM jackpots should generate additional consumption on EGMs above machines that do not have such lottery-like features. Rational motivations are likely to lead to consumer surplus, whereas biased and irrational motivations are likely to contribute to excessive consumption. Moreover, there is evidence that excessive gambling consumption is strongly associated with gambling-related harm. Future research should identify how the structural features of different types of jackpots; such as progressive, deterministic, hidden, mystery, linked and wide-area jackpots; may differentially appeal to rational, biased and irrational gambling motivations. Jackpots are common feature of EGM games, and therefore it is important to have a better understanding of how jackpot features influence play on the machines.
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Meister, Hartmut; Lausberg, Isabel; Kiessling, Juergen; von Wedel, Hasso; Walger, Martin
2002-11-01
Older patients represent the majority of hearing-aid users. The needs of elderly, hearing-impaired subjects are not entirely identified. The present study aims to determine the importance of fundamental hearing-aid attributes and to elicit the utility of associated hypothetical hearing aids for older patients. This was achieved using a questionnaire-based conjoint analysis--a decompositional approach to preference measurement offering a realistic study design. A random sample of 200 experienced hearing-aid users participated in the study. Though three out of the six examined attributes revealed age-related dependencies, the only significant effect was found for the attribute "handling", which was considerably more important for older than younger hearing-aid users. A trend of decreasing importance of speech intelligibility in noise and increasing significance of speech in quiet was observed for subjects older than 70 years. In general, the utility of various hypothetical hearing aids was similar for older and younger subjects. Apart from the attribute "handling", older and younger subjects have comparable needs regarding hearing-aid features. On the basis of the examined attributes, there is no requirement for hearing aids designed specifically for elderly hearing-aid users, provided that ergonomic features are considered and the benefits of modern technology are made fully available for older patients.
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Klong-Klaew, Tunwadee; Sukontason, Kom; Sribanditmongkol, Pongruk; Moophayak, Kittikhun; Sanit, Sangob; Sukontason, Kabkaew L
2012-11-01
Lucilia porphyrina (Walker) is a blow fly of forensic importance, and shares its geographical distribution with a related forensically important species, Lucilia cuprina (Wiedemann). The immature stages of both species are similar in general appearance; therefore, correct identification should be given special consideration. This study highlighted the main features of L. porphyrina larvae, as observed under light microscopy and scanning electron microscopy. Particular attention is given to the anterior and posterior spiracles, cephalopharyngeal skeleton, and characteristics of the dorsal spines between the prothorax and mesothorax. In the third instar specifically, morphological information on L. porphyrina showed several features that are shared by L. cuprina, and therefore need certain identification to distinguish between them. Such key features are (1) greater posterior spiracle, (2) apparent inner projection between the middle and lower slits of the posterior spiracle, and (3) strongly sclerotized peritreme. The number of papillae on the anterior spiracle may be a supplement, five to nine and three to six in L. porphyrina and L. cuprina, respectively. The key for identifying third instar of forensically important flies in Thailand has been updated to include L. porphyrina.
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Search for ultraviolet emission lines from a hot gaseous halo in the edge-on galaxy NGC 4244
NASA Technical Reports Server (NTRS)
Deharveng, J.-M.; Joubert, M.; Bixler, J.; Bowyer, S.; Malina, R.
1986-01-01
Short and long wavelength IUE spectra of the halo region in the edge-on galaxy NGC 4244 are analyzed in order to identify evidence of line emission at the level of 0.000001 ergs per cu cm sr/s. Features are found at 1245 A and 1402 A, having peaks four times greater than the rms intensity fluctuations of nearby spectra. The spectral features are identified with semi-forbidden N V, semi-forbidden S IV at 1240 A, and Si IV and semi-forbidden O IV multiplets at 1400 A, respectively. The appearance of high-peak features and the lack of astrophysically important lines in the sample are evidence of a gas near T = 10 exp 5.2 and emission measure (EM) equal to about 0.000001 pc. However, the case for the existence of such a gas is weakened due to the existence of two other similarly sized features with no identifiable astrophysical origin and the extremely faint nature of the candidate features. The assumed upper limit for the line intensities in NGC 4244 leads to the conclusion that at T less than 100,000 K any emitting gas is either highly clumped or has a p/k value of less than 1000 per cu cm K. It is suggested that if the observed low level features in the short wavelength spectrum are real, the temperature and emission measures allow for a single component gas in the halo of NGC 4244, and are in agreement with those derived by Paresce et al. (1983).
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Talking the Talk: Translating Research to Practice
ERIC Educational Resources Information Center
Grifenhagen, Jill F.; Barnes, Erica M.; Collins, Molly F.; Dickinson, David K.
2017-01-01
Decades of research have identified features of classrooms and teachers' talk that are associated with children's language growth. Unfortunately, much of this work has not yet translated to widespread practice in early childhood classrooms. Given the important contributions that early language development makes to later academic achievement,…
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ERIC Educational Resources Information Center
Kluger, Miriam P., Ed.; Alexander, Gina, Ed.; Curtis, Patrick A., Ed.
Noting the importance of identifying the effectiveness of child welfare programs for future policy planning, this book examines features of successful programs. The book is presented in six sections: family preservation and family support services, child protective services, out-of-home care, adoption, child care, and adolescent services. Each…
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Guidelines for a cancer prevention smartphone application: A mixed-methods study.
Ribeiro, Nuno; Moreira, Luís; Barros, Ana; Almeida, Ana Margarida; Santos-Silva, Filipe
2016-10-01
This study sought to explore the views and experiences of healthy young adults concerning the fundamental features of a cancer prevention smartphone app that seeks behaviour change. Three focus groups were conducted with 16 healthy young adults that explored prior experiences, points of view and opinions about currently available health-related smartphone apps. Then, an online questionnaire was designed and applied to a larger sample of healthy young adults. Focus group and online questionnaire data were analysed and confronted. Study results identified behaviour tracking, goal setting, tailored information and use of reminders as the most desired features in a cancer prevention app. Participants highlighted the importance of privacy and were reluctant to share personal health information with other users. The results also point out important dimensions to be considered for long-term use of health promotion apps related with usability and perceived usefulness. Participants didn't consider gamification features as important dimensions for long-term use of apps. This study allowed the definition of a guideline set for the development of a cancer prevention app. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney
2016-01-01
Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079
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Landscape of the spliced leader trans-splicing mechanism in Schistosoma mansoni.
Boroni, Mariana; Sammeth, Michael; Gava, Sandra Grossi; Jorge, Natasha Andressa Nogueira; Macedo, Andréa Mara; Machado, Carlos Renato; Mourão, Marina Moraes; Franco, Glória Regina
2018-03-01
Spliced leader dependent trans-splicing (SLTS) has been described as an important RNA regulatory process that occurs in different organisms, including the trematode Schistosoma mansoni. We identified more than seven thousand putative SLTS sites in the parasite, comprising genes with a wide spectrum of functional classes, which underlines the SLTS as a ubiquitous mechanism in the parasite. Also, SLTS gene expression levels span several orders of magnitude, showing that SLTS frequency is not determined by the expression level of the target gene, but by the presence of particular gene features facilitating or hindering the trans-splicing mechanism. Our in-depth investigation of SLTS events demonstrates widespread alternative trans-splicing (ATS) acceptor sites occurring in different regions along the entire gene body, highlighting another important role of SLTS generating alternative RNA isoforms in the parasite, besides the polycistron resolution. Particularly for introns where SLTS directly competes for the same acceptor substrate with cis-splicing, we identified for the first time additional and important features that might determine the type of splicing. Our study substantially extends the current knowledge of RNA processing by SLTS in S. mansoni, and provide basis for future studies on the trans-splicing mechanism in other eukaryotes.
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Bøe, Tormod; Lundervold, Arvid
2017-01-01
Inattention in childhood is associated with academic problems later in life. The contribution of specific aspects of inattentive behaviour is, however, less known. We investigated feature importance of primary school teachers’ reports on nine aspects of inattentive behaviour, gender and age in predicting future academic achievement. Primary school teachers of n = 2491 children (7–9 years) rated nine items reflecting different aspects of inattentive behaviour in 2002. A mean academic achievement score from the previous semester in high school (2012) was available for each youth from an official school register. All scores were at a categorical level. Feature importances were assessed by using multinominal logistic regression, classification and regression trees analysis, and a random forest algorithm. Finally, a comprehensive pattern classification procedure using k-fold cross-validation was implemented. Overall, inattention was rated as more severe in boys, who also obtained lower academic achievement scores in high school than girls. Problems related to sustained attention and distractibility were together with age and gender defined as the most important features to predict future achievement scores. Using these four features as input to a collection of classifiers employing k-fold cross-validation for prediction of academic achievement level, we obtained classification accuracy, precision and recall that were clearly better than chance levels. Primary school teachers’ reports of problems related to sustained attention and distractibility were identified as the two most important features of inattentive behaviour predicting academic achievement in high school. Identification and follow-up procedures of primary school children showing these characteristics should be prioritised to prevent future academic failure. PMID:29182663
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Lundervold, Astri J; Bøe, Tormod; Lundervold, Arvid
2017-01-01
Inattention in childhood is associated with academic problems later in life. The contribution of specific aspects of inattentive behaviour is, however, less known. We investigated feature importance of primary school teachers' reports on nine aspects of inattentive behaviour, gender and age in predicting future academic achievement. Primary school teachers of n = 2491 children (7-9 years) rated nine items reflecting different aspects of inattentive behaviour in 2002. A mean academic achievement score from the previous semester in high school (2012) was available for each youth from an official school register. All scores were at a categorical level. Feature importances were assessed by using multinominal logistic regression, classification and regression trees analysis, and a random forest algorithm. Finally, a comprehensive pattern classification procedure using k-fold cross-validation was implemented. Overall, inattention was rated as more severe in boys, who also obtained lower academic achievement scores in high school than girls. Problems related to sustained attention and distractibility were together with age and gender defined as the most important features to predict future achievement scores. Using these four features as input to a collection of classifiers employing k-fold cross-validation for prediction of academic achievement level, we obtained classification accuracy, precision and recall that were clearly better than chance levels. Primary school teachers' reports of problems related to sustained attention and distractibility were identified as the two most important features of inattentive behaviour predicting academic achievement in high school. Identification and follow-up procedures of primary school children showing these characteristics should be prioritised to prevent future academic failure.
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Visual search for feature and conjunction targets with an attention deficit.
Arguin, M; Joanette, Y; Cavanagh, P
1993-01-01
Abstract Brain-damaged subjects who had previously been identified as suffering from a visual attention deficit for contralesional stimulation were tested on a series of visual search tasks. The experiments examined the hypothesis that the processing of single features is preattentive but that feature integration, necessary for the correct perception of conjunctions of features, requires attention (Treisman & Gelade, 1980 Treisman & Sato, 1990). Subjects searched for a feature target (orientation or color) or for a conjunction target (orientation and color) in unilateral displays in which the number of items presented was variable. Ocular fixation was controlled so that trials on which eye movements occurred were cancelled. While brain-damaged subjects with a visual attention disorder (VAD subjects) performed similarly to normal controls in feature search tasks, they showed a marked deficit in conjunction search. Specifically, VAD subjects exhibited an important reduction of their serial search rates for a conjunction target with contralesional displays. In support of Treisman's feature integration theory, a visual attention deficit leads to a marked impairment in feature integration whereas it does not appear to affect feature encoding.
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A Photographic Atlas of Rock Breakdown Features in Geomorphic Environments
NASA Technical Reports Server (NTRS)
Bourke, Mary C. (Editor); Brearley, J. Alexander; Haas, Randall; Viles, Heather A.
2007-01-01
A primary goal of geomorphological enquiry is to make genetic associations between process and form. In rock breakdown studies, the links between process, inheritance and lithology are not well constrained. In particular, there is a need to establish an understanding of feature persistence. That is, to determine the extent to which in situ rock breakdown (e.g., aeolian abrasion or salt weathering) masks signatures of earlier geomorphic transport processes (e.g., fluvial transport or crater ejecta). Equally important is the extent to which breakdown during geomorphic transport masks the imprint of past weathering. The use of rock features in this way raises the important question: Can features on the surface of a rock reliably indicate its geomorphic history? This has not been determined for rock surfaces on Earth or other planets. A first step towards constraining the links between process, inheritance, and morphology is to identify pristine features produced by different process regimes. The purpose of this atlas is to provide a comprehensive image collection of breakdown features commonly observed on boulders in different geomorphic environments. The atlas is intended as a tool for planetary geoscientists and their students to assist in identifying features found on rocks on planetary surfaces. In compiling this atlas, we have attempted to include features that have formed 'recently' and where the potential for modification by another geomorphic process is low. However, we acknowledge that this is, in fact, difficult to achieve when selecting rocks in their natural environment. We group breakdown features according to their formative environment and process. In selecting images for inclusion in the atlas we were mindful to cover a wide range of climatic zones. For example, in the weathering chapter, clast features are shown from locations such as the hyper-arid polar desert of Antarctica and the semi-arid canyons of central Australia. This is important as some features (e.g., alveoli) occur across climate regimes. We have drawn on the published geomorphological literature and our own field experience. We use, where possible, images of extrusive igneous rocks as the data returned from Mars, Venus and the Moon indicates that this is the predominant rock type. One of the purposes of this atlas is to expand the range of surface features that are known to indicate a particular geomorphic environment or process history. The surface features on boulders in some environments such as aeolian and weathering are well understood. In contrast, those in fluvial or ejecta environments are not. Therefore we have presented a comprehensive assemblage of features that are likely to be produced in each of the geomorphic environments. We hope that this atlas will trigger more research on diagnostic features, particularly their morphometry and detailed morphology, their persistence and rates of formation. In this first edition of the atlas we detail the features found on clasts in three geomorphic environments: aeolian, fluvial and weathering. Future editions of the atlas will include chapters on ejecta, micro-impacts, coastal, colluvial, glacial and structural features.
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Pian, Cong; Chen, Yuan-Yuan; Zhang, Jin; Chen, Zhi; Zhang, Guang-Le; Li, Qiang; Yang, Tao; Zhang, Liang-Yun
2017-02-01
Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this paper, we introduce a series of new features with 80 dimension called short sequence motifs (SSM). A hybrid feature vector with 1444 dimension can be formed by combining 1364 features of [Formula: see text]-mer strings and 80 features of SSM features. We optimize the 1444 dimension features using the feature score criterion (FSC) and list them in descending order according to the scores. The first 462 are selected as the input feature vector in the classifier. Moreover, eight of 80 SSM features appear in the top 20. This indicates that these eight SSM features play an important part in the identification of piRNAs. Since five of the above eight SSM features are associated with nucleotide A and G ('A*G', 'A**G', 'A***G', 'A****G', 'A*****G'). So, we guess there may exist some biological significance. We also use a neural network algorithm called voting-based extreme learning machine (V-ELM) to identify real piRNAs. The Specificity (Sp) and Sensitivity (Sn) of our method are 95.48% and 94.61%, respectively in human species. This result shows that our method is more effective compared with those of the piRPred, piRNApredictor, Asym-Pibomd, Piano and McRUMs. The web service of V-ELMpiRNAPred is available for free at http://mm20132014.wicp.net:38601/velmprepiRNA/Main.jsp .
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Tamilvanan, Thangaraju; Hopper, Waheeta
2014-01-01
Yersinia pestis, a Gram negative bacillus, spreads via lymphatic to lymph nodes and to all organs through the bloodstream, causing plague. Yersinia outer protein H (YopH) is one of the important effector proteins, which paralyzes lymphocytes and macrophages by dephosphorylating critical tyrosine kinases and signal transduction molecules. The purpose of the study is to generate a three-dimensional (3D) pharmacophore model by using diverse sets of YopH inhibitors, which would be useful for designing of potential antitoxin. In this study, we have selected 60 biologically active inhibitors of YopH to perform Ligand based pharmacophore study to elucidate the important structural features responsible for biological activity. Pharmacophore model demonstrated the importance of two acceptors, one hydrophobic and two aromatic features toward the biological activity. Based on these features, different databases were screened to identify novel compounds and these ligands were subjected for docking, ADME properties and Binding energy prediction. Post docking validation was performed using molecular dynamics simulation for selected ligands to calculate the Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF). The ligands, ASN03270114, Mol_252138, Mol_31073 and ZINC04237078 may act as inhibitors against YopH of Y. pestis.
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New opportunities for future small civil turbine engines: Overviewing the GATE studies
NASA Technical Reports Server (NTRS)
Strack, W. C.
1979-01-01
An overview of four independent studies forecasts the potential impact of advanced technology turbine engines in the post 1988 market, identifies important aircraft and missions, desirable engine sizes, engine performance, and cost goals. Parametric evaluations of various engine cycles, configurations, design features, and advanced technology elements defined baseline conceptual engines for each of the important missions identified by the market analysis. Both fixed-wing and helicopter aircraft, and turboshaft, turboprop, and turbofan engines were considered. Sizable performance gains (e.g., 20% SFC decrease), and large engine cost reductions of sufficient magnitude are predicted to challenge the reciprocating engine in the 300-500 SHP class.
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2016-01-01
Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. PMID:27806075
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Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue
2016-01-01
Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.
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Analysis of spike-wave discharges in rats using discrete wavelet transform.
Ubeyli, Elif Derya; Ilbay, Gül; Sahin, Deniz; Ateş, Nurbay
2009-03-01
A feature is a distinctive or characteristic measurement, transform, structural component extracted from a segment of a pattern. Features are used to represent patterns with the goal of minimizing the loss of important information. The discrete wavelet transform (DWT) as a feature extraction method was used in representing the spike-wave discharges (SWDs) records of Wistar Albino Glaxo/Rijswijk (WAG/Rij) rats. The SWD records of WAG/Rij rats were decomposed into time-frequency representations using the DWT and the statistical features were calculated to depict their distribution. The obtained wavelet coefficients were used to identify characteristics of the signal that were not apparent from the original time domain signal. The present study demonstrates that the wavelet coefficients are useful in determining the dynamics in the time-frequency domain of SWD records.
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Examining Overgeneral Autobiographical Memory as a Risk Factor for Adolescent Depression
ERIC Educational Resources Information Center
Rawal, Adhip; Rice, Frances
2012-01-01
Objective: Identifying risk factors for adolescent depression is an important research aim. Overgeneral autobiographical memory (OGM) is a feature of adolescent depression and a candidate cognitive risk factor for future depression. However, no study has ascertained whether OGM predicts the onset of adolescent depressive disorder. OGM was…
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Various origins of the duplicated middle cerebral artery.
Tutar, Nihal Uslu; Töre, Hüseyin Gürkan; Kirbaş, Ismail; Tarhan, Nefise Cağla; Coşkun, Mehmet
2008-10-01
We describe the features of a duplicated middle cerebral artery identified by computed tomographic angiography that originates from a previously undefined origin, ie, from the petrous portion of the internal carotid artery. Recognition of this anomaly is important in patients with a possible aneurysm, which was not present in our patient.
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Interarticulator Coordination in Dysarthria: An X-Ray Microbeam Study
ERIC Educational Resources Information Center
Weismer, Gary; Yunusova, Yana; Westbury, John R.
2003-01-01
Articulatory discoordination is often said to be an important feature of the speech production disorder in dysarthria, but little experimental work has been done to identify and specify the coordination difficulties. The present study evaluated the coordination of labial and lingual gestures for /u/ production in persons with Parkinson's disease…
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Billis, Evdokia V; McCarthy, Christopher J; Stathopoulos, Ioannis; Kapreli, Eleni; Pantzou, Paulina; Oldham, Jacqueline A
2007-06-01
Identifying homogenous subgroups of low back pain (LBP) patients is considered a priority in musculoskeletal rehabilitation and is believed to enhance clinical outcomes. In order to achieve this, the specific features of each subgroup need to be identified. The aim of this study was to develop a list of clinical and cultural features that are included in the assessment of LBP patients in Greece, among health professionals. This 'list' will be, utilized in a clinical study for developing LBP subgroups. Three focus groups were conducted, each one comprising health professionals with homogenous characteristics and all coordinated by a single moderator. There were: 11 physiotherapists (PTs) with clinical experience in LBP patients, seven PTs specialized in LBP management, and five doctors with a particular spinal interest. The focus of discussions was to develop a list of clinical and cultural features that were important in the examination of LBP. Content analysis was performed by two researchers. Clinicians and postgraduates developed five categories within the History (Present Symptoms, History of Symptoms, Function, Psychosocial, Medical History) and six categories within the Physical Examination (Observation, Neurological Examination, Active and Passive Movements, Muscle Features and Palpation). The doctors identified four categories in History (Symptomatology, Function, Psychosocial, Medical History) and an additional in Physical Examination (Special Tests). All groups identified three cultural categories; Attitudes of Health Professionals, Patients' Attitudes and Health System influences. An extensive Greek 'list' of clinical and cultural features was developed from the groups' analysis. Although similarities existed in most categories, there were several differences across the three focus groups which will be discussed.
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Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.
Ginsburg, Shoshana; Ali, Sahirzeeshan; Lee, George; Basavanhally, Ajay; Madabhushi, Anant
2013-01-01
Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of variable importance in nonlinear kernels (VINK). We show how VINK can be implemented in conjunction with the popular Isomap and Laplacian eigenmap algorithms. VINK is evaluated in the contexts of three different problems in digital pathology: (1) predicting five year PSA failure following radical prostatectomy, (2) predicting Oncotype DX recurrence risk scores for ER+ breast cancers, and (3) distinguishing good and poor outcome p16+ oropharyngeal tumors. We demonstrate that subsets of features identified by VINK provide similar or better classification or regression performance compared to the original high dimensional feature sets.
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Philip, Nada; Kayyali, Reem; Nabhani-Gebara, Shereen; Pierscionek, Barbara; Vaes, Anouk W; Spruit, Martijn A; Kaimakamis, Evangelos
2017-01-01
Background Chronic obstructive pulmonary disease (COPD) is a serious long-term lung disease in which the airflow from the lungs is progressively reduced. By 2030, COPD will become the third cause of mortality and seventh cause of morbidity worldwide. With advances in technology and mobile communications, significant progress in the mobile health (mHealth) sector has been recently observed. Mobile phones with app capabilities (smartphones) are now considered as potential media for the self-management of certain types of diseases such as asthma, cancer, COPD, or cardiovascular diseases. While many mobile apps for patients with COPD are currently found on the market, there is little published material on the effectiveness of most of them, their features, and their adoption in health care settings. Objectives The aim of this study was to search the literature for current systems related to COPD and identify any missing links and studies that were carried out to evaluate the effectiveness of COPD mobile apps. In addition, we reviewed existing mHealth apps from different stores in order to identify features that can be considered in the initial design of a COPD support tool to improve health care services and patient outcomes. Methods In total, 206 articles related to COPD management systems were identified from different databases. Irrelevant materials and duplicates were excluded. Of those, 38 articles were reviewed to extract important features. We identified 214 apps from online stores. Following exclusion of irrelevant apps, 48 were selected and 20 of them were downloaded to review some of their common features. Results Our review found that out of the 20 apps downloaded, 13 (65%, 13/20) had an education section, 5 (25%, 5/20) consisted of medication and guidelines, 6 (30%, 6/20) included a calendar or diary and other features such as reminders or symptom tracking. There was little published material on the effectiveness of the identified COPD apps. Features such as (1) a social networking tool; (2) personalized education; (3) feedback; (4) e-coaching; and (5) psychological motivation to enhance behavioral change were found to be missing in many of the downloaded apps. Conclusions This paper summarizes the features of a COPD patient-support mobile app that can be taken into consideration for the initial design of an integrated care system to encourage the self-management of their condition at home. PMID:28219878
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Bashiri, Azadeh; Shahmoradi, Leila; Beigy, Hamid; Savareh, Behrouz A; Nosratabadi, Masood; N Kalhori, Sharareh R; Ghazisaeedi, Marjan
2018-06-01
Quantitative EEG gives valuable information in the clinical evaluation of psychological disorders. The purpose of the present study is to identify the most prominent features of quantitative electroencephalography (QEEG) that affect attention and response control parameters in children with attention deficit hyperactivity disorder. The QEEG features and the Integrated Visual and Auditory-Continuous Performance Test ( IVA-CPT) of 95 attention deficit hyperactivity disorder subjects were preprocessed by Independent Evaluation Criterion for Binary Classification. Then, the importance of selected features in the classification of desired outputs was evaluated using the artificial neural network. Findings uncovered the highest rank of QEEG features in each IVA-CPT parameters related to attention and response control. Using the designed model could help therapists to determine the existence or absence of defects in attention and response control relying on QEEG.
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Visual observations over oceans
NASA Technical Reports Server (NTRS)
Terry, R. D.
1979-01-01
Important factors in locating, identifying, describing, and photographing ocean features from space are presented. On the basis of crew comments and other findings, the following recommendations can be made for Earth observations on Space Shuttle missions: (1) flyover exercises must include observations and photography of both temperate and tropical/subtropical waters; (2) sunglint must be included during some observations of ocean features; (3) imaging remote sensors should be used together with conventional photographic systems to document visual observations; (4) greater consideration must be given to scheduling earth observation targets likely to be obscured by clouds; and (5) an annotated photographic compilation of ocean features can be used as a training aid before the mission and as a reference book during space flight.
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Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers' Perspectives.
Hartzler, Andrea Lisabeth; BlueSpruce, June; Catz, Sheryl L; McClure, Jennifer B
2016-08-05
Smoking remains the leading cause of preventable disease and death in the United States. Therefore, researchers are constantly exploring new ways to promote smoking cessation. Mobile health (mHealth) technologies could be effective cessation tools. Despite the availability of commercial quit-smoking apps, little research to date has examined smokers' preferred treatment intervention components (ie, design features). Honoring these preferences is important for designing programs that are appealing to smokers and may be more likely to be adopted and used. The aim of this study was to understand smokers' preferred design features of mHealth quit-smoking tools. We used a mixed-methods approach consisting of focus groups and written surveys to understand the design preferences of adult smokers who were interested in quitting smoking (N=40). Focus groups were stratified by age to allow differing perspectives to emerge between older (>40 years) and younger (<40 years) participants. Focus group discussion included a "blue-sky" brainstorming exercise followed by participant reactions to contrasting design options for communicating with smokers, providing social support, and incentivizing program use. Participants rated the importance of preselected design features on an exit survey. Qualitative analyses examined emergent discussion themes and quantitative analyses compared feature ratings to determine which were perceived as most important. Participants preferred a highly personalized and adaptive mHealth experience. Their ideal mHealth quit-smoking tool would allow personalized tracking of their progress, adaptively tailored feedback, and real-time peer support to help manage smoking cravings. Based on qualitative analysis of focus group discussion, participants preferred pull messages (ie, delivered upon request) over push messages (ie, sent automatically) and preferred interaction with other smokers through closed social networks. Preferences for entertaining games or other rewarding incentives to encourage program use differed by age group. Based on quantitative analysis of surveys, participants rated the importance of select design features significantly differently (P<.001). Design features rated as most important included personalized content, the ability to track one's progress, and features designed to help manage nicotine withdrawal and medication side effects. Design features rated least important were quit-smoking videos and posting on social media. Communicating with stop-smoking experts was rated more important than communicating with family and friends about quitting (P=.03). Perceived importance of various design features varied by age, experience with technology, and frequency of smoking. Future mHealth cessation aids should be designed with an understanding of smokers' needs and preferences for these tools. Doing so does not guarantee treatment effectiveness, but balancing user preferences with best-practice treatment considerations could enhance program adoption and improve treatment outcomes. Grounded in the perspectives of smokers, we identify several design considerations, which should be prioritized when designing future mHealth cessation tools and which warrant additional empirical validation.
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Nie, Guoping; Li, Yong; Wang, Feichi; Wang, Siwen; Hu, Xuehai
2015-01-01
G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew's correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances.
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Acoustic features of objects matched by an echolocating bottlenose dolphin.
Delong, Caroline M; Au, Whitlow W L; Lemonds, David W; Harley, Heidi E; Roitblat, Herbert L
2006-03-01
The focus of this study was to investigate how dolphins use acoustic features in returning echolocation signals to discriminate among objects. An echolocating dolphin performed a match-to-sample task with objects that varied in size, shape, material, and texture. After the task was completed, the features of the object echoes were measured (e.g., target strength, peak frequency). The dolphin's error patterns were examined in conjunction with the between-object variation in acoustic features to identify the acoustic features that the dolphin used to discriminate among the objects. The present study explored two hypotheses regarding the way dolphins use acoustic information in echoes: (1) use of a single feature, or (2) use of a linear combination of multiple features. The results suggested that dolphins do not use a single feature across all object sets or a linear combination of six echo features. Five features appeared to be important to the dolphin on four or more sets: the echo spectrum shape, the pattern of changes in target strength and number of highlights as a function of object orientation, and peak and center frequency. These data suggest that dolphins use multiple features and integrate information across echoes from a range of object orientations.
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CpG islands: algorithms and applications in methylation studies.
Zhao, Zhongming; Han, Leng
2009-05-15
Methylation occurs frequently at 5'-cytosine of the CpG dinucleotides in vertebrate genomes; however, this epigenetic feature is rarely observed in CpG islands (CGIs) or CpG clusters in the promoter regions of genes. Aberrant methylation of the promoter-associated CGIs might influence gene expression and cause carcinogenesis. Because of the functional importance, multiple algorithms have been available for identifying CGIs in a genome or a sequence. They can be categorized into the traditional algorithms (e.g., Gardiner-Garden and Frommer (1987), Takai and Jones (2002), and CpGPRoD (2002)) or statistical property based algorithms (CpGcluster (2006) and CG cluster (2007)). We reviewed the features of these algorithms and evaluated their performance on identifying functional CGIs using genome-wide methylation data. Moreover, identification of CGIs is an initial step in many recent studies for predicting methylation status as well as in the design of methylation detection platforms. We reviewed the benchmarks and features used in these studies.
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Adaptive multiscale processing for contrast enhancement
NASA Astrophysics Data System (ADS)
Laine, Andrew F.; Song, Shuwu; Fan, Jian; Huda, Walter; Honeyman, Janice C.; Steinbach, Barbara G.
1993-07-01
This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms within a continuum of scale space and used to enhance features of importance to mammography. Choosing analyzing functions that are well localized in both space and frequency, results in a powerful methodology for image analysis. We describe methods of contrast enhancement based on two overcomplete (redundant) multiscale representations: (1) Dyadic wavelet transform (2) (phi) -transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by non-linear, logarithmic and constant scale-space weight functions. Multiscale edges identified within distinct levels of transform space provide a local support for enhancement throughout each decomposition. We demonstrate that features extracted from wavelet spaces can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification.
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NASA Technical Reports Server (NTRS)
Colwell, R. N. (Principal Investigator)
1983-01-01
The geometric quality of the TM and MSS film products were evaluated by making selective photo measurements such as scale, linear and area determinations; and by measuring the coordinates of known features on both the film products and map products and then relating these paired observations using a standard linear least squares regression approach. Quantitative interpretation tests are described which evaluate the quality and utility of the TM film products and various band combinations for detecting and identifying important forest and agricultural features.
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Intestinal microbiome-gut-brain axis and irritable bowel syndrome.
Moser, Gabriele; Fournier, Camille; Peter, Johannes
2018-03-01
Psychological comorbidity is highly present in irritable bowel syndrome (IBS). Recent research points to a role of intestinal microbiota in visceral hypersensitivity, anxiety, and depression. Increased disease reactivity to psychological stress has been described too. A few clinical studies have attempted to identify features of dysbiosis in IBS. While animal studies revealed strong associations between stress and gut microbiota, studies in humans are rare. This review covers the most important studies on intestinal microbial correlates of psychological and clinical features in IBS, including stress, anxiety, and depression.
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Why replication is important in landscape genetics: American black bear in the Rocky Mountains
Short, Bull R.A.; Cushman, S.A.; MacE, R.; Chilton, T.; Kendall, K.C.; Landguth, E.L.; Schwartz, Maurice L.; McKelvey, K.; Allendorf, F.W.; Luikart, G.
2011-01-01
We investigated how landscape features influence gene flow of black bears by testing the relative support for 36 alternative landscape resistance hypotheses, including isolation by distance (IBD) in each of 12 study areas in the north central U.S. Rocky Mountains. The study areas all contained the same basic elements, but differed in extent of forest fragmentation, altitude, variation in elevation and road coverage. In all but one of the study areas, isolation by landscape resistance was more supported than IBD suggesting gene flow is likely influenced by elevation, forest cover, and roads. However, the landscape features influencing gene flow varied among study areas. Using subsets of loci usually gave models with the very similar landscape features influencing gene flow as with all loci, suggesting the landscape features influencing gene flow were correctly identified. To test if the cause of the variability of supported landscape features in study areas resulted from landscape differences among study areas, we conducted a limiting factor analysis. We found that features were supported in landscape models only when the features were highly variable. This is perhaps not surprising but suggests an important cautionary note – that if landscape features are not found to influence gene flow, researchers should not automatically conclude that the features are unimportant to the species’ movement and gene flow. Failure to investigate multiple study areas that have a range of variability in landscape features could cause misleading inferences about which landscape features generally limit gene flow. This could lead to potentially erroneous identification of corridors and barriers if models are transferred between areas with different landscape characteristics.
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Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest
Ma, Suliang; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan
2018-01-01
Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods. PMID:29659548
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PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine
Manavalan, Balachandran; Shin, Tae H.; Lee, Gwang
2018-01-01
Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html. PMID:29616000
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PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.
Manavalan, Balachandran; Shin, Tae H; Lee, Gwang
2018-01-01
Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.
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Analysis of geometric moments as features for firearm identification.
Md Ghani, Nor Azura; Liong, Choong-Yeun; Jemain, Abdul Aziz
2010-05-20
The task of identifying firearms from forensic ballistics specimens is exacting in crime investigation since the last two decades. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint'. These fingerprints transfer when a firearm is fired to the fired bullet and cartridge case. The components that are involved in producing these unique characteristics are the firing chamber, breech face, firing pin, ejector, extractor and the rifling of the barrel. These unique characteristics are the critical features in identifying firearms. It allows investigators to decide on which particular firearm that has fired the bullet. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. Therefore, the main objective of this study is the extraction and identification of suitable features from firing pin impression of cartridge case images for firearm recognition. Some previous studies have shown that firing pin impression of cartridge case is one of the most important characteristics used for identifying an individual firearm. In this study, data are gathered using 747 cartridge case images captured from five different pistols of type 9mm Parabellum Vektor SP1, made in South Africa. All the images of the cartridge cases are then segmented into three regions, forming three different set of images, i.e. firing pin impression image, centre of firing pin impression image and ring of firing pin impression image. Then geometric moments up to the sixth order were generated from each part of the images to form a set of numerical features. These 48 features were found to be significantly different using the MANOVA test. This high dimension of features is then reduced into only 11 significant features using correlation analysis. Classification results using cross-validation under discriminant analysis show that 96.7% of the images were classified correctly. These results demonstrate the value of geometric moments technique for producing a set of numerical features, based on which the identification of firearms are made.
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Aledavood, Talayeh; Triana Hoyos, Ana Maria; Alakörkkö, Tuomas; Kaski, Kimmo; Saramäki, Jari; Isometsä, Erkki; Darst, Richard K
2017-06-09
Mental and behavioral disorders are the main cause of disability worldwide. However, their diagnosis is challenging due to a lack of reliable biomarkers; current detection is based on structured clinical interviews which can be biased by the patient's recall ability, affective state, changing in temporal frames, etc. While digital platforms have been introduced as a possible solution to this complex problem, there is little evidence on the extent of usability and usefulness of these platforms. Therefore, more studies where digital data is collected in larger scales are needed to collect scientific evidence on the capacities of these platforms. Most of the existing platforms for digital psychiatry studies are designed as monolithic systems for a certain type of study; publications from these studies focus on their results, rather than the design features of the data collection platform. Inevitably, more tools and platforms will emerge in the near future to fulfill the need for digital data collection for psychiatry. Currently little knowledge is available from existing digital platforms for future data collection platforms to build upon. The objective of this work was to identify the most important features for designing a digital platform for data collection for mental health studies, and to demonstrate a prototype platform that we built based on these design features. We worked closely in a multidisciplinary collaboration with psychiatrists, software developers, and data scientists and identified the key features which could guarantee short-term and long-term stability and usefulness of the platform from the designing stage to data collection and analysis of collected data. The key design features that we identified were flexibility of access control, flexibility of data sources, and first-order privacy protection. We also designed the prototype platform Non-Intrusive Individual Monitoring Architecture (Niima), where we implemented these key design features. We described why each of these features are important for digital data collection for psychiatry, gave examples of projects where Niima was used or is going to be used in the future, and demonstrated how incorporating these design principles opens new possibilities for studies. The new methods of digital psychiatry are still immature and need further research. The design features we suggested are a first step to design platforms which can adapt to the upcoming requirements of digital psychiatry. ©Talayeh Aledavood, Ana Maria Triana Hoyos, Tuomas Alakörkkö, Kimmo Kaski, Jari Saramäki, Erkki Isometsä, Richard K Darst. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 09.06.2017.
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Klobusicky, Joseph J.; Aryasomayajula, Arun; Marko, Nicholas
2015-01-01
Efforts toward improving patient compliance in medication focus on either identifying trends in patient features or studying changes through an intervention. Our study seeks to provide an important link between these two approaches through defining trends of evolving compliance. In addition to using clinical covariates provided through insurance claims and health records, we also extracted census based data to provide socioeconomic covariates such as income and population density. Through creating quadrants based on periods of medicine intake, we derive several novel definitions of compliance. These definitions revealed additional compliance trends through considering refill histories later in a patient’s length of therapy. These results suggested that the link between patient features and compliance includes a temporal component, and should be considered in policymaking when identifying compliant subgroups. PMID:26958212
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Discriminative prediction of mammalian enhancers from DNA sequence
Lee, Dongwon; Karchin, Rachel; Beer, Michael A.
2011-01-01
Accurately predicting regulatory sequences and enhancers in entire genomes is an important but difficult problem, especially in large vertebrate genomes. With the advent of ChIP-seq technology, experimental detection of genome-wide EP300/CREBBP bound regions provides a powerful platform to develop predictive tools for regulatory sequences and to study their sequence properties. Here, we develop a support vector machine (SVM) framework which can accurately identify EP300-bound enhancers using only genomic sequence and an unbiased set of general sequence features. Moreover, we find that the predictive sequence features identified by the SVM classifier reveal biologically relevant sequence elements enriched in the enhancers, but we also identify other features that are significantly depleted in enhancers. The predictive sequence features are evolutionarily conserved and spatially clustered, providing further support of their functional significance. Although our SVM is trained on experimental data, we also predict novel enhancers and show that these putative enhancers are significantly enriched in both ChIP-seq signal and DNase I hypersensitivity signal in the mouse brain and are located near relevant genes. Finally, we present results of comparisons between other EP300/CREBBP data sets using our SVM and uncover sequence elements enriched and/or depleted in the different classes of enhancers. Many of these sequence features play a role in specifying tissue-specific or developmental-stage-specific enhancer activity, but our results indicate that some features operate in a general or tissue-independent manner. In addition to providing a high confidence list of enhancer targets for subsequent experimental investigation, these results contribute to our understanding of the general sequence structure of vertebrate enhancers. PMID:21875935
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Modelling assistive technology adoption for people with dementia.
Chaurasia, Priyanka; McClean, Sally I; Nugent, Chris D; Cleland, Ian; Zhang, Shuai; Donnelly, Mark P; Scotney, Bryan W; Sanders, Chelsea; Smith, Ken; Norton, Maria C; Tschanz, JoAnn
2016-10-01
Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment. The research described in this paper builds upon our previous work on modelling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user's background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models. With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants. We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile. Copyright © 2016 Elsevier Inc. All rights reserved.
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Ma, Xin; Guo, Jing; Sun, Xiao
2015-01-01
The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR) method, followed by incremental feature selection (IFS). We incorporated features of conjoint triad features and three novel features: binding propensity (BP), nonbinding propensity (NBP), and evolutionary information combined with physicochemical properties (EIPP). The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient). High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.
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NASA Astrophysics Data System (ADS)
An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang
2017-03-01
Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.
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NASA Astrophysics Data System (ADS)
Li, Tingting; Fu, Xing; Chen, Kun; Dorantes-Gonzalez, Dante J.; Li, Yanning; Wu, Sen; Hu, Xiaotang
2015-12-01
Despite the seriously increasing number of people contracting skin cancer every year, limited attention has been given to the investigation of human skin tissues. To this regard, Laser-induced Surface Acoustic Wave (LSAW) technology, with its accurate, non-invasive and rapid testing characteristics, has recently shown promising results in biological and biomedical tissues. In order to improve the measurement accuracy and efficiency of detecting important features in highly opaque and soft surfaces such as human skin, this paper identifies the most important parameters of a pulse laser source, as well as provides practical guidelines to recommended proper ranges to generate Surface Acoustic Waves (SAWs) for characterization purposes. Considering that melanoma is a serious type of skin cancer, we conducted a finite element simulation-based research on the generation and propagation of surface waves in human skin containing a melanoma-like feature, determine best pulse laser parameter ranges of variation, simulation mesh size and time step, working bandwidth, and minimal size of detectable melanoma.
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Extraction of Pharmacokinetic Evidence of Drug–Drug Interactions from the Literature
Kolchinsky, Artemy; Lourenço, Anália; Wu, Heng-Yi; Li, Lang; Rocha, Luis M.
2015-01-01
Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence. PMID:25961290
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Cyberprints: Identifying Cyber Attackers by Feature Analysis
ERIC Educational Resources Information Center
Blakely, Benjamin A.
2012-01-01
The problem of attributing cyber attacks is one of increasing importance. Without a solid method of demonstrating the origin of a cyber attack, any attempts to deter would-be cyber attackers are wasted. Existing methods of attribution make unfounded assumptions about the environment in which they will operate: omniscience (the ability to gather,…
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ERIC Educational Resources Information Center
Lee, Phyllis; Bierman, Karen L.
2015-01-01
For socioeconomically disadvantaged children, a positive experience in kindergarten may play a particularly important role in fostering the behavioral adjustment and learning engagement necessary for school success. Prior research has identified supportive student--teacher relationships and classroom emotional support as two features of the…
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Modeling and Measuring the Structure of Professional Vision in Preservice Teachers
ERIC Educational Resources Information Center
Seidel, Tina; Stürmer, Kathleen
2014-01-01
Professional vision has been identified as an important element of teacher expertise that can be developed in teacher education. It describes the use of knowledge to notice and interpret significant features of classroom situations. Three aspects of professional vision have been described by qualitative research: describe, explain, and predict…
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ERIC Educational Resources Information Center
Allen, Phyllis
Findings from a study of living accommodations for young people are given in the first part. Features are identified that are regarded as important by management and residents. Suggestions are made as to how user response may be predicted and the responses of the residents to eight schemes are examined in detail. Also considered are--(1) the…
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Psychotherapeutic Function of the Kazakh Traditional Music
ERIC Educational Resources Information Center
Shakerimova, Zere S.; Nussupova, Aizada S.; Burambaeva, Maryam N.; Yermanov, Zhanat R.; Emreyeva, Akmaral E.; Janseitova, Sveta S.
2016-01-01
This article considers the psychotherapeutic parameters of traditional Kazakh music, best practices that were achieved in practical psychology. From the one hand, it allows us to see the music features in a new light, and from the other hand--to identify the ethnic psychology of the Kazakh nation. An important step in the study of the…
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ERIC Educational Resources Information Center
Phillips, Kristin D.; Klein-Tasman, Bonita P.
2009-01-01
The refinement of the Williams syndrome phenotype has frequently included the study of behavioral and temperamental features common to individuals with this disorder. Within this line of research, the importance of evaluating incidence of psychopathology has been increasingly recognized, with studies consistently identifying an increased risk for…
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Workbook on the Identification of Anopheles Larvae. Preliminary Issue.
ERIC Educational Resources Information Center
Pratt, Harry D.; Stojanovich, Chester J.
This self-instructional booklet is designed to enable malarial control workers to identify the larvae of "Anopheles" species that are important malaria vectors. The morphological features of the larvae are illustrated in a programed booklet, which also contains an illustrated taxonomic key to 25 species of anopheline larvae. A glossary and a short…
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Workbook on the Identification of Anopheles Adults. Preliminary Issue.
ERIC Educational Resources Information Center
Pratt, Harry D.; Stojanovich, Chester J.
This self-instructional workbook is designed to enable malaria control workers to identify adults of "Anopheles" species that are important malaria vectors. The morphological features of the adults are illustrated in a programed booklet, which also contains an illustrated taxonomic key to adult females of 29 anopheline species. A glossary and a…
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Workbook on the Identification of Mosquito Larvae.
ERIC Educational Resources Information Center
Pratt, Harry D.; And Others
This self-instructional booklet is designed to enable public health workers identify larvae of some important North American mosquito species. The morphological features of larvae of the various genera and species are illustrated in a programed booklet, which also contains illustrated taxonomic keys to the larvae of 11 North American genera and to…
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An exploration of social support as a factor in the return-to-work process.
Lysaght, Rosemary M; Larmour-Trode, Sherrey
2008-01-01
Despite evidence that inter-personal relationships are important in human resource management, little is understood about the nature of workplace social support in a disability context, or what features of support are important to the success of return-to-work programs. The purpose of this qualitative study was to explore workplace disability support from worker and supervisory perspectives and to identify salient features for work re-entry. A total of 8 supervisors and 18 previously injured workers from a range of work units in a Canadian municipality were interviewed, and their views concerning supportive and unsupportive behaviours in work-re-entry situations were recorded and analyzed. A full range of social support dimensions were reported to be relevant, and were seen as arising from a variety of sources (e.g. supervisor, co-workers, disability manager, work unit, and outside of work). Respondents identified trust, communication and knowledge of disability as key precursors to a successful return-to-work process. Future research should explore the specific contributions of support to work rehabilitation outcomes as well as interventions to enhance available supports.
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Bloch, Steven
2011-01-01
The study described here investigates the practice of anticipatory completion of augmentative and alternative communication (AAC) utterances in progress. The aims were to identify and analyse features of this practice as they occur in natural conversation between a person using an AAC system and a family member. The methods and principles of Conversation Analysis (CA) were used to video record conversations between people with progressive neurological diseases and a progressive speech disorder (dysarthria) and their family members. Key features of interaction were identified and extracts transcribed. Four extracts of talk between a man with motor neurone disease/amyotrophic lateral sclerosis and his mother are presented here. Anticipatory completion of AAC utterances is intimately related to the sequential context in which such utterances occur. Difficulties can arise from topic shifts, understanding the intended action of an AAC word in progress and in recognising the possible end point an utterance. The analysis highlights the importance of understanding how AAC talk works in everyday interaction. The role of co-participants is particularly important here. These results may have implications for both AAC software design and clinical intervention.
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Radiological features for the approach in trans-sphenoidal pituitary surgery.
Twigg, Victoria; Carr, Simon D; Balakumar, Ramkishan; Sinha, Saurabh; Mirza, Showkat
2017-08-01
In order to perform trans-sphenoidal endoscopic pituitary surgery safely and efficiently it is important to identify anatomical and pituitary disease features on the pre-operative CT and MRI scans; thereby minimising the risk to surrounding structures and optimising outcomes. We aim to create a checklist to streamline pre-operative planning. We retrospectively reviewed pre-operative CT and MRI scans of 100 adults undergoing trans-sphenoidal endoscopic pituitary surgery. Radiological findings and their incidence included deviated nasal septum (62%), concha bullosa (32%), bony dehiscence of the carotid arteries (18%), sphenoid septation overlying the internal carotid artery (24% at the sella) and low lying CSF (32%). The mean distance of the sphenoid ostium to the skull base was 10 mm (range 2.7-17.6 mm). We also describe the 'teddy bear' sign which when present on an axial CT indicates the carotid arteries will be identifiable intra-operatively. There are significant variations in the anatomical and pituitary disease features between patients. We describe a number of features on pre-operative scans and have devised a checklist including a new 'teddy bear' sign to aid the surgeon in the anatomical assessment of patients undergoing trans-sphenoidal pituitary surgery.
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A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.
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.
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NASA Astrophysics Data System (ADS)
Mazurowski, Maciej A.; Zhang, Jing; Lo, Joseph Y.; Kuzmiak, Cherie M.; Ghate, Sujata V.; Yoon, Sora
2014-03-01
Providing high quality mammography education to radiology trainees is essential, as good interpretation skills potentially ensure the highest benefit of screening mammography for patients. We have previously proposed a computer-aided education system that utilizes trainee models, which relate human-assessed image characteristics to interpretation error. We proposed that these models be used to identify the most difficult and therefore the most educationally useful cases for each trainee. In this study, as a next step in our research, we propose to build trainee models that utilize features that are automatically extracted from images using computer vision algorithms. To predict error, we used a logistic regression which accepts imaging features as input and returns error as output. Reader data from 3 experts and 3 trainees were used. Receiver operating characteristic analysis was applied to evaluate the proposed trainee models. Our experiments showed that, for three trainees, our models were able to predict error better than chance. This is an important step in the development of adaptive computer-aided education systems since computer-extracted features will allow for faster and more extensive search of imaging databases in order to identify the most educationally beneficial cases.
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Liu, Zhenqiu; Sun, Fengzhu; McGovern, Dermot P
2017-01-01
Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L 1 , SCAD and MC+. However, none of the existing algorithms optimizes L 0 , which penalizes the number of nonzero features directly. In this paper, we develop a novel sparse generalized linear model (GLM) with L 0 approximation for feature selection and prediction with big omics data. The proposed approach approximate the L 0 optimization directly. Even though the original L 0 problem is non-convex, the problem is approximated by sequential convex optimizations with the proposed algorithm. The proposed method is easy to implement with only several lines of code. Novel adaptive ridge algorithms ( L 0 ADRIDGE) for L 0 penalized GLM with ultra high dimensional big data are developed. The proposed approach outperforms the other cutting edge regularization methods including SCAD and MC+ in simulations. When it is applied to integrated analysis of mRNA, microRNA, and methylation data from TCGA ovarian cancer, multilevel gene signatures associated with suboptimal debulking are identified simultaneously. The biological significance and potential clinical importance of those genes are further explored. The developed Software L 0 ADRIDGE in MATLAB is available at https://github.com/liuzqx/L0adridge.
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Jain, Anil K; Feng, Jianjiang
2011-01-01
Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.
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Quantitative imaging features: extension of the oncology medical image database
NASA Astrophysics Data System (ADS)
Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.
2015-03-01
Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.
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[Regression analysis to select native-like structures from decoys of antigen-antibody docking].
Chen, Zhengshan; Chi, Xiangyang; Fan, Pengfei; Zhang, Guanying; Wang, Meirong; Yu, Changming; Chen, Wei
2018-06-25
Given the increasing exploitation of antibodies in different contexts such as molecular diagnostics and therapeutics, it would be beneficial to unravel properties of antigen-antibody interaction with modeling of computational protein-protein docking, especially, in the absence of a cocrystal structure. However, obtaining a native-like antigen-antibody structure remains challenging due in part to failing to reliably discriminate accurate from inaccurate structures among tens of thousands of decoys after computational docking with existing scoring function. We hypothesized that some important physicochemical and energetic features could be used to describe antigen-antibody interfaces and identify native-like antigen-antibody structure. We prepared a dataset, a subset of Protein-Protein Docking Benchmark Version 4.0, comprising 37 nonredundant 3D structures of antigen-antibody complexes, and used it to train and test multivariate logistic regression equation which took several important physicochemical and energetic features of decoys as dependent variables. Our results indicate that the ability to identify native-like structures of our method is superior to ZRANK and ZDOCK score for the subset of antigen-antibody complexes. And then, we use our method in workflow of predicting epitope of anti-Ebola glycoprotein monoclonal antibody-4G7 and identify three accurate residues in its epitope.
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Smartphone medication adherence apps: Potential benefits to patients and providers
Dayer, Lindsey; Heldenbrand, Seth; Anderson, Paul; Gubbins, Paul O.; Martin, Bradley C.
2014-01-01
Objectives To provide an overview of medication adherence, discuss the potential for smartphone medication adherence applications (adherence apps) to improve medication nonadherence, evaluate features of adherence apps across operating systems (OSs), and identify future opportunities and barriers facing adherence apps. Practice description Medication nonadherence is a common, complex, and costly problem that contributes to poor treatment outcomes and consumes health care resources. Nonadherence is difficult to measure precisely, and interventions to mitigate it have been largely unsuccessful. Practice innovation Using smartphone adherence apps represents a novel approach to improving adherence. This readily available technology offers many features that can be designed to help patients and health care providers improve medication-taking behavior. Main outcome measures Currently available apps were identified from the three main smartphone OSs (Apple, Android, and Blackberry). In addition, desirable features for adherence apps were identified and ranked by perceived importance to user desirability using a three-point rating system: 1, modest; 2, moderate; or 3, high. The 10 highest-rated apps were installed and subjected to user testing to assess app attributes using a standard medication regimen. Results 160 adherence apps were identified and ranked. These apps were most prevalent for the Android OS. Adherence apps with advanced functionality were more prevalent on the Apple iPhone OS. Among all apps, MyMedSchedule, MyMeds, and RxmindMe rated the highest because of their basic medication reminder features coupled with their enhanced levels of functionality. Conclusion Despite being untested, medication apps represent a possible strategy that pharmacists can recommend to nonadherent patients and incorporate into their practice. PMID:23571625
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Absorption Efficiencies of Forsterite. I: DDA Explorations in Grain Shape and Size
NASA Technical Reports Server (NTRS)
Lindsay, Sean S.; Wooden, Diane; Harker, David E.; Kelley, Michael S.; Woodward, Charles E.; Murphy, Jim R.
2013-01-01
We compute the absorption efficiency (Q(sub abs)) of forsterite using the discrete dipole approximation (DDA) in order to identify and describe what characteristics of crystal grain shape and size are important to the shape, peak location, and relative strength of spectral features in the 8 - 40 micron wavelength range. Using the DDSCAT code, we compute Q(sub abs) for non-spherical polyhedral grain shapes with a(sub eff) = 0.1 micron. The shape characteristics identified are: 1) elongation/reduction along one of three crystallographic axes; 2) asymmetry, such that all three crystallographic axes are of different lengths; and 3) the presence of crystalline faces that are not parallel to a specific crystallographic axis, e.g., non-rectangular prisms and (di)pyramids. Elongation/reduction dominates the locations and shapes of spectral features near 10, 11, 16, 23.5, 27, and 33.5 micron, while asymmetry and tips are secondary shape effects. Increasing grain sizes (0.1 - 1.0 micron) shifts the 10, 11 micron features systematically towards longer wavelengths and relative to the 11 micron feature increases the strengths and slightly broadens the longer wavelength features. Seven spectral shape classes are established for crystallographic a-, b-, and c-axes and include columnar and platelet shapes plus non-elongated or equant grain shapes. The spectral shape classes and the effects of grain size have practical application in identifying or excluding columnar, platelet or equant forsterite grain shapes in astrophysical environs. Identification of the shape characteristics of forsterite from 8 - 40 micron spectra provides a potential means to probe the temperatures at which forsterite formed.
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Identifying the domains of context important to implementation science: a study protocol.
Squires, Janet E; Graham, Ian D; Hutchinson, Alison M; Michie, Susan; Francis, Jill J; Sales, Anne; Brehaut, Jamie; Curran, Janet; Ivers, Noah; Lavis, John; Linklater, Stefanie; Fenton, Shannon; Noseworthy, Thomas; Vine, Jocelyn; Grimshaw, Jeremy M
2015-09-28
There is growing recognition that "context" can and does modify the effects of implementation interventions aimed at increasing healthcare professionals' use of research evidence in clinical practice. However, conceptual clarity about what exactly comprises "context" is lacking. The purpose of this research program is to develop, refine, and validate a framework that identifies the key domains of context (and their features) that can facilitate or hinder (1) healthcare professionals' use of evidence in clinical practice and (2) the effectiveness of implementation interventions. A multi-phased investigation of context using mixed methods will be conducted. The first phase is a concept analysis of context using the Walker and Avant method to distinguish between the defining and irrelevant attributes of context. This phase will result in a preliminary framework for context that identifies its important domains and their features according to the published literature. The second phase is a secondary analysis of qualitative data from 13 studies of interviews with 312 healthcare professionals on the perceived barriers and enablers to their application of research evidence in clinical practice. These data will be analyzed inductively using constant comparative analysis. For the third phase, we will conduct semi-structured interviews with key health system stakeholders and change agents to elicit their knowledge and beliefs about the contextual features that influence the effectiveness of implementation interventions and healthcare professionals' use of evidence in clinical practice. Results from all three phases will be synthesized using a triangulation protocol to refine the context framework drawn from the concept analysis. The framework will then be assessed for content validity using an iterative Delphi approach with international experts (researchers and health system stakeholders/change agents). This research program will result in a framework that identifies the domains of context and their features that can facilitate or hinder: (1) healthcare professionals' use of evidence in clinical practice and (2) the effectiveness of implementation interventions. The framework will increase the conceptual clarity of the term "context" for advancing implementation science, improving healthcare professionals' use of evidence in clinical practice, and providing greater understanding of what interventions are likely to be effective in which contexts.
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Integrated feature extraction and selection for neuroimage classification
NASA Astrophysics Data System (ADS)
Fan, Yong; Shen, Dinggang
2009-02-01
Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.
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MIIB: A Metric to Identify Top Influential Bloggers in a Community.
Khan, Hikmat Ullah; Daud, Ali; Malik, Tahir Afzal
2015-01-01
Social networking has revolutionized the use of conventional web and has converted World Wide Web into the social web as users can generate their own content. This change has been possible due to social web platforms like forums, wikis, and blogs. Blogs are more commonly being used as a form of virtual communication to express an opinion about an event, product or experience and can reach a large audience. Users can influence others to buy a product, have certain political or social views, etc. Therefore, identifying the most influential bloggers has become very significant as this can help us in the fields of commerce, advertisement and product knowledge searching. Existing approaches consider some basic features, but lack to consider some other features like the importance of the blog on which the post has been created. This paper presents a new metric, MIIB (Metric for Identification of Influential Bloggers), based on various features of bloggers' productivity and popularity. Productivity refers to bloggers' blogging activity and popularity measures bloggers' influence in the blogging community. The novel module of BlogRank depicts the importance of blog sites where bloggers create their posts. The MIIB has been evaluated against the standard model and existing metrics for finding the influential bloggers using dataset from the real-world blogosphere. The obtained results confirm that the MIIB is able to find the most influential bloggers in a more effective manner.
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Lowry, David B.; Logan, Tierney L.; Santuari, Luca; Hardtke, Christian S.; Richards, James H.; DeRose-Wilson, Leah J.; McKay, John K.; Sen, Saunak; Juenger, Thomas E.
2013-01-01
The regulation of gene expression is crucial for an organism’s development and response to stress, and an understanding of the evolution of gene expression is of fundamental importance to basic and applied biology. To improve this understanding, we conducted expression quantitative trait locus (eQTL) mapping in the Tsu-1 (Tsushima, Japan) × Kas-1 (Kashmir, India) recombinant inbred line population of Arabidopsis thaliana across soil drying treatments. We then used genome resequencing data to evaluate whether genomic features (promoter polymorphism, recombination rate, gene length, and gene density) are associated with genes responding to the environment (E) or with genes with genetic variation (G) in gene expression in the form of eQTLs. We identified thousands of genes that responded to soil drying and hundreds of main-effect eQTLs. However, we identified very few statistically significant eQTLs that interacted with the soil drying treatment (GxE eQTL). Analysis of genome resequencing data revealed associations of several genomic features with G and E genes. In general, E genes had lower promoter diversity and local recombination rates. By contrast, genes with eQTLs (G) had significantly greater promoter diversity and were located in genomic regions with higher recombination. These results suggest that genomic architecture may play an important a role in the evolution of gene expression. PMID:24045022
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Recurrence time statistics: versatile tools for genomic DNA sequence analysis.
Cao, Yinhe; Tung, Wen-Wen; Gao, J B
2004-01-01
With the completion of the human and a few model organisms' genomes, and the genomes of many other organisms waiting to be sequenced, it has become increasingly important to develop faster computational tools which are capable of easily identifying the structures and extracting features from DNA sequences. One of the more important structures in a DNA sequence is repeat-related. Often they have to be masked before protein coding regions along a DNA sequence are to be identified or redundant expressed sequence tags (ESTs) are to be sequenced. Here we report a novel recurrence time based method for sequence analysis. The method can conveniently study all kinds of periodicity and exhaustively find all repeat-related features from a genomic DNA sequence. An efficient codon index is also derived from the recurrence time statistics, which has the salient features of being largely species-independent and working well on very short sequences. Efficient codon indices are key elements of successful gene finding algorithms, and are particularly useful for determining whether a suspected EST belongs to a coding or non-coding region. We illustrate the power of the method by studying the genomes of E. coli, the yeast S. cervisivae, the nematode worm C. elegans, and the human, Homo sapiens. Computationally, our method is very efficient. It allows us to carry out analysis of genomes on the whole genomic scale by a PC.
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Cameron, D W
1997-01-01
This paper examines sexually dimorphic skeletal characters within the face and upper dentition of extant hominids (great ape), not including members of the Hominini. Specimens of Pan paniscus, Pan troglodytes, Gorilla gorilla, and Pongo pygmaeus are used to help identify likely sex specific characters for the Hominidae. The aim of this paper is to identify extant hominid faciodental sexual features which can be used to help sex fossil specimens. A morphometric and skeletal study of sexual variability demonstrates relatively diverse patterns of sexual variability within the extant hominids. In terms of morphometrics, P. paniscus is relatively non-dimorphic, while P. troglodytes, Gorilla and Pongo display a large degree of sexual dimorphism. In their respective skeletal anatomies, however, each has specific characters which tend to differentiate between the sexes. Some faciodental sex features are shown to be common amongst all four taxa and as such are likely to be important criteria for determining the sex of Miocene and Plio-Pleistocene fossil hominid specimens. The construction of extant great ape sexual ranges of variability are also important in helping to test the fossil ape single species hypotheses. The testing of sex and species ranges of variability should employ range based statistics not only because they are sample size independent, (relative to C.V.) but also because they are of low power.
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Neonatal Seizure Detection Using Deep Convolutional Neural Networks.
Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine
2018-04-02
Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.
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Jakobsen, Tim Holm; Hansen, Martin Asser; Jensen, Peter Østrup; Hansen, Lars; Riber, Leise; Cockburn, April; Kolpen, Mette; Rønne Hansen, Christine; Ridderberg, Winnie; Eickhardt, Steffen; Hansen, Marlene; Kerpedjiev, Peter; Alhede, Morten; Qvortrup, Klaus; Burmølle, Mette; Moser, Claus; Kühl, Michael; Ciofu, Oana; Givskov, Michael; Sørensen, Søren J.; Høiby, Niels; Bjarnsholt, Thomas
2013-01-01
Achromobacter xylosoxidans is an environmental opportunistic pathogen, which infects an increasing number of immunocompromised patients. In this study we combined genomic analysis of a clinical isolated A. xylosoxidans strain with phenotypic investigations of its important pathogenic features. We present a complete assembly of the genome of A. xylosoxidans NH44784-1996, an isolate from a cystic fibrosis patient obtained in 1996. The genome of A. xylosoxidans NH44784-1996 contains approximately 7 million base pairs with 6390 potential protein-coding sequences. We identified several features that render it an opportunistic human pathogen, We found genes involved in anaerobic growth and the pgaABCD operon encoding the biofilm adhesin poly-β-1,6-N-acetyl-D-glucosamin. Furthermore, the genome contains a range of antibiotic resistance genes coding efflux pump systems and antibiotic modifying enzymes. In vitro studies of A. xylosoxidans NH44784-1996 confirmed the genomic evidence for its ability to form biofilms, anaerobic growth via denitrification, and resistance to a broad range of antibiotics. Our investigation enables further studies of the functionality of important identified genes contributing to the pathogenicity of A. xylosoxidans and thereby improves our understanding and ability to treat this emerging pathogen. PMID:23894309
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Multi-resolution analysis for ear recognition using wavelet features
NASA Astrophysics Data System (ADS)
Shoaib, M.; Basit, A.; Faye, I.
2016-11-01
Security is very important and in order to avoid any physical contact, identification of human when they are moving is necessary. Ear biometric is one of the methods by which a person can be identified using surveillance cameras. Various techniques have been proposed to increase the ear based recognition systems. In this work, a feature extraction method for human ear recognition based on wavelet transforms is proposed. The proposed features are approximation coefficients and specific details of level two after applying various types of wavelet transforms. Different wavelet transforms are applied to find the suitable wavelet. Minimum Euclidean distance is used as a matching criterion. Results achieved by the proposed method are promising and can be used in real time ear recognition system.
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Perski, Olga; Blandford, Ann; Ubhi, Harveen Kaur; West, Robert; Michie, Susan
2017-02-28
Public health organisations such as the National Health Service in the United Kingdom and the National Institutes of Health in the United States provide access to online libraries of publicly endorsed smartphone applications (apps); however, there is little evidence that users rely on this guidance. Rather, one of the most common methods of finding new apps is to search an online store. As hundreds of smoking cessation and alcohol-related apps are currently available on the market, smokers and drinkers must actively choose which app to download prior to engaging with it. The influences on this choice are yet to be identified. This study aimed to investigate 1) design features that shape users' choice of smoking cessation or alcohol reduction apps, and 2) design features judged to be important for engagement. Adult smokers (n = 10) and drinkers (n = 10) interested in using an app to quit/cut down were asked to search an online store to identify and explore a smoking cessation or alcohol reduction app of their choice whilst thinking aloud. Semi-structured interview techniques were used to allow participants to elaborate on their statements. An interpretivist theoretical framework informed the analysis. Verbal reports were audio recorded, transcribed verbatim and analysed using inductive thematic analysis. Participants chose apps based on their immediate look and feel, quality as judged by others' ratings and brand recognition ('social proof'), and titles judged to be realistic and relevant. Monitoring and feedback, goal setting, rewards and prompts were identified as important for engagement, fostering motivation and autonomy. Tailoring of content, a non-judgmental communication style, privacy and accuracy were viewed as important for engagement, fostering a sense of personal relevance and trust. Sharing progress on social media and the use of craving management techniques in social settings were judged not to be engaging because of concerns about others' negative reactions. Choice of a smoking cessation or alcohol reduction app may be influenced by its immediate look and feel, 'social proof' and titles that appear realistic. Design features that enhance motivation, autonomy, personal relevance and credibility may be important for engagement.
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Assessment of chronic post-surgical pain after knee replacement: development of a core outcome set.
Wylde, V; MacKichan, F; Bruce, J; Gooberman-Hill, R
2015-05-01
Approximately 20% of patients experience chronic post-surgical pain (CPSP) after total knee replacement (TKR). There is scope to improve assessment of CPSP after TKR, and this study aimed to develop a core outcome set. Eighty patients and 43 clinicians were recruited into a three-round modified Delphi study. In Round 1, participants were presented with 56 pain features identified from a systematic review, structured interviews with patients and focus groups with clinicians. Participants assigned importance ratings, using a 1-9 scale, to individual pain features; those features rated as most important were retained in subsequent rounds. Consensus that a pain feature should be included in the core outcome set was defined as the feature having a rating of 7-9 by ≥70% of both panels (patients and clinicians) and 1-3 by ≤15% of both panels or rated as 7-9 by ≥90% of one panel. Round 1 was completed by 71 patients and 39 clinicians, and Round 3 by 62 patients and 33 clinicians. The final consensus was that 33 pain features were important. These were grouped into an 8-item core outcome set comprising: pain intensity, pain interference with daily living, pain and physical functioning, temporal aspects of pain, pain description, emotional aspects of pain, use of pain medication, and improvement and satisfaction with pain relief. This core outcome set serves to guide assessment of CPSP after TKR. Consistency in assessment can promote standardized reporting and facilitate comparability between studies that address a common but understudied type of CPSP. © 2014 The Authors. European Journal of Pain published by John Wiley & Sons Ltd on behalf of European Pain Federation - EFIC®.
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NASA Astrophysics Data System (ADS)
Tu, Shiqi; Yuan, Guo-Cheng; Shao, Zhen
2017-01-01
Recently, long non-coding RNAs (lncRNAs) have emerged as an important class of molecules involved in many cellular processes. One of their primary functions is to shape epigenetic landscape through interactions with chromatin modifying proteins. However, mechanisms contributing to the specificity of such interactions remain poorly understood. Here we took the human and mouse lncRNAs that were experimentally determined to have physical interactions with Polycomb repressive complex 2 (PRC2), and systematically investigated the sequence features of these lncRNAs by developing a new computational pipeline for sequences composition analysis, in which each sequence is considered as a series of transitions between adjacent nucleotides. Through that, PRC2-binding lncRNAs were found to be associated with a set of distinctive and evolutionarily conserved sequence features, which can be utilized to distinguish them from the others with considerable accuracy. We further identified fragments of PRC2-binding lncRNAs that are enriched with these sequence features, and found they show strong PRC2-binding signals and are more highly conserved across species than the other parts, implying their functional importance.
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A mobile user-interface for elderly care from the perspective of relatives.
Warpenius, Erika; Alasaarela, Esko; Sorvoja, Hannu; Kinnunen, Matti
2015-03-01
As the number of elderly people rises, relatives' care-taking responsibilities increase accordingly. This creates a need for developing new systems that enable relatives to keep track of aged family members. To develop new mobile services for elderly healthcare we tried to identify the most wanted features of a mobile user-interface from the perspective of relatives. Feature mapping was based on two online surveys: one administered to the relatives (N = 32) and nurses (N = 3) of senior citizens and the other to nursing students (N = 18). Results of the surveys, confirmed by face-to-face interviews of the relatives (N = 8), indicated that the most valued features of the mobile user-interface are Accident Reporting (e.g. falling), Alarms (e.g. fire-alarm), Doctor Visits and evaluation of the General Condition of the Senior. The averaged importance ratings of these features were 9.2, 9.0, 8.6 and 8.5, respectively (on a scale from 0 to 10). Other important considerations for the user-interface development are aspiration to simplicity and ease-of-use. We recommend that the results are taken into account, when designing and implementing mobile services for elderly healthcare.
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Multimodal emotional state recognition using sequence-dependent deep hierarchical features.
Barros, Pablo; Jirak, Doreen; Weber, Cornelius; Wermter, Stefan
2015-12-01
Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5% reported in the literature to 91.3% for a benchmark dataset on spontaneous emotion expressions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
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A New Scheme to Characterize and Identify Protein Ubiquitination Sites.
Nguyen, Van-Nui; Huang, Kai-Yao; Huang, Chien-Hsun; Lai, K Robert; Lee, Tzong-Yi
2017-01-01
Protein ubiquitination, involving the conjugation of ubiquitin on lysine residue, serves as an important modulator of many cellular functions in eukaryotes. Recent advancements in proteomic technology have stimulated increasing interest in identifying ubiquitination sites. However, most computational tools for predicting ubiquitination sites are focused on small-scale data. With an increasing number of experimentally verified ubiquitination sites, we were motivated to design a predictive model for identifying lysine ubiquitination sites for large-scale proteome dataset. This work assessed not only single features, such as amino acid composition (AAC), amino acid pair composition (AAPC) and evolutionary information, but also the effectiveness of incorporating two or more features into a hybrid approach to model construction. The support vector machine (SVM) was applied to generate the prediction models for ubiquitination site identification. Evaluation by five-fold cross-validation showed that the SVM models learned from the combination of hybrid features delivered a better prediction performance. Additionally, a motif discovery tool, MDDLogo, was adopted to characterize the potential substrate motifs of ubiquitination sites. The SVM models integrating the MDDLogo-identified substrate motifs could yield an average accuracy of 68.70 percent. Furthermore, the independent testing result showed that the MDDLogo-clustered SVM models could provide a promising accuracy (78.50 percent) and perform better than other prediction tools. Two cases have demonstrated the effective prediction of ubiquitination sites with corresponding substrate motifs.
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Taguchi, Y-H
2016-05-10
MicroRNA(miRNA)-mRNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA-mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA-mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches.
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Fleming, Richard; Kelly, Fiona; Stillfried, Gillian
2015-05-12
The design of environments in which people with dementia live should be understandable, reinforce personal identity and maintain their abilities. The focus on supporting people with dementia to live well has omitted considering the needs or wishes for a supportive physical environment of those who are nearing the end of their lives. Using a combination of focus groups and a Delphi survey, this study explored the views of people with dementia, family carers and professionals on what aspects of the physical environment would be important to support a good quality of life to the very end. Three focus groups were carried out in three cities along the East Coast of Australia using a discussion guide informed by a literature review. Focus groups comprised recently bereaved family carers of people with dementia (FG1), people with dementia and family carers of people with dementia (FG2) and practitioners caring for people with dementia nearing or at the end of their lives (FG3). Focus group conversations were audio-recorded with participants' consent. Audio files were transcribed verbatim and analysed thematically to identify environmental features that could contribute to achieving the goal of providing a comfortable life to the end. A list of design features derived from analysis of focus group transcripts was distributed to a range of experts in the dementia field and a consensus sought on their appropriateness. From this, a set of features to inform the design of environments for people with dementia nearing the end of life was defined. Eighteen people took part in three focus groups: two with dementia, eleven current or recently bereaved family carers and five practitioners. There were differences in opinion on what were important environmental considerations. People with dementia and family carers identified comfort through engagement, feeling at home, a calm environment, privacy and dignity and use of technology to remain connected as important. For practitioners, design to facilitate duty of care and institutional influences on their practice were salient themes. Twenty one experts in the dementia field took part in the survey to agree a consensus on the desirable features derived from analysis of focus group transcripts, with fifteen features agreed. The fifteen features are compatible with the design principles for people with dementia who are mobile, but include a stronger focus on sensory engagement. We suggest that considering these features as part of a continuum of care will support good practice and offer those with dementia the opportunity to live well until the end and give their families a more positive experience in the last days of their lives together.
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Zhai, Binxu; Chen, Jianguo
2018-04-18
A stacked ensemble model is developed for forecasting and analyzing the daily average concentrations of fine particulate matter (PM 2.5 ) in Beijing, China. Special feature extraction procedures, including those of simplification, polynomial, transformation and combination, are conducted before modeling to identify potentially significant features based on an exploratory data analysis. Stability feature selection and tree-based feature selection methods are applied to select important variables and evaluate the degrees of feature importance. Single models including LASSO, Adaboost, XGBoost and multi-layer perceptron optimized by the genetic algorithm (GA-MLP) are established in the level 0 space and are then integrated by support vector regression (SVR) in the level 1 space via stacked generalization. A feature importance analysis reveals that nitrogen dioxide (NO 2 ) and carbon monoxide (CO) concentrations measured from the city of Zhangjiakou are taken as the most important elements of pollution factors for forecasting PM 2.5 concentrations. Local extreme wind speeds and maximal wind speeds are considered to extend the most effects of meteorological factors to the cross-regional transportation of contaminants. Pollutants found in the cities of Zhangjiakou and Chengde have a stronger impact on air quality in Beijing than other surrounding factors. Our model evaluation shows that the ensemble model generally performs better than a single nonlinear forecasting model when applied to new data with a coefficient of determination (R 2 ) of 0.90 and a root mean squared error (RMSE) of 23.69μg/m 3 . For single pollutant grade recognition, the proposed model performs better when applied to days characterized by good air quality than when applied to days registering high levels of pollution. The overall classification accuracy level is 73.93%, with most misclassifications made among adjacent categories. The results demonstrate the interpretability and generalizability of the stacked ensemble model. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
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Nembhard, Ingrid M
2009-01-01
Objective To understand participants' views on the relative helpfulness of various features of collaboratives, why each feature was helpful and which features the most successful participants viewed as most central to their success. Data Sources Primary data collected from 53 teams in four 2004–2005 Institute for Healthcare Improvement (IHI) Breakthrough Series collaboratives; secondary data from IHI and demographic sources. Study Design Cross-sectional analyses were conducted to assess participants' views of 12 features, and the relationship between their views and performance improvement. Data Collection Methods Participants' views on features were obtained via self-administered surveys and semi-structured telephone interviews. Performance improvement data were obtained from IHI and demographic data from secondary sources. Principal Findings Participants viewed six features as most helpful for advancing their improvement efforts overall and knowledge acquisition in particular: collaborative faculty, solicitation of their staff's ideas, change package, Plan-Do-Study-Act cycles, Learning Session interactions, and collaborative extranet. These features also provided participants with motivation, social support, and project management skills. Features enabling interorganizational learning were rated higher by teams whose organizations improved significantly than by other teams. Conclusions Findings identify features of collaborative design and implementation that participants view as most helpful and highlight the importance of interorganizational features, at least for those organizations that most improve. PMID:19040423
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Authentic Mars Research in the High School
NASA Astrophysics Data System (ADS)
Kortekaas, Katie; Leach, Dani
2015-01-01
As a 11th and 12th grade Astrobiology class we were charged with developing a scientific research question about the potential for life on Mars. We narrowed our big picture question to, 'Where should the next Mars rover land in order to study the volcanic and water features to find evidence of past or present extremophiles on Mars?'After a lot of searching through images on JMARS (although not extensive due to high school time constraints) we narrowed our interest to three areas of Mars we thought could be good candidates to land a rover there to do further research. We know from extremophiles on Earth that microscopic life need water and energy. It seems reasonable that Mars would be no different. We developed a research question, 'Does Kasei Valles, Dzigai Vallis and Hecate Tholus have volcanic features (lava flow, fractures, volcanoes, cryovolcanoes) and water features (layers of ice, hematite, carbonate, chaos)?'This question is important and interesting because by having a deeper understanding of whether these places have evidence of volcanic and water features, we will be able to decide where the best place to land a future rover would be. Evidence of volcanic and water features are important to help determine where to land our rover because in those areas, temperatures could have been warm and the land could be wet. In these conditions, the probability of life is higher.We individually did research through JMARS (CTX, THEMIS) in order to establish if those three areas could contain certain land features (volcanic and water features) that could possibly lead to the discovery of extremophiles. We evaluated the images to determine if the three areas have evidence of those volcanic and water features.Although we are not experts at identifying features we believe we have evidence to say that all three areas are interesting, astrobiologically, but Dzigai Vallis shows the most number of types of volcanic and water features. More importantly, through this process we as a class began to understand true authentic science and how it is performed.Thank you to Arizona State University for the curriculum and guidance.
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Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers’ Perspectives
BlueSpruce, June; Catz, Sheryl L; McClure, Jennifer B
2016-01-01
Background Smoking remains the leading cause of preventable disease and death in the United States. Therefore, researchers are constantly exploring new ways to promote smoking cessation. Mobile health (mHealth) technologies could be effective cessation tools. Despite the availability of commercial quit-smoking apps, little research to date has examined smokers’ preferred treatment intervention components (ie, design features). Honoring these preferences is important for designing programs that are appealing to smokers and may be more likely to be adopted and used. Objective The aim of this study was to understand smokers’ preferred design features of mHealth quit-smoking tools. Methods We used a mixed-methods approach consisting of focus groups and written surveys to understand the design preferences of adult smokers who were interested in quitting smoking (N=40). Focus groups were stratified by age to allow differing perspectives to emerge between older (>40 years) and younger (<40 years) participants. Focus group discussion included a “blue-sky” brainstorming exercise followed by participant reactions to contrasting design options for communicating with smokers, providing social support, and incentivizing program use. Participants rated the importance of preselected design features on an exit survey. Qualitative analyses examined emergent discussion themes and quantitative analyses compared feature ratings to determine which were perceived as most important. Results Participants preferred a highly personalized and adaptive mHealth experience. Their ideal mHealth quit-smoking tool would allow personalized tracking of their progress, adaptively tailored feedback, and real-time peer support to help manage smoking cravings. Based on qualitative analysis of focus group discussion, participants preferred pull messages (ie, delivered upon request) over push messages (ie, sent automatically) and preferred interaction with other smokers through closed social networks. Preferences for entertaining games or other rewarding incentives to encourage program use differed by age group. Based on quantitative analysis of surveys, participants rated the importance of select design features significantly differently (P<.001). Design features rated as most important included personalized content, the ability to track one’s progress, and features designed to help manage nicotine withdrawal and medication side effects. Design features rated least important were quit-smoking videos and posting on social media. Communicating with stop-smoking experts was rated more important than communicating with family and friends about quitting (P=.03). Perceived importance of various design features varied by age, experience with technology, and frequency of smoking. Conclusions Future mHealth cessation aids should be designed with an understanding of smokers’ needs and preferences for these tools. Doing so does not guarantee treatment effectiveness, but balancing user preferences with best-practice treatment considerations could enhance program adoption and improve treatment outcomes. Grounded in the perspectives of smokers, we identify several design considerations, which should be prioritized when designing future mHealth cessation tools and which warrant additional empirical validation. PMID:27496593
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NASA Astrophysics Data System (ADS)
Arvind, Pratul
2012-11-01
The ability to identify and classify all ten types of faults in a distribution system is an important task for protection engineers. Unlike transmission system, distribution systems have a complex configuration and are subjected to frequent faults. In the present work, an algorithm has been developed for identifying all ten types of faults in a distribution system by collecting current samples at the substation end. The samples are subjected to wavelet packet transform and artificial neural network in order to yield better classification results. A comparison of results between wavelet transform and wavelet packet transform is also presented thereby justifying the feature extracted from wavelet packet transform yields promising results. It should also be noted that current samples are collected after simulating a 25kv distribution system in PSCAD software.
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Chondritic Meteorites: Nebular and Parent-Body Formation Processes
NASA Technical Reports Server (NTRS)
Rubin, Alan E.; Lindstrom, David (Technical Monitor)
2002-01-01
It is important to identify features in chondrites that formed as a result of parent-body modification in order to disentangle nebular and asteroidal processes. However, this task is difficult because unmetamorphosed chondritic meteorites are mixtures of diverse components including various types of chondrules, chondrule fragments, refractory and mafic inclusions, metal-sulfide grains and fine-grained matrix material. Shocked chondrites can contain melt pockets, silicate-darkened material, metal veins, silicate melt veins, and impact-melt-rock clasts. This grant paid for several studies that went far in helping to distinguish primitive nebular features from those produced during asteroidal modification processes.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Rao, D.V.; Zhong, Z.; Akatsuka, T.
Images of the cork used for wine and other bottles are visualized with the use of diffraction-enhanced imaging (DEI) technique. Present experimental studies allowed us to identify the cracks, holes, porosity, and importance of soft-matter (soft-material) and associated biology by visualization of the embedded internal complex features of the biological material such as cork and its microstructure. Highlighted the contrast mechanisms above and below the K-absorption edge of iodine and studied the attenuation through a combination of weakly and strongly attenuating materials.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Donepudi, R.; Cesareo, R; Brunetti, A
Images of the cork used for wine and other bottles are visualized with the use of diffraction-enhanced imaging (DEI) technique. Present experimental studies allowed us to identify the cracks, holes, porosity, and importance of soft-matter (soft-material) and associated biology by visualization of the embedded internal complex features of the biological material such as cork and its microstructure. Highlighted the contrast mechanisms above and below the K-absorption edge of iodine and studied the attenuation through a combination of weakly and strongly attenuating materials.
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Che Hasan, Rozaimi; Ierodiaconou, Daniel; Laurenson, Laurie; Schimel, Alexandre
2014-01-01
Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management. PMID:24824155
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Moolhuijzen, P; Cakir, M; Hunter, A; Schibeci, D; Macgregor, A; Smith, C; Francki, M; Jones, M G K; Appels, R; Bellgard, M
2006-06-01
The identification of markers in legume pasture crops, which can be associated with traits such as protein and lipid production, disease resistance, and reduced pod shattering, is generally accepted as an important strategy for improving the agronomic performance of these crops. It has been demonstrated that many quantitative trait loci (QTLs) identified in one species can be found in other plant species. Detailed legume comparative genomic analyses can characterize the genome organization between model legume species (e.g., Medicago truncatula, Lotus japonicus) and economically important crops such as soybean (Glycine max), pea (Pisum sativum), chickpea (Cicer arietinum), and lupin (Lupinus angustifolius), thereby identifying candidate gene markers that can be used to track QTLs in lupin and pasture legume breeding. LegumeDB is a Web-based bioinformatics resource for legume researchers. LegumeDB analysis of Medicago truncatula expressed sequence tags (ESTs) has identified novel simple sequence repeat (SSR) markers (16 tested), some of which have been putatively linked to symbiosome membrane proteins in root nodules and cell-wall proteins important in plant-pathogen defence mechanisms. These novel markers by preliminary PCR assays have been detected in Medicago truncatula and detected in at least one other legume species, Lotus japonicus, Glycine max, Cicer arietinum, and (or) Lupinus angustifolius (15/16 tested). Ongoing research has validated some of these markers to map them in a range of legume species that can then be used to compile composite genetic and physical maps. In this paper, we outline the features and capabilities of LegumeDB as an interactive application that provides legume genetic and physical comparative maps, and the efficient feature identification and annotation of the vast tracks of model legume sequences for convenient data integration and visualization. LegumeDB has been used to identify potential novel cross-genera polymorphic legume markers that map to agronomic traits, supporting the accelerated identification of molecular genetic factors underpinning important agronomic attributes in lupin.
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Sobnath, Drishty D; Philip, Nada; Kayyali, Reem; Nabhani-Gebara, Shereen; Pierscionek, Barbara; Vaes, Anouk W; Spruit, Martijn A; Kaimakamis, Evangelos
2017-02-20
Chronic obstructive pulmonary disease (COPD) is a serious long-term lung disease in which the airflow from the lungs is progressively reduced. By 2030, COPD will become the third cause of mortality and seventh cause of morbidity worldwide. With advances in technology and mobile communications, significant progress in the mobile health (mHealth) sector has been recently observed. Mobile phones with app capabilities (smartphones) are now considered as potential media for the self-management of certain types of diseases such as asthma, cancer, COPD, or cardiovascular diseases. While many mobile apps for patients with COPD are currently found on the market, there is little published material on the effectiveness of most of them, their features, and their adoption in health care settings. The aim of this study was to search the literature for current systems related to COPD and identify any missing links and studies that were carried out to evaluate the effectiveness of COPD mobile apps. In addition, we reviewed existing mHealth apps from different stores in order to identify features that can be considered in the initial design of a COPD support tool to improve health care services and patient outcomes. In total, 206 articles related to COPD management systems were identified from different databases. Irrelevant materials and duplicates were excluded. Of those, 38 articles were reviewed to extract important features. We identified 214 apps from online stores. Following exclusion of irrelevant apps, 48 were selected and 20 of them were downloaded to review some of their common features. Our review found that out of the 20 apps downloaded, 13 (65%, 13/20) had an education section, 5 (25%, 5/20) consisted of medication and guidelines, 6 (30%, 6/20) included a calendar or diary and other features such as reminders or symptom tracking. There was little published material on the effectiveness of the identified COPD apps. Features such as (1) a social networking tool; (2) personalized education; (3) feedback; (4) e-coaching; and (5) psychological motivation to enhance behavioral change were found to be missing in many of the downloaded apps. This paper summarizes the features of a COPD patient-support mobile app that can be taken into consideration for the initial design of an integrated care system to encourage the self-management of their condition at home. ©Drishty D Sobnath, Nada Philip, Reem Kayyali, Shereen Nabhani-Gebara, Barbara Pierscionek, Anouk W Vaes, Martijn A Spruit, Evangelos Kaimakamis. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 20.02.2017.
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Tania Barros; Samuel A. Cushman; Joao Carvalho; Carlos Fonseca
2016-01-01
Identifying the environmental features affecting gene flow across a species range is of extreme importance for conservation planning. We investigated the genetic structure of the Egyptian mongoose (Herpestes ichneumon) in Western Iberian Peninsula by analyzing the correlations between genetic distances and landscape resistance models. We evaluated several...
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Knowledge Enriched Learning by Converging Knowledge Object & Learning Object
ERIC Educational Resources Information Center
Sabitha, Sai; Mehrotra, Deepti; Bansal, Abhay
2015-01-01
The most important dimension of learning is the content, and a Learning Management System (LMS) suffices this to a certain extent. The present day LMS are designed to primarily address issues like ease of use, search, content and performance. Many surveys had been conducted to identify the essential features required for the improvement of LMS,…
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Wetland features and landscape context predict the risk of wetland habitat loss
Kevin J. Gutzwiller; Curtis H. Flather
2011-01-01
Wetlands generally provide significant ecosystem services and function as important harbors of biodiversity. To ensure that these habitats are conserved, an efficient means of identifying wetlands at risk of conversion is needed, especially in the southern United States where the rate of wetland loss has been highest in recent decades. We used multivariate adaptive...
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EPCAL: ETS Platform for Collaborative Assessment and Learning. Research Report. ETS RR-17-49
ERIC Educational Resources Information Center
Hao, Jiangang; Liu, Lei; von Davier, Alina A.; Lederer, Nathan; Zapata-Rivera, Diego; Jaki, Peter; Bakkenson, Michael
2017-01-01
Most existing software tools for online collaboration are designed to support the collaboration itself instead of the study of collaboration with a systematic team and task management system. In this report, we identify six important features for a platform to facilitate the study of online collaboration. We then introduce the Educational Testing…
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Fatal methanol poisoning: features of liver histopathology.
Akhgari, Maryam; Panahianpour, Mohammad Hadi; Bazmi, Elham; Etemadi-Aleagha, Afshar; Mahdavi, Amirhosein; Nazari, Saeed Hashemi
2013-03-01
Methanol poisoning has become a considerable problem in Iran. Liver can show some features of poisoning after methanol ingestion. Therefore, our concern was to examine liver tissue histopathology in fatal methanol poisoning cases in Iranian population. In this study, 44 cases of fatal methanol poisoning were identified in a year. The histological changes of the liver were reviewed. The most striking features of liver damage by light microscopy were micro-vesicular steatosis, macro-vesicular steatosis, focal hepatocyte necrosis, mild intra-hepatocyte bile stasis, feathery degeneration and hydropic degeneration. Blood and vitreous humor methanol concentrations were examined to confirm the proposed history of methanol poisoning. The majority of cases were men (86.36%). In conclusion, methanol poisoning can cause histological changes in liver tissues. Most importantly in cases with mean blood and vitreous humor methanol levels greater than 127 ± 38.9 mg/dL more than one pathologic features were detected.
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Intrinsic two-dimensional features as textons
NASA Technical Reports Server (NTRS)
Barth, E.; Zetzsche, C.; Rentschler, I.
1998-01-01
We suggest that intrinsic two-dimensional (i2D) features, computationally defined as the outputs of nonlinear operators that model the activity of end-stopped neurons, play a role in preattentive texture discrimination. We first show that for discriminable textures with identical power spectra the predictions of traditional models depend on the type of nonlinearity and fail for energy measures. We then argue that the concept of intrinsic dimensionality, and the existence of end-stopped neurons, can help us to understand the role of the nonlinearities. Furthermore, we show examples in which models without strong i2D selectivity fail to predict the correct ranking order of perceptual segregation. Our arguments regarding the importance of i2D features resemble the arguments of Julesz and co-workers regarding textons such as terminators and crossings. However, we provide a computational framework that identifies textons with the outputs of nonlinear operators that are selective to i2D features.
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Image feature based GPS trace filtering for road network generation and road segmentation
Yuan, Jiangye; Cheriyadat, Anil M.
2015-10-19
We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less
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Conserved and variable domains of RNase MRP RNA.
Dávila López, Marcela; Rosenblad, Magnus Alm; Samuelsson, Tore
2009-01-01
Ribonuclease MRP is a eukaryotic ribonucleoprotein complex consisting of one RNA molecule and 7-10 protein subunits. One important function of MRP is to catalyze an endonucleolytic cleavage during processing of rRNA precursors. RNase MRP is evolutionary related to RNase P which is critical for tRNA processing. A large number of MRP RNA sequences that now are available have been used to identify conserved primary and secondary structure features of the molecule. MRP RNA has structural features in common with P RNA such as a conserved catalytic core, but it also has unique features and is characterized by a domain highly variable between species. Information regarding primary and secondary structure features is of interest not only in basic studies of the function of MRP RNA, but also because mutations in the RNA give rise to human genetic diseases such as cartilage-hair hypoplasia.
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Image feature based GPS trace filtering for road network generation and road segmentation
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yuan, Jiangye; Cheriyadat, Anil M.
We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less
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Prediction of essential proteins based on gene expression programming.
Zhong, Jiancheng; Wang, Jianxin; Peng, Wei; Zhang, Zhen; Pan, Yi
2013-01-01
Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods. The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.
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NASA Astrophysics Data System (ADS)
Lim, Meng-Hui; Teoh, Andrew Beng Jin
2011-12-01
Biometric discretization derives a binary string for each user based on an ordered set of biometric features. This representative string ought to be discriminative, informative, and privacy protective when it is employed as a cryptographic key in various security applications upon error correction. However, it is commonly believed that satisfying the first and the second criteria simultaneously is not feasible, and a tradeoff between them is always definite. In this article, we propose an effective fixed bit allocation-based discretization approach which involves discriminative feature extraction, discriminative feature selection, unsupervised quantization (quantization that does not utilize class information), and linearly separable subcode (LSSC)-based encoding to fulfill all the ideal properties of a binary representation extracted for cryptographic applications. In addition, we examine a number of discriminative feature-selection measures for discretization and identify the proper way of setting an important feature-selection parameter. Encouraging experimental results vindicate the feasibility of our approach.
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Arabic writer identification based on diacritic's features
NASA Astrophysics Data System (ADS)
Maliki, Makki; Al-Jawad, Naseer; Jassim, Sabah A.
2012-06-01
Natural languages like Arabic, Kurdish, Farsi (Persian), Urdu, and any other similar languages have many features, which make them different from other languages like Latin's script. One of these important features is diacritics. These diacritics are classified as: compulsory like dots which are used to identify/differentiate letters, and optional like short vowels which are used to emphasis consonants. Most indigenous and well trained writers often do not use all or some of these second class of diacritics, and expert readers can infer their presence within the context of the writer text. In this paper, we investigate the use of diacritics shapes and other characteristic as parameters of feature vectors for Arabic writer identification/verification. Segmentation techniques are used to extract the diacritics-based feature vectors from examples of Arabic handwritten text. The results of evaluation test will be presented, which has been carried out on an in-house database of 50 writers. Also the viability of using diacritics for writer recognition will be demonstrated.
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Support-vector-based emergent self-organising approach for emotional understanding
NASA Astrophysics Data System (ADS)
Nguwi, Yok-Yen; Cho, Siu-Yeung
2010-12-01
This study discusses the computational analysis of general emotion understanding from questionnaires methodology. The questionnaires method approaches the subject by investigating the real experience that accompanied the emotions, whereas the other laboratory approaches are generally associated with exaggerated elements. We adopted a connectionist model called support-vector-based emergent self-organising map (SVESOM) to analyse the emotion profiling from the questionnaires method. The SVESOM first identifies the important variables by giving discriminative features with high ranking. The classifier then performs the classification based on the selected features. Experimental results show that the top rank features are in line with the work of Scherer and Wallbott [(1994), 'Evidence for Universality and Cultural Variation of Differential Emotion Response Patterning', Journal of Personality and Social Psychology, 66, 310-328], which approached the emotions physiologically. While the performance measures show that using the full features for classifications can degrade the performance, the selected features provide superior results in terms of accuracy and generalisation.
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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.
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Tiedeman, C.R.; Hill, M.C.; D'Agnese, F. A.; Faunt, C.C.
2003-01-01
Calibrated models of groundwater systems can provide substantial information for guiding data collection. This work considers using such models to guide hydrogeologic data collection for improving model predictions by identifying model parameters that are most important to the predictions. Identification of these important parameters can help guide collection of field data about parameter values and associated flow system features and can lead to improved predictions. Methods for identifying parameters important to predictions include prediction scaled sensitivities (PSS), which account for uncertainty on individual parameters as well as prediction sensitivity to parameters, and a new "value of improved information" (VOII) method presented here, which includes the effects of parameter correlation in addition to individual parameter uncertainty and prediction sensitivity. In this work, the PSS and VOII methods are demonstrated and evaluated using a model of the Death Valley regional groundwater flow system. The predictions of interest are advective transport paths originating at sites of past underground nuclear testing. Results show that for two paths evaluated the most important parameters include a subset of five or six of the 23 defined model parameters. Some of the parameters identified as most important are associated with flow system attributes that do not lie in the immediate vicinity of the paths. Results also indicate that the PSS and VOII methods can identify different important parameters. Because the methods emphasize somewhat different criteria for parameter importance, it is suggested that parameters identified by both methods be carefully considered in subsequent data collection efforts aimed at improving model predictions.
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Russo, Isa-Rita M.; Sole, Catherine L.; Barbato, Mario; von Bramann, Ullrich; Bruford, Michael W.
2016-01-01
Small mammals provide ecosystem services, acting, for example, as pollinators and seed dispersers. In addition, they are also disease reservoirs that can be detrimental to human health and they can also act as crop pests. Knowledge of their dispersal preferences is therefore useful for population management and landscape planning. Genetic data were used alongside landscape data to examine the influence of the landscape on the demographic connectedness of the Natal multimammate mouse (Mastomys natalensis) and to identify landscape characteristics that influence the genetic structure of this species across a spatially and temporally varying environment. The most significant landscape features shaping gene flow were aspect, vegetation cover, topographic complexity (TC) and rivers, with western facing slopes, topographic complexity and rivers restricting gene flow. In general, thicket vegetation was correlated with increased gene flow. Identifying features of the landscape that facilitate movement/dispersal in M. natalensis potentially has application for other small mammals in similar ecosystems. As the primary reservoir host of the zoonotic Lassa virus, a landscape genetics approach may have applications in determining areas of high disease risk to humans. Identifying these landscape features may also be important in crop management due to damage by rodent pests. PMID:27406468
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Russo, Isa-Rita M; Sole, Catherine L; Barbato, Mario; von Bramann, Ullrich; Bruford, Michael W
2016-07-13
Small mammals provide ecosystem services, acting, for example, as pollinators and seed dispersers. In addition, they are also disease reservoirs that can be detrimental to human health and they can also act as crop pests. Knowledge of their dispersal preferences is therefore useful for population management and landscape planning. Genetic data were used alongside landscape data to examine the influence of the landscape on the demographic connectedness of the Natal multimammate mouse (Mastomys natalensis) and to identify landscape characteristics that influence the genetic structure of this species across a spatially and temporally varying environment. The most significant landscape features shaping gene flow were aspect, vegetation cover, topographic complexity (TC) and rivers, with western facing slopes, topographic complexity and rivers restricting gene flow. In general, thicket vegetation was correlated with increased gene flow. Identifying features of the landscape that facilitate movement/dispersal in M. natalensis potentially has application for other small mammals in similar ecosystems. As the primary reservoir host of the zoonotic Lassa virus, a landscape genetics approach may have applications in determining areas of high disease risk to humans. Identifying these landscape features may also be important in crop management due to damage by rodent pests.
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Automatic detection and classification of artifacts in single-channel EEG.
Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D
2014-01-01
Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.
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Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals
NASA Astrophysics Data System (ADS)
Wang, Hui; Li, Jingchao; Guo, Lili; Dou, Zheng; Lin, Yun; Zhou, Ruolin
How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of -10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.
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An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins.
Rawat, Puneet; Kumar, Sandeep; Michael Gromiha, M
2018-06-24
Newly synthesized polypeptides must pass stringent quality controls in cells to ensure appropriate folding and function. However, mutations, environmental stresses and aging can reduce efficiencies of these controls, leading to accumulation of protein aggregates, amyloid fibrils and plaques. In-vitro experiments have shown that even single amino acid substitutions can drastically enhance or mitigate protein aggregation kinetics. In this work, we have collected a dataset of 220 unique mutations in 25 proteins and classified them as enhancers or mitigators on the basis of their effect on protein aggregation rate. The data were analyzed via machine learning to identify features capable of distinguishing between aggregation rate enhancers and mitigators. Our initial Support Vector Machine (SVM) model separated such mutations with an overall accuracy of 69%. When local secondary structures at the mutation sites were considered, the accuracies further improved by 13-15%. The machine-learnt features are distinct for each secondary structure class at mutation sites. Protein stability and flexibility changes are important features for mutations in α-helices. β-strand propensity, polarity and charge become important when mutations occur in β-strands and ability to form secondary structure, helical tendency and aggregation propensity are important for mutations lying in coils. These results have been incorporated into a sequence-based algorithm (available at http://www.iitm.ac.in/bioinfo/aggrerate-disc/) capable of predicting whether a mutation will enhance or mitigate a protein's aggregation rate. This algorithm will find several applications towards understanding protein aggregation in human diseases, enable in-silico optimization of biopharmaceuticals and enzymes for improved biophysical attributes and de novo design of bio-nanomaterials. Copyright © 2018. Published by Elsevier B.V.
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Bioethics in Mediterranean culture: the Spanish experience.
Busquets, Ester; Roman, Begoña; Terribas, Núria
2012-11-01
This article presents a view of bioethics in the Spanish context. We may identify several features common to Mediterranean countries because of their relatively similar social organisation. Each country has its own distinguishing features but we would point two aspects which are of particular interest: the Mediterranean view of autonomy and the importance of Catholicism in Mediterranean culture. The Spanish experience on bioethics field has been marked by these elements, trying to build a civic ethics alternative, with the law as an important support. So, Spanish bioethics has been developed in two parallel levels: in the academic and policy maker field (University and Parliament) and in clinical practice (hospitals and healthcare ethics committees), with different paces and methods. One of the most important changes in the paternalistic mentality has been promoted through the recognition by law of the patient's rights and also through the new generation of citizens, clearly aware on the exercise of autonomy. Now, the healthcare professionals have a new challenge: adapt their practice to this new paradigm.
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Predicting protein amidation sites by orchestrating amino acid sequence features
NASA Astrophysics Data System (ADS)
Zhao, Shuqiu; Yu, Hua; Gong, Xiujun
2017-08-01
Amidation is the fourth major category of post-translational modifications, which plays an important role in physiological and pathological processes. Identifying amidation sites can help us understanding the amidation and recognizing the original reason of many kinds of diseases. But the traditional experimental methods for predicting amidation sites are often time-consuming and expensive. In this study, we propose a computational method for predicting amidation sites by orchestrating amino acid sequence features. Three kinds of feature extraction methods are used to build a feature vector enabling to capture not only the physicochemical properties but also position related information of the amino acids. An extremely randomized trees algorithm is applied to choose the optimal features to remove redundancy and dependence among components of the feature vector by a supervised fashion. Finally the support vector machine classifier is used to label the amidation sites. When tested on an independent data set, it shows that the proposed method performs better than all the previous ones with the prediction accuracy of 0.962 at the Matthew's correlation coefficient of 0.89 and area under curve of 0.964.
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Juhlin, Kristina; Norén, G. Niklas
2017-01-01
Abstract Purpose To develop a method for data‐driven exploration in pharmacovigilance and illustrate its use by identifying the key features of individual case safety reports related to medication errors. Methods We propose vigiPoint, a method that contrasts the relative frequency of covariate values in a data subset of interest to those within one or more comparators, utilizing odds ratios with adaptive statistical shrinkage. Nested analyses identify higher order patterns, and permutation analysis is employed to protect against chance findings. For illustration, a total of 164 000 adverse event reports related to medication errors were characterized and contrasted to the other 7 833 000 reports in VigiBase, the WHO global database of individual case safety reports, as of May 2013. The initial scope included 2000 features, such as patient age groups, reporter qualifications, and countries of origin. Results vigiPoint highlighted 109 key features of medication error reports. The most prominent were that the vast majority of medication error reports were from the United States (89% compared with 49% for other reports in VigiBase); that the majority of reports were sent by consumers (53% vs 17% for other reports); that pharmacists (12% vs 5.3%) and lawyers (2.9% vs 1.5%) were overrepresented; and that there were more medication error reports than expected for patients aged 2‐11 years (10% vs 5.7%), particularly in Germany (16%). Conclusions vigiPoint effectively identified key features of medication error reports in VigiBase. More generally, it reduces lead times for analysis and ensures reproducibility and transparency. An important next step is to evaluate its use in other data. PMID:28815800
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Salimi, Nima; Loh, Kar Hoe; Kaur Dhillon, Sarinder; Chong, Ving Ching
2016-01-01
Background. Fish species may be identified based on their unique otolith shape or contour. Several pattern recognition methods have been proposed to classify fish species through morphological features of the otolith contours. However, there has been no fully-automated species identification model with the accuracy higher than 80%. The purpose of the current study is to develop a fully-automated model, based on the otolith contours, to identify the fish species with the high classification accuracy. Methods. Images of the right sagittal otoliths of 14 fish species from three families namely Sciaenidae, Ariidae, and Engraulidae were used to develop the proposed identification model. Short-time Fourier transform (STFT) was used, for the first time in the area of otolith shape analysis, to extract important features of the otolith contours. Discriminant Analysis (DA), as a classification technique, was used to train and test the model based on the extracted features. Results. Performance of the model was demonstrated using species from three families separately, as well as all species combined. Overall classification accuracy of the model was greater than 90% for all cases. In addition, effects of STFT variables on the performance of the identification model were explored in this study. Conclusions. Short-time Fourier transform could determine important features of the otolith outlines. The fully-automated model proposed in this study (STFT-DA) could predict species of an unknown specimen with acceptable identification accuracy. The model codes can be accessed at http://mybiodiversityontologies.um.edu.my/Otolith/ and https://peerj.com/preprints/1517/. The current model has flexibility to be used for more species and families in future studies.
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Banerjee, Amit; Misra, Milind; Pai, Deepa; Shih, Liang-Yu; Woodley, Rohan; Lu, Xiang-Jun; Srinivasan, A R; Olson, Wilma K; Davé, Rajesh N; Venanzi, Carol A
2007-01-01
Six rigid-body parameters (Shift, Slide, Rise, Tilt, Roll, Twist) are commonly used to describe the relative displacement and orientation of successive base pairs in a nucleic acid structure. The present work adapts this approach to describe the relative displacement and orientation of any two planes in an arbitrary molecule-specifically, planes which contain important pharmacophore elements. Relevant code from the 3DNA software package (Nucleic Acids Res. 2003, 31, 5108-5121) was generalized to treat molecular fragments other than DNA bases as input for the calculation of the corresponding rigid-body (or "planes") parameters. These parameters were used to construct feature vectors for a fuzzy relational clustering study of over 700 conformations of a flexible analogue of the dopamine reuptake inhibitor, GBR 12909. Several cluster validity measures were used to determine the optimal number of clusters. Translational (Shift, Slide, Rise) rather than rotational (Tilt, Roll, Twist) features dominate clustering based on planes that are relatively far apart, whereas both types of features are important to clustering when the pair of planes are close by. This approach was able to classify the data set of molecular conformations into groups and to identify representative conformers for use as template conformers in future Comparative Molecular Field Analysis studies of GBR 12909 analogues. The advantage of using the planes parameters, rather than the combination of atomic coordinates and angles between molecular planes used in our previous fuzzy relational clustering of the same data set (J. Chem. Inf. Model. 2005, 45, 610-623), is that the present clustering results are independent of molecular superposition and the technique is able to identify clusters in the molecule considered as a whole. This approach is easily generalizable to any two planes in any molecule.
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Ellington, M J; Ganner, M; Warner, M; Boakes, E; Cookson, B D; Hill, R L; Kearns, A M
2010-07-01
We report the first international spread and dissemination of ST93-SCCmecIV (Queensland clone) methicillin-resistant Staphylococcus aureus (MRSA), previously identified in communities and hospitals in Australia. Ten highly genetically related MRSA isolates and one methicillin-susceptible S. aureus (MSSA) isolate were identified in England between 2005 and June 2008. The demography and clinical features were typical for community-associated-MRSA. One female with MRSA infection died from necrotizing pneumonia. Travel between Australia and the UK, and some onward transmission, suggested that both importation and clonal dissemination of this strain had occurred, albeit to a small extent. Nosocomial transmission was not detected, but we remain vigilant for further importations and/or spread.
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The gate studies: Assessing the potential of future small general aviation turbine engines
NASA Technical Reports Server (NTRS)
Strack, W. C.
1979-01-01
Four studies were completed that explore the opportunities for future General Aviation turbine engines (GATE) in the 150-1000 SHP class. These studies forecasted the potential impact of advanced technology turbine engines in the post-1988 market, identified important aircraft and missions, desirable engine sizes, engine performance, and cost goals. Parametric evaluations of various engine cycles, configurations, design features, and advanced technology elements defined baseline conceptual engines for each of the important missions identified by the market analysis. Both fixed-wing and helicopter aircraft, and turboshaft, turboprop, and turbofan engines were considered. Sizable performance gains (e.g., 20% SFC decrease), and large engine cost reductions of sufficient magnitude to challenge the reciprocating engine in the 300-500 SHP class were predicted.
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Black, R.W.; Moran, P.W.; Frankforter, J.D.
2011-01-01
Many streams within the United States are impaired due to nutrient enrichment, particularly in agricultural settings. The present study examines the response of benthic algal communities in agricultural and minimally disturbed sites from across the western United States to a suite of environmental factors, including nutrients, collected at multiple scales. The first objective was to identify the relative importance of nutrients, habitat and watershed features, and macroinvertebrate trophic structure to explain algal metrics derived from deposition and erosion habitats. The second objective was to determine if thresholds in total nitrogen (TN) and total phosphorus (TP) related to algal metrics could be identified and how these thresholds varied across metrics and habitats. Nutrient concentrations within the agricultural areas were elevated and greater than published threshold values. All algal metrics examined responded to nutrients as hypothesized. Although nutrients typically were the most important variables in explaining the variation in each of the algal metrics, environmental factors operating at multiple scales also were important. Calculated thresholds for TN or TP based on the algal metrics generated from samples collected from erosion and deposition habitats were not significantly different. Little variability in threshold values for each metric for TN and TP was observed. The consistency of the threshold values measured across multiple metrics and habitats suggest that the thresholds identified in this study are ecologically relevant. Additional work to characterize the relationship between algal metrics, physical and chemical features, and nuisance algal growth would be of benefit to the development of nutrient thresholds and criteria. ?? 2010 The Author(s).
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Black, Robert W; Moran, Patrick W; Frankforter, Jill D
2011-04-01
Many streams within the United States are impaired due to nutrient enrichment, particularly in agricultural settings. The present study examines the response of benthic algal communities in agricultural and minimally disturbed sites from across the western United States to a suite of environmental factors, including nutrients, collected at multiple scales. The first objective was to identify the relative importance of nutrients, habitat and watershed features, and macroinvertebrate trophic structure to explain algal metrics derived from deposition and erosion habitats. The second objective was to determine if thresholds in total nitrogen (TN) and total phosphorus (TP) related to algal metrics could be identified and how these thresholds varied across metrics and habitats. Nutrient concentrations within the agricultural areas were elevated and greater than published threshold values. All algal metrics examined responded to nutrients as hypothesized. Although nutrients typically were the most important variables in explaining the variation in each of the algal metrics, environmental factors operating at multiple scales also were important. Calculated thresholds for TN or TP based on the algal metrics generated from samples collected from erosion and deposition habitats were not significantly different. Little variability in threshold values for each metric for TN and TP was observed. The consistency of the threshold values measured across multiple metrics and habitats suggest that the thresholds identified in this study are ecologically relevant. Additional work to characterize the relationship between algal metrics, physical and chemical features, and nuisance algal growth would be of benefit to the development of nutrient thresholds and criteria.
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Designing coastal conservation to deliver ecosystem and human well-being benefits.
Annis, Gust M; Pearsall, Douglas R; Kahl, Katherine J; Washburn, Erika L; May, Christopher A; Franks Taylor, Rachael; Cole, James B; Ewert, David N; Game, Edward T; Doran, Patrick J
2017-01-01
Conservation scientists increasingly recognize that incorporating human values into conservation planning increases the chances for success by garnering broader project acceptance. However, methods for defining quantitative targets for the spatial representation of human well-being priorities are less developed. In this study we employ an approach for identifying regionally important human values and establishing specific spatial targets for their representation based on stakeholder outreach. Our primary objective was to develop a spatially-explicit conservation plan that identifies the most efficient locations for conservation actions to meet ecological goals while sustaining or enhancing human well-being values within the coastal and nearshore areas of the western Lake Erie basin (WLEB). We conducted an optimization analysis using 26 features representing ecological and human well-being priorities (13 of each), and included seven cost layers. The influence that including human well-being had on project results was tested by running five scenarios and setting targets for human well-being at different levels in each scenario. The most important areas for conservation to achieve multiple goals are clustered along the coast, reflecting a concentration of existing or potentially restorable coastal wetlands, coastal landbird stopover habitat and terrestrial biodiversity, as well as important recreational activities. Inland important areas tended to cluster around trails and high quality inland landbird stopover habitat. Most concentrated areas of importance also are centered on lands that are already conserved, reflecting the lower costs and higher benefits of enlarging these conserved areas rather than conserving isolated, dispersed areas. Including human well-being features in the analysis only influenced the solution at the highest target levels.
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Designing coastal conservation to deliver ecosystem and human well-being benefits
Pearsall, Douglas R.; Kahl, Katherine J.; Washburn, Erika L.; May, Christopher A.; Franks Taylor, Rachael; Cole, James B.; Ewert, David N.; Game, Edward T.; Doran, Patrick J.
2017-01-01
Conservation scientists increasingly recognize that incorporating human values into conservation planning increases the chances for success by garnering broader project acceptance. However, methods for defining quantitative targets for the spatial representation of human well-being priorities are less developed. In this study we employ an approach for identifying regionally important human values and establishing specific spatial targets for their representation based on stakeholder outreach. Our primary objective was to develop a spatially-explicit conservation plan that identifies the most efficient locations for conservation actions to meet ecological goals while sustaining or enhancing human well-being values within the coastal and nearshore areas of the western Lake Erie basin (WLEB). We conducted an optimization analysis using 26 features representing ecological and human well-being priorities (13 of each), and included seven cost layers. The influence that including human well-being had on project results was tested by running five scenarios and setting targets for human well-being at different levels in each scenario. The most important areas for conservation to achieve multiple goals are clustered along the coast, reflecting a concentration of existing or potentially restorable coastal wetlands, coastal landbird stopover habitat and terrestrial biodiversity, as well as important recreational activities. Inland important areas tended to cluster around trails and high quality inland landbird stopover habitat. Most concentrated areas of importance also are centered on lands that are already conserved, reflecting the lower costs and higher benefits of enlarging these conserved areas rather than conserving isolated, dispersed areas. Including human well-being features in the analysis only influenced the solution at the highest target levels. PMID:28241018
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Learning about the internal structure of categories through classification and feature inference.
Jee, Benjamin D; Wiley, Jennifer
2014-01-01
Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.
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Nanoscale Morphology to Macroscopic Performance in Ultra High Molecular Weight Polyethylene Fibers
NASA Astrophysics Data System (ADS)
McDaniel, Preston B.
Ultra high molecular weight polyethylene (UHMWPE) fibers are increasingly used in high -performance applications where strength, stiffness, and the ability to dissipate energy are of critical importance. Despite their use in a variety of applications, the influence of morphological features at the meso/nanoscale on the macroscopic performance of the fibers has not been well understood. There is particular interest in gaining a better understanding of the nanoscale structure-property relationships in UHMWPE fibers used in ballistics applications. In order to accurately model and predict failure in the fiber, a more complete understanding of the complex load pathways that dictate the ways in which load is transferred through the fiber, across interfaces and length scales is required. The goal of the work discussed herein is to identify key meso/nanostructural features evolved in high performance fibers and determine how these features influence the performance of the fiber through a variety of different loading mechanisms. The important structural features in high-performance UHMWPE fibers are first identified through examination of the meso/nanostructure of a series of fibers with different processing conditions. This is achieved primarily through the use of wide-angle x-ray diffraction (WAXD) and atomic force microscopy (AFM). Analysis of AFM images and WAXD data allows identification and quantifications of important structural features at these length scales. Key meso/nanostructural features are then examined with respect to their influence on the transverse compression behavior of single fibers. Through post-mortem AFM analysis of samples at incremental compressive strains, the evolution of damage is examined and compared with macroscopic fiber mechanical response. It was found that collapse of mesoscale voids, followed by nanoscale fibrillation and reorganization of a fibrillar network has a significant influence on the mechanical response of the fiber. Through this work, the importance of nanoscale fibril adhesive interactions is highlighted. However, very little information exists in the literature as to the nature and magnitude of these interactions. Examination of nanoscale fibrillar adhesive interactions is experimentally difficult, and necessitated the development of an AFM based nanoscale splitting technique to quantify the interactions between fibrils. Through analysis of split geometry and careful partitioning of energies, the adhesive energy between fibrils in UHMWPE fibers are determined. The calculated average adhesive energies are significantly larger than the estimated energy due to van der Waals interactions, suggesting that there are physical connections (e.g., tie chains, tie fibrils, and lamellar crystalline bridges) that influence the interactions between fibrils. The interactions identified through this work are believed to be responsible for the creation of load pathways across fibril interfaces where load may be translated through the fiber in tension, compression, and shear. Finally, the nature of the mesoscale fibrillar network is explored through the development of a variable angle, single fiber peel test. This peel test enables the quantification of Mode I and Mode II peel energies. The modes of deformation observed in the peel test are representative of the mechanisms experienced during tensile and transverse compression loading. The quantification of peel energies in both Mode I and Mode II failure highlight the importance of the fibrillar network as a key mechanism for the translation of load through the fiber. In both modes of failure, the fibril network acts as a framework for the orientation and subsequent failure of nanoscale fibrils.
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Global screening for Critical Habitat in the terrestrial realm
Blyth, Simon; Bennun, Leon; Butchart, Stuart H. M.; Hoffmann, Michael; Burgess, Neil D.; Cuttelod, Annabelle; Jones, Matt I.; Kapos, Val; Pilgrim, John; Tolley, Melissa J.; Underwood, Emma C.; Weatherdon, Lauren V.
2018-01-01
Critical Habitat has become an increasingly important concept used by the finance sector and businesses to identify areas of high biodiversity value. The International Finance Corporation (IFC) defines Critical Habitat in their highly influential Performance Standard 6 (PS6), requiring projects in Critical Habitat to achieve a net gain of biodiversity. Here we present a global screening layer of Critical Habitat in the terrestrial realm, derived from global spatial datasets covering the distributions of 12 biodiversity features aligned with guidance provided by the IFC. Each biodiversity feature is categorised as ‘likely’ or ‘potential’ Critical Habitat based on: 1. Alignment between the biodiversity feature and the IFC Critical Habitat definition; and 2. Suitability of the spatial resolution for indicating a feature’s presence on the ground. Following the initial screening process, Critical Habitat must then be assessed in-situ by a qualified assessor. This analysis indicates that a total of 10% and 5% of the global terrestrial environment can be considered as likely and potential Critical Habitat, respectively, while the remaining 85% did not overlap with any of the biodiversity features assessed and was classified as ‘unknown’. Likely Critical Habitat was determined principally by the occurrence of Key Biodiversity Areas and Protected Areas. Potential Critical Habitat was predominantly characterised by data representing highly threatened and unique ecosystems such as ever-wet tropical forests and tropical dry forests. The areas we identified as likely or potential Critical Habitat are based on the best available global-scale data for the terrestrial realm that is aligned with IFC’s Critical Habitat definition. Our results can help businesses screen potential development sites at the early project stage based on a range of biodiversity features. However, the study also demonstrates several important data gaps and highlights the need to incorporate new and improved global spatial datasets as they become available. PMID:29565977
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Chan, Thomas Y. K.
2016-01-01
In this review, the main objective was to describe the characteristic features of fatal ciguatera fish poisoning and identify contributory factors, with a view to promote prevention and public education. Ciguatera-related deaths, although rare, have been reported from the Pacific, Caribbean, and Indian Ocean regions. The clinical features were generally dominated by convulsions and coma, with various focal neurological signs. Several contributory factors could be identified, including consumption of ciguatoxin (CTX)-rich fish parts (viscera and head) in larger amounts, the most ciguatoxic fish species (e.g., Gymnothorax flavimarginatus) and reef fish collected after storms and individuals' susceptibility. Mass ciguatera fish poisoning with mortalities also occurred when G. flavimarginatus and other ciguatoxic fish species were shared in gatherings and parties. The characteristic features of fatal ciguatera fish poisoning must be recognized early. The public should be repeatedly reminded to avoid eating the most ciguatoxic fish species and the CTX-rich parts of reef fish. To prevent mass poisoning in gatherings and parties, the most ciguatoxic fish species and potentially toxic fish species must be avoided. Particularly after hits by disastrous storms, it is important to monitor the toxicity of reef fish and the incidence rates of ciguatera. PMID:26787145
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Chan, Thomas Y K
2016-04-01
In this review, the main objective was to describe the characteristic features of fatal ciguatera fish poisoning and identify contributory factors, with a view to promote prevention and public education. Ciguatera-related deaths, although rare, have been reported from the Pacific, Caribbean, and Indian Ocean regions. The clinical features were generally dominated by convulsions and coma, with various focal neurological signs. Several contributory factors could be identified, including consumption of ciguatoxin (CTX)-rich fish parts (viscera and head) in larger amounts, the most ciguatoxic fish species (e.g.,Gymnothorax flavimarginatus) and reef fish collected after storms and individuals' susceptibility. Mass ciguatera fish poisoning with mortalities also occurred when G. flavimarginatus and other ciguatoxic fish species were shared in gatherings and parties. The characteristic features of fatal ciguatera fish poisoning must be recognized early. The public should be repeatedly reminded to avoid eating the most ciguatoxic fish species and the CTX-rich parts of reef fish. To prevent mass poisoning in gatherings and parties, the most ciguatoxic fish species and potentially toxic fish species must be avoided. Particularly after hits by disastrous storms, it is important to monitor the toxicity of reef fish and the incidence rates of ciguatera. © The American Society of Tropical Medicine and Hygiene.
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Rothman, Linda; Buliung, Ron; Macarthur, Colin; To, Teresa; Howard, Andrew
2014-02-01
The child active transportation literature has focused on walking, with little attention to risk associated with increased traffic exposure. This paper reviews the literature related to built environment correlates of walking and pedestrian injury in children together, to broaden the current conceptualization of walkability to include injury prevention. Two independent searches were conducted focused on walking in children and child pedestrian injury within nine electronic databases until March, 2012. Studies were included which: 1) were quantitative 2) set in motorized countries 3) were either urban or suburban 4) investigated specific built environment risk factors 5) had outcomes of either walking in children and/or child pedestrian roadway collisions (ages 0-12). Built environment features were categorized according to those related to density, land use diversity or roadway design. Results were cross-tabulated to identify how built environment features associate with walking and injury. Fifty walking and 35 child pedestrian injury studies were identified. Only traffic calming and presence of playgrounds/recreation areas were consistently associated with more walking and less pedestrian injury. Several built environment features were associated with more walking, but with increased injury. Many features had inconsistent results or had not been investigated for either outcome. The findings emphasise the importance of incorporating safety into the conversation about creating more walkable cities.
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Kumar, B V S Suneel; Kotla, Rohith; Buddiga, Revanth; Roy, Jyoti; Singh, Sardar Shamshair; Gundla, Rambabu; Ravikumar, Muttineni; Sarma, Jagarlapudi A R P
2011-01-01
Structure and ligand based pharmacophore modeling and docking studies carried out using diversified set of c-Jun N-terminal kinase-3 (JNK3) inhibitors are presented in this paper. Ligand based pharmacophore model (LBPM) was developed for 106 inhibitors of JNK3 using a training set of 21 compounds to reveal structural and chemical features necessary for these molecules to inhibit JNK3. Hypo1 consisted of two hydrogen bond acceptors (HBA), one hydrogen bond donor (HBD), and a hydrophobic (HY) feature with a correlation coefficient (r²) of 0.950. This pharmacophore model was validated using test set containing 85 inhibitors and had a good r² of 0.846. All the molecules were docked using Glide software and interestingly, all the docked conformations showed hydrogen bond interactions with important hinge region amino acids (Gln155 and Met149)and these interactions were compared with Hypo1 features. The results of ligand based pharmacophore model (LBPM)and docking studies are validated each other. The structure based pharmacophore model (SBPM) studies have identified additional features, two hydrogen bond donors and one hydrogen bond acceptor. The combination of these methodologies is useful in designing ideal pharmacophore which provides a powerful tool for the discovery of novel and selective JNK3 inhibitors.
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Machine Learning for Medical Imaging
Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L.
2017-01-01
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017 PMID:28212054
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Machine Learning for Medical Imaging.
Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L
2017-01-01
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.
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Maansson, Maria; Vynne, Nikolaj G.; Klitgaard, Andreas; Nybo, Jane L.; Melchiorsen, Jette; Nguyen, Don D.; Sanchez, Laura M.; Ziemert, Nadine; Dorrestein, Pieter C.
2016-01-01
ABSTRACT Microorganisms are a rich source of bioactives; however, chemical identification is a major bottleneck. Strategies that can prioritize the most prolific microbial strains and novel compounds are of great interest. Here, we present an integrated approach to evaluate the biosynthetic richness in bacteria and mine the associated chemical diversity. Thirteen strains closely related to Pseudoalteromonas luteoviolacea isolated from all over the Earth were analyzed using an untargeted metabolomics strategy, and metabolomic profiles were correlated with whole-genome sequences of the strains. We found considerable diversity: only 2% of the chemical features and 7% of the biosynthetic genes were common to all strains, while 30% of all features and 24% of the genes were unique to single strains. The list of chemical features was reduced to 50 discriminating features using a genetic algorithm and support vector machines. Features were dereplicated by tandem mass spectrometry (MS/MS) networking to identify molecular families of the same biosynthetic origin, and the associated pathways were probed using comparative genomics. Most of the discriminating features were related to antibacterial compounds, including the thiomarinols that were reported from P. luteoviolacea here for the first time. By comparative genomics, we identified the biosynthetic cluster responsible for the production of the antibiotic indolmycin, which could not be predicted with standard methods. In conclusion, we present an efficient, integrative strategy for elucidating the chemical richness of a given set of bacteria and link the chemistry to biosynthetic genes. IMPORTANCE We here combine chemical analysis and genomics to probe for new bioactive secondary metabolites based on their pattern of distribution within bacterial species. We demonstrate the usefulness of this combined approach in a group of marine Gram-negative bacteria closely related to Pseudoalteromonas luteoviolacea, which is a species known to produce a broad spectrum of chemicals. The approach allowed us to identify new antibiotics and their associated biosynthetic pathways. Combining chemical analysis and genetics is an efficient “mining” workflow for identifying diverse pharmaceutical candidates in a broad range of microorganisms and therefore of great use in bioprospecting. PMID:27822535
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Kay, Robert T.; Mills, Patrick C.; Dunning, Charles P.; Yeskis, Douglas J.; Ursic, James R.; Vendl, Mark
2004-01-01
The effectiveness of 28 methods used to characterize the fractured Galena-Platteville aquifer at eight sites in northern Illinois and Wisconsin is evaluated. Analysis of government databases, previous investigations, topographic maps, aerial photographs, and outcrops was essential to understanding the hydrogeology in the area to be investigated. The effectiveness of surface-geophysical methods depended on site geology. Lithologic logging provided essential information for site characterization. Cores were used for stratigraphy and geotechnical analysis. Natural-gamma logging helped identify the effect of lithology on the location of secondary- permeability features. Caliper logging identified large secondary-permeability features. Neutron logs identified trends in matrix porosity. Acoustic-televiewer logs identified numerous secondary-permeability features and their orientation. Borehole-camera logs also identified a number of secondary-permeability features. Borehole ground-penetrating radar identified lithologic and secondary-permeability features. However, the accuracy and completeness of this method is uncertain. Single-point-resistance, density, and normal resistivity logs were of limited use. Water-level and water-quality data identified flow directions and indicated the horizontal and vertical distribution of aquifer permeability and the depth of the permeable features. Temperature, spontaneous potential, and fluid-resistivity logging identified few secondary-permeability features at some sites and several features at others. Flowmeter logging was the most effective geophysical method for characterizing secondary-permeability features. Aquifer tests provided insight into the permeability distribution, identified hydraulically interconnected features, the presence of heterogeneity and anisotropy, and determined effective porosity. Aquifer heterogeneity prevented calculation of accurate hydraulic properties from some tests. Different methods, such as flowmeter logging and slug testing, occasionally produced different interpretations. Aquifer characterization improved with an increase in the number of data points, the period of data collection, and the number of methods used.
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Alfano, Massimo; Nebuloni, Manuela; Allevi, Raffaele; Zerbi, Pietro; Longhi, Erika; Lucianò, Roberta; Locatelli, Irene; Pecoraro, Angela; Indrieri, Marco; Speziali, Chantal; Doglioni, Claudio; Milani, Paolo; Montorsi, Francesco; Salonia, Andrea
2016-10-25
In the fields of biomaterials and tissue engineering simulating the native microenvironment is of utmost importance. As a major component of the microenvironment, the extracellular matrix (ECM) contributes to tissue homeostasis, whereas modifications of native features are associated with pathological conditions. Furthermore, three-dimensional (3D) geometry is an important feature of synthetic scaffolds favoring cell stemness, maintenance and differentiation. We analyzed the 3D structure, geometrical measurements and anisotropy of the ECM isolated from (i) human bladder mucosa (basal lamina and lamina propria) and muscularis propria; and, (ii) bladder carcinoma (BC). Next, binding and invasion of bladder metastatic cell line was observed on synthetic scaffold recapitulating anisotropy of tumoral ECM, but not on scaffold with disorganized texture typical of non-neoplastic lamina propria. This study provided information regarding the ultrastructure and geometry of healthy human bladder and BC ECMs. Likewise, using synthetic scaffolds we identified linearization of the texture as a mandatory feature for BC cell invasion. Integrating microstructure and geometry with biochemical and mechanical factors could support the development of an innovative synthetic bladder substitute or a tumoral scaffold predictive of chemotherapy outcomes.
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Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.
Wu, Yonghui; Jiang, Min; Lei, Jianbo; Xu, Hua
2015-01-01
Rapid growth in electronic health records (EHRs) use has led to an unprecedented expansion of available clinical data in electronic formats. However, much of the important healthcare information is locked in the narrative documents. Therefore Natural Language Processing (NLP) technologies, e.g., Named Entity Recognition that identifies boundaries and types of entities, has been extensively studied to unlock important clinical information in free text. In this study, we investigated a novel deep learning method to recognize clinical entities in Chinese clinical documents using the minimal feature engineering approach. We developed a deep neural network (DNN) to generate word embeddings from a large unlabeled corpus through unsupervised learning and another DNN for the NER task. The experiment results showed that the DNN with word embeddings trained from the large unlabeled corpus outperformed the state-of-the-art CRF's model in the minimal feature engineering setting, achieving the highest F1-score of 0.9280. Further analysis showed that word embeddings derived through unsupervised learning from large unlabeled corpus remarkably improved the DNN with randomized embedding, denoting the usefulness of unsupervised feature learning.
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NASA Astrophysics Data System (ADS)
Fiori, C.
2016-02-01
The offshore Mediterranean oceanographic systems are complex and partially unknown. Some features such as seamounts are ususally reported as stepping-stones for feeding of many species among which top-predators despite specific studies on the Mediterranean realm are missing. In the framework of the PROMETEOS project, 64 seamounts were identified in Tyrrhenian Sea (NW Mediterranean) and specific morphological characteristics have been calculated for each seamount aiming at the identification of those features triggering the greatest attraction effect. The distributions of striped dolphins and sea turtles have been considered in this analysis as proxies of the seamount's importance for pelagic fauna. A total of 467 sightings of striped dolphins (Stenella coeruleoalba) have been considered together with 127 sightings of sea turtles (Caretta caretta). Random Forest regression technique have been applied to assess the importance of seamounts and the relevance of different seamounts' features on this species.
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A novel feature extraction approach for microarray data based on multi-algorithm fusion
Jiang, Zhu; Xu, Rong
2015-01-01
Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277
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A novel feature extraction approach for microarray data based on multi-algorithm fusion.
Jiang, Zhu; Xu, Rong
2015-01-01
Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.
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Objectively classifying Southern Hemisphere extratropical cyclones
NASA Astrophysics Data System (ADS)
Catto, Jennifer
2016-04-01
There has been a long tradition in attempting to separate extratropical cyclones into different classes depending on their cloud signatures, airflows, synoptic precursors, or upper-level flow features. Depending on these features, the cyclones may have different impacts, for example in their precipitation intensity. It is important, therefore, to understand how the distribution of different cyclone classes may change in the future. Many of the previous classifications have been performed manually. In order to be able to evaluate climate models and understand how extratropical cyclones might change in the future, we need to be able to use an automated method to classify cyclones. Extratropical cyclones have been identified in the Southern Hemisphere from the ERA-Interim reanalysis dataset with a commonly used identification and tracking algorithm that employs 850 hPa relative vorticity. A clustering method applied to large-scale fields from ERA-Interim at the time of cyclone genesis (when the cyclone is first detected), has been used to objectively classify identified cyclones. The results are compared to the manual classification of Sinclair and Revell (2000) and the four objectively identified classes shown in this presentation are found to match well. The relative importance of diabatic heating in the clusters is investigated, as well as the differing precipitation characteristics. The success of the objective classification shows its utility in climate model evaluation and climate change studies.
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Betel, Doron; Koppal, Anjali; Agius, Phaedra; Sander, Chris; Leslie, Christina
2010-01-01
mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
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Investigators from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) who comprehensively analyzed 95 human colorectal tumor samples, have determined how gene alterations identified in previous analyses of the same samples are expressed at the protein level. The integration of proteomic and genomic data, or proteogenomics, provides a more comprehensive view of the biological features that drive cancer than genomic analysis alone and may help identify the most important targets for cancer detection and intervention.
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Identifying significant environmental features using feature recognition.
DOT National Transportation Integrated Search
2015-10-01
The Department of Environmental Analysis at the Kentucky Transportation Cabinet has expressed an interest in feature-recognition capability because it may help analysts identify environmentally sensitive features in the landscape, : including those r...
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Rowan, Lawrence C.; Goetz, Alexander F.H.; Abbott, Elsa
1987-01-01
During the November 12–14, 1981, mission of the space shuttle Columbia, the Shuttle Multispectral Infrared Radiometer (SMIRR) recorded radiances in ten channels along a 100 m wide groundtrack across the western Saudi Arabian shield. The ten channels are located in the 0.5 to 2.4 μm region, with five positioned between 2.0 and 2.40 μm for measuring absorption features that are diagnostic of OH‐bearing and CO3‐bearing">CO3‐bearing minerals. This exceptionally well exposed area consists of late Proterozoic metamorphic, intermediate to silicic intrusive, and interlayered clastic sedimentary and intermediate silicic volcanic rocks that have not been studied previously using SMIRR data. Plots or traces of unnormalized SMIRR channel ratios were examined before field studies to locate areas with high spectral contrast, especially in the 2.0 μm to 2.40 μm channels. Reflectance spectra were measured in the laboratory for rock and soil samples collected in these areas, and the mineralogic causes of the main absorption features were determined using X‐ray diffraction. Laboratory SMIRR spectra were produced by convolving the ten SMIRR filters with the laboratory spectra. Then, normalized SMIRR reflectance spectra were generated along the groundtrack using normalization coefficients calculated for a field sample representing a uniform, low‐spectral contrast area. Field evaluation shows that unnormalized SMIRR ratio traces are useful, even without specific mineralogic information, for distinguishing rocks that are characterized by Al‐OH, Mg‐OH, and/or CO3">CO3">CO3, Fe3+">Fe3+, and Fe2+ absorption features. Analysis of field samples permits suites of minerals causing absorption features to be identified. However, specific mineral identification cannot be achieved consistently using the SMIRR ratio traces or normalized SMIRR spectra, because the Al‐OH and Mg‐OH absorption features can be caused by more than one of the minerals commonly present. The normalized SMIRR spectra are especially useful for identifying subtle Al‐OH and Mg‐OH absorption features that are difficult to identify in the unnormalized ratio traces and for comparing the relative intensities of absorption features. Al‐OH absorption is related to muscovite, smectite, illite, and kaolinite, whereas Mg‐OH absorption is caused by chlorite, amphibole, and biotite. The principal sources of error in using SMIRR spectral measurements for identifying mineral groups along the orbit 27 groundtrack are inaccuracies in field location and lithologic heterogeneity that is not represented adequately by field samples. Calibration errors may account for systematic albedo and absorption intensity differences between calculated laboratory SMIRR spectra and normalized SMIRR spectra. SMIRR instrument noise and atmospheric factors appear to be less important sources of error. However, as higher spectral and spatial resolution systems are developed for mineral identification, radiometric precision and atmospheric factors will become more important.
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Evaluating key landscape features of a climate- induced forest decline (Project WC-EM-07-01)
Paul Hennon; Dustin Wittwer
2013-01-01
Yellow-cedar is a culturally, economically, and ecologically important tree in coastal Alaska that has been experiencing a widespread mortality known as yellow-cedar decline for about 100 years. Mapping during annual aerial detection surveys has identified nearly the entire geographical distribution of the problem, which totals over 500,000 acres in Alaska (Lamb and...
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A Snapshot of the Role of the Textbook in English Secondary Mathematics Classrooms
ERIC Educational Resources Information Center
O'Keeffe, Lisa; White, Bruce
2017-01-01
The role and function of the mathematics textbook has been widely discussed since its inclusion in the Trends in International Mathematics and Science study (TIMSS) in the late nineties. It is a common feature in many classrooms worldwide and has been identified as an important vehicle for the promotion of curricula. However, there has also been…
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ERIC Educational Resources Information Center
Tipton, Charles M.
2013-01-01
Society members whose research publication during the past 125 yr had an important impact on the discipline of physiology were featured at the American Physiological Society (APS)'s 125th Anniversary symposium. The daunting and challenging task of identifying and selecting significant publications was assumed by the Steering Committee of the…
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ERIC Educational Resources Information Center
Stocco, Andrea
2018-01-01
Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of…
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ERIC Educational Resources Information Center
Almutairi, Mashal
2013-01-01
The main purpose of this research was to survey the literature about the U.S. education system and synthesize the important conclusions that could be identified as the main features of the education system in general as they relate to student achievement. The criteria were set and the meta-analysis procedures were carefully followed. This process…
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ERIC Educational Resources Information Center
Schneider, Christoph; Bodensohn, Rainer
2017-01-01
Competence in assessment has been identified as a key feature in teachers' professional success. However, assessment competence is a complex field, comprising capacity in both summative and formative assessment. Hence, a detailed view on how student teachers perceive assessment is the focus of this study. Based on an official catalogue of…
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ERIC Educational Resources Information Center
Norte, Edmundo
1999-01-01
Explores key features of processes school leaders employ to create positive interethnic school communities, identifying five elements for effective intervention and applying an analytical model to each to provide a schema for framing elements of central importance. Addresses how school leaders use their power and authority and how they determine…
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Ding, Xuemei; Bucholc, Magda; Wang, Haiying; Glass, David H; Wang, Hui; Clarke, Dave H; Bjourson, Anthony John; Dowey, Le Roy C; O'Kane, Maurice; Prasad, Girijesh; Maguire, Liam; Wong-Lin, KongFatt
2018-06-27
There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.
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Hereditary spastic paraplegia.
Blackstone, Craig
2018-01-01
The hereditary spastic paraplegias (HSPs) are a heterogeneous group of neurologic disorders with the common feature of prominent lower-extremity spasticity, resulting from a length-dependent axonopathy of corticospinal upper motor neurons. The HSPs exist not only in "pure" forms but also in "complex" forms that are associated with additional neurologic and extraneurologic features. The HSPs are among the most genetically diverse neurologic disorders, with well over 70 distinct genetic loci, for which about 60 mutated genes have already been identified. Numerous studies elucidating the molecular pathogenesis underlying HSPs have highlighted the importance of basic cellular functions - especially membrane trafficking, mitochondrial function, organelle shaping and biogenesis, axon transport, and lipid/cholesterol metabolism - in axon development and maintenance. An encouragingly small number of converging cellular pathogenic themes have been identified for the most common HSPs, and some of these pathways present compelling targets for future therapies. Copyright © 2018 Elsevier B.V. All rights reserved.
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New approaches to investigating social gestures in autism spectrum disorder
2012-01-01
The combination of economic games and human neuroimaging presents the possibility of using economic probes to identify biomarkers for quantitative features of healthy and diseased cognition. These probes span a range of important cognitive functions, but one new use is in the domain of reciprocating social exchange with other humans - a capacity perturbed in a number of psychopathologies. We summarize the use of a reciprocating exchange game to elicit neural and behavioral signatures for subjects diagnosed with autism spectrum disorder (ASD). Furthermore, we outline early efforts to capture features of social exchange in computational models and use these to identify quantitative behavioral differences between subjects with ASD and matched controls. Lastly, we summarize a number of subsequent studies inspired by the modeling results, which suggest new neural and behavioral signatures that could be used to characterize subtle deficits in information processing during interactions with other humans. PMID:22958572
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Yuan, Rui; Singh, Sudhanshu S.; Chawla, Nikhilesh
2016-08-15
We present a robust method for automating removal of “segregation artifacts” in segmented tomographic images of three-dimensional heterogeneous microstructures. The objective of this method is to accurately identify and separate discrete features in composite materials where limitations in imaging resolution lead to spurious connections near close contacts. The method utilizes betweenness centrality, a measure of the importance of a node in the connectivity of a graph network, to identify voxels that create artificial bridges between otherwise distinct geometric features. To facilitate automation of the algorithm, we develop a relative centrality metric to allow for the selection of a threshold criterionmore » that is not sensitive to inclusion size or shape. As a demonstration of the effectiveness of the algorithm, we report on the segmentation of a 3D reconstruction of a SiC particle reinforced aluminum alloy, imaged by X-ray synchrotron tomography.« less
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The geology of Pine and Crater Buttes: Two basaltic constructs on the far eastern Snake River Plain
NASA Technical Reports Server (NTRS)
Mazierski, Paul F.; King, John S.
1987-01-01
The emplacement history and petrochemical evolution of the volcanics associated with Pine Butte, Crater Butte, and other nearby vents are developed and described. Four major vents were identified in the study area and their associated eruptive products were mapped. All of the vents show a marked physical elongation or linear orientation coincident with the observed rift set. Planetary exploration has revealed the importance of volcanic processes in the genesis and modification of extraterrestrial surfaces. Interpretation of surface features has identified plains-type basaltic volcanism in various mare regions of the Moon and the volcanic provinces of Mars. Identification of these areas with features that appear analogous to those observed in the Pine Butte area suggests similar styles of eruption and mode of emplacement. Such terrestrial analogies serve as a method to interpret the evolution of volcanic planetary surfaces on the inner planets.
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Wang, Dong; Wang, Haifeng; Hu, P
2015-01-21
Using density functional theory calculations with HSE 06 functional, we obtained the structures of spin-polarized radicals on rutile TiO2(110), which is crucial to understand the photooxidation at the atomic level, and further calculate the thermodynamic stabilities of these radicals. By analyzing the results, we identify the structural features for hole trapping in the system, and reveal the mutual effects among the geometric structures, the energy levels of trapped hole states and their hole trapping capacities. Furthermore, the results from HSE 06 functional are compared to those from DFT + U and the stability trend of radicals against the number of slabs is tested. The effect of trapped holes on two important steps of the oxygen evolution reaction, i.e. water dissociation and the oxygen removal, is investigated and discussed.
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Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions
Venkatraman, S.; Doktycz, M. J.; Qi, H.; ...
2006-01-01
The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction.more » Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.« less
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Qualitative evaluation of pretreatment patient concerns in orthodontics.
Twigge, Eugene; Roberts, Rachel M; Jamieson, Lisa; Dreyer, Craig W; Sampson, Wayne J
2016-07-01
A discrepancy exists between objective and subjective measures of orthodontic treatment need, highlighting the importance of patients' perceptions. Limited qualitative information is available regarding patients' perceptions and orthodontic concerns. For the first time, patient facial images and qualitative methodology were used to assess patients' orthodontic concerns, which are incorporated into and are important in treatment planning and consent. An interview-based, cross-sectional study of adolescent patients eligible to receive orthodontic treatment in a public dental hospital was conducted with 105 adolescents (42 boys, 63 girls) aged between 12 and 17 years. Each patient's face was video recorded, and 3 images were selected from each recording to assess the patient's orthodontic concerns. The initial chief concerns were compared with concerns articulated after the patients assessed their facial images. In addition, patient concerns were compared with occlusal features visible on smiling using the Dental Aesthetic Index and patient study casts. For 37% of the adolescent patients, smiling images helped to identify additional concerns. For 87%, their smiling images helped them to describe their concerns in more detail. In addition, a few patients did not articulate any concern about features measurable on the Dental Aesthetic Index that were visible on smiling. Showing adolescent patients images of their face and smile helped them to identify and better describe their concerns. Adolescents are not always overly concerned about visible and quantifiable malocclusion features. This might influence orthodontic treatment planning and consent. Copyright © 2016 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.
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Change of the image of the city in process of using traffic infrastructure
NASA Astrophysics Data System (ADS)
Alihodžić, Rifat; Vasiljević Tomić, Dragana; Iablonskii, Leonid
2017-10-01
Unique urban image cannot be experienced without moving within its structure. This paper deals with phenomenology considering changes of images of the city and influential factors closely related to it. Infrastructure gives basic structural scheme of every city, so its planning requires a high level proficiency. Some changes in these images can be observed during longer period of time. Sometimes it includes rapid changes of temporal layers, generated by building new urban elements on the exact same place where the old ones existed; while lighter change during the time passing is a regular occurrence. Creating completely new urban frames, caused by expanding the city, represents its dynamical variant. Topography is a significant factor, giving distinctive feature to the urbanity. This paper considers factors identified as generators of the change of the urban image, based on research so far. The structural elements are considered with the utmost attention. The importance of the city landmark, monumental complexes not possessing these features but having the importance in image of the city stability (as well as the inhabitants’ memory) are crucial elements of identifying its picture. Another significant factor is related to individual personal experience. However, there are also certain factors of significance features, but not considered within this paper. One such factor is change in coloring, being the special topic itself. The purpose of this work is to indicate that urban planning requires special attention in order to keep continuous nature of the urban image for the city to preserve its visual identity.
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Online coupled camera pose estimation and dense reconstruction from video
Medioni, Gerard; Kang, Zhuoliang
2016-11-01
A product may receive each image in a stream of video image of a scene, and before processing the next image, generate information indicative of the position and orientation of an image capture device that captured the image at the time of capturing the image. The product may do so by identifying distinguishable image feature points in the image; determining a coordinate for each identified image feature point; and for each identified image feature point, attempting to identify one or more distinguishable model feature points in a three dimensional (3D) model of at least a portion of the scene that appears likely to correspond to the identified image feature point. Thereafter, the product may find each of the following that, in combination, produce a consistent projection transformation of the 3D model onto the image: a subset of the identified image feature points for which one or more corresponding model feature points were identified; and, for each image feature point that has multiple likely corresponding model feature points, one of the corresponding model feature points. The product may update a 3D model of at least a portion of the scene following the receipt of each video image and before processing the next video image base on the generated information indicative of the position and orientation of the image capture device at the time of capturing the received image. The product may display the updated 3D model after each update to the model.
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Kulshrestha, Saurabh; Hallan, Vipin; Sharma, Anshul; Seth, Chandrika Attri; Chauhan, Anjali; Zaidi, Aijaz Asghar
2013-09-01
Coat protein (CP) and RNA3 from Prunus necrotic ringspot virus (PNRSV-rose), the most prevalent virus infecting rose in India, were characterized and regions in the coat protein important for self-interaction, during dimer formation were identified. The sequence analysis of CP and partial RNA 3 revealed that the rose isolate of PNRSV in India belongs to PV-32 group of PNRSV isolates. Apart from the already established group specific features of PV-32 group member's additional group-specific and host specific features were also identified. Presence of methionine at position 90 in the amino acid sequence alignment of PNRSV CP gene (belonging to PV-32 group) was identified as the specific conserved feature for the rose isolates of PNRSV. As protein-protein interaction plays a vital role in the infection process, an attempt was made to identify the portions of PNRSV CP responsible for self-interaction using yeast two-hybrid system. It was found (after analysis of the deletion clones) that the C-terminal region of PNRSV CP (amino acids 153-226) plays a vital role in this interaction during dimer formation. N-terminal of PNRSV CP is previously known to be involved in CP-RNA interactions, but our results also suggested that N-terminal of PNRSV CP represented by amino acids 1-77 also interacts with C-terminal (amino acids 153-226) in yeast two-hybrid system, suggesting its probable involvement in the CP-CP interaction.
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Ridge-like lava tube systems in southeast Tharsis, Mars
NASA Astrophysics Data System (ADS)
Zhao, Jiannan; Huang, Jun; Kraft, Michael D.; Xiao, Long; Jiang, Yun
2017-10-01
Lava tubes are widely distributed in volcanic fields on a planetary surface and they are important means of lava transportation. We have identified 38 sinuous ridges with a lava-tube origin in southeast Tharsis. The lengths vary between 14 and 740 km, and most of them occur in areas with slopes < 0.3°. We analyzed their geomorphology in detail with CTX (Context Camera) and HiRISE (High Resolution Imaging Science Experiment) images and DTM (digital terrain model) derived from them. We identified three cross-sectional shapes of these sinuous ridges: round-crested, double-ridged, and flat-crested and described features associated with the lava tubes, including branches, axial cracks, collapsed pits, breakout lobes, and tube-fed lava deltas. Age determination results showed that most of the lava tubes formed in Late Hesperian and were active until the Hesperian-Amazonian boundary. We proposed that these lava tubes formed at relatively low local flow rate, low lava viscosity, and sustained magma supply during a long period. Besides, lava flow inflation is also important in the formation of the ridge-like lava tubes and some associated features. These lava tubes provide efficient lateral pathways for magma transportation over the relatively low topographic slopes in southeast Tharsis, and they are important for the formation of long lava flows in this region. The findings of this study provide an alternative formation mechanism for sinuous ridges on the martian surface.
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Riemer, Valentin; Frommel, Julian; Layher, Georg; Neumann, Heiko; Schrader, Claudia
2017-01-01
The importance of emotions experienced by learners during their interaction with multimedia learning systems, such as serious games, underscores the need to identify sources of information that allow the recognition of learners’ emotional experience without interrupting the learning process. Bodily expression is gaining in attention as one of these sources of information. However, to date, the question of how bodily expression can convey different emotions has largely been addressed in research relying on acted emotion displays. Following a more contextualized approach, the present study aims to identify features of bodily expression (i.e., posture and activity of the upper body and the head) that relate to genuine emotional experience during interaction with a serious game. In a multimethod approach, 70 undergraduates played a serious game relating to financial education while their bodily expression was captured using an off-the-shelf depth-image sensor (Microsoft Kinect). In addition, self-reports of experienced enjoyment, boredom, and frustration were collected repeatedly during gameplay, to address the dynamic changes in emotions occurring in educational tasks. Results showed that, firstly, the intensities of all emotions indeed changed significantly over the course of the game. Secondly, by using generalized estimating equations, distinct features of bodily expression could be identified as significant indicators for each emotion under investigation. A participant keeping their head more turned to the right was positively related to frustration being experienced, whereas keeping their head more turned to the left was positively related to enjoyment. Furthermore, having their upper body positioned more closely to the gaming screen was also positively related to frustration. Finally, increased activity of a participant’s head emerged as a significant indicator of boredom being experienced. These results confirm the value of bodily expression as an indicator of emotional experience in multimedia learning systems. Furthermore, the findings may guide developers of emotion recognition procedures by focusing on the identified features of bodily expression. PMID:28798717
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Texture analysis of pulmonary parenchyma in normal and emphysematous lung
NASA Astrophysics Data System (ADS)
Uppaluri, Renuka; Mitsa, Theophano; Hoffman, Eric A.; McLennan, Geoffrey; Sonka, Milan
1996-04-01
Tissue characterization using texture analysis is gaining increasing importance in medical imaging. We present a completely automated method for discriminating between normal and emphysematous regions from CT images. This method involves extracting seventeen features which are based on statistical, hybrid and fractal texture models. The best subset of features is derived from the training set using the divergence technique. A minimum distance classifier is used to classify the samples into one of the two classes--normal and emphysema. Sensitivity and specificity and accuracy values achieved were 80% or greater in most cases proving that texture analysis holds great promise in identifying emphysema.
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Roth 401(k): asking the right questions.
Joyner, James F
2006-01-01
Roth 401(k) provisions are a newly available feature of 401(k) plans. Roth 401(k) provisions are after-tax savings that generally are tax-free at the time of distribution. Questions arise for plan sponsors about whether the new feature is beneficial, and to whom, and what needs to be done if the plan sponsor decides to offer this provision to its employees. This article tries to answer some of those common questions, including a simple computational analysis to try to answer the important question of how much an employee-participant genuinely benefits from this savings approach. Some practical issues of implementation are touched on, and some unanswered questions are identified.
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Identifying the features of an exercise addiction: A Delphi study
Macfarlane, Lucy; Owens, Glynn; Cruz, Borja del Pozo
2016-01-01
Objectives There remains limited consensus regarding the definition and conceptual basis of exercise addiction. An understanding of the factors motivating maintenance of addictive exercise behavior is important for appropriately targeting intervention. The aims of this study were twofold: first, to establish consensus on features of an exercise addiction using Delphi methodology and second, to identify whether these features are congruous with a conceptual model of exercise addiction adapted from the Work Craving Model. Methods A three-round Delphi process explored the views of participants regarding the features of an exercise addiction. The participants were selected from sport and exercise relevant domains, including physicians, physiotherapists, coaches, trainers, and athletes. Suggestions meeting consensus were considered with regard to the proposed conceptual model. Results and discussion Sixty-three items reached consensus. There was concordance of opinion that exercising excessively is an addiction, and therefore it was appropriate to consider the suggestions in light of the addiction-based conceptual model. Statements reaching consensus were consistent with all three components of the model: learned (negative perfectionism), behavioral (obsessive–compulsive drive), and hedonic (self-worth compensation and reduction of negative affect and withdrawal). Conclusions Delphi methodology allowed consensus to be reached regarding the features of an exercise addiction, and these features were consistent with our hypothesized conceptual model of exercise addiction. This study is the first to have applied Delphi methodology to the exercise addiction field, and therefore introduces a novel approach to exercise addiction research that can be used as a template to stimulate future examination using this technique. PMID:27554504
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Keita, Akilah Dulin; Whittaker, Shannon; Wynter, Jamila; Kidanu, Tamnnet Woldemichael; Chhay, Channavy; Cardel, Michelle; Gans, Kim M
2016-01-01
This study identifies Southeast Asian refugee parents' and grandparents' perceptions of the risk and protective factors for childhood obesity. We used a mixed methods approach (concept mapping) for data collection and analyses. Fifty-nine participants engaged in modified nominal group meetings where they generated statements about children's weight status and structuring meetings where they sorted statements into piles based on similarity and rated statements on relative importance. Concept Systems® software generated clusters of ideas, cluster ratings, and pattern matches. Eleven clusters emerged. Participants rated "Healthy Food Changes Made within the School" and "Parent-related Physical Activity Factors" as most important, whereas "Neighborhood Built Features" was rated as the least important. Cambodian and Hmong participants agreed the most on cluster ratings of relative importance (r = 0.62). The study findings may be used to inform the development of culturally appropriate obesity prevention interventions for Southeast Asian refugee communities.
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Identifying Potential Collapse Features Under Highways
DOT National Transportation Integrated Search
2003-01-01
In 1994, subsidence features were identified on Interstate 70 in eastern Ohio. These : features were caused by collapse of old mine workings beneath the highway. An attempt : was made to delineate these features using geophysical methods with no avai...
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Eiff, M Patrice; Green, Larry A; Jones, Geoff; Devlaeminck, Alex Verdieck; Waller, Elaine; Dexter, Eve; Marino, Miguel; Carney, Patricia A
2017-03-01
Little is known about how the patient-centered medical home (PCMH) is being implemented in residency practices. We describe both the trends in implementation of PCMH features and the influence that working with PCMH features has on resident attitudes toward their importance in 14 family medicine residencies associated with the P4 Project. We assessed 24 residency continuity clinics annually between 2007-2011 on presence or absence of PCMH features. Annual resident surveys (n=690) assessed perceptions of importance of PCMH features using a 4-point scale (not at all important to very important). We used generalized estimating equations logistic regression to assess trends and ordinal-response proportional odds regression models to determine if resident ratings of importance were associated with working with those features during training. Implementation of electronic health record (EHR) features increased significantly from 2007-2011, such as email communication with patients (33% to 67%), preventive services registries (23% to 64%), chronic disease registries (63% to 82%), and population-based quality assurance (46% to 79%). Team-based care was the only process of care feature to change significantly (54% to 93%). Residents with any exposure to EHR-based features had higher odds of rating the features more important compared to those with no exposure. We observed consistently lower odds of the resident rating process of care features as more important with any exposure compared to no exposure. Residencies engaged in educational transformation were more successful in implementing EHR-based PCMH features, and exposure during training appears to positively influence resident ratings of importance, while exposure to process of care features are slower to implement with less influence on importance ratings.
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Planillo, Aimara; Malo, Juan E
2018-01-01
Human disturbance is widespread across landscapes in the form of roads that alter wildlife populations. Knowing which road features are responsible for the species response and their relevance in comparison with environmental variables will provide useful information for effective conservation measures. We sampled relative abundance of European rabbits, a very widespread species, in motorway verges at regional scale, in an area with large variability in environmental and infrastructure conditions. Environmental variables included vegetation structure, plant productivity, distance to water sources, and altitude. Infrastructure characteristics were the type of vegetation in verges, verge width, traffic volume, and the presence of embankments. We performed a variance partitioning analysis to determine the relative importance of two sets of variables on rabbit abundance. Additionally, we identified the most important variables and their effects model averaging after model selection by AICc on hypothesis-based models. As a group, infrastructure features explained four times more variability in rabbit abundance than environmental variables, being the effects of the former critical in motorway stretches located in altered landscapes with no available habitat for rabbits, such as agricultural fields. Model selection and Akaike weights showed that verge width and traffic volume are the most important variables explaining rabbit abundance index, with positive and negative effects, respectively. In the light of these results, the response of species to the infrastructure can be modulated through the modification of motorway features, being some of them manageable in the design phase. The identification of such features leads to suggestions for improvement through low-cost corrective measures and conservation plans. As a general indication, keeping motorway verges less than 10 m wide will prevent high densities of rabbits and avoid the unwanted effects that rabbit populations can generate in some areas.
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Mahoney, Colin J.; Beck, Jon; Rohrer, Jonathan D.; Lashley, Tammaryn; Mok, Kin; Shakespeare, Tim; Yeatman, Tom; Warrington, Elizabeth K.; Schott, Jonathan M.; Fox, Nick C.; Rossor, Martin N.; Hardy, John; Collinge, John; Revesz, Tamas; Mead, Simon
2012-01-01
An expanded hexanucleotide repeat in the C9ORF72 gene has recently been identified as a major cause of familial frontotemporal lobar degeneration and motor neuron disease, including cases previously identified as linked to chromosome 9. Here we present a detailed retrospective clinical, neuroimaging and histopathological analysis of a C9ORF72 mutation case series in relation to other forms of genetically determined frontotemporal lobar degeneration ascertained at a specialist centre. Eighteen probands (19 cases in total) were identified, representing 35% of frontotemporal lobar degeneration cases with identified mutations, 36% of cases with clinical evidence of motor neuron disease and 7% of the entire cohort. Thirty-three per cent of these C9ORF72 cases had no identified relevant family history. Families showed wide variation in clinical onset (43–68 years) and duration (1.7–22 years). The most common presenting syndrome (comprising a half of cases) was behavioural variant frontotemporal dementia, however, there was substantial clinical heterogeneity across the C9ORF72 mutation cohort. Sixty per cent of cases developed clinical features consistent with motor neuron disease during the period of follow-up. Anxiety and agitation and memory impairment were prominent features (between a half to two-thirds of cases), and dominant parietal dysfunction was also frequent. Affected individuals showed variable magnetic resonance imaging findings; however, relative to healthy controls, the group as a whole showed extensive thinning of frontal, temporal and parietal cortices, subcortical grey matter atrophy including thalamus and cerebellum and involvement of long intrahemispheric, commissural and corticospinal tracts. The neuroimaging profile of the C9ORF72 expansion was significantly more symmetrical than progranulin mutations with significantly less temporal lobe involvement than microtubule-associated protein tau mutations. Neuropathological examination in six cases with C9ORF72 mutation from the frontotemporal lobar degeneration series identified histomorphological features consistent with either type A or B TAR DNA-binding protein-43 deposition; however, p62-positive (in excess of TAR DNA-binding protein-43 positive) neuronal cytoplasmic inclusions in hippocampus and cerebellum were a consistent feature of these cases, in contrast to the similar frequency of p62 and TAR DNA-binding protein-43 deposition in 53 control cases with frontotemporal lobar degeneration–TAR DNA-binding protein. These findings corroborate the clinical importance of the C9ORF72 mutation in frontotemporal lobar degeneration, delineate phenotypic and neuropathological features that could help to guide genetic testing, and suggest hypotheses for elucidating the neurobiology of a culprit subcortical network. PMID:22366791
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Zhang, Chenwang; Gao, Liuze; Xu, Eugene Yujun
2016-11-01
Spermatogenesis is one of the fundamental processes of sexual reproduction, present in almost all metazoan animals. Like many other reproductive traits, developmental features and traits of spermatogenesis are under strong selective pressure to change, both at morphological and underlying molecular levels. Yet evidence suggests that some fundamental features of spermatogenesis may be ancient and conserved among metazoan species. Identifying the underlying conserved molecular mechanisms could reveal core components of metazoan spermatogenic machinery and provide novel insight into causes of human infertility. Conserved RNA-binding proteins and their interacting RNA network emerge to be a common theme important for animal sperm development. We review research on the recent addition to the RNA family - Long non-coding RNA (lncRNA) and its roles in spermatogenesis in the context of the expanding RNA-protein network. Copyright © 2016 Elsevier Ltd. All rights reserved.
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The Geological Susceptibility of Induced Earthquakes in the Duvernay Play
NASA Astrophysics Data System (ADS)
Pawley, Steven; Schultz, Ryan; Playter, Tiffany; Corlett, Hilary; Shipman, Todd; Lyster, Steven; Hauck, Tyler
2018-02-01
Presently, consensus on the incorporation of induced earthquakes into seismic hazard has yet to be established. For example, the nonstationary, spatiotemporal nature of induced earthquakes is not well understood. Specific to the Western Canada Sedimentary Basin, geological bias in seismogenic activation potential has been suggested to control the spatial distribution of induced earthquakes regionally. In this paper, we train a machine learning algorithm to systemically evaluate tectonic, geomechanical, and hydrological proxies suspected to control induced seismicity. Feature importance suggests that proximity to basement, in situ stress, proximity to fossil reef margins, lithium concentration, and rate of natural seismicity are among the strongest model predictors. Our derived seismogenic potential map faithfully reproduces the current distribution of induced seismicity and is suggestive of other regions which may be prone to induced earthquakes. The refinement of induced seismicity geological susceptibility may become an important technique to identify significant underlying geological features and address induced seismic hazard forecasting issues.
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Redefining cerebral malaria by including malaria retinopathy.
Beare, Nicholas A V; Lewallen, Susan; Taylor, Terrie E; Molyneux, Malcolm E
2011-03-01
Accurate diagnosis of cerebral malaria (CM) is important for patient management, epidemiological and end point surveillance, and enrolling patients with CM in studies of pathogenesis or therapeutic trials. In malaria-endemic areas, where asymptomatic Plasmodium falciparum parasitemia is common, a positive blood film in a comatose individual does not prove that the coma is due to malaria. A retinopathy consisting of two unique features - patchy retinal whitening and focal changes of vessel color - is highly specific for encephalopathy of malarial etiology. White-centered retinal hemorrhages are a common but less specific feature. Either indirect or direct ophthalmoscopy can be used to identify the changes, and both procedures can be learned and practiced by nonspecialist clinicians. In view of its important contributions to both clinical care and research, examination of the retina should become a routine component of the assessment of a comatose child or adult when CM is a possible diagnosis.
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Redefining cerebral malaria by including malaria retinopathy
Beare, Nicholas AV; Lewallen, Susan; Taylor, Terrie E; Molyneux, Malcolm E
2011-01-01
Accurate diagnosis of cerebral malaria (CM) is important for patient management, epidemiological and end point surveillance, and enrolling patients with CM in studies of pathogenesis or therapeutic trials. In malaria-endemic areas, where asymptomatic Plasmodium falciparum parasitemia is common, a positive blood film in a comatose individual does not prove that the coma is due to malaria. A retinopathy consisting of two unique features – patchy retinal whitening and focal changes of vessel color – is highly specific for encephalopathy of malarial etiology. White-centered retinal hemorrhages are a common but less specific feature. Either indirect or direct ophthalmoscopy can be used to identify the changes, and both procedures can be learned and practiced by nonspecialist clinicians. In view of its important contributions to both clinical care and research, examination of the retina should become a routine component of the assessment of a comatose child or adult when CM is a possible diagnosis. PMID:21449844
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Intertumoral Heterogeneity within Medulloblastoma Subgroups.
Cavalli, Florence M G; Remke, Marc; Rampasek, Ladislav; Peacock, John; Shih, David J H; Luu, Betty; Garzia, Livia; Torchia, Jonathon; Nor, Carolina; Morrissy, A Sorana; Agnihotri, Sameer; Thompson, Yuan Yao; Kuzan-Fischer, Claudia M; Farooq, Hamza; Isaev, Keren; Daniels, Craig; Cho, Byung-Kyu; Kim, Seung-Ki; Wang, Kyu-Chang; Lee, Ji Yeoun; Grajkowska, Wieslawa A; Perek-Polnik, Marta; Vasiljevic, Alexandre; Faure-Conter, Cecile; Jouvet, Anne; Giannini, Caterina; Nageswara Rao, Amulya A; Li, Kay Ka Wai; Ng, Ho-Keung; Eberhart, Charles G; Pollack, Ian F; Hamilton, Ronald L; Gillespie, G Yancey; Olson, James M; Leary, Sarah; Weiss, William A; Lach, Boleslaw; Chambless, Lola B; Thompson, Reid C; Cooper, Michael K; Vibhakar, Rajeev; Hauser, Peter; van Veelen, Marie-Lise C; Kros, Johan M; French, Pim J; Ra, Young Shin; Kumabe, Toshihiro; López-Aguilar, Enrique; Zitterbart, Karel; Sterba, Jaroslav; Finocchiaro, Gaetano; Massimino, Maura; Van Meir, Erwin G; Osuka, Satoru; Shofuda, Tomoko; Klekner, Almos; Zollo, Massimo; Leonard, Jeffrey R; Rubin, Joshua B; Jabado, Nada; Albrecht, Steffen; Mora, Jaume; Van Meter, Timothy E; Jung, Shin; Moore, Andrew S; Hallahan, Andrew R; Chan, Jennifer A; Tirapelli, Daniela P C; Carlotti, Carlos G; Fouladi, Maryam; Pimentel, José; Faria, Claudia C; Saad, Ali G; Massimi, Luca; Liau, Linda M; Wheeler, Helen; Nakamura, Hideo; Elbabaa, Samer K; Perezpeña-Diazconti, Mario; Chico Ponce de León, Fernando; Robinson, Shenandoah; Zapotocky, Michal; Lassaletta, Alvaro; Huang, Annie; Hawkins, Cynthia E; Tabori, Uri; Bouffet, Eric; Bartels, Ute; Dirks, Peter B; Rutka, James T; Bader, Gary D; Reimand, Jüri; Goldenberg, Anna; Ramaswamy, Vijay; Taylor, Michael D
2017-06-12
While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials. Copyright © 2017 Elsevier Inc. All rights reserved.
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Identifying Potential Collapse Features Under Highways : Executive Summary
DOT National Transportation Integrated Search
2003-03-01
In 1994, subsidence features were identified on Interstate 70 in eastern Ohio. These : features were caused by collapse of old mine workings beneath the highway. An attempt : was made to delineate these features using geophysical methods with no avai...
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Identifying potential collapse features under highways.
DOT National Transportation Integrated Search
2003-03-01
In 1994, subsidence features were identified on Interstate 70 in eastern Ohio. These features were caused by collapse of old mine workings beneath the highway. An attempt was made to delineate these features using geophysical methods with no avail. T...
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Automated Recognition of 3D Features in GPIR Images
NASA Technical Reports Server (NTRS)
Park, Han; Stough, Timothy; Fijany, Amir
2007-01-01
A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.
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NASA Astrophysics Data System (ADS)
Shavers, E. J.; Ghulam, A.; Encarnacion, J. P.
2016-12-01
Spectroscopic reflectance in the visible to short-wave infrared region is an important tool for remote geologic mapping and is applied at scales from satellite to field measurements. Remote geologic mapping is challenging in regions subject to significant surficial weathering. Here we identify absorption features found in altered volcanic pipes and dikes in the Avon Volcanic District, Missouri, that are inherited from the original ultramafic and carbonatite lithology. Alteration ranges from small degree hydrothermal alteration to extensive laterization. The absorption features are three broad minima centered near 690, 890, and 1100 nm. Features in this region are recognized to be caused by ferric and ferrous Fe minerals including olivine, carbonates, chlorite, and goethite all of which are found among the Avon pipes and dikes that are in various stages of alteration. Iron-related intervalence charge transfer and crystal field perturbations of ions are the principal causes of the spectroscopic features in the visible to near-infrared region yet spectra are also distorted by factors like texture and the presence of opaque minerals known to reduce overall reflectance. In the Avon samples, Fe oxide content can reach >15 wt% leading to prominent absorption features even in the less altered ultramafics with reflectance curve maxima as low as 5%. The exaggerated minima allow the altered intrusive rocks to stand out among other weathered lithologies that will often have clay features in the region yet have lower iron concentration. The absorption feature centered near 690 nm is particularly noteworthy. Broad mineral-related absorption features centered at this wavelength are rare but have been linked to Ti3+ in octahedral coordination. The reduced form of Ti is not common in surface lithologies. Titanium-rich andradite has Ti3+ in the octahedral position, is resistant to weathering, is found among the Avon lithologies including ultramafic, carbonatite, and carbonated breccia, and is identified here as the cause of the 690 nm absorption feature. The Ti3+ absorption feature centered near 690 nm and strong Fe absorption features at 890 and 1100 nm may be useful indicators of rare intrusive lithologies in remote geologic mapping.
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On designing of a low leakage patient-centric provider network.
Zheng, Yuchen; Lin, Kun; White, Thomas; Pickreign, Jeremy; Yuen-Reed, Gigi
2018-03-27
When a patient in a provider network seeks services outside of their community, the community experiences a leakage. Leakage is undesirable as it typically leads to higher out-of-network cost for patient and increases barrier for care coordination, which is particularly problematic for Accountable Care Organization (ACO) as the in-network providers are financially responsible for quality of care and outcome. We aim to design a data-driven method to identify naturally occurring provider networks driven by diabetic patient choices, and understand the relationship among provider composition, patient composition, and service leakage pattern. By doing so, we learn the features of low service leakage provider networks that can be generalized to different patient population. Data used for this study include de-identified healthcare insurance administrative data acquired from Capital District Physicians' Health Plan (CDPHP) for diabetic patients who resided in four New York state counties (Albany, Rensselaer, Saratoga, and Schenectady) in 2014. We construct a healthcare provider network based on patients' historical medical insurance claims. A community detection algorithm is used to identify naturally occurring communities of collaborating providers. For each detected community, a profile is built using several new key measures to elucidate stakeholders of our findings. Finally, import-export analysis is conducted to benchmark their leakage pattern and identify further leakage reduction opportunity. The design yields six major provider communities with diverse profiles. Some communities are geographically concentrated, while others tend to draw patients with certain diabetic co-morbidities. Providers from the same healthcare institution are likely to be assigned to the same community. While most communities have high within-community utilization and spending, at 85% and 86% respectively, leakage still persists. Hence, we utilize a metric from import-export analysis to detect leakage, gaining insight on how to minimize leakage. We identify patient-driven provider organization by surfacing providers who share a large number of patients. By analyzing the import-export behavior of each identified community using a novel approach and profiling community patient and provider composition we understand the key features of having a balanced number of PCP and specialists and provider heterogeneity.
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Gao, JianZhao; Tao, Xue-Wen; Zhao, Jia; Feng, Yuan-Ming; Cai, Yu-Dong; Zhang, Ning
2017-01-01
Lysine acetylation, as one type of post-translational modifications (PTM), plays key roles in cellular regulations and can be involved in a variety of human diseases. However, it is often high-cost and time-consuming to use traditional experimental approaches to identify the lysine acetylation sites. Therefore, effective computational methods should be developed to predict the acetylation sites. In this study, we developed a position-specific method for epsilon lysine acetylation site prediction. Sequences of acetylated proteins were retrieved from the UniProt database. Various kinds of features such as position specific scoring matrix (PSSM), amino acid factors (AAF), and disorders were incorporated. A feature selection method based on mRMR (Maximum Relevance Minimum Redundancy) and IFS (Incremental Feature Selection) was employed. Finally, 319 optimal features were selected from total 541 features. Using the 319 optimal features to encode peptides, a predictor was constructed based on dagging. As a result, an accuracy of 69.56% with MCC of 0.2792 was achieved. We analyzed the optimal features, which suggested some important factors determining the lysine acetylation sites. We developed a position-specific method for epsilon lysine acetylation site prediction. A set of optimal features was selected. Analysis of the optimal features provided insights into the mechanism of lysine acetylation sites, providing guidance of experimental validation. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Evaluation of Sexual Communication Message Strategies
2011-01-01
Parent-child communication about sex is an important proximal reproductive health outcome. But while campaigns to promote it such as the Parents Speak Up National Campaign (PSUNC) have been effective, little is known about how messages influence parental cognitions and behavior. This study examines which message features explain responses to sexual communication messages. We content analyzed 4 PSUNC ads to identify specific, measurable message and advertising execution features. We then develop quantitative measures of those features, including message strategies, marketing strategies, and voice and other stylistic features, and merged the resulting data into a dataset drawn from a national media tracking survey of the campaign. Finally, we conducted multivariable logistic regression models to identify relationships between message content and ad reactions/receptivity, and between ad reactions/receptivity and parents' cognitions related to sexual communication included in the campaign's conceptual model. We found that overall parents were highly receptive to the PSUNC ads. We did not find significant associations between message content and ad reactions/receptivity. However, we found that reactions/receptivity to specific PSUNC ads were associated with increased norms, self-efficacy, short- and long-term expectations about parent-child sexual communication, as theorized in the conceptual model. This study extends previous research and methods to analyze message content and reactions/receptivity. The results confirm and extend previous PSUNC campaign evaluation and provide further evidence for the conceptual model. Future research should examine additional message content features and the effects of reactions/receptivity. PMID:21599875
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Constrained clusters of gene expression profiles with pathological features.
Sese, Jun; Kurokawa, Yukinori; Monden, Morito; Kato, Kikuya; Morishita, Shinichi
2004-11-22
Gene expression profiles should be useful in distinguishing variations in disease, since they reflect accurately the status of cells. The primary clustering of gene expression reveals the genotypes that are responsible for the proximity of members within each cluster, while further clustering elucidates the pathological features of the individual members of each cluster. However, since the first clustering process and the second classification step, in which the features are associated with clusters, are performed independently, the initial set of clusters may omit genes that are associated with pathologically meaningful features. Therefore, it is important to devise a way of identifying gene expression clusters that are associated with pathological features. We present the novel technique of 'itemset constrained clustering' (IC-Clustering), which computes the optimal cluster that maximizes the interclass variance of gene expression between groups, which are divided according to the restriction that only divisions that can be expressed using common features are allowed. This constraint automatically labels each cluster with a set of pathological features which characterize that cluster. When applied to liver cancer datasets, IC-Clustering revealed informative gene expression clusters, which could be annotated with various pathological features, such as 'tumor' and 'man', or 'except tumor' and 'normal liver function'. In contrast, the k-means method overlooked these clusters.
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Facial expression identification using 3D geometric features from Microsoft Kinect device
NASA Astrophysics Data System (ADS)
Han, Dongxu; Al Jawad, Naseer; Du, Hongbo
2016-05-01
Facial expression identification is an important part of face recognition and closely related to emotion detection from face images. Various solutions have been proposed in the past using different types of cameras and features. Microsoft Kinect device has been widely used for multimedia interactions. More recently, the device has been increasingly deployed for supporting scientific investigations. This paper explores the effectiveness of using the device in identifying emotional facial expressions such as surprise, smile, sad, etc. and evaluates the usefulness of 3D data points on a face mesh structure obtained from the Kinect device. We present a distance-based geometric feature component that is derived from the distances between points on the face mesh and selected reference points in a single frame. The feature components extracted across a sequence of frames starting and ending by neutral emotion represent a whole expression. The feature vector eliminates the need for complex face orientation correction, simplifying the feature extraction process and making it more efficient. We applied the kNN classifier that exploits a feature component based similarity measure following the principle of dynamic time warping to determine the closest neighbors. Preliminary tests on a small scale database of different facial expressions show promises of the newly developed features and the usefulness of the Kinect device in facial expression identification.
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Tahir, Fahima; Fahiem, Muhammad Abuzar
2014-01-01
The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, K-nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers.
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Alu expression in human cell lines and their retrotranspositional potential.
Oler, Andrew J; Traina-Dorge, Stephen; Derbes, Rebecca S; Canella, Donatella; Cairns, Brad R; Roy-Engel, Astrid M
2012-06-20
The vast majority of the 1.1 million Alu elements are retrotranspositionally inactive, where only a few loci referred to as 'source elements' can generate new Alu insertions. The first step in identifying the active Alu sources is to determine the loci transcribed by RNA polymerase III (pol III). Previous genome-wide analyses from normal and transformed cell lines identified multiple Alu loci occupied by pol III factors, making them candidate source elements. Analysis of the data from these genome-wide studies determined that the majority of pol III-bound Alus belonged to the older subfamilies Alu S and Alu J, which varied between cell lines from 62.5% to 98.7% of the identified loci. The pol III-bound Alus were further scored for estimated retrotransposition potential (ERP) based on the absence or presence of selected sequence features associated with Alu retrotransposition capability. Our analyses indicate that most of the pol III-bound Alu loci candidates identified lack the sequence characteristics important for retrotransposition. These data suggest that Alu expression likely varies by cell type, growth conditions and transformation state. This variation could extend to where the same cell lines in different laboratories present different Alu expression patterns. The vast majority of Alu loci potentially transcribed by RNA pol III lack important sequence features for retrotransposition and the majority of potentially active Alu loci in the genome (scored high ERP) belong to young Alu subfamilies. Our observations suggest that in an in vivo scenario, the contribution of Alu activity on somatic genetic damage may significantly vary between individuals and tissues.
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Chin, Wei-Chien-Benny; Wen, Tzai-Hung
2015-01-01
A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms-Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)-that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.
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Epilepsy with auditory features
Licchetta, Laura; Baldassari, Sara; Palombo, Flavia; Menghi, Veronica; D'Aurizio, Romina; Leta, Chiara; Stipa, Carlotta; Boero, Giovanni; d'Orsi, Giuseppe; Magi, Alberto; Scheffer, Ingrid; Seri, Marco; Tinuper, Paolo; Bisulli, Francesca
2015-01-01
Objective: To identify novel genes implicated in epilepsy with auditory features (EAF) in phenotypically heterogeneous families with unknown molecular basis. Methods: We identified 15 probands with EAF in whom an LGI1 mutation had been excluded. We performed electroclinical phenotyping on all probands and available affected relatives. We used whole-exome sequencing (WES) in 20 individuals with EAF (including all the probands and 5 relatives) to identify single nucleotide variants, small insertions/deletions, and copy number variants. Results: WES revealed likely pathogenic variants in genes that had not been previously associated with EAF: a CNTNAP2 intragenic deletion, 2 truncating mutations of DEPDC5, and a missense SCN1A change. Conclusions: EAF is a clinically and molecularly heterogeneous disease. The association of EAF with CNTNAP2, DEPDC5, and SCN1A mutations widens the phenotypic spectrum related to these genes. CNTNAP2 encodes CASPR2, a member of the voltage-gated potassium channel complex in which LGI1 plays a role. The finding of a CNTNAP2 deletion emphasizes the importance of this complex in EAF and shows biological convergence. PMID:27066544
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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
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Modelling above Ground Biomass of Mangrove Forest Using SENTINEL-1 Imagery
NASA Astrophysics Data System (ADS)
Labadisos Argamosa, Reginald Jay; Conferido Blanco, Ariel; Balidoy Baloloy, Alvin; Gumbao Candido, Christian; Lovern Caboboy Dumalag, John Bart; Carandang Dimapilis, Lee, , Lady; Camero Paringit, Enrico
2018-04-01
Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength 23 cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75 cm to 7.5 cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a-c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r2 of 0.79 and an RMSE of 0.44 Mg using only four features, namely, σ°VH GLCM variance, σ°VH GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.
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NASA Astrophysics Data System (ADS)
Zhao, Yiqun; Wang, Zhihui
2015-12-01
The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.
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Immunohistochemical mismatch in a case of rhabdomyoblastic metastatic melanoma.
Dumitru, Adrian Vasile; Tampa, Mircea Ştefan; Georgescu, Simona Roxana; Păunică, Stana; Matei, Clara Nicoleta; Nica, Adriana Elena; Costache, Mariana; Motofei, Ion; Sajin, Maria; Păunică, Ioana; Georgescu, Tiberiu Augustin
2018-01-01
Melanomas can exhibit a wide range of unusual morphologies due to the neural crest origin of melanocytes. Several authors have documented variations in size and shape of cells, cytoplasmic features and inclusions, nuclear features and cell architecture. Metastatic melanoma with rhabdomyoblastic differentiation is an extremely rare condition with poor prognosis. Few studies concerning rhabdoid or rhabdomyoblastic differentiation in melanoma are currently available and the current report highlights some of the most important immunohistochemical features of this rare entity. We report on a case of a rhabdomyoblastic metastatic melanoma showing intense positivity for both melanocytic and rhabdoid markers in two cell populations dissociated within the tumor with multiple mismatches in immunomarker expression. Improved recognition of this rare morphological pattern may provide the means for developing new techniques to identify novel therapeutic targets, which would improve the prognostic outlook for these patients.
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On the structural context and identification of enzyme catalytic residues.
Chien, Yu-Tung; Huang, Shao-Wei
2013-01-01
Enzymes play important roles in most of the biological processes. Although only a small fraction of residues are directly involved in catalytic reactions, these catalytic residues are the most crucial parts in enzymes. The study of the fundamental and unique features of catalytic residues benefits the understanding of enzyme functions and catalytic mechanisms. In this work, we analyze the structural context of catalytic residues based on theoretical and experimental structure flexibility. The results show that catalytic residues have distinct structural features and context. Their neighboring residues, whether sequence or structure neighbors within specific range, are usually structurally more rigid than those of noncatalytic residues. The structural context feature is combined with support vector machine to identify catalytic residues from enzyme structure. The prediction results are better or comparable to those of recent structure-based prediction methods.
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Forecasting air quality time series using deep learning.
Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse
2018-04-13
This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.
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Holmes, Tyson H; He, Xiao-Song
2016-10-01
Small, wide data sets are commonplace in human immunophenotyping research. As defined here, a small, wide data set is constructed by sampling a small to modest quantity n,1
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Holmes, Tyson H.; He, Xiao-Song
2016-01-01
Small, wide data sets are commonplace in human immunophenotyping research. As defined here, a small, wide data set is constructed by sampling a small to modest quantity n, 1 < n < 50, of human participants for the purpose of estimating many parameters p, such that n < p < 1,000. We offer a set of prescriptions that are designed to facilitate low-variance (i.e. stable), low-bias, interpretive regression modeling of small, wide data sets. These prescriptions are distinctive in their especially heavy emphasis on minimizing use of out-of-sample information for conducting statistical inference. That allows the working immunologist to proceed without being encumbered by imposed and often untestable statistical assumptions. Problems of unmeasured confounders, confidence-interval coverage, feature selection, and shrinkage/denoising are defined clearly and treated in detail. We propose an extension of an existing nonparametric technique for improved small-sample confidence-interval tail coverage from the univariate case (single immune feature) to the multivariate (many, possibly correlated immune features). An important role for derived features in the immunological interpretation of regression analyses is stressed. Areas of further research are discussed. Presented principles and methods are illustrated through application to a small, wide data set of adults spanning a wide range in ages and multiple immunophenotypes that were assayed before and after immunization with inactivated influenza vaccine (IIV). Our regression modeling prescriptions identify some potentially important topics for future immunological research. 1) Immunologists may wish to distinguish age-related differences in immune features from changes in immune features caused by aging. 2) A form of the bootstrap that employs linear extrapolation may prove to be an invaluable analytic tool because it allows the working immunologist to obtain accurate estimates of the stability of immune parameter estimates with a bare minimum of imposed assumptions. 3) Liberal inclusion of immune features in phenotyping panels can facilitate accurate separation of biological signal of interest from noise. In addition, through a combination of denoising and potentially improved confidence interval coverage, we identify some candidate immune correlates (frequency of cell subset and concentration of cytokine) with B cell response as measured by quantity of IIV-specific IgA antibody-secreting cells and quantity of IIV-specific IgG antibody-secreting cells. PMID:27196789
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Wright, Courtney J; Zeeman, Heidi; Kendall, Elizabeth; Whitty, Jennifer A
2017-07-01
Despite the recent emphasis in Australian political, academic, and legislative narratives to more actively promote real housing choice for people with high healthcare and support needs, there is a lack of understanding regarding the specific housing features that might constitute better housing solutions for this population. Inclusive housing provision in Australia rightly emphasises safety and accessibility issues but often fails to incorporate factors related to broader psychosocial elements of housing such as dwelling location, neighbourhood quality, and overall design. While the importance of these broader elements appears obvious, it is not yet clear what specific housing features relate to these elements and how they might contribute to housing solutions for people with high healthcare and support needs. For individuals with complex neurological conditions such as brain injury or cerebral palsy, who require maximum support on a daily basis yet want to live independently and away from a primary care hospital or health facility, a more detailed understanding of the housing features that might influence design and development is needed. Thus, in order to clarify the broader factors related to housing solutions for this population, a systematic review was conducted to identify and synthesise the current research evidence (post-2003) and guide future housing design and development opportunities. From the included studies (n=26), 198 unique housing features were identified. From the 198 features, 142 related to housing design (i.e., internal or external characteristics of the dwelling and its land), 12 related to the dwelling's location (i.e., its proximity to available resources), and 54 related to the nature of the surrounding neighbourhood (i.e., the physical, social, and economic conditions of the area). The findings of this review contribute significantly to the literature by reporting a broader scope of relevant housing features for people with neurological disability, presenting preliminary guiding principles for housing design and development for this population, and identifying opportunities for future research. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Prioritizing Scientific Data for Transmission
NASA Technical Reports Server (NTRS)
Castano, Rebecca; Anderson, Robert; Estlin, Tara; DeCoste, Dennis; Gaines, Daniel; Mazzoni, Dominic; Fisher, Forest; Judd, Michele
2004-01-01
A software system has been developed for prioritizing newly acquired geological data onboard a planetary rover. The system has been designed to enable efficient use of limited communication resources by transmitting the data likely to have the most scientific value. This software operates onboard a rover by analyzing collected data, identifying potential scientific targets, and then using that information to prioritize data for transmission to Earth. Currently, the system is focused on the analysis of acquired images, although the general techniques are applicable to a wide range of data modalities. Image prioritization is performed using two main steps. In the first step, the software detects features of interest from each image. In its current application, the system is focused on visual properties of rocks. Thus, rocks are located in each image and rock properties, such as shape, texture, and albedo, are extracted from the identified rocks. In the second step, the features extracted from a group of images are used to prioritize the images using three different methods: (1) identification of key target signature (finding specific rock features the scientist has identified as important), (2) novelty detection (finding rocks we haven t seen before), and (3) representative rock sampling (finding the most average sample of each rock type). These methods use techniques such as K-means unsupervised clustering and a discrimination-based kernel classifier to rank images based on their interest level.
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Yeung, Kit San; Tso, Winnie Wan Yee; Ip, Janice Jing Kun; Mak, Christopher Chun Yu; Leung, Gordon Ka Chun; Tsang, Mandy Ho Yin; Ying, Dingge; Pei, Steven Lim Cho; Lee, So Lun; Yang, Wanling; Chung, Brian Hon-Yin
2017-01-01
Macrocephaly, which is defined as a head circumference greater than or equal to + 2 standard deviations, is a feature commonly observed in children with developmental delay and/or autism spectrum disorder. Although PTEN is a well-known gene identified in patients with this syndromic presentation, other genes in the PI3K-AKT-mTOR signalling pathway have also recently been suggested to have important roles. The aim of this study is to characterise the mutation spectrum of this group of patients. We performed whole-exome sequencing of 21 patients with macrocephaly and developmental delay/autism spectrum disorder. Sources of genomic DNA included blood, buccal mucosa and saliva. Germline mutations were validated by Sanger sequencing, whereas somatic mutations were validated by droplet digital PCR. We identified ten pathogenic/likely pathogenic mutations in PTEN ( n = 4), PIK3CA ( n = 3), MTOR ( n = 1) and PPP2R5D ( n = 2) in ten patients. An additional PTEN mutation, which was classified as variant of unknown significance, was identified in a patient with a pathogenic PTEN mutation, making him harbour bi-allelic germline PTEN mutations. Two patients harboured somatic PIK3CA mutations, and the level of somatic mosaicism in blood DNA was low. Patients who tested positive for mutations in the PI3K-AKT-mTOR pathway had a lower developmental quotient than the rest of the cohort (DQ = 62.8 vs. 76.1, p = 0.021). Their dysmorphic features were non-specific, except for macrocephaly. Among the ten patients with identified mutations, brain magnetic resonance imaging was performed in nine, all of whom showed megalencephaly. We identified mutations in the PI3K-AKT-mTOR signalling pathway in nearly half of our patients with macrocephaly and developmental delay/autism spectrum disorder. These patients have subtle dysmorphic features and mild developmental issues. Clinically, patients with germline mutations are difficult to distinguish from patients with somatic mutations, and therefore, sequencing of buccal or saliva DNA is important to identify somatic mosaicism. Given the high diagnostic yield and the management implications, we suggest implementing comprehensive genetic testing in the PI3K-AKT-mTOR pathway in the clinical evaluation of patients with macrocephaly and developmental delay and/or autism spectrum disorder.
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Issenberg, S Barry; McGaghie, William C; Petrusa, Emil R; Lee Gordon, David; Scalese, Ross J
2005-01-01
1969 to 2003, 34 years. Simulations are now in widespread use in medical education and medical personnel evaluation. Outcomes research on the use and effectiveness of simulation technology in medical education is scattered, inconsistent and varies widely in methodological rigor and substantive focus. Review and synthesize existing evidence in educational science that addresses the question, 'What are the features and uses of high-fidelity medical simulations that lead to most effective learning?'. The search covered five literature databases (ERIC, MEDLINE, PsycINFO, Web of Science and Timelit) and employed 91 single search terms and concepts and their Boolean combinations. Hand searching, Internet searches and attention to the 'grey literature' were also used. The aim was to perform the most thorough literature search possible of peer-reviewed publications and reports in the unpublished literature that have been judged for academic quality. Four screening criteria were used to reduce the initial pool of 670 journal articles to a focused set of 109 studies: (a) elimination of review articles in favor of empirical studies; (b) use of a simulator as an educational assessment or intervention with learner outcomes measured quantitatively; (c) comparative research, either experimental or quasi-experimental; and (d) research that involves simulation as an educational intervention. Data were extracted systematically from the 109 eligible journal articles by independent coders. Each coder used a standardized data extraction protocol. Qualitative data synthesis and tabular presentation of research methods and outcomes were used. Heterogeneity of research designs, educational interventions, outcome measures and timeframe precluded data synthesis using meta-analysis. Coding accuracy for features of the journal articles is high. The extant quality of the published research is generally weak. The weight of the best available evidence suggests that high-fidelity medical simulations facilitate learning under the right conditions. These include the following: providing feedback--51 (47%) journal articles reported that educational feedback is the most important feature of simulation-based medical education; repetitive practice--43 (39%) journal articles identified repetitive practice as a key feature involving the use of high-fidelity simulations in medical education; curriculum integration--27 (25%) journal articles cited integration of simulation-based exercises into the standard medical school or postgraduate educational curriculum as an essential feature of their effective use; range of difficulty level--15 (14%) journal articles address the importance of the range of task difficulty level as an important variable in simulation-based medical education; multiple learning strategies--11 (10%) journal articles identified the adaptability of high-fidelity simulations to multiple learning strategies as an important factor in their educational effectiveness; capture clinical variation--11 (10%) journal articles cited simulators that capture a wide variety of clinical conditions as more useful than those with a narrow range; controlled environment--10 (9%) journal articles emphasized the importance of using high-fidelity simulations in a controlled environment where learners can make, detect and correct errors without adverse consequences; individualized learning--10 (9%) journal articles highlighted the importance of having reproducible, standardized educational experiences where learners are active participants, not passive bystanders; defined outcomes--seven (6%) journal articles cited the importance of having clearly stated goals with tangible outcome measures that will more likely lead to learners mastering skills; simulator validity--four (3%) journal articles provided evidence for the direct correlation of simulation validity with effective learning. While research in this field needs improvement in terms of rigor and quality, high-fidelity medical simulations are educationally effective and simulation-based education complements medical education in patient care settings.
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Tensor-driven extraction of developmental features from varying paediatric EEG datasets.
Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier
2018-05-21
Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.
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Janczyk, Markus; Pfister, Roland; Hommel, Bernhard; Kunde, Wilfried
2014-07-01
Responses in the second of two subsequently performed tasks can speed up compatible responses in the temporally preceding first task. Such backward crosstalk effects (BCEs) represent a challenge to the assumption of serial processing in stage models of human information processing, because they indicate that certain features of the second response have to be represented before the first response is emitted. Which of these features are actually relevant for BCEs is an open question, even though identifying these features is important for understanding the nature of parallel and serial response selection processes in dual-task performance. Motivated by effect-based models of action control, we show in three experiments that the BCE to a considerable degree reflects features of intended action effects, although features of the response proper (or response-associated kinesthetic feedback) also seem to play a role. These findings suggest that the codes of action effects (or action goals) can become activated simultaneously rather than serially, thereby creating BCEs. Copyright © 2014 Elsevier B.V. All rights reserved.
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Delong, Caroline M; Au, Whitlow W L; Harley, Heidi E; Roitblat, Herbert L; Pytka, Lisa
2007-08-01
Echolocating bottlenose dolphins (Tursiops truncatus) discriminate between objects on the basis of the echoes reflected by the objects. However, it is not clear which echo features are important for object discrimination. To gain insight into the salient features, the authors had a dolphin perform a match-to-sample task and then presented human listeners with echoes from the same objects used in the dolphin's task. In 2 experiments, human listeners performed as well or better than the dolphin at discriminating objects, and they reported the salient acoustic cues. The error patterns of the humans and the dolphin were compared to determine which acoustic features were likely to have been used by the dolphin. The results indicate that the dolphin did not appear to use overall echo amplitude, but that it attended to the pattern of changes in the echoes across different object orientations. Human listeners can quickly identify salient combinations of echo features that permit object discrimination, which can be used to generate hypotheses that can be tested using dolphins as subjects.
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Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra
2014-01-01
Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.
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Al-Rajab, Murad; Lu, Joan; Xu, Qiang
2017-07-01
This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.
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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.
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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
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Understanding Deep Representations Learned in Modeling Users Likes.
Guntuku, Sharath Chandra; Zhou, Joey Tianyi; Roy, Sujoy; Lin, Weisi; Tsang, Ivor W
2016-08-01
Automatically understanding and discriminating different users' liking for an image is a challenging problem. This is because the relationship between image features (even semantic ones extracted by existing tools, viz., faces, objects, and so on) and users' likes is non-linear, influenced by several subtle factors. This paper presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. Feature selection is applied before learning deep representation to identify the important features for a user to like an image. The proposed representation is shown to be effective in discriminating users based on images they like and also in recommending images that a given user likes, outperforming the state-of-the-art feature representations by ∼ 15 %-20%. Beyond this test-set performance, an attempt is made to qualitatively understand the representations learned by the deep architecture used to model user likes.
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Crowding by a single bar: probing pattern recognition mechanisms in the visual periphery.
Põder, Endel
2014-11-06
Whereas visual crowding does not greatly affect the detection of the presence of simple visual features, it heavily inhibits combining them into recognizable objects. Still, crowding effects have rarely been directly related to general pattern recognition mechanisms. In this study, pattern recognition mechanisms in visual periphery were probed using a single crowding feature. Observers had to identify the orientation of a rotated T presented briefly in a peripheral location. Adjacent to the target, a single bar was presented. The bar was either horizontal or vertical and located in a random direction from the target. It appears that such a crowding bar has very strong and regular effects on the identification of the target orientation. The observer's responses are determined by approximate relative positions of basic visual features; exact image-based similarity to the target is not important. A version of the "standard model" of object recognition with second-order features explains the main regularities of the data. © 2014 ARVO.
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Eskofier, Bjoern M; Kraus, Martin; Worobets, Jay T; Stefanyshyn, Darren J; Nigg, Benno M
2012-01-01
The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified. The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task. Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.
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Accounts of bullying on Twitter in relation to dentofacial features and orthodontic treatment.
Chan, A; Antoun, J S; Morgaine, K C; Farella, M
2017-04-01
Social media offers an accessible resource for gaining valuable insights into the social culture of bullying. The purpose of this study was to qualitatively analyse Twitter posts for common themes relating to dentofacial features, braces and bullying. Twitter's database was searched from 2010 to 2014 using keywords relevant to bullying, teeth and orthodontics. Two investigators assessed the Twitter posts, and selected those that conveyed the experiences or opinions of bullying victims. The posts were qualitatively analysed using thematic analysis. Of the 548 posts screened, 321 were included in the final sample. Four primary categories relating to 'dental-related bullying' were identified: (i) morphological features, (ii) psychological and psychosocial impact, (iii) coping mechanisms and (iv) the role of family. Bullied individuals reported a diverse range of psychological impacts and coping mechanisms. Secondary categories were also identified. Family members, for example, were found to play both a contributory and mediatory role in bullying. In summary, social media can provide new and valuable information about the causal factors and social issues associated with oral health-related bullying. Importantly, some coping mechanisms may mitigate the negative effects of bullying. © 2017 John Wiley & Sons Ltd.
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DiDonato, Kristen L; Liu, Yifei; Lindsey, Cameron C; Hartwig, David Matthew; Stoner, Steven C
2015-10-01
To determine patient perceptions of using a demonstration application (app) of mobile technology to improve medication adherence and to identify desired features to assist in the management of medications. A qualitative study using key informant interviews was conducted in a community pharmacy chain for patients aged 50 and older, on statin therapy and owning a smart device. Three main themes emerged from 24 interviews at four pharmacy locations, which included benefits, barriers and desired features of the app. Benefits such as accessibility, privacy, pros of appearance and beneficiaries were more likely to lead to usage of the app. Barriers that might prevent usage of the app were related to concerns of appearance, the burden it might cause for others, cost, privacy, motivation and reliability. Specific features patients desired were categorized under appearance, customization, communication, functionality, input and the app platform. Patients provided opinions about using a mobile app to improve medication adherence and assist with managing medications. Patients envisioned the app within their lifestyle and expressed important considerations, identifying benefits to using this technology and voicing relevant concerns. App developers can use patient perceptions to guide development of a mobile app addressing patient medication-related needs. © 2015 Royal Pharmaceutical Society.
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Extending information retrieval methods to personalized genomic-based studies of disease.
Ye, Shuyun; Dawson, John A; Kendziorski, Christina
2014-01-01
Genomic-based studies of disease now involve diverse types of data collected on large groups of patients. A major challenge facing statistical scientists is how best to combine the data, extract important features, and comprehensively characterize the ways in which they affect an individual's disease course and likelihood of response to treatment. We have developed a survival-supervised latent Dirichlet allocation (survLDA) modeling framework to address these challenges. Latent Dirichlet allocation (LDA) models have proven extremely effective at identifying themes common across large collections of text, but applications to genomics have been limited. Our framework extends LDA to the genome by considering each patient as a "document" with "text" detailing his/her clinical events and genomic state. We then further extend the framework to allow for supervision by a time-to-event response. The model enables the efficient identification of collections of clinical and genomic features that co-occur within patient subgroups, and then characterizes each patient by those features. An application of survLDA to The Cancer Genome Atlas ovarian project identifies informative patient subgroups showing differential response to treatment, and validation in an independent cohort demonstrates the potential for patient-specific inference.
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Banna, Jinan; Grace Lin, Meng-Fen; Stewart, Maria; Fialkowski, Marie K
2015-06-01
Fostering interaction in the online classroom is an important consideration in ensuring that students actively create their own knowledge and reach a high level of achievement in science courses. This study focuses on fostering interaction in an online introductory nutrition course offered in a public institution of higher education in Hawai'i, USA. Interactive features included synchronous discussions and polls in scheduled sessions, and social media tools for sharing of information and resources. Qualitative student feedback was solicited regarding the new course features. Findings indicated that students who attended monthly synchronous sessions valued live interaction with peers and the instructor. Issues identified included technical difficulties during synchronous sessions, lack of participation on the part of fellow students in discussion and inability to attend synchronous sessions due to scheduling conflicts. In addition, few students made use of the opportunity to interact via social media. While students indicated that the interactive components of the course were valuable, several areas in which improvement may be made remain. Future studies may explore potential solutions to issues identified with new features to further promote interaction and foster learning in the course. Recommendations for instructors who are interested in offering online science courses in higher education are provided.
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de Vries, Tamar I; Monroe, Glen R; van Belzen, Martine J; van der Lans, Christian A; Savelberg, Sanne Mc; Newman, William G; van Haaften, Gijs; Nievelstein, Rutger A; van Haelst, Mieke M
2016-08-01
Rubinstein-Taybi syndrome (RTS, OMIM 180849) and Filippi syndrome (FLPIS, OMIM 272440) are both rare syndromes, with multiple congenital anomalies and intellectual deficit (MCA/ID). We present a patient with intellectual deficit, short stature, bilateral syndactyly of hands and feet, broad thumbs, ocular abnormalities, and dysmorphic facial features. These clinical features suggest both RTS and FLPIS. Initial DNA analysis of DNA isolated from blood did not identify variants to confirm either of these syndrome diagnoses. Whole-exome sequencing identified a homozygous variant in C9orf173, which was novel at the time of analysis. Further Sanger sequencing analysis of FLPIS cases tested negative for CKAP2L variants did not, however, reveal any further variants. Subsequent analysis using DNA isolated from buccal mucosa revealed a mosaic variant in CREBBP. This report highlights the importance of excluding mosaic variants in patients with a strong but atypical clinical presentation of a MCA/ID syndrome if no disease-causing variants can be detected in DNA isolated from blood samples. As the striking syndactyly observed in the present case is typical for FLPIS, we suggest CREBBP analysis in saliva samples for FLPIS syndrome cases in which no causal CKAP2L variant is detected.
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Cortical processing of dynamic sound envelope transitions.
Zhou, Yi; Wang, Xiaoqin
2010-12-08
Slow envelope fluctuations in the range of 2-20 Hz provide important segmental cues for processing communication sounds. For a successful segmentation, a neural processor must capture envelope features associated with the rise and fall of signal energy, a process that is often challenged by the interference of background noise. This study investigated the neural representations of slowly varying envelopes in quiet and in background noise in the primary auditory cortex (A1) of awake marmoset monkeys. We characterized envelope features based on the local average and rate of change of sound level in envelope waveforms and identified envelope features to which neurons were selective by reverse correlation. Our results showed that envelope feature selectivity of A1 neurons was correlated with the degree of nonmonotonicity in their static rate-level functions. Nonmonotonic neurons exhibited greater feature selectivity than monotonic neurons in quiet and in background noise. The diverse envelope feature selectivity decreased spike-timing correlation among A1 neurons in response to the same envelope waveforms. As a result, the variability, but not the average, of the ensemble responses of A1 neurons represented more faithfully the dynamic transitions in low-frequency sound envelopes both in quiet and in background noise.
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Application of musical timbre discrimination features to active sonar classification
NASA Astrophysics Data System (ADS)
Young, Victor W.; Hines, Paul C.; Pecknold, Sean
2005-04-01
In musical acoustics significant effort has been devoted to uncovering the physical basis of timbre perception. Most investigations into timbre rely on multidimensional scaling (MDS), in which different musical sounds are arranged as points in multidimensional space. The Euclidean distance between points corresponds to the perceptual distance between sounds and the multidimensional axes are linked to measurable properties of the sounds. MDS has identified numerous temporal and spectral features believed to be important to timbre perception. There is reason to believe that some of these features may have wider application in the disparate field of underwater acoustics, since anecdotal evidence suggests active sonar returns from metallic objects sound different than natural clutter returns when auralized by human operators. This is particularly encouraging since attempts to develop robust automatic classifiers capable of target-clutter discrimination over a wide range of operational conditions have met with limited success. Spectral features relevant to target-clutter discrimination are believed to include click-pitch and envelope irregularity; relevant temporal features are believed to include duration, sub-band attack/decay time, and time separation pitch. Preliminary results from an investigation into the role of these timbre features in target-clutter discrimination will be presented. [Work supported by NSERC and GDC.
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NASA Astrophysics Data System (ADS)
Callicó Fortunato, Roberta; Benedito Durà, Vicent; Volpedo, Alejandra
2014-06-01
In the Northeastern Atlantic and Mediterranean Sea there are 8 species of the Mugilidae family: Mugil cephalus, Liza aurata, Liza ramada, Oedalechilus labeo, Chelon labrosus, Liza saliens, Liza carinata and Liza haematocheila. The identification of mugilids is very important for local fisheries management and regulations, but it is difficult using gross morphological characters. This work aims to contribute to the identification of mullets present in the Northeastern Atlantic Ocean and Mediterranean Sea using saccular otolith features of each species. Specimens of C. labrosus, L. aurata, L. ramada, L. saliens and M. cephalus were obtained from Delta del Ebro (40°38'N-0°44'E) in artisanal catches. For L. carinata and O. labeo photographs extracted from AFORO online database were used. L. haematocheila was not studied for lack of otolith samples. A general pattern of the saccular otoliths for this family was identified: the shape of the otoliths are rectangular to oblong with irregular margins; they present a heterosulcoid, ostial sulcus acusticus, with an open funnel-like ostium to the anterior margin and a closed, tubular cauda, ending towards the posterior ventral corner, always larger than the ostium. In the present study, the mugilid species could be recognized using their saccular otolith morphology. Here we give the first key to identify Northeastern Atlantic and Mediterranean mullets. The distinctive features between the species were the position and centrality of the sulcus, the curvature of the cauda, the presence of areal depositions and plateaus, and the type of anterior and posterior regions. These features could be used not only to reinforce the identification keys through morphological and meristic characters of the species, but also to identify the species consumed by piscivores, being the otoliths the only identifiable remains of the individuals.
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Multispectra CWT-based algorithm (MCWT) in mass spectra for peak extraction.
Hsueh, Huey-Miin; Kuo, Hsun-Chih; Tsai, Chen-An
2008-01-01
An important objective in mass spectrometry (MS) is to identify a set of biomarkers that can be used to potentially distinguish patients between distinct treatments (or conditions) from tens or hundreds of spectra. A common two-step approach involving peak extraction and quantification is employed to identify the features of scientific interest. The selected features are then used for further investigation to understand underlying biological mechanism of individual protein or for development of genomic biomarkers to early diagnosis. However, the use of inadequate or ineffective peak detection and peak alignment algorithms in peak extraction step may lead to a high rate of false positives. Also, it is crucial to reduce the false positive rate in detecting biomarkers from ten or hundreds of spectra. Here a new procedure is introduced for feature extraction in mass spectrometry data that extends the continuous wavelet transform-based (CWT-based) algorithm to multiple spectra. The proposed multispectra CWT-based algorithm (MCWT) not only can perform peak detection for multiple spectra but also carry out peak alignment at the same time. The author' MCWT algorithm constructs a reference, which integrates information of multiple raw spectra, for feature extraction. The algorithm is applied to a SELDI-TOF mass spectra data set provided by CAMDA 2006 with known polypeptide m/z positions. This new approach is easy to implement and it outperforms the existing peak extraction method from the Bioconductor PROcess package.
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A field study of air flow and turbulent features of advection fog
NASA Technical Reports Server (NTRS)
Connell, J. D.
1979-01-01
The setup and initial operation of a set of specialized meteorological data collection hardware are described. To study the life cycle of advection fogs at a lake test site, turbulence levels in the fog are identified, and correlated with the temperature gradients and mean wind profiles. A meteorological tower was instrumented to allow multiple-level measurements of wind and temperature on a continuous basis. Additional instrumentation was: (1)hydrothermograph, (2)microbarograph, (3)transmissometers, and (4)a boundary layer profiler. Two types of fogs were identified, and important differences in the turbulence scales were noted.
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Cheng, Qiang; Zhou, Hongbo; Cheng, Jie
2011-06-01
Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.
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Amaral, Katrina E; Palace, Michael; O'Brien, Kathleen M; Fenderson, Lindsey E; Kovach, Adrienne I
2016-01-01
Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists.
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Amaral, Katrina E.; Palace, Michael; O’Brien, Kathleen M.; Fenderson, Lindsey E.; Kovach, Adrienne I.
2016-01-01
Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists. PMID:26954014
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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.
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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
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Aksu, Yaman; Miller, David J; Kesidis, George; Yang, Qing X
2010-05-01
Feature selection for classification in high-dimensional spaces can improve generalization, reduce classifier complexity, and identify important, discriminating feature "markers." For support vector machine (SVM) classification, a widely used technique is recursive feature elimination (RFE). We demonstrate that RFE is not consistent with margin maximization, central to the SVM learning approach. We thus propose explicit margin-based feature elimination (MFE) for SVMs and demonstrate both improved margin and improved generalization, compared with RFE. Moreover, for the case of a nonlinear kernel, we show that RFE assumes that the squared weight vector 2-norm is strictly decreasing as features are eliminated. We demonstrate this is not true for the Gaussian kernel and, consequently, RFE may give poor results in this case. MFE for nonlinear kernels gives better margin and generalization. We also present an extension which achieves further margin gains, by optimizing only two degrees of freedom--the hyperplane's intercept and its squared 2-norm--with the weight vector orientation fixed. We finally introduce an extension that allows margin slackness. We compare against several alternatives, including RFE and a linear programming method that embeds feature selection within the classifier design. On high-dimensional gene microarray data sets, University of California at Irvine (UCI) repository data sets, and Alzheimer's disease brain image data, MFE methods give promising results.
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Investigation of kinematic features for dismount detection and tracking
NASA Astrophysics Data System (ADS)
Narayanaswami, Ranga; Tyurina, Anastasia; Diel, David; Mehra, Raman K.; Chinn, Janice M.
2012-05-01
With recent changes in threats and methods of warfighting and the use of unmanned aircrafts, ISR (Intelligence, Surveillance and Reconnaissance) activities have become critical to the military's efforts to maintain situational awareness and neutralize the enemy's activities. The identification and tracking of dismounts from surveillance video is an important step in this direction. Our approach combines advanced ultra fast registration techniques to identify moving objects with a classification algorithm based on both static and kinematic features of the objects. Our objective was to push the acceptable resolution beyond the capability of industry standard feature extraction methods such as SIFT (Scale Invariant Feature Transform) based features and inspired by it, SURF (Speeded-Up Robust Feature). Both of these methods utilize single frame images. We exploited the temporal component of the video signal to develop kinematic features. Of particular interest were the easily distinguishable frequencies characteristic of bipedal human versus quadrupedal animal motion. We examine limits of performance, frame rates and resolution required for human, animal and vehicles discrimination. A few seconds of video signal with the acceptable frame rate allow us to lower resolution requirements for individual frames as much as by a factor of five, which translates into the corresponding increase of the acceptable standoff distance between the sensor and the object of interest.
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Wells, Stephen A; van der Kamp, Marc W; McGeagh, John D; Mulholland, Adrian J
2015-01-01
Large-scale conformational change is a common feature in the catalytic cycles of enzymes. Many enzymes function as homodimers with active sites that contain elements from both chains. Symmetric and anti-symmetric cooperative motions in homodimers can potentially lead to correlated active site opening and/or closure, likely to be important for ligand binding and release. Here, we examine such motions in two different domain-swapped homodimeric enzymes: the DcpS scavenger decapping enzyme and citrate synthase. We use and compare two types of all-atom simulations: conventional molecular dynamics simulations to identify physically meaningful conformational ensembles, and rapid geometric simulations of flexible motion, biased along normal mode directions, to identify relevant motions encoded in the protein structure. The results indicate that the opening/closure motions are intrinsic features of both unliganded enzymes. In DcpS, conformational change is dominated by an anti-symmetric cooperative motion, causing one active site to close as the other opens; however a symmetric motion is also significant. In CS, we identify that both symmetric (suggested by crystallography) and asymmetric motions are features of the protein structure, and as a result the behaviour in solution is largely non-cooperative. The agreement between two modelling approaches using very different levels of theory indicates that the behaviours are indeed intrinsic to the protein structures. Geometric simulations correctly identify and explore large amplitudes of motion, while molecular dynamics simulations indicate the ranges of motion that are energetically feasible. Together, the simulation approaches are able to reveal unexpected functionally relevant motions, and highlight differences between enzymes.
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McGeagh, John D.; Mulholland, Adrian J.
2015-01-01
Large-scale conformational change is a common feature in the catalytic cycles of enzymes. Many enzymes function as homodimers with active sites that contain elements from both chains. Symmetric and anti-symmetric cooperative motions in homodimers can potentially lead to correlated active site opening and/or closure, likely to be important for ligand binding and release. Here, we examine such motions in two different domain-swapped homodimeric enzymes: the DcpS scavenger decapping enzyme and citrate synthase. We use and compare two types of all-atom simulations: conventional molecular dynamics simulations to identify physically meaningful conformational ensembles, and rapid geometric simulations of flexible motion, biased along normal mode directions, to identify relevant motions encoded in the protein structure. The results indicate that the opening/closure motions are intrinsic features of both unliganded enzymes. In DcpS, conformational change is dominated by an anti-symmetric cooperative motion, causing one active site to close as the other opens; however a symmetric motion is also significant. In CS, we identify that both symmetric (suggested by crystallography) and asymmetric motions are features of the protein structure, and as a result the behaviour in solution is largely non-cooperative. The agreement between two modelling approaches using very different levels of theory indicates that the behaviours are indeed intrinsic to the protein structures. Geometric simulations correctly identify and explore large amplitudes of motion, while molecular dynamics simulations indicate the ranges of motion that are energetically feasible. Together, the simulation approaches are able to reveal unexpected functionally relevant motions, and highlight differences between enzymes. PMID:26241964
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Aveling, Emma-Louise; Martin, Graham; Jiménez García, Senai; Martin, Lisa; Herbert, Georgia; Armstrong, Natalie; Dixon-Woods, Mary; Woolhouse, Ian
2012-12-01
Peer review offers a promising way of promoting improvement in health systems, but the optimal model is not yet clear. We aimed to describe a specific peer review model-reciprocal peer-to-peer review (RP2PR)-to identify the features that appeared to support optimal functioning. We conducted an ethnographic study involving observations, interviews and documentary analysis of the Improving Lung Cancer Outcomes Project, which involved 30 paired multidisciplinary lung cancer teams participating in facilitated reciprocal site visits. Analysis was based on the constant comparative method. Fundamental features of the model include multidisciplinary participation, a focus on discussion and observation of teams in action, rather than paperwork; facilitated reflection and discussion on data and observations; support to develop focused improvement plans. Five key features were identified as important in optimising this model: peers and pairing methods; minimising logistic burden; structure of visits; independent facilitation; and credibility of the process. Facilitated RP2PR was generally a positive experience for participants, but implementing improvement plans was challenging and required substantial support. RP2PR appears to be optimised when it is well organised; a safe environment for learning is created; credibility is maximised; implementation and impact are supported. RP2PR is seen as credible and legitimate by lung cancer teams and can act as a powerful stimulus to produce focused quality improvement plans and to support implementation. Our findings have identified how RP2PR functioned and may be optimised to provide a constructive, open space for identifying opportunities for improvement and solutions.
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Turmezei, T D; Lomas, D J; Hopper, M A; Poole, K E S
2014-10-01
Plain radiography has been the mainstay of imaging assessment in osteoarthritis for over 50 years, but it does have limitations. Here we present the methodology and results of a new technique for identifying, grading, and mapping the severity and spatial distribution of osteoarthritic disease features at the hip in 3D with clinical computed tomography (CT). CT imaging of 456 hips from 230 adult female volunteers (mean age 66 ± 17 years) was reviewed using 3D multiplanar reformatting to identify bone-related radiological features of osteoarthritis, namely osteophytes, subchondral cysts and joint space narrowing. Scoresheets dividing up the femoral head, head-neck region and the joint space were used to register the location and severity of each feature (scored from 0 to 3). Novel 3D cumulative feature severity maps were then created to display where the most severe disease features from each individual were anatomically located across the cohort. Feature severity maps showed a propensity for osteophytes at the inferoposterior and superolateral femoral head-neck junction. Subchondral cysts were a less common and less localised phenomenon. Joint space narrowing <1.5 mm was recorded in at least one sector of 83% of hips, but most frequently in the posterolateral joint space. This is the first description of hip osteoarthritis using unenhanced clinical CT in which we describe the co-localisation of posterior osteophytes and joint space narrowing for the first time. We believe this technique can perform several important roles in future osteoarthritis research, including phenotyping and sensitive disease assessment in 3D. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.
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Bacterial chemoreceptors: high-performance signaling in networked arrays.
Hazelbauer, Gerald L; Falke, Joseph J; Parkinson, John S
2008-01-01
Chemoreceptors are crucial components in the bacterial sensory systems that mediate chemotaxis. Chemotactic responses exhibit exquisite sensitivity, extensive dynamic range and precise adaptation. The mechanisms that mediate these high-performance functions involve not only actions of individual proteins but also interactions among clusters of components, localized in extensive patches of thousands of molecules. Recently, these patches have been imaged in native cells, important features of chemoreceptor structure and on-off switching have been identified, and new insights have been gained into the structural basis and functional consequences of higher order interactions among sensory components. These new data suggest multiple levels of molecular interactions, each of which contribute specific functional features and together create a sophisticated signaling device.
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Bacterial chemoreceptors: high-performance signaling in networked arrays
Hazelbauer, Gerald L.; Falke, Joseph J.; Parkinson, John S.
2010-01-01
Chemoreceptors are crucial components in the bacterial sensory systems that mediate chemotaxis. Chemotactic responses exhibit exquisite sensitivity, extensive dynamic range and precise adaptation. The mechanisms that mediate these high-performance functions involve not only actions of individual proteins but also interactions among clusters of components, localized in extensive patches of thousands of molecules. Recently, these patches have been imaged in native cells, important features of chemoreceptor structure and on–off switching have been identified, and new insights have been gained into the structural basis and functional consequences of higher order interactions among sensory components. These new data suggest multiple levels of molecular interactions, each of which contribute specific functional features and together create a sophisticated signaling device. PMID:18165013
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Island of Hawaii, State of Hawaii seen from Skylab
NASA Technical Reports Server (NTRS)
1974-01-01
A vertical view of the Island of Hawaii, State of Hawaii (19.5N, 155.5W), as photographed from the Skylab space station in Earth orbit by a Skylab 4 crewman. This photograph, taken on January 8, 1974, is very useful in studies of volcanic areas. Prominent volcanic features such as the summit caldera on Mauna Loa, the extinct volcano Mauna Kea, the Kilauea caldera, and the pit crater at Halo Mau Mau within the caldera are easily identified. Kilauea was undergoing frequent eruption during the mission. Detailed features such as the extent and delineation of historic lava flows on Mauna Loa can be determined and are important parameters in volcanic studies.
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Microresonator soliton dual-comb spectroscopy
NASA Astrophysics Data System (ADS)
Suh, Myoung-Gyun; Yang, Qi-Fan; Yang, Ki Youl; Yi, Xu; Vahala, Kerry J.
2016-11-01
Measurement of optical and vibrational spectra with high resolution provides a way to identify chemical species in cluttered environments and is of general importance in many fields. Dual-comb spectroscopy has emerged as a powerful approach for acquiring nearly instantaneous Raman and optical spectra with unprecedented resolution. Spectra are generated directly in the electrical domain, without the need for bulky mechanical spectrometers. We demonstrate a miniature soliton-based dual-comb system that can potentially transfer the approach to a chip platform. These devices achieve high-coherence pulsed mode locking. They also feature broad, reproducible spectral envelopes, an essential feature for dual-comb spectroscopy. Our work shows the potential for integrated spectroscopy with high signal-to-noise ratios and fast acquisition rates.
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Noise and autism spectrum disorder in children: An exploratory survey.
Kanakri, Shireen M; Shepley, Mardelle; Varni, James W; Tassinary, Louis G
2017-04-01
With more students being educated in schools for Autism Spectrum Disorder (ASD) than ever before, architects and interior designers need to consider the environmental features that may be modified to enhance the academic and social success of autistic students in school. This study explored existing empirical research on the impact of noise on children with ASD and provides recommendations regarding design features that can contribute to noise reduction. A survey, which addressed the impact of architectural design elements on autism-related behavior, was developed for teachers of children with ASD and distributed to three schools. Most teachers found noise control to be an important issue for students with autism and many observed children using ear defenders. In terms of managing issues related to noise, most teachers agreed that thick or soundproof walls and carpet in the classroom were the most important issues for children with ASD. Suggested future research should address architectural considerations for building an acoustically friendly environment for children with autism, identifying patterns of problematic behaviors in response to acoustical features of the built environment of the classroom setting, and ways to manage maladaptive behaviors in acoustically unfriendly environments. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Noronha, Jyothi M; Liu, Mengya; Squires, R Burke; Pickett, Brett E; Hale, Benjamin G; Air, Gillian M; Galloway, Summer E; Takimoto, Toru; Schmolke, Mirco; Hunt, Victoria; Klem, Edward; García-Sastre, Adolfo; McGee, Monnie; Scheuermann, Richard H
2012-05-01
Genetic drift of influenza virus genomic sequences occurs through the combined effects of sequence alterations introduced by a low-fidelity polymerase and the varying selective pressures experienced as the virus migrates through different host environments. While traditional phylogenetic analysis is useful in tracking the evolutionary heritage of these viruses, the specific genetic determinants that dictate important phenotypic characteristics are often difficult to discern within the complex genetic background arising through evolution. Here we describe a novel influenza virus sequence feature variant type (Flu-SFVT) approach, made available through the public Influenza Research Database resource (www.fludb.org), in which variant types (VTs) identified in defined influenza virus protein sequence features (SFs) are used for genotype-phenotype association studies. Since SFs have been defined for all influenza virus proteins based on known structural, functional, and immune epitope recognition properties, the Flu-SFVT approach allows the rapid identification of the molecular genetic determinants of important influenza virus characteristics and their connection to underlying biological functions. We demonstrate the use of the SFVT approach to obtain statistical evidence for effects of NS1 protein sequence variations in dictating influenza virus host range restriction.
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Lu, Bin; Yang, Yi; Sharma, Santosh K; Zambare, Prachi; Madane, Mayura A
2014-12-23
A method identifies electric load types of a plurality of different electric loads. The method includes providing a load feature database of a plurality of different electric load types, each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal and a current signal for each of the different electric loads; determining a second load feature vector comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types of the load feature database.
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The life-cycle of upper-tropospheric jet streams identified with a novel data segmentation algorithm
NASA Astrophysics Data System (ADS)
Limbach, S.; Schömer, E.; Wernli, H.
2010-09-01
Jet streams are prominent features of the upper-tropospheric atmospheric flow. Through the thermal wind relationship these regions with intense horizontal wind speed (typically larger than 30 m/s) are associated with pronounced baroclinicity, i.e., with regions where extratropical cyclones develop due to baroclinic instability processes. Individual jet streams are non-stationary elongated features that can extend over more than 2000 km in the along-flow and 200-500 km in the across-flow direction, respectively. Their lifetime can vary between a few days and several weeks. In recent years, feature-based algorithms have been developed that allow compiling synoptic climatologies and typologies of upper-tropospheric jet streams based upon objective selection criteria and climatological reanalysis datasets. In this study a novel algorithm to efficiently identify jet streams using an extended region-growing segmentation approach is introduced. This algorithm iterates over a 4-dimensional field of horizontal wind speed from ECMWF analyses and decides at each grid point whether all prerequisites for a jet stream are met. In a single pass the algorithm keeps track of all adjacencies of these grid points and creates the 4-dimensional connected segments associated with each jet stream. In addition to the detection of these sets of connected grid points, the algorithm analyzes the development over time of the distinct 3-dimensional features each segment consists of. Important events in the development of these features, for example mergings and splittings, are detected and analyzed on a per-grid-point and per-feature basis. The output of the algorithm consists of the actual sets of grid-points augmented with information about the particular events, and of the so-called event graphs, which are an abstract representation of the distinct 3-dimensional features and events of each segment. This technique provides comprehensive information about the frequency of upper-tropospheric jet streams, their preferred regions of genesis, merging, splitting, and lysis, and statistical information about their size, amplitude and lifetime. The presentation will introduce the technique, provide example visualizations of the time evolution of the identified 3-dimensional jet stream features, and present results from a first multi-month "climatology" of upper-tropospheric jets. In the future, the technique can be applied to longer datasets, for instance reanalyses and output from global climate model simulations - and provide detailed information about key characteristics of jet stream life cycles.
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Special requirements for electronic health record systems in ophthalmology.
Chiang, Michael F; Boland, Michael V; Brewer, Allen; Epley, K David; Horton, Mark B; Lim, Michele C; McCannel, Colin A; Patel, Sayjal J; Silverstone, David E; Wedemeyer, Linda; Lum, Flora
2011-08-01
The field of ophthalmology has a number of unique features compared with other medical and surgical specialties regarding clinical workflow and data management. This has important implications for the design of electronic health record (EHR) systems that can be used intuitively and efficiently by ophthalmologists and that can promote improved quality of care. Ophthalmologists often lament the absence of these specialty-specific features in EHRs, particularly in systems that were developed originally for primary care physicians or other medical specialists. The purpose of this article is to summarize the special requirements of EHRs that are important for ophthalmology. The hope is that this will help ophthalmologists to identify important features when searching for EHR systems, to stimulate vendors to recognize and incorporate these functions into systems, and to assist federal agencies to develop future guidelines regarding meaningful use of EHRs. More broadly, the American Academy of Ophthalmology believes that these functions are elements of good system design that will improve access to relevant information at the point of care between the ophthalmologist and the patient, will enhance timely communications between primary care providers and ophthalmologists, will mitigate risk, and ultimately will improve the ability of physicians to deliver the highest-quality medical care. Proprietary or commercial interest disclosure may be found after the references. Copyright © 2011 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.
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Soderquist, Craig R; Ewalt, Mark D; Czuchlewski, David R; Geyer, Julia T; Rogers, Heesun J; Hsi, Eric D; Wang, Sa A; Bueso-Ramos, Carlos E; Orazi, Attilio; Arber, Daniel A; Hexner, Elizabeth O; Babushok, Daria V; Bagg, Adam
2018-05-01
Myeloproliferative neoplasms arise from hematopoietic stem cells with somatically altered tyrosine kinase signaling. Classification of myeloproliferative neoplasms is based on hematologic, histopathologic and molecular characteristics including the presence of the BCR-ABL1 and JAK2 V617F. Although thought to be mutually exclusive, a number of cases with co-occurring BCR-ABL1 and JAK2 V617F have been identified. To characterize the clinicopathologic features of myeloproliferative neoplasms with concomitant BCR-ABL1 and JAK2 V617F, and define the frequency of co-occurrence, we conducted a retrospective multi-institutional study. Cases were identified using a search of electronic databases over a decade at six major institutions. Of 1570 patients who were tested for both BCR-ABL1 and JAK2 V617F, six were positive for both. An additional five patients were identified via clinical records providing a total of 11 cases for detailed evaluation. For each case, clinical variables, hematologic and genetic data, and bone marrow histomorphologic features were analyzed. The sequence of identification of the genetic abnormalities varied: five patients were initially diagnosed with a JAK2 V617F+ myeloproliferative neoplasm, one patient initially had BCR-ABL1+ chronic myeloid leukemia, while both alterations were identified simultaneously in five patients. Classification of the BCR-ABL1-negative myeloproliferative neoplasms varied, and in some cases, features only became apparent following tyrosine kinase inhibitor therapy. Seven of the 11 patients showed myelofibrosis, in some cases before identification of the second genetic alteration. Our data, reflecting the largest reported study comprehensively detailing clinicopathologic features and response to therapy, show that the co-occurrence of BCR-ABL1 and JAK2 V617F is rare, with an estimated frequency of 0.4%, and most often reflects two distinct ('composite') myeloproliferative neoplasms. Although uncommon, it is important to be aware of this potentially confounding genetic combination, lest these features be misinterpreted to reflect resistance to therapy or disease progression, considerations that could lead to inappropriate management.
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ERIC Educational Resources Information Center
Copa, George H.; Pease, Virginia H.
A 2-year project to specify new designs for the comprehensive high school of the future identified the following important features: (1) a guaranteed set of learner outcomes closely linked to present and future life roles and responsibilities for all students; (2) learning applied to life situations, using authentic assessment; (3) multiple ways…
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Self-organization and clustering algorithms
NASA Technical Reports Server (NTRS)
Bezdek, James C.
1991-01-01
Kohonen's feature maps approach to clustering is often likened to the k or c-means clustering algorithms. Here, the author identifies some similarities and differences between the hard and fuzzy c-Means (HCM/FCM) or ISODATA algorithms and Kohonen's self-organizing approach. The author concludes that some differences are significant, but at the same time there may be some important unknown relationships between the two methodologies. Several avenues of research are proposed.
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ERIC Educational Resources Information Center
General Accounting Office, Washington, DC. Program Evaluation and Methodology Div.
The importance of identifying effective drug abuse prevention strategies is clear not only from the increasing federal expenses, but also from the costs to individuals and families, and the overall damage to U.S. productivity and competitiveness caused by drug use. This report is about comprehensive community-based approaches to drug abuse…
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Silva, Patrícia S.; Tauber, Catherine A.; Albuquerque, Gilberto S.; Tauber, Maurice J.
2013-01-01
Abstract An expanded list of generic level larval characteristics is presented for Chrysopodes; it includes a reinterpretation of the mesothoracic and metathoracic structure and setation. Keys, descriptions and images of Semaphoront A (first instar) and Semaphoront B (second and third instars) are offered for identifying five species of Chrysopodes (Chrysopodes) that are commonly reported from horticultural habitats in the Neotropical region. PMID:23653514
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Chinstrap penguin foraging area associated with a seamount in Bransfield Strait, Antarctica
NASA Astrophysics Data System (ADS)
Kokubun, Nobuo; Lee, Won Young; Kim, Jeong-Hoon; Takahashi, Akinori
2015-12-01
Identifying marine features that support high foraging performance of predators is useful to determine areas of ecological importance. This study aimed to identify marine features that are important for foraging of chinstrap penguins (Pygoscelis antarcticus), an abundant upper-trophic level predator in the Antarctic Peninsula region. We investigated the foraging locations of penguins breeding on King George Island using GPS-depth loggers. Tracking data from 18 birds (4232 dives), 11 birds (2095 dives), and 19 birds (3947 dives) were obtained in 2007, 2010, and 2015, respectively. In all three years, penguins frequently visited an area near a seamount (Orca Seamount) in Bransfield Strait. The percentage of dives (27.8% in 2007, 36.1% in 2010, and 19.1% in 2015) and depth wiggles (27.1% in 2007, 37.2% in 2010, and 22.3% in 2015) performed in this area was higher than that expected from the size of the area and distance from the colony (8.4% for 2007, 14.7% for 2010, and 6.3% for 2015). Stomach content analysis showed that the penguins fed mainly on Antarctic krill. These results suggest that the seamount provided a favorable foraging area for breeding chinstrap penguins, with high availability of Antarctic krill, possibly related to local upwelling.
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How Can Public Health Approaches and Perspectives Advance Hearing Health Care?
Reavis, Kelly M; Tremblay, Kelly L; Saunders, Gabrielle
2016-01-01
This commentary explores the role of public health programs and themes on hearing health care. Ongoing engagement within the hearing professional community is needed to determine how to change the landscape and identify important features in the evolution of population hearing health care. Why and how to leverage existing public health programs and develop new programs to improve hearing health in older individuals is an important topic. Hearing professionals are encouraged to reflect on these themes and recommendations and join the discussion about the future of hearing science on a population level. PMID:27232072
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A review of clinical practice guidelines for lung cancer
Ball, David; Silvestri, Gerard A.
2013-01-01
Clinical practice guidelines are important evidence-based resources to guide complex clinical decision making. However, it is challenging for health professionals to keep abreast available guidelines and to know how and where to access relevant guidelines. This review examines currently available guidelines for lung cancer published in the English language. Important key features are listed for each identified guideline. The methodology, approaches to dissemination and implementation, and associated resources are summarised. General challenges in the area of guideline development are highlighted. The potential to collaborate more widely across lung cancer guideline developers by sharing literature searches and assessments is discussed. PMID:24163752
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Corwin, Lisa A.; Runyon, Christopher; Robinson, Aspen; Dolan, Erin L.
2015-01-01
Course-based undergraduate research experiences (CUREs) are increasingly being offered as scalable ways to involve undergraduates in research. Yet few if any design features that make CUREs effective have been identified. We developed a 17-item survey instrument, the Laboratory Course Assessment Survey (LCAS), that measures students’ perceptions of three design features of biology lab courses: 1) collaboration, 2) discovery and relevance, and 3) iteration. We assessed the psychometric properties of the LCAS using established methods for instrument design and validation. We also assessed the ability of the LCAS to differentiate between CUREs and traditional laboratory courses, and found that the discovery and relevance and iteration scales differentiated between these groups. Our results indicate that the LCAS is suited for characterizing and comparing undergraduate biology lab courses and should be useful for determining the relative importance of the three design features for achieving student outcomes. PMID:26466990
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Principal curve detection in complicated graph images
NASA Astrophysics Data System (ADS)
Liu, Yuncai; Huang, Thomas S.
2001-09-01
Finding principal curves in an image is an important low level processing in computer vision and pattern recognition. Principal curves are those curves in an image that represent boundaries or contours of objects of interest. In general, a principal curve should be smooth with certain length constraint and allow either smooth or sharp turning. In this paper, we present a method that can efficiently detect principal curves in complicated map images. For a given feature image, obtained from edge detection of an intensity image or thinning operation of a pictorial map image, the feature image is first converted to a graph representation. In graph image domain, the operation of principal curve detection is performed to identify useful image features. The shortest path and directional deviation schemes are used in our algorithm os principal verve detection, which is proven to be very efficient working with real graph images.
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LRRTM1 underlies synaptic convergence in visual thalamus
Monavarfeshani, Aboozar; Stanton, Gail; Van Name, Jonathan; Su, Kaiwen; Mills, William A; Swilling, Kenya; Kerr, Alicia; Huebschman, Natalie A; Su, Jianmin
2018-01-01
It has long been thought that the mammalian visual system is organized into parallel pathways, with incoming visual signals being parsed in the retina based on feature (e.g. color, contrast and motion) and then transmitted to the brain in unmixed, feature-specific channels. To faithfully convey feature-specific information from retina to cortex, thalamic relay cells must receive inputs from only a small number of functionally similar retinal ganglion cells. However, recent studies challenged this by revealing substantial levels of retinal convergence onto relay cells. Here, we sought to identify mechanisms responsible for the assembly of such convergence. Using an unbiased transcriptomics approach and targeted mutant mice, we discovered a critical role for the synaptic adhesion molecule Leucine Rich Repeat Transmembrane Neuronal 1 (LRRTM1) in the emergence of retinothalamic convergence. Importantly, LRRTM1 mutant mice display impairment in visual behaviors, suggesting a functional role of retinothalamic convergence in vision. PMID:29424692
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Ning, Kaida; Chen, Bo; Sun, Fengzhu; Hobel, Zachary; Zhao, Lu; Matloff, Will; Toga, Arthur W
2018-08-01
A long-standing question is how to best use brain morphometric and genetic data to distinguish Alzheimer's disease (AD) patients from cognitively normal (CN) subjects and to predict those who will progress from mild cognitive impairment (MCI) to AD. Here, we use a neural network (NN) framework on both magnetic resonance imaging-derived quantitative structural brain measures and genetic data to address this question. We tested the effectiveness of NN models in classifying and predicting AD. We further performed a novel analysis of the NN model to gain insight into the most predictive imaging and genetics features and to identify possible interactions between features that affect AD risk. Data were obtained from the AD Neuroimaging Initiative cohort and included baseline structural MRI data and single nucleotide polymorphism (SNP) data for 138 AD patients, 225 CN subjects, and 358 MCI patients. We found that NN models with both brain and SNP features as predictors perform significantly better than models with either alone in classifying AD and CN subjects, with an area under the receiver operating characteristic curve (AUC) of 0.992, and in predicting the progression from MCI to AD (AUC=0.835). The most important predictors in the NN model were the left middle temporal gyrus volume, the left hippocampus volume, the right entorhinal cortex volume, and the APOE (a gene that encodes apolipoprotein E) ɛ4 risk allele. Furthermore, we identified interactions between the right parahippocampal gyrus and the right lateral occipital gyrus, the right banks of the superior temporal sulcus and the left posterior cingulate, and SNP rs10838725 and the left lateral occipital gyrus. Our work shows the ability of NN models to not only classify and predict AD occurrence but also to identify important AD risk factors and interactions among them. Copyright © 2018 Elsevier Inc. All rights reserved.
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Fine-Scale Analysis Reveals Cryptic Landscape Genetic Structure in Desert Tortoises
Latch, Emily K.; Boarman, William I.; Walde, Andrew; Fleischer, Robert C.
2011-01-01
Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately. PMID:22132143
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Fine-scale analysis reveals cryptic landscape genetic structure in desert tortoises.
Latch, Emily K; Boarman, William I; Walde, Andrew; Fleischer, Robert C
2011-01-01
Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately.
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Clinical features distinguishing grief from depressive episodes: A qualitative analysis.
Parker, Gordon; McCraw, Stacey; Paterson, Amelia
2015-05-01
The independence or interdependence of grief and major depression has been keenly argued in relation to recent DSM definitions and encouraged the current study. We report a phenomenological study seeking to identify the experiential and phenomenological differences between depression and grief as judged qualitatively by those who had experienced clinical (n=125) or non-clinical depressive states (n=28). Analyses involving the whole sample indicated that, in contrast to grief, depression involved feelings of hopelessness and helplessness, being endless and was associated with a lack of control, having an internal self-focus impacting on self-esteem, being more severe and stressful, being marked by physical symptoms and often lacking a justifiable cause. Grief was distinguished from depression by the individual viewing their experience as natural and to be expected, a consequence of a loss, and with an external focus (i.e. the loss of the other). Some identified differences may have reflected the impact of depressive "type" (e.g. melancholia) rather than depression per se, and argue for a two-tiered model differentiating normative depressive and grief states at their base level and then "clinical" depressive and 'pathological' grief states by their associated clinical features. Comparative analyses between the clinical and non-clinical groups were limited by the latter sub-set being few in number. The provision of definitions may have shaped subjects׳ nominated differentiating features. The study identified a distinct number of phenomenological and clinical differences between grief and depression and few shared features, but more importantly, argued for the development of a two-tiered model defining both base states and clinical expressions. Copyright © 2015 Elsevier B.V. All rights reserved.
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Spectroscopic remote sensing for material identification, vegetation characterization, and mapping
Kokaly, Raymond F.; Lewis, Paul E.; Shen, Sylvia S.
2012-01-01
Identifying materials by measuring and analyzing their reflectance spectra has been an important procedure in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow materials to be mapped across the landscape. With many existing airborne sensors and new satellite-borne sensors planned for the future, robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral feature analyses of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described. MICA is a module of the PRISM (Processing Routines in IDL for Spectroscopic Measurements) software, available to the public from the U.S. Geological Survey (USGS) at http://pubs.usgs.gov/of/2011/1155/. The core concepts of MICA include continuum removal and linear regression to compare key diagnostic absorption features in reference laboratory/field spectra and the spectra being analyzed. The reference spectra, diagnostic features, and threshold constraints are defined within a user-developed MICA command file (MCF). Building on several decades of experience in mineral mapping, a broadly-applicable MCF was developed to detect a set of minerals frequently occurring on the Earth's surface and applied to map minerals in the country-wide coverage of the 2007 Afghanistan HyMap data set. MICA has also been applied to detect sub-pixel oil contamination in marshes impacted by the Deepwater Horizon incident by discriminating the C-H absorption features in oil residues from background vegetation. These two recent examples demonstrate the utility of a spectroscopic approach to remote sensing for identifying and mapping the distributions of materials in imaging spectrometer data.
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Wilkinson, Sarah; Ogée, Jérôme; Domec, Jean-Christophe; Rayment, Mark; Wingate, Lisa
2015-03-01
Process-based models that link seasonally varying environmental signals to morphological features within tree rings are essential tools to predict tree growth response and commercially important wood quality traits under future climate scenarios. This study evaluated model portrayal of radial growth and wood anatomy observations within a mature maritime pine (Pinus pinaster (L.) Aït.) stand exposed to seasonal droughts. Intra-annual variations in tracheid anatomy and wood density were identified through image analysis and X-ray densitometry on stem cores covering the growth period 1999-2010. A cambial growth model was integrated with modelled plant water status and sugar availability from the soil-plant-atmosphere transfer model MuSICA to generate estimates of cell number, cell volume, cell mass and wood density on a weekly time step. The model successfully predicted inter-annual variations in cell number, ring width and maximum wood density. The model was also able to predict the occurrence of special anatomical features such as intra-annual density fluctuations (IADFs) in growth rings. Since cell wall thickness remained surprisingly constant within and between growth rings, variations in wood density were primarily the result of variations in lumen diameter, both in the model and anatomical data. In the model, changes in plant water status were identified as the main driver of the IADFs through a direct effect on cell volume. The anatomy data also revealed that a trade-off existed between hydraulic safety and hydraulic efficiency. Although a simplified description of cambial physiology is presented, this integrated modelling approach shows potential value for identifying universal patterns of tree-ring growth and anatomical features over a broad climatic gradient. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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Rough sets and Laplacian score based cost-sensitive feature selection
Yu, Shenglong
2018-01-01
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884
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Rough sets and Laplacian score based cost-sensitive feature selection.
Yu, Shenglong; Zhao, Hong
2018-01-01
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.
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Predicting Key Events in the Popularity Evolution of Online Information.
Hu, Ying; Hu, Changjun; Fu, Shushen; Fang, Mingzhe; Xu, Wenwen
2017-01-01
The popularity of online information generally experiences a rising and falling evolution. This paper considers the "burst", "peak", and "fade" key events together as a representative summary of popularity evolution. We propose a novel prediction task-predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify "burst", "peak", and "fade" in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution.
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Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review.
Sudarshan, Vidya K; Mookiah, Muthu Rama Krishnan; Acharya, U Rajendra; Chandran, Vinod; Molinari, Filippo; Fujita, Hamido; Ng, Kwan Hoong
2016-02-01
Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Predicting Key Events in the Popularity Evolution of Online Information
Fu, Shushen; Fang, Mingzhe; Xu, Wenwen
2017-01-01
The popularity of online information generally experiences a rising and falling evolution. This paper considers the “burst”, “peak”, and “fade” key events together as a representative summary of popularity evolution. We propose a novel prediction task—predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify “burst”, “peak”, and “fade” in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution. PMID:28046121
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Spatial Analysis of Geohazards using ArcGIS--A web-based Course.
NASA Astrophysics Data System (ADS)
Harbert, W.; Davis, D.
2003-12-01
As part of the Environmental Systems Research Incorporated (ESRI) Virtual Campus program, a course was designed to present the benefits of Geographical Information Systems (GIS) based spatial analysis as applied towards a variety of geohazards. We created this on-line ArcGIS 8.x-based course to aid the motivated student or professional in his or her efforts to use GIS in determining where geohazards are likely to occur and for assessing their potential impact on the human community. Our course is broadly designed for earth scientists, public sector professionals, students, and others who want to apply GIS to the study of geohazards. Participants work with ArcGIS software and diverse datasets to display, visualize and analyze a wide variety of data sets and map a variety of geohazards including earthquakes, volcanoes, landslides, tsunamis, and floods. Following the GIS-based methodology of posing a question, decomposing the question into specific criteria, applying the criteria to spatial or tabular geodatasets and then analyzing feature relationships, from the beginning the course content was designed in order to enable the motivated student to answer questions. For example, to explain the relationship between earth quake location, earthquake depth, and plate boundaries; use a seismic hazard map to identify population and features at risk from an earthquake; import data from an earthquake catalog and visualize these data in 3D; explain the relationship between earthquake damage and local geology; use a flood scenario map to identify features at risk for forecast river discharges; use a tsunami inundation map to identify population and features at risk from tsunami; use a hurricane inundation map to identify the population at risk for any given category hurricane; estimate accumulated precipitation by integrating time-series Doppler radar data; and model a real-life landslide event. The six on-line modules for our course are Earthquakes I, Earthquakes II, Volcanoes, Floods, Coastal Geohazards and Landslides. Earthquake I can be viewed and accessed for no cost at http://campus.esri.com.
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Akuta, Norio; Kawamura, Yusuke; Arase, Yasuji; Suzuki, Fumitaka; Sezaki, Hitomi; Hosaka, Tetsuya; Kobayashi, Masahiro; Kobayashi, Mariko; Saitoh, Satoshi; Suzuki, Yoshiyuki; Ikeda, Kenji; Kumada, Hiromitsu
2016-05-23
It is important to determine the noninvasive parameters of histological features in nonalcoholic fatty liver disease (NAFLD). The aim of this study was to investigate the value of genetic variations as surrogate markers of histological features. The parameters that affected the histological features of NAFLD were investigated in 211 Japanese patients with biopsy-proven NAFLD. The relationships between genetic variations in PNPLA3 rs738409 or TM6SF2 rs58542926 and histological features were analyzed. Furthermore, the impact of genetic variations that affected the pathological criteria for the diagnosis of nonalcoholic steatohepatitis (NASH) (Matteoni classification and NAFLD activity score) was evaluated. The fibrosis stage of PNPLA3 GG was significantly more progressive than that of CG by multiple comparisons. Multivariate analysis identified PNPLA3 genotypes as predictors of fibrosis of stage 2 or more, but the impact tended to decrease at stage 3 or greater. There were no significant differences among the histological features of the three genotypes of TM6SF2. PNPLA3 genotypes partly affected the definition of NASH by the NAFLD activity score, but TM6SF2 genotypes did not affect the definition of NASH. In Japanese patients with biopsy-proven NAFLD, PNPLA3 genotypes may partly affect histological features, including stage of fibrosis, but the TM6SF2 genotype does not affect histological features.
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Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing
2018-05-15
Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Mairota, Paola; Cafarelli, Barbara; Labadessa, Rocco; Lovergine, Francesco; Tarantino, Cristina; Lucas, Richard M.; Nagendra, Harini; Didham, Raphael K.
2015-05-01
Monitoring the status and future trends in biodiversity can be prohibitively expensive using ground-based surveys. Consequently, significant effort is being invested in the use of satellite remote sensing to represent aspects of the proximate mechanisms (e.g., resource availability) that can be related to biodiversity surrogates (BS) such as species community descriptors. We explored the potential of very high resolution (VHR) satellite Earth observation (EO) features as proxies for habitat structural attributes that influence spatial variation in habitat quality and biodiversity change. In a semi-natural grassland mosaic of conservation concern in southern Italy, we employed a hierarchical nested sampling strategy to collect field and VHR-EO data across three spatial extent levels (landscape, patch and plot). Species incidence and abundance data were collected at the plot level for plant, insect and bird functional groups. Spectral and textural VHR-EO image features were derived from a Worldview-2 image. Three window sizes (grains) were tested for analysis and computation of textural features, guided by the perception limits of different organisms. The modelled relationships between VHR-EO features and BS responses differed across scales, suggesting that landscape, patch and plot levels are respectively most appropriate when dealing with birds, plants and insects. This research demonstrates the potential of VHR-EO for biodiversity mapping and habitat modelling, and highlights the importance of identifying the appropriate scale of analysis for specific taxonomic groups of interest. Further, textural features are important in the modelling of functional group-specific indices which represent BS in high conservation value habitat types, and provide a more direct link to species interaction networks and ecosystem functioning, than provided by traditional taxonomic diversity indices.
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Hereditary myopathies with early respiratory insufficiency in adults.
Naddaf, Elie; Milone, Margherita
2017-11-01
Hereditary myopathies with early respiratory insufficiency as a predominant feature of the clinical phenotype are uncommon and underestimated in adults. We reviewed the clinical and laboratory data of patients with hereditary myopathies who demonstrated early respiratory insufficiency before the need for ambulatory assistance. Only patients with disease-causing mutations or a specific histopathological diagnosis were included. Patients with cardiomyopathy were excluded. We identified 22 patients; half had isolated respiratory symptoms at onset. The diagnosis of the myopathy was often delayed, resulting in delayed ventilatory support. The most common myopathies were adult-onset Pompe disease, myofibrillar myopathy, multi-minicore disease, and myotonic dystrophy type 1. Single cases of laminopathy, MELAS (mitochondrial encephalomyopathy with lactic acidosis and strokelike events), centronuclear myopathy, and cytoplasmic body myopathy were identified. We highlighted the most common hereditary myopathies associated with early respiratory insufficiency as the predominant clinical feature, and underscored the importance of a timely diagnosis for patient care. Muscle Nerve 56: 881-886, 2017. © 2017 Wiley Periodicals, Inc.
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Optimal designs for prediction studies of whiplash.
Kamper, Steven J; Hancock, Mark J; Maher, Christopher G
2011-12-01
Commentary. To provide guidance for the design and interpretation of predictive studies of whiplash associated disorders (WAD). Numerous studies have sought to define and explain the clinical course and response to treatment of people with WAD. Design of these studies is often suboptimal, which can lead to biased findings and issues with interpreting the results. Literature review and commentary. Predictive studies can be grouped into four broad categories; studies of symptomatic course, studies that aim to identify factors that predict outcome, studies that aim to isolate variables that are causally responsible for outcome, and studies that aim to identify patients who respond best to particular treatments. Although the specific research question will determine the optimal methods, there are a number of generic features that should be incorporated into design of such studies. The aim of these features is to minimize bias, generate adequately precise prognostic estimates, and ensure generalizability of the findings. This paper provides a summary of important considerations in the design, conduct, and reporting of prediction studies in the field of whiplash.
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NASA Technical Reports Server (NTRS)
Poulton, C. E.
1975-01-01
Comparative statistics were presented on the capability of LANDSAT-1 and three of the Skylab remote sensing systems (S-190A, S-190B, S-192) for the recognition and inventory of analogous natural vegetations and landscape features important in resource allocation and management. Two analogous regions presenting vegetational zonation from salt desert to alpine conditions above the timberline were observed, emphasizing the visual interpretation mode in the investigation. An hierarchical legend system was used as the basic classification of all land surface features. Comparative tests were run on image identifiability with the different sensor systems, and mapping and interpretation tests were made both in monocular and stereo interpretation with all systems except the S-192. Significant advantage was found in the use of stereo from space when image analysis is by visual or visual-machine-aided interactive systems. Some cost factors in mapping from space are identified. The various image types are compared and an operational system is postulated.
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Social-aware data dissemination in opportunistic mobile social networks
NASA Astrophysics Data System (ADS)
Yang, Yibo; Zhao, Honglin; Ma, Jinlong; Han, Xiaowei
Opportunistic Mobile Social Networks (OMSNs), formed by mobile users with social relationships and characteristics, enhance spontaneous communication among users that opportunistically encounter each other. Such networks can be exploited to improve the performance of data forwarding. Discovering optimal relay nodes is one of the important issues for efficient data propagation in OMSNs. Although traditional centrality definitions to identify the nodes features in network, they cannot identify effectively the influential nodes for data dissemination in OMSNs. Existing protocols take advantage of spatial contact frequency and social characteristics to enhance transmission performance. However, existing protocols have not fully exploited the benefits of the relations and the effects between geographical information, social features and user interests. In this paper, we first evaluate these three characteristics of users and design a routing protocol called Geo-Social-Interest (GSI) protocol to select optimal relay nodes. We compare the performance of GSI using real INFOCOM06 data sets. The experiment results demonstrate that GSI overperforms the other protocols with highest data delivery ratio and low communication overhead.
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Gandomkar, Ziba; Brennan, Patrick C.; Mello-Thoms, Claudia
2017-01-01
Context: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Aims: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. Materials and Methods: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. Statistical Analysis Used: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. Results: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Conclusions: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects. PMID:28966834
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Britton, Philip N; Dale, Russell C; Blyth, Christopher C; Macartney, Kristine; Crawford, Nigel W; Marshall, Helen; Clark, Julia E; Elliott, Elizabeth J; Webster, Richard I; Cheng, Allen C; Booy, Robert; Jones, Cheryl A
2017-11-01
Influenza-associated encephalitis/encephalopathy (IAE) is an important cause of acute encephalitis syndrome in children. IAE includes a series of clinicoradiologic syndromes or acute encephalopathy syndromes that have been infrequently reported outside East Asia. We aimed to describe cases of IAE identified by the Australian Childhood Encephalitis study. Children ≤ 14 years of age with suspected encephalitis were prospectively identified in 5 hospitals in Australia. Demographic, clinical, laboratory, imaging, and outcome at discharge data were reviewed by an expert panel and cases were categorized by using predetermined case definitions. We extracted cases associated with laboratory identification of influenza virus for this analysis; among these cases, specific IAE syndromes were identified where clinical and radiologic features were consistent with descriptions in the published literature. We identified 13 cases of IAE during 3 southern hemisphere influenza seasons at 5 tertiary children's hospitals in Australia; 8 children with specific acute encephalopathy syndromes including: acute necrotizing encephalopathy, acute encephalopathy with biphasic seizures and late diffusion restriction, mild encephalopathy with reversible splenial lesion, and hemiconvulsion-hemiplegia syndrome. Use of influenza-specific antiviral therapy and prior influenza vaccination were infrequent. In contrast, death or significant neurologic morbidity occurred in 7 of the 13 children (54%). The conditions comprising IAE are heterogeneous with varied clinical features, magnetic resonance imaging changes, and outcomes. Overall, outcome of IAE is poor emphasizing the need for optimized prevention, early recognition, and empiric management.
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Moore, Hannah E; Adam, Craig D; Drijfhout, Falko P
2014-07-01
Calliphoridae are known to be the most forensically important insects when it comes to establishing the minimum post mortem interval (PMImin) in criminal investigations. The first step in calculating the PMImin is to identify the larvae present to species level. Accurate identification which is conventionally carried out by morphological analysis is crucial because different insects have different life stage timings. Rapid identification in the immature larvae stages would drastically cut time in criminal investigations as it would eliminate the need to rear larvae to adult flies to determine the species. Cuticular hydrocarbon analysis on 1st instar larvae has been applied to three forensically important blowflies; Lucilia sericata, Calliphora vicina and Calliphora vomitoria, using gas chromatography-mass spectrometry (GC-MS) and principal component analysis (PCA). The results show that each species holds a distinct "fingerprint" hydrocarbon profile, allowing for accurate identification to be established in 1-day old larvae, when it can be challenging to apply morphological criteria. Consequently, this GC-MS based technique could accelerate and strengthen the identification process, not only for forensically important species, but also for other entomological samples which are hard to identify using morphological features. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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Pakshir, Keyvan; Ghiasi, Moosa Rahimi; Zomorodian, Kamiar; Gharavi, Ali Reza
2013-01-01
Keratinophilic fungi are an important group of fungi that live in soil. The aim of this study was to isolate and identify keratinophilic fungi from the soil of different parks in Shiraz. A total of 196 soil samples from 43 parks were collected. Isolation of the fungi was performed by hair bait technique. The isolated colonies were identified by morphologic feature of macro- and microconidia and molecular method, using DNA sequence analysis. ITS region of ribosomal DNA was amplified and the PCR products were sequenced. Results. 411 isolates from 22 genera were identified. Fusarium (23.8%), Chrysosporium (13.13%), Acremonium (12.65%), Penicillium (12.39%), Microsporum gypseum (1.94%), Bionectria ochroleuca (1.21%), Bipolaris spicifera (1.21%), Scedosporium apiospermum (0.82%), Phialophora reptans (0.82%), Cephalosporium curtipes (0.49%), Scedosporium dehoogii (0.24%), Ochroconis constricta (0.24%), Nectria mauritiicola (0.49%), Chaetomium (0.49%), Scopulariopsis (0.24%), Malbranchea (0.24%), and Tritirachium (0.24%) were the most important isolates. Most of the fungi were isolated from the soils with the PH range of 7 to 8. Our study results showed that many keratinophilic fungi isolated from the parks soil are important for public health and children are an important group at a high risk of being exposed to these fungi.
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Resource selection and its implications for wide-ranging mammals of the brazilian cerrado.
Vynne, Carly; Keim, Jonah L; Machado, Ricardo B; Marinho-Filho, Jader; Silveira, Leandro; Groom, Martha J; Wasser, Samuel K
2011-01-01
Conserving animals beyond protected areas is critical because even the largest reserves may be too small to maintain viable populations for many wide-ranging species. Identification of landscape features that will promote persistence of a diverse array of species is a high priority, particularly, for protected areas that reside in regions of otherwise extensive habitat loss. This is the case for Emas National Park, a small but important protected area located in the Brazilian Cerrado, the world's most biologically diverse savanna. Emas Park is a large-mammal global conservation priority area but is too small to protect wide-ranging mammals for the long-term and conserving these populations will depend on the landscape surrounding the park. We employed novel, noninvasive methods to determine the relative importance of resources found within the park, as well as identify landscape features that promote persistence of wide-ranging mammals outside reserve borders. We used scat detection dogs to survey for five large mammals of conservation concern: giant armadillo (Priodontes maximus), giant anteater (Myrmecophaga tridactyla), maned wolf (Chrysocyon brachyurus), jaguar (Panthera onca), and puma (Puma concolor). We estimated resource selection probability functions for each species from 1,572 scat locations and 434 giant armadillo burrow locations. Results indicate that giant armadillos and jaguars are highly selective of natural habitats, which makes both species sensitive to landscape change from agricultural development. Due to the high amount of such development outside of the Emas Park boundary, the park provides rare resource conditions that are particularly important for these two species. We also reveal that both woodland and forest vegetation remnants enable use of the agricultural landscape as a whole for maned wolves, pumas, and giant anteaters. We identify those features and their landscape compositions that should be prioritized for conservation, arguing that a multi-faceted approach is required to protect these species.
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Important features of home-based support services for older Australians and their informal carers.
McCaffrey, Nikki; Gill, Liz; Kaambwa, Billingsley; Cameron, Ian D; Patterson, Jan; Crotty, Maria; Ratcliffe, Julie
2015-11-01
In Australia, newly initiated, publicly subsidised 'Home-Care Packages' designed to assist older people (≥ 65 years of age) living in their own home must now be offered on a 'consumer-directed care' (CDC) basis by service providers. However, CDC models have largely developed in the absence of evidence on users' views and preferences. The aim of this study was to determine what features (attributes) of consumer-directed, home-based support services are important to older people and their informal carers to inform the design of a discrete choice experiment (DCE). Semi-structured, face-to-face interviews were conducted in December 2012-November 2013 with 17 older people receiving home-based support services and 10 informal carers from 5 providers located in South Australia and New South Wales. Salient service characteristics important to participants were determined using thematic and constant comparative analysis and formulated into attributes and attribute levels for presentation within a DCE. Initially, eight broad themes were identified: information and knowledge, choice and control, self-managed continuum, effective co-ordination, effective communication, responsiveness and flexibility, continuity and planning. Attributes were formulated for the DCE by combining overlapping themes such as effective communication and co-ordination, and the self-managed continuum and planning into single attributes. Six salient service features that characterise consumer preferences for the provision of home-based support service models were identified: choice of provider, choice of support worker, flexibility in care activities provided, contact with the service co-ordinator, managing the budget and saving unspent funds. Best practice indicates that qualitative research with individuals who represent the population of interest should guide attribute selection for a DCE and this is the first study to employ such methods in aged care service provision. Further development of services could incorporate methods of consumer engagement such as DCEs which facilitate the identification and quantification of users' views and preferences on alternative models of delivery. © 2015 John Wiley & Sons Ltd.
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Resource Selection and Its Implications for Wide-Ranging Mammals of the Brazilian Cerrado
Vynne, Carly; Keim, Jonah L.; Machado, Ricardo B.; Marinho-Filho, Jader; Silveira, Leandro; Groom, Martha J.; Wasser, Samuel K.
2011-01-01
Conserving animals beyond protected areas is critical because even the largest reserves may be too small to maintain viable populations for many wide-ranging species. Identification of landscape features that will promote persistence of a diverse array of species is a high priority, particularly, for protected areas that reside in regions of otherwise extensive habitat loss. This is the case for Emas National Park, a small but important protected area located in the Brazilian Cerrado, the world's most biologically diverse savanna. Emas Park is a large-mammal global conservation priority area but is too small to protect wide-ranging mammals for the long-term and conserving these populations will depend on the landscape surrounding the park. We employed novel, noninvasive methods to determine the relative importance of resources found within the park, as well as identify landscape features that promote persistence of wide-ranging mammals outside reserve borders. We used scat detection dogs to survey for five large mammals of conservation concern: giant armadillo (Priodontes maximus), giant anteater (Myrmecophaga tridactyla), maned wolf (Chrysocyon brachyurus), jaguar (Panthera onca), and puma (Puma concolor). We estimated resource selection probability functions for each species from 1,572 scat locations and 434 giant armadillo burrow locations. Results indicate that giant armadillos and jaguars are highly selective of natural habitats, which makes both species sensitive to landscape change from agricultural development. Due to the high amount of such development outside of the Emas Park boundary, the park provides rare resource conditions that are particularly important for these two species. We also reveal that both woodland and forest vegetation remnants enable use of the agricultural landscape as a whole for maned wolves, pumas, and giant anteaters. We identify those features and their landscape compositions that should be prioritized for conservation, arguing that a multi-faceted approach is required to protect these species. PMID:22205984
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Santo, Karla; Richtering, Sarah S; Chalmers, John; Thiagalingam, Aravinda; Chow, Clara K; Redfern, Julie
2016-12-02
There are a growing number of mobile phone apps available to support people in taking their medications and to improve medication adherence. However, little is known about how these apps differ in terms of features, quality, and effectiveness. We aimed to systematically review the medication reminder apps available in the Australian iTunes store and Google Play to assess their features and their quality in order to identify high-quality apps. This review was conducted in a similar manner to a systematic review by using a stepwise approach that included (1) a search strategy; (2) eligibility assessment; (3) app selection process through an initial screening of all retrieved apps and full app review of the included apps; (4) data extraction using a predefined set of features considered important or desirable in medication reminder apps; (5) analysis by classifying the apps as basic and advanced medication reminder apps and scoring and ranking them; and (6) a quality assessment by using the Mobile App Rating Scale (MARS), a reliable tool to assess mobile health apps. We identified 272 medication reminder apps, of which 152 were found only in Google Play, 87 only in iTunes, and 33 in both app stores. Apps found in Google Play had more customer reviews, higher star ratings, and lower cost compared with apps in iTunes. Only 109 apps were available for free and 124 were recently updated in 2015 or 2016. Overall, the median number of features per app was 3.0 (interquartile range 4.0) and only 18 apps had ≥9 of the 17 desirable features. The most common features were flexible scheduling that was present in 56.3% (153/272) of the included apps, medication tracking history in 54.8% (149/272), snooze option in 34.9% (95/272), and visual aids in 32.4% (88/272). We classified 54.8% (149/272) of the included apps as advanced medication reminder apps and 45.2% (123/272) as basic medication reminder apps. The advanced apps had a higher number of features per app compared with the basic apps. Using the MARS instrument, we were able to identify high-quality apps that were rated as being very interesting and entertaining, highly interactive and customizable, intuitive, and easy to use and to navigate as well as having a high level of visual appeal and good-quality information. Many medication reminder apps are available in the app stores; however, the majority of them did not have many of the desirable features and were, therefore, considered low quality. Through a systematic stepwise process, we were able to identify high-quality apps to be tested in a future study that will provide evidence on the use of medication reminder apps to improve medication adherence. ©Karla Santo, Sarah S Richtering, John Chalmers, Aravinda Thiagalingam, Clara K Chow, Julie Redfern. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 02.12.2016.
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Richtering, Sarah S; Chalmers, John; Thiagalingam, Aravinda; Chow, Clara K; Redfern, Julie
2016-01-01
Background There are a growing number of mobile phone apps available to support people in taking their medications and to improve medication adherence. However, little is known about how these apps differ in terms of features, quality, and effectiveness. Objective We aimed to systematically review the medication reminder apps available in the Australian iTunes store and Google Play to assess their features and their quality in order to identify high-quality apps. Methods This review was conducted in a similar manner to a systematic review by using a stepwise approach that included (1) a search strategy; (2) eligibility assessment; (3) app selection process through an initial screening of all retrieved apps and full app review of the included apps; (4) data extraction using a predefined set of features considered important or desirable in medication reminder apps; (5) analysis by classifying the apps as basic and advanced medication reminder apps and scoring and ranking them; and (6) a quality assessment by using the Mobile App Rating Scale (MARS), a reliable tool to assess mobile health apps. Results We identified 272 medication reminder apps, of which 152 were found only in Google Play, 87 only in iTunes, and 33 in both app stores. Apps found in Google Play had more customer reviews, higher star ratings, and lower cost compared with apps in iTunes. Only 109 apps were available for free and 124 were recently updated in 2015 or 2016. Overall, the median number of features per app was 3.0 (interquartile range 4.0) and only 18 apps had ≥9 of the 17 desirable features. The most common features were flexible scheduling that was present in 56.3% (153/272) of the included apps, medication tracking history in 54.8% (149/272), snooze option in 34.9% (95/272), and visual aids in 32.4% (88/272). We classified 54.8% (149/272) of the included apps as advanced medication reminder apps and 45.2% (123/272) as basic medication reminder apps. The advanced apps had a higher number of features per app compared with the basic apps. Using the MARS instrument, we were able to identify high-quality apps that were rated as being very interesting and entertaining, highly interactive and customizable, intuitive, and easy to use and to navigate as well as having a high level of visual appeal and good-quality information. Conclusions Many medication reminder apps are available in the app stores; however, the majority of them did not have many of the desirable features and were, therefore, considered low quality. Through a systematic stepwise process, we were able to identify high-quality apps to be tested in a future study that will provide evidence on the use of medication reminder apps to improve medication adherence. PMID:27913373
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NASA Astrophysics Data System (ADS)
Kutzleb, C. D.
1997-02-01
The high incidence of recidivism (repeat offenders) in the criminal population makes the use of the IAFIS III/FBI criminal database an important tool in law enforcement. The problems and solutions employed by IAFIS III/FBI criminal subject searches are discussed for the following topics: (1) subject search selectivity and reliability; (2) the difficulty and limitations of identifying subjects whose anonymity may be a prime objective; (3) database size, search workload, and search response time; (4) techniques and advantages of normalizing the variability in an individual's name and identifying features into identifiable and discrete categories; and (5) the use of database demographics to estimate the likelihood of a match between a search subject and database subjects.
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Costigan, Sarah A; Veitch, Jenny; Crawford, David; Carver, Alison; Timperio, Anna
2017-11-02
Parks in the US and Australia are generally underutilised, and park visitors typically engage in low levels of physical activity (PA). Better understanding park features that may encourage visitors to be active is important. This study examined the perceived importance of park features for encouraging park-based PA and examined differences by sex, age, parental-status and participation in PA. Cross-sectional surveys were completed by local residents ( n = 2775) living near two parks (2013/2015). Demographic variables, park visitation and leisure-time PA were self-reported, respondents rated the importance of 20 park features for encouraging park-based PA in the next fortnight. Chi-square tests of independence examined differences in importance of park features for PA among sub-groups of local residents (sex, age, parental-status, PA). Park features ranked most important for park-based PA were: well maintained (96.2%), feel safe (95.4%), relaxing atmosphere (91.2%), easy to get to (91.7%), and shady trees (90.3%). All subgroups ranked 'well maintained' as most important. Natural and built environment features of parks are important for promoting adults' park-based PA, and should be considered in park (re)design.
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Barr, Paul J; Forcino, Rachel C; Mishra, Manish; Blitzer, Rachel; Elwyn, Glyn
2016-01-08
To identify information priorities for consumers and clinicians making depression treatment decisions and assess shared decision-making (SDM) in routine depression care. 20 questions related to common features of depression treatments were provided. Participants were initially asked to select which features were important, and in a second stage they were asked to rank their top 5 'important features' in order of importance. Clinicians were asked to provide rankings according to both consumer and clinician perspectives. Consumers completed CollaboRATE, a measure of SDM. Multiple logistic regression analysis identified consumer characteristics associated with CollaboRATE scores. Online cross-sectional surveys fielded in September to December 2014. We administered surveys to convenience samples of US adults with depression and clinicians who treat depression. Consumer sampling was targeted to reflect age, gender and educational attainment of adults with depression in the USA. Information priority rankings; CollaboRATE, a 3-item consumer-reported measure of SDM. 972 consumers and 244 clinicians completed the surveys. The highest ranked question for both consumers and clinicians was 'Will the treatment work?' Clinicians were aware of consumers' priorities, yet did not always prioritise that information themselves, particularly insurance coverage and cost of treatment. Only 18% of consumers reported high levels of SDM. Working with a psychiatrist (OR 1.87; 95% CI 1.07 to 3.26) and female gender (OR 2.04; 95% CI 1.25 to 3.34) were associated with top CollaboRATE scores. While clinicians know what information is important to consumers making depression treatment decisions, they do not always address these concerns. This mismatch, coupled with low SDM, adversely affects the quality of depression care. Development of a decision support intervention based on our findings can improve levels of SDM and provide clinicians and consumers with a tool to address the existing misalignment in information priorities. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
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Clinical features, proximate causes, and consequences of active convulsive epilepsy in Africa
Kariuki, Symon M; Matuja, William; Akpalu, Albert; Kakooza-Mwesige, Angelina; Chabi, Martin; Wagner, Ryan G; Connor, Myles; Chengo, Eddie; Ngugi, Anthony K; Odhiambo, Rachael; Bottomley, Christian; White, Steven; Sander, Josemir W; Neville, Brian G R; Newton, Charles R J C
2014-01-01
Purpose Epilepsy is common in sub-Saharan Africa (SSA), but the clinical features and consequences are poorly characterized. Most studies are hospital-based, and few studies have compared different ecological sites in SSA. We described active convulsive epilepsy (ACE) identified in cross-sectional community-based surveys in SSA, to understand the proximate causes, features, and consequences. Methods We performed a detailed clinical and neurophysiologic description of ACE cases identified from a community survey of 584,586 people using medical history, neurologic examination, and electroencephalography (EEG) data from five sites in Africa: South Africa; Tanzania; Uganda; Kenya; and Ghana. The cases were examined by clinicians to discover risk factors, clinical features, and consequences of epilepsy. We used logistic regression to determine the epilepsy factors associated with medical comorbidities. Key Findings Half (51%) of the 2,170 people with ACE were children and 69% of seizures began in childhood. Focal features (EEG, seizure types, and neurologic deficits) were present in 58% of ACE cases, and these varied significantly with site. Status epilepticus occurred in 25% of people with ACE. Only 36% received antiepileptic drugs (phenobarbital was the most common drug [95%]), and the proportion varied significantly with the site. Proximate causes of ACE were adverse perinatal events (11%) for onset of seizures before 18 years; and acute encephalopathy (10%) and head injury prior to seizure onset (3%). Important comorbidities were malnutrition (15%), cognitive impairment (23%), and neurologic deficits (15%). The consequences of ACE were burns (16%), head injuries (postseizure) (1%), lack of education (43%), and being unmarried (67%) or unemployed (57%) in adults, all significantly more common than in those without epilepsy. Significance There were significant differences in the comorbidities across sites. Focal features are common in ACE, suggesting identifiable and preventable causes. Malnutrition and cognitive and neurologic deficits are common in people with ACE and should be integrated into the management of epilepsy in this region. Consequences of epilepsy such as burns, lack of education, poor marriage prospects, and unemployment need to be addressed. PMID:24116877
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Clinical features, proximate causes, and consequences of active convulsive epilepsy in Africa.
Kariuki, Symon M; Matuja, William; Akpalu, Albert; Kakooza-Mwesige, Angelina; Chabi, Martin; Wagner, Ryan G; Connor, Myles; Chengo, Eddie; Ngugi, Anthony K; Odhiambo, Rachael; Bottomley, Christian; White, Steven; Sander, Josemir W; Neville, Brian G R; Newton, Charles R J C; Twine, Rhian; Gómez Olivé, F Xavier; Collinson, Mark; Kahn, Kathleen; Tollman, Stephen; Masanja, Honratio; Mathew, Alexander; Pariyo, George; Peterson, Stefan; Ndyomughenyi, Donald; Bauni, Evasius; Kamuyu, Gathoni; Odera, Victor Mung'ala; Mageto, James O; Ae-Ngibise, Ken; Akpalu, Bright; Agbokey, Francis; Adjei, Patrick; Owusu-Agyei, Seth; Kleinschmidt, Immo; Doku, Victor C K; Odermatt, Peter; Nutman, Thomas; Wilkins, Patricia; Noh, John
2014-01-01
Epilepsy is common in sub-Saharan Africa (SSA), but the clinical features and consequences are poorly characterized. Most studies are hospital-based, and few studies have compared different ecological sites in SSA. We described active convulsive epilepsy (ACE) identified in cross-sectional community-based surveys in SSA, to understand the proximate causes, features, and consequences. We performed a detailed clinical and neurophysiologic description of ACE cases identified from a community survey of 584,586 people using medical history, neurologic examination, and electroencephalography (EEG) data from five sites in Africa: South Africa; Tanzania; Uganda; Kenya; and Ghana. The cases were examined by clinicians to discover risk factors, clinical features, and consequences of epilepsy. We used logistic regression to determine the epilepsy factors associated with medical comorbidities. Half (51%) of the 2,170 people with ACE were children and 69% of seizures began in childhood. Focal features (EEG, seizure types, and neurologic deficits) were present in 58% of ACE cases, and these varied significantly with site. Status epilepticus occurred in 25% of people with ACE. Only 36% received antiepileptic drugs (phenobarbital was the most common drug [95%]), and the proportion varied significantly with the site. Proximate causes of ACE were adverse perinatal events (11%) for onset of seizures before 18 years; and acute encephalopathy (10%) and head injury prior to seizure onset (3%). Important comorbidities were malnutrition (15%), cognitive impairment (23%), and neurologic deficits (15%). The consequences of ACE were burns (16%), head injuries (postseizure) (1%), lack of education (43%), and being unmarried (67%) or unemployed (57%) in adults, all significantly more common than in those without epilepsy. There were significant differences in the comorbidities across sites. Focal features are common in ACE, suggesting identifiable and preventable causes. Malnutrition and cognitive and neurologic deficits are common in people with ACE and should be integrated into the management of epilepsy in this region. Consequences of epilepsy such as burns, lack of education, poor marriage prospects, and unemployment need to be addressed. Wiley Periodicals, Inc. © 2013 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of the International League Against Epilepsy.
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Lee, Hyoung Shin; Park, Chanwoo; Kim, Sung Won; Noh, Woong Jae; Lim, Soo Jin; Chun, Bong Kwon; Kim, Beom Su; Hong, Jong Chul; Lee, Kang Dae
2016-10-01
The importance of pathologic features of metastatic lymph nodes (LNs), such as size, number, and extranodal extension, has been recently emphasized in patients with papillary thyroid carcinoma (PTC). We evaluated the characteristics of metastatic LNs identified after prophylactic central neck dissection (CND) in patients with PTC. We performed a retrospective review of 1,046 patients who underwent unilateral or bilateral thyroidectomy with ipsilateral prophylactic CND. We reviewed the characteristics of the metastatic LNs and analyzed their correlation to the clinicopathologic characteristics of the primary tumor. Cervical LN metastasis after prophylactic CND was identified in 280 out of 1046 patients (26.8 %). The size of metastatic foci (≥2 mm) was independently correlated with primary tumor size (≥1 cm) (p = 0.016, OR = 1.88). Primary tumor size (≥1 cm) was also correlated to the number of metastatic LNs (≥5) (p = 0.004, OR = 3.14) and extranodal extension (p = 0.021, OR = 2.41) in univariate analysis. The size of the primary tumor affects pathologic features of subclinical LN metastasis in patients with PTC. Patients with primary tumors ≥1 cm have an increased risk of larger LN metastases (≥2 mm), an increased number of LN metastases (≥5), and a higher incidence of ENE, which should be considered in decision for prophylactic CND.
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Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra
2014-01-01
Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. Objective The aims were to describe how to: (i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii) automatically identify the features that best distinguish the groups. Methods The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described – simple or complex; presentation order – which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders. Results and Conclusion A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods. PMID:29213908
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Vrkljan, Brenda H; Anaby, Dana
2011-02-01
Certain vehicle features can help drivers avoid collisions and/or protect occupants in the event of a crash, and therefore, might play an important role when deciding which vehicle to purchase. The objective of this study was to examine the importance attributed to key vehicle features (including safety) that drivers consider when buying a car and its association with age and gender. A sample of 2,002 Canadian drivers aged 18 years and older completed a survey that asked them to rank the importance of eight vehicle features if they were to purchase a vehicle (storage, mileage, safety, price, comfort, performance, design, and reliability). ANOVA tests were performed to: (a) determine if there were differences in the level of importance between features and; (b) examine the effect of age and gender on the importance attributed to these features. Of the features examined, safety and reliability were the most highly rated in terms of importance, whereas design and performance had the lowest rating. Differences in safety and performance across age groups were dependent on gender. This effect was most evident in the youngest and oldest age groups. Safety and reliability were considered the most important features. Age and gender play a significant role in explaining the importance of certain features. Targeted efforts for translating safety-related information to the youngest and oldest consumers should be emphasized due to their high collision, injury, and fatality rates. Copyright © 2011 National Safety Council and Elsevier Ltd. All rights reserved.
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Community-acquired pneumonia: identification and evaluation of nonresponders.
Gonçalves-Pereira, João; Conceição, Catarina; Póvoa, Pedro
2013-02-01
Community acquired pneumonia (CAP) is a relevant public health problem, constituting an important cause of morbidity and mortality. It accounts for a significant number of adult hospital admissions and a large number of those patients ultimately die, especially the population who needed mechanical ventilation or vasopressor support. Thus, early identification of CAP patients and its rapid and appropriate treatment are important features with impact on hospital resource consumption and overall mortality. Although CAP diagnosis may sometimes be straightforward, the diagnostic criteria commonly used are highly sensitive but largely unspecific. Biomarkers and microbiological documentation may be useful but have important limitations. Evaluation of clinical response is also critical especially to identify patients who fail to respond to initial treatment since these patients have a high risk of in-hospital death. However, the criteria of definition of non-response in CAP are largely empirical and frequently markedly diverse between different studies. In this review, we aim to identify criteria defining nonresponse in CAP and the pitfalls associated with this diagnosis. We also aim to overview the main causes of treatment failure especially in severe CAP and the possible strategies to identify and reassess non-responders trying to change the dismal prognosis associated with this condition.
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Vanoni, Federica; Federici, Silvia; Antón, Jordi; Barron, Karyl S; Brogan, Paul; De Benedetti, Fabrizio; Dedeoglu, Fatma; Demirkaya, Erkan; Hentgen, Veronique; Kallinich, Tilmann; Laxer, Ronald; Russo, Ricardo; Toplak, Natasa; Uziel, Yosef; Martini, Alberto; Ruperto, Nicolino; Gattorno, Marco; Hofer, Michael
2018-04-18
Diagnosis of Periodic fever, aphthous stomatitis, pharyngitis and cervical adenitis (PFAPA) is currently based on a set of criteria proposed in 1999 modified from Marshall's criteria. Nevertheless no validated evidence based set of classification criteria for PFAPA has been established so far. The aim of this study was to identify candidate classification criteria PFAPA syndrome using international consensus formation through a Delphi questionnaire survey. A first open-ended questionnaire was sent to adult and pediatric clinicians/researchers, asking to identify the variables thought most likely to be helpful and relevant for the diagnosis of PFAPA. In a second survey, respondents were asked to select, from the list of variables coming from the first survey, the 10 features that they felt were most important, and to rank them in descending order from most important to least important. The response rate to the first and second Delphi was respectively 109/124 (88%) and 141/162 (87%). The number of participants that completed the first and second Delphi was 69/124 (56%) and 110/162 (68%). From the first Delphi we obtained a list of 92 variables, of which 62 were selected in the second Delphi. Variables reaching the top five position of the rank were regular periodicity, aphthous stomatitis, response to corticosteroids, cervical adenitis, and well-being between flares. Our process led to identification of features that were felt to be the most important as candidate classification criteria for PFAPA by a large sample of international rheumatologists. The performance of these items will be tested further in the next phase of the study, through analysis of real patient data.
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2015-01-01
A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms—Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)—that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility. PMID:26437000
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Identification of Load Categories in Rotor System Based on Vibration Analysis
Yang, Zhaojian
2017-01-01
Rotating machinery is often subjected to variable loads during operation. Thus, monitoring and identifying different load types is important. Here, five typical load types have been qualitatively studied for a rotor system. A novel load category identification method for rotor system based on vibration signals is proposed. This method is a combination of ensemble empirical mode decomposition (EEMD), energy feature extraction, and back propagation (BP) neural network. A dedicated load identification test bench for rotor system was developed. According to loads characteristics and test conditions, an experimental plan was formulated, and loading tests for five loads were conducted. Corresponding vibration signals of the rotor system were collected for each load condition via eddy current displacement sensor. Signals were reconstructed using EEMD, and then features were extracted followed by energy calculations. Finally, characteristics were input to the BP neural network, to identify different load types. Comparison and analysis of identifying data and test data revealed a general identification rate of 94.54%, achieving high identification accuracy and good robustness. This shows that the proposed method is feasible. Due to reliable and experimentally validated theoretical results, this method can be applied to load identification and fault diagnosis for rotor equipment used in engineering applications. PMID:28726754
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Aphinyanaphongs, Yin; Fu, Lawrence D; Aliferis, Constantin F
2013-01-01
Building machine learning models that identify unproven cancer treatments on the Health Web is a promising approach for dealing with the dissemination of false and dangerous information to vulnerable health consumers. Aside from the obvious requirement of accuracy, two issues are of practical importance in deploying these models in real world applications. (a) Generalizability: The models must generalize to all treatments (not just the ones used in the training of the models). (b) Scalability: The models can be applied efficiently to billions of documents on the Health Web. First, we provide methods and related empirical data demonstrating strong accuracy and generalizability. Second, by combining the MapReduce distributed architecture and high dimensionality compression via Markov Boundary feature selection, we show how to scale the application of the models to WWW-scale corpora. The present work provides evidence that (a) a very small subset of unproven cancer treatments is sufficient to build a model to identify unproven treatments on the web; (b) unproven treatments use distinct language to market their claims and this language is learnable; (c) through distributed parallelization and state of the art feature selection, it is possible to prepare the corpora and build and apply models with large scalability.
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Consumer-identified barriers and strategies for optimizing technology use in the workplace.
De Jonge, Desleigh M; Rodger, Sylvia A
2006-01-01
This article explores the experiences of 26 assistive technology (AT) users having a range of physical impairments as they optimized their use of technology in the workplace. A qualitative research design was employed using in-depth, open-ended interviews and observations of AT users in the workplace. Participants identified many factors that limited their use of technology such as discomfort and pain, limited knowledge of the technology's features, and the complexity of the technology. The amount of time required for training, limited work time available for mastery, cost of training and limitations of the training provided, resulted in an over-reliance on trial and error and informal support networks and a sense of isolation. AT users enhanced their use of technology by addressing the ergonomics of the workstation and customizing the technology to address individual needs and strategies. Other key strategies included tailored training and learning support as well as opportunities to practice using the technology and explore its features away from work demands. This research identified structures important for effective AT use in the workplace which need to be put in place to ensure that AT users are able to master and optimize their use of technology.
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Infrared photography and imagery in water resources research
Robinove, Charles J.
1965-01-01
Infrared photography has restricted usefulness in general water resources studies but is particularly useful in special problems such as shoreline mapping. Infrared imagery is beginning to be used in water resources studies for the identification of surface and sub surface thermal anomalies as expressed at the surface and the measurement of apparent water surface temperatures. It will attain its maximum usefulness only when interpretation criteria for infrared imagery are fully developed. Several important hydrologic problems to which infrared imagery may be applied are: (1) determination of circulation and cooling of water in power plant cooling ponds, (2) measurement of river temperature and temperature decline downstream from power plants discharging heated water, (3) identification of submarine springs along coasts, and (4) measurement of temperature differences along streams as indicators of effluent seepage of ground water. Although it is possible at this time to identify many features of importance to hydrology by the use of infrared imagery, the task remaining is to develop criteria to show the hydrologic significance of the features.
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Kokaly, Raymond F.
2011-01-01
This report describes procedures for installing and using the U.S. Geological Survey Processing Routines in IDL for Spectroscopic Measurements (PRISM) software. PRISM provides a framework to conduct spectroscopic analysis of measurements made using laboratory, field, airborne, and space-based spectrometers. Using PRISM functions, the user can compare the spectra of materials of unknown composition with reference spectra of known materials. This spectroscopic analysis allows the composition of the material to be identified and characterized. Among its other functions, PRISM contains routines for the storage of spectra in database files, import/export of ENVI spectral libraries, importation of field spectra, correction of spectra to absolute reflectance, arithmetic operations on spectra, interactive continuum removal and comparison of spectral features, correction of imaging spectrometer data to ground-calibrated reflectance, and identification and mapping of materials using spectral feature-based analysis of reflectance data. This report provides step-by-step instructions for installing the PRISM software and running its functions.
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On biodiversity conservation and poverty traps.
Barrett, Christopher B; Travis, Alexander J; Dasgupta, Partha
2011-08-23
This paper introduces a special feature on biodiversity conservation and poverty traps. We define and explain the core concepts and then identify four distinct classes of mechanisms that define important interlinkages between biodiversity and poverty. The multiplicity of candidate mechanisms underscores a major challenge in designing policy appropriate across settings. This framework is then used to introduce the ensuing set of papers, which empirically explore these various mechanisms linking poverty traps and biodiversity conservation.
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On biodiversity conservation and poverty traps
Barrett, Christopher B.; Travis, Alexander J.; Dasgupta, Partha
2011-01-01
This paper introduces a special feature on biodiversity conservation and poverty traps. We define and explain the core concepts and then identify four distinct classes of mechanisms that define important interlinkages between biodiversity and poverty. The multiplicity of candidate mechanisms underscores a major challenge in designing policy appropriate across settings. This framework is then used to introduce the ensuing set of papers, which empirically explore these various mechanisms linking poverty traps and biodiversity conservation. PMID:21873176
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ERIC Educational Resources Information Center
Ouvrier, Robert; Grew, Simon
2010-01-01
Mitofusin 2, a large transmembrane GTPase located in the outer mitochondrial membrane, promotes membrane fusion and is involved in the maintenance of the morphology of axonal mitochondria. Mutations of the gene encoding mitofusin 2 ("MFN2") have recently been identified as the cause of approximately one-third of dominantly inherited cases of the…
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ERIC Educational Resources Information Center
Adams, John S.; And Others
This report is the second in a series on What the 1990 Census Says about Minnesota. A group of urban specialists gathered to examine a set of metropolitan areas that share important features that were thought to be related to central-city decline as evidenced in Minnesota's Twin Cities, Minneapolis and Saint Paul. Six cities were identified as…
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Gwini, Stella May; Shaw, Deborah; Iqbal, Mohammad; Spaight, Anne; Siriwardena, Aloysius Niroshan
2011-10-01
To investigate the factors associated with adverse clinical features presented by drug overdose/self-poisoning patients and the treatments provided. Historical patient records collected over 3 months from ambulance crews attending non-fatal overdoses/self-poisoning incidents were reviewed. Logistic regression was used to investigate predictors of adverse clinical features (reduced consciousness, obstructed airway, hypotension or bradycardia, hypoglycaemia) and treatment. Of 22,728 calls attended to over 3 months, 585 (rate 26/1000 calls) were classified as overdose or self-poisoning. In the 585 patients identified, paracetamol-containing drugs were most commonly involved (31.5%). At least one adverse clinical feature occurred in 103 (17.7%) patients, with higher odds in men and opiate overdose or illegal drugs. Older patients and patients with reduced consciousness were more likely to receive oxygen. The latter also had a greater chance of receiving saline. Non-fatal overdose/self-poisoning accounted for 2.6% of patients attended by an ambulance. Gender, illegal drugs or opiates were important predictors of adverse clinical features. The treatments most often provided to patients were oxygen and saline.
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Tabei, Yasuo; Pauwels, Edouard; Stoven, Véronique; Takemoto, Kazuhiro; Yamanishi, Yoshihiro
2012-01-01
Motivation: Drug effects are mainly caused by the interactions between drug molecules and their target proteins including primary targets and off-targets. Identification of the molecular mechanisms behind overall drug–target interactions is crucial in the drug design process. Results: We develop a classifier-based approach to identify chemogenomic features (the underlying associations between drug chemical substructures and protein domains) that are involved in drug–target interaction networks. We propose a novel algorithm for extracting informative chemogenomic features by using L1 regularized classifiers over the tensor product space of possible drug–target pairs. It is shown that the proposed method can extract a very limited number of chemogenomic features without loosing the performance of predicting drug–target interactions and the extracted features are biologically meaningful. The extracted substructure–domain association network enables us to suggest ligand chemical fragments specific for each protein domain and ligand core substructures important for a wide range of protein families. Availability: Softwares are available at the supplemental website. Contact: yamanishi@bioreg.kyushu-u.ac.jp Supplementary Information: Datasets and all results are available at http://cbio.ensmp.fr/~yyamanishi/l1binary/ . PMID:22962471
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Prediction of enhancer-promoter interactions via natural language processing.
Zeng, Wanwen; Wu, Mengmeng; Jiang, Rui
2018-05-09
Precise identification of three-dimensional genome organization, especially enhancer-promoter interactions (EPIs), is important to deciphering gene regulation, cell differentiation and disease mechanisms. Currently, it is a challenging task to distinguish true interactions from other nearby non-interacting ones since the power of traditional experimental methods is limited due to low resolution or low throughput. We propose a novel computational framework EP2vec to assay three-dimensional genomic interactions. We first extract sequence embedding features, defined as fixed-length vector representations learned from variable-length sequences using an unsupervised deep learning method in natural language processing. Then, we train a classifier to predict EPIs using the learned representations in supervised way. Experimental results demonstrate that EP2vec obtains F1 scores ranging from 0.841~ 0.933 on different datasets, which outperforms existing methods. We prove the robustness of sequence embedding features by carrying out sensitivity analysis. Besides, we identify motifs that represent cell line-specific information through analysis of the learned sequence embedding features by adopting attention mechanism. Last, we show that even superior performance with F1 scores 0.889~ 0.940 can be achieved by combining sequence embedding features and experimental features. EP2vec sheds light on feature extraction for DNA sequences of arbitrary lengths and provides a powerful approach for EPIs identification.
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Factors related to HIV-associated neurocognitive impairment differ with age.
Fogel, Gary B; Lamers, Susanna L; Levine, Andrew J; Valdes-Sueiras, Miguel; McGrath, Michael S; Shapshak, Paul; Singer, Elyse J
2015-02-01
Over 50% of HIV-infected (HIV+) persons are expected to be over age 50 by 2015. The pathogenic effects of HIV, particularly in cases of long-term infection, may intersect with those of age-related illnesses and prolonged exposure to combined antiretroviral therapy (cART). One potential outcome is an increased prevalence of neurocognitive impairment in older HIV+ individuals, as well as an altered presentation of HIV-associated neurocognitive disorders (HANDs). In this study, we employed stepwise regression to examine 24 features sometimes associated with HAND in 40 older (55-73 years of age) and 30 younger (32-50 years of age) HIV+, cART-treated participants without significant central nervous system confounds. The features most effective in generating a true assessment of the likelihood of HAND diagnosis differed between older and younger cohorts, with the younger cohort containing features associated with drug abuse that were correlated to HAND and the older cohort containing features that were associated with lipid disorders mildly associated with HAND. As the HIV-infected population grows and the demographics of the epidemic change, it is increasingly important to re-evaluate features associated with neurocognitive impairment. Here, we have identified features, routinely collected in primary care settings, that provide more accurate diagnostic value than a neurocognitive screening measure among younger and older HIV individuals.
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NASA Astrophysics Data System (ADS)
Shi, Yue; Huang, Wenjiang; Zhou, Xianfeng
2017-04-01
Hyperspectral absorption features are important indicators of characterizing plant biophysical variables for the automatic diagnosis of crop diseases. Continuous wavelet analysis has proven to be an advanced hyperspectral analysis technique for extracting absorption features; however, specific wavelet features (WFs) and their relationship with pathological characteristics induced by different infestations have rarely been summarized. The aim of this research is to determine the most sensitive WFs for identifying specific pathological lesions from yellow rust and powdery mildew in winter wheat, based on 314 hyperspectral samples measured in field experiments in China in 2002, 2003, 2005, and 2012. The resultant WFs could be used as proxies to capture the major spectral absorption features caused by infestation of yellow rust or powdery mildew. Multivariate regression analysis based on these WFs outperformed conventional spectral features in disease detection; meanwhile, a Fisher discrimination model exhibited considerable potential for generating separable clusters for each infestation. Optimal classification returned an overall accuracy of 91.9% with a Kappa of 0.89. This paper also emphasizes the WFs and their relationship with pathological characteristics in order to provide a foundation for the further application of this approach in monitoring winter wheat diseases at the regional scale.
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LINKING LUNG AIRWAY STRUCTURE TO PULMONARY FUNCTION VIA COMPOSITE BRIDGE REGRESSION
Chen, Kun; Hoffman, Eric A.; Seetharaman, Indu; Jiao, Feiran; Lin, Ching-Long; Chan, Kung-Sik
2017-01-01
The human lung airway is a complex inverted tree-like structure. Detailed airway measurements can be extracted from MDCT-scanned lung images, such as segmental wall thickness, airway diameter, parent-child branch angles, etc. The wealth of lung airway data provides a unique opportunity for advancing our understanding of the fundamental structure-function relationships within the lung. An important problem is to construct and identify important lung airway features in normal subjects and connect these to standardized pulmonary function test results such as FEV1%. Among other things, the problem is complicated by the fact that a particular airway feature may be an important (relevant) predictor only when it pertains to segments of certain generations. Thus, the key is an efficient, consistent method for simultaneously conducting group selection (lung airway feature types) and within-group variable selection (airway generations), i.e., bi-level selection. Here we streamline a comprehensive procedure to process the lung airway data via imputation, normalization, transformation and groupwise principal component analysis, and then adopt a new composite penalized regression approach for conducting bi-level feature selection. As a prototype of composite penalization, the proposed composite bridge regression method is shown to admit an efficient algorithm, enjoy bi-level oracle properties, and outperform several existing methods. We analyze the MDCT lung image data from a cohort of 132 subjects with normal lung function. Our results show that, lung function in terms of FEV1% is promoted by having a less dense and more homogeneous lung comprising an airway whose segments enjoy more heterogeneity in wall thicknesses, larger mean diameters, lumen areas and branch angles. These data hold the potential of defining more accurately the “normal” subject population with borderline atypical lung functions that are clearly influenced by many genetic and environmental factors. PMID:28280520
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Subduction, Extension, and a Mantle Plume in the Pacific Northwest
NASA Astrophysics Data System (ADS)
Hawley, W. B.; Allen, R. M.; Richards, M. A.
2016-12-01
Subduction zones are some of the most important systems that control the dynamics and evolution of the earth. The Cascadia Subduction Zone offers a unique natural laboratory for understanding the subduction process, and how subduction interacts with other large-scale geodynamical phenomena. The small size of the Juan de Fuca (JdF) plate and the proximity of the system to the Yellowstone Hotspot and the extensional Basin and Range province allow for detailed study of the effects these important systems have on each other. We present both a P-wave and an S-wave tomographic model of the Pacific Northwestern United States using regional seismic arrays, including the amphibious Cascadia Initiative. These models share important features, such as the Yellowstone plume, the subducting JdF slab, a gap in the subducting slab, and a low-velocity feature beneath the shallowest portions of the slab. But subtle differences in these features between the models—the size of the gap in the subducting JdF slab and the shape of the Yellowstone plume shaft above the transition zone, for example—provide physical insight into the interpretation of these models. The physics that we infer from our seismic tomography and other studies of the region will refine our understanding of subduction zones worldwide, and will help to identify targets for future amphibious seismic array studies. The discovery of a pronounced low-velocity feature beneath the JdF slab as it subducts beneath the coastal Pacific Northwest is, thus far, the most surprising result from our imaging work, and implies a heretofore unanticipated regime of dynamical interaction between the sublithospheric oceanic asthenosphere and the subduction process. Such discoveries are made possible, and rendered interpretable, by ever-increasing resolution that the Cascadia Initiative affords seismic tomography models.
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Measuring Polycentricity of Mega-City Regions in China Based on the Intercity Migration Flows
NASA Astrophysics Data System (ADS)
Mu, Xiaoyan; Yeh, Anthony G. O.
2016-06-01
This paper uses the intercity migration flows to examine relations between Chinese cities, identify the important mega-city regions and measure each region's polycentricity from an interaction perspective. Data set contains the long-term residential migration trajectories of three million Sina weibo users across 345 cities. Cities with close connectivity deployed around one or several mega cities are identified as mega-city regions. Features of the mega-city regions are characterized by the strength of migration flows, density of connections, and regional migration patterns. The results show that the disparities exist in different mega-city regions; most mega-city regions are lack of polycentricity.
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Data DOIs - virtues and weak points from the perspective of the data journal ESSD
NASA Astrophysics Data System (ADS)
Pfeiffenberger, Hans; Carlson, David
2016-04-01
Fundamentally, data publication seeks to provide improved access, trust and utility for data users accompanied by improved recognition for data providers. The features of persistent identifier schemes used in this context and the policies of providers of their resolver systems can greatly contribute towards these aims! Unfortunately, we also recognize situations where identifier systems undermine some of our lofty data publication aims. From our 7 years of successful experience with the data journal ESSD, we will present our practical observations as well as future requirements on the DOI-system in particular, such as: * Most scientists contributing to or drawing benefit from ESSD seem to understand the notion of published data in the sense of a static, citable entity to build on, relying on its long term availability and integrity (as is the case with journal articles), secured by DOIs. * It is still true that many data providers - individuals or projects - can not find a repository for their (type of) data which would provide a DOI (or other persistent identifier) and that many data centers and databases holding valuable data have a hard time adding DOIs as a feature of their systems for conceptual, legacy or funding reasons. * The typical evolution of many important data collections or data products - such as the Global Carbon Budget - can adequately be tracked with yearly (or less frequent) issues of dataset and data article, each having a new DOI. However, issuing a new DOI for a dataset extended in time clashes with many producers' wish to amass all references to the developing dataset under one citation (and one DOI). * Current practises with DOIs and metadata associated with DOIs need to be clarified and amended so the identifier systems can and do express and track the relationships between versions of data, addressing extensions, corrections, modifications, revisions and retractions in an unambiguous (and machine readable) manner, while also removing extraneous information (dates, page numbers) from the identifier itself. * Similarly, we recognize a clear need to hold one or more copies of important data in different locations (continents), under different governance and technologies but to still be able to identify these copies as intellectually identical. Today, however, we observe identical datasets held in separate repositories with different metadata and DOIs without a process to easily identify the duplicates. * The dataset and data article - coupled with a research article citing both and drawing conclusions and perhaps with an additional article describing and publishing the software - offer a valid and vivid example of linking objects reliably across many players' (publishers') systems. Too often, however, establishing such linkages requires substantial human intervention to initiate and synchronize the necessary processes, in particular to ensure high-fidelity mutual citation among the individual items. As editors of a data journal we firmly embrace the notion of persistence and persistent identifiers, in particular DOIs. We see the need and applaud all efforts to enhance its features to serve the important needs of published and linked data.
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Paños, A; Arnaldos, M I; García, M D; Ubero-Pascal, N
2013-11-01
Piophila Fallén, 1810 is a genus of small flies composed of two species: Piophila casei (P. casei ) (Linnaeus, 1758), worldwide distributed, and Piophila megastigmata (P. megastigmata ) McAlpine, 1978, recently referred in the Palaearctic Region, from the Iberian Peninsula. Both species share ecological niche and are very interesting for forensic purposes, since they are present in carrion in advance stages of decay and have been found to be related to human corpses. The immature stages of P. megastigmata have ever been described, so this paper gives the ultrastructural morphologies of all preimaginal stages of P. megastigmata studied by light microscopy and scanning electron microscopy (SEM). Particular attention is given to pseudocephalon features—antenna, maxillary palps, facial mask, etc.—cephalopharyngeal skeleton, anterior and posterior spiracles, tegumentary sculpturing, and anal division among others. A comparative analysis of the main distinguishing features is made in order to understand how those features evolve along the developmental process, while larvae II and III are morphologically similar to each other, the larva I shows particular features. Larvae of all stages and pupae are easily distinguishable from other Diptera of forensic importance just based on the presence of trichoid sensilla associated to respiratory slits, instead of peristigmatig tufts, as well as on thewell-known disposition of anal papillae. The shapes of both dorsal edge at the basal part of mouthhook and dorsal bridge of cephalopharyngeal skeleton, and the tegumental ornamentationmay be considered as good features to distinguish the Piophila species, especially for P. megastigmata and P. casei . At the SEM level, shape, number, and arrangement of oral combs, oral ridges, sensilla of maxillary palpus, papillae of anterior spiracle, scales of spiracular field, and posterior spiracles represent good features to distinguish P. megastigmata from P. casei, but further studies will be necessary in West-Paleartic specimens of latter species. The key for identifying third instar larvae of forensically important Piophilidae in the Iberian Peninsula has been updated to include P. megastigmata.
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Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry.
Katuwal, Gajendra J; Baum, Stefi A; Cahill, Nathan D; Michael, Andrew M
2016-01-01
Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7-8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4-5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13-18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD.
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Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry
Baum, Stefi A.; Cahill, Nathan D.; Michael, Andrew M.
2016-01-01
Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7–8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4–5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13–18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD. PMID:27065101
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NASA Astrophysics Data System (ADS)
Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Kim, Renaid
2017-03-01
Understanding the key radiogenomic associations for breast cancer between DCE-MRI and micro-RNA expressions is the foundation for the discovery of radiomic features as biomarkers for assessing tumor progression and prognosis. We conducted a study to analyze the radiogenomic associations for breast cancer using the TCGA-TCIA data set. The core idea that tumor etiology is a function of the behavior of miRNAs is used to build the regression models. The associations based on regression are analyzed for three study outcomes: diagnosis, prognosis, and treatment. The diagnosis group consists of miRNAs associated with clinicopathologic features of breast cancer and significant aberration of expression in breast cancer patients. The prognosis group consists of miRNAs which are closely associated with tumor suppression and regulation of cell proliferation and differentiation. The treatment group consists of miRNAs that contribute significantly to the regulation of metastasis thereby having the potential to be part of therapeutic mechanisms. As a first step, important miRNA expressions were identified and their ability to classify the clinical phenotypes based on the study outcomes was evaluated using the area under the ROC curve (AUC) as a figure-of-merit. The key mapping between the selected miRNAs and radiomic features were determined using least absolute shrinkage and selection operator (LASSO) regression analysis within a two-loop leave-one-out cross-validation strategy. These key associations indicated a number of radiomic features from DCE-MRI to be potential biomarkers for the three study outcomes.
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FitzGerald, Liesel M.; Kwon, Erika M.; Koopmeiners, Joseph S.; Salinas, Claudia A.; Stanford, Janet L.; Ostrander, Elaine A.
2009-01-01
Purpose Two recent genome-wide association studies have highlighted several SNPs purported to be associated with prostate cancer risk. We investigated the significance of these SNPs in a population-based study of Caucasian men, testing the effects of each SNP in relation to family history of prostate cancer and clinicopathological features of disease. Experimental Design We genotyped 13 SNPs in 1,308 prostate cancer patients and 1,267 unaffected controls frequency matched to cases by five-year age groups. The association of each SNP with disease risk and stratified by family history of prostate cancer and clinicopathological features of disease was calculated using logistic and polytomous regression. Results These results confirm the importance of multiple previously reported SNPs in relation to prostate cancer susceptibility; 11 of the 13 SNPs were significantly associated with risk of developing prostate cancer. However, none of the SNP associations were of comparable magnitude to that associated with having a first-degree family history of the disease. Risk estimates associated with SNPs rs4242382 and rs2735839 varied by family history, while risk estimates for rs10993994 and rs5945619 varied by Gleason score. Conclusions Our results confirm that several recently identified SNPs are associated with prostate cancer risk; however the variant alleles only confer a low to moderate relative risk of disease and are generally not associated with more aggressive disease features. PMID:19366831
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Stewart, Heather; Rutherford, Nicola J; Briemberg, Hannah; Krieger, Charles; Cashman, Neil; Fabros, Marife; Baker, Matt; Fok, Alice; DeJesus-Hernandez, Mariely; Eisen, Andrew; Rademakers, Rosa; Mackenzie, Ian R A
2012-03-01
Two studies recently identified a GGGGCC hexanucleotide repeat expansion in a non-coding region of the chromosome 9 open-reading frame 72 gene (C9ORF72) as the cause of chromosome 9p-linked amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). In a cohort of 231 probands with ALS, we identified the C9ORF72 mutation in 17 familial (27.4%) and six sporadic (3.6%) cases. Patients with the mutation presented with typical motor features of ALS, although subjects with the C9ORF72 mutation had more frequent bulbar onset, compared to those without this mutation. Dementia was significantly more common in ALS patients and families with the C9ORF72 mutation and was usually early-onset FTD. There was striking clinical heterogeneity among the members of individual families with the mutation. The associated neuropathology was a combination of ALS with TDP-ir inclusions and FTLD-TDP. In addition to TDP-43-immunoreactive pathology, a consistent and specific feature of cases with the C9ORF72 mutation was the presence of ubiquitin-positive, TDP-43-negative inclusions in a variety of neuroanatomical regions, such as the cerebellar cortex. These findings support the C9ORF72 mutation as an important newly recognized cause of ALS, provide a more detailed characterization of the associated clinical and pathological features and further demonstrate the clinical and molecular overlap between ALS and FTD.
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Peer assessment of personality traits and pathology in female college students.
Oltmanns, T F; Turkheimer, E; Strauss, M E
1998-03-01
Assessment procedures for personality disorders (PDs) typically rely on self-reports, even though some people with PDs may be unable to view themselves realistically or are unwilling to report socially undesirable traits. Close associates may provide important information regarding the presence of PD traits. Peer nomination is a reliable and valid assessment procedure that can be adapted to the study of PDs for research purposes. This study focused on characteristic features that define narcissistic, dependent, and obsessive-compulsive PDs using information collected from both self and others in a nonclinical sample of women. It was designed to identify specific areas of agreement and discrepancy between self-report and peer assessment in the measurement of characteristic features of these disorders.
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Visual Pattern Analysis in Histopathology Images Using Bag of Features
NASA Astrophysics Data System (ADS)
Cruz-Roa, Angel; Caicedo, Juan C.; González, Fabio A.
This paper presents a framework to analyse visual patterns in a collection of medical images in a two stage procedure. First, a set of representative visual patterns from the image collection is obtained by constructing a visual-word dictionary under a bag-of-features approach. Second, an analysis of the relationships between visual patterns and semantic concepts in the image collection is performed. The most important visual patterns for each semantic concept are identified using correlation analysis. A matrix visualization of the structure and organization of the image collection is generated using a cluster analysis. The experimental evaluation was conducted on a histopathology image collection and results showed clear relationships between visual patterns and semantic concepts, that in addition, are of easy interpretation and understanding.
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Gstruct: a system for extracting schemas from GML documents
NASA Astrophysics Data System (ADS)
Chen, Hui; Zhu, Fubao; Guan, Jihong; Zhou, Shuigeng
2008-10-01
Geography Markup Language (GML) becomes the de facto standard for geographic information representation on the internet. GML schema provides a way to define the structure, content, and semantic of GML documents. It contains useful structural information of GML documents and plays an important role in storing, querying and analyzing GML data. However, GML schema is not mandatory, and it is common that a GML document contains no schema. In this paper, we present Gstruct, a tool for GML schema extraction. Gstruct finds the features in the input GML documents, identifies geometry datatypes as well as simple datatypes, then integrates all these features and eliminates improper components to output the optimal schema. Experiments demonstrate that Gstruct is effective in extracting semantically meaningful schemas from GML documents.
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NASA Astrophysics Data System (ADS)
Zhang, Ruizhi; Du, Baoli; Chen, Kan; Reece, Mike; Materials Research Insititute Team
With the increasing computational power and reliable databases, high-throughput screening is playing a more and more important role in the search of new thermoelectric materials. Rather than the well established density functional theory (DFT) calculation based methods, we propose an alternative approach to screen for new TE materials: using crystal structural features as 'descriptors'. We show that a non-distorted transition metal sulphide polyhedral network can be a good descriptor for high power factor according to crystal filed theory. By using Cu/S containing compounds as an example, 1600+ Cu/S containing entries in the Inorganic Crystal Structure Database (ICSD) were screened, and of those 84 phases are identified as promising thermoelectric materials. The screening results are validated by both electronic structure calculations and experimental results from the literature. We also fabricated some new compounds to test our screening results. Another advantage of using crystal structure features as descriptors is that we can easily establish structural relationships between the identified phases. Based on this, two material design approaches are discussed: 1) High-pressure synthesis of metastable phase; 2) In-situ 2-phase composites with coherent interface. This work was supported by a Marie Curie International Incoming Fellowship of the European Community Human Potential Program.
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Participation-based environment accessibility assessment tool (P-BEAAT) in the Zambian context.
Banda-Chalwe, Martha; Nitz, Jennifer C; de Jonge, Desleigh
2012-01-01
The purpose of this study was to describe the preliminary development and validation of a potential measure for assessing the accessibility of the built environment in Zambia. It was designed to identify environmental features that present barriers to participation for people with mobility limitations (PWML) using mobility devices such as wheelchairs or crutches. The Participation-Based Environment Accessibility Assessment Tool (P-BEAAT) was developed through focus group discussions and personal interviews with 88 PWML from five provinces of Zambia regarding the accessibility of their built environment. The content validity of the P-BEAAT checklist was accomplished through three phases of development with data gathered from 11 focus groups and nine personal interviews. Participants described accessibility barriers which affect their participation in daily life. This information generated the P-BEAAT with 66 items describing eight environmental features with potential for identifying environmental barriers. The P-BEAAT has shown good homogeneity with Cronbach's α score of 0.91. The P-BEAAT was constructed grounded in the reality of people's experiences in Zambia for use in assessing environmental features important in the participation of daily life of PWML pertinent to developing countries. Further clinimetric testing of the properties of the P-BEAAT to establish reliability should be conducted next.
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Cheng, Chao; Ung, Matthew; Grant, Gavin D.; Whitfield, Michael L.
2013-01-01
Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and a combination of the two types of features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division. The model we propose here provides not only a practical tool for identifying novel cell cycle genes with high accuracy, but also new insights on cell cycle regulation by TFs and cis-regulatory elements. PMID:23874175
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Banna, Jinan; Grace Lin, Meng-Fen; Stewart, Maria; Fialkowski, Marie K.
2016-01-01
Fostering interaction in the online classroom is an important consideration in ensuring that students actively create their own knowledge and reach a high level of achievement in science courses. This study focuses on fostering interaction in an online introductory nutrition course offered in a public institution of higher education in Hawai‘i, USA. Interactive features included synchronous discussions and polls in scheduled sessions, and social media tools for sharing of information and resources. Qualitative student feedback was solicited regarding the new course features. Findings indicated that students who attended monthly synchronous sessions valued live interaction with peers and the instructor. Issues identified included technical difficulties during synchronous sessions, lack of participation on the part of fellow students in discussion and inability to attend synchronous sessions due to scheduling conflicts. In addition, few students made use of the opportunity to interact via social media. While students indicated that the interactive components of the course were valuable, several areas in which improvement may be made remain. Future studies may explore potential solutions to issues identified with new features to further promote interaction and foster learning in the course. Recommendations for instructors who are interested in offering online science courses in higher education are provided. PMID:27441032
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de Vries, Tamar I; R Monroe, Glen; van Belzen, Martine J; van der Lans, Christian A; Savelberg, Sanne MC; Newman, William G; van Haaften, Gijs; Nievelstein, Rutger A; van Haelst, Mieke M
2016-01-01
Rubinstein–Taybi syndrome (RTS, OMIM 180849) and Filippi syndrome (FLPIS, OMIM 272440) are both rare syndromes, with multiple congenital anomalies and intellectual deficit (MCA/ID). We present a patient with intellectual deficit, short stature, bilateral syndactyly of hands and feet, broad thumbs, ocular abnormalities, and dysmorphic facial features. These clinical features suggest both RTS and FLPIS. Initial DNA analysis of DNA isolated from blood did not identify variants to confirm either of these syndrome diagnoses. Whole-exome sequencing identified a homozygous variant in C9orf173, which was novel at the time of analysis. Further Sanger sequencing analysis of FLPIS cases tested negative for CKAP2L variants did not, however, reveal any further variants. Subsequent analysis using DNA isolated from buccal mucosa revealed a mosaic variant in CREBBP. This report highlights the importance of excluding mosaic variants in patients with a strong but atypical clinical presentation of a MCA/ID syndrome if no disease-causing variants can be detected in DNA isolated from blood samples. As the striking syndactyly observed in the present case is typical for FLPIS, we suggest CREBBP analysis in saliva samples for FLPIS syndrome cases in which no causal CKAP2L variant is detected. PMID:26956253
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Towards a taxonomy for integrated care: a mixed-methods study
Valentijn, Pim P.; Boesveld, Inge C.; van der Klauw, Denise M.; Ruwaard, Dirk; Struijs, Jeroen N.; Molema, Johanna J.W.; Bruijnzeels, Marc A.; Vrijhoef, Hubertus JM.
2015-01-01
Introduction Building integrated services in a primary care setting is considered an essential important strategy for establishing a high-quality and affordable health care system. The theoretical foundations of such integrated service models are described by the Rainbow Model of Integrated Care, which distinguishes six integration dimensions (clinical, professional, organisational, system, functional and normative integration). The aim of the present study is to refine the Rainbow Model of Integrated Care by developing a taxonomy that specifies the underlying key features of the six dimensions. Methods First, a literature review was conducted to identify features for achieving integrated service delivery. Second, a thematic analysis method was used to develop a taxonomy of key features organised into the dimensions of the Rainbow Model of Integrated Care. Finally, the appropriateness of the key features was tested in a Delphi study among Dutch experts. Results The taxonomy consists of 59 key features distributed across the six integration dimensions of the Rainbow Model of Integrated Care. Key features associated with the clinical, professional, organisational and normative dimensions were considered appropriate by the experts. Key features linked to the functional and system dimensions were considered less appropriate. Discussion This study contributes to the ongoing debate of defining the concept and typology of integrated care. This taxonomy provides a development agenda for establishing an accepted scientific framework of integrated care from an end-user, professional, managerial and policy perspective. PMID:25759607
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Yahya, Noorazrul; Chua, Xin-Jane; Manan, Hanani A; Ismail, Fuad
2018-05-17
This systematic review evaluates the completeness of dosimetric features and their inclusion as covariates in genetic-toxicity association studies. Original research studies associating genetic features and normal tissue complications following radiotherapy were identified from PubMed. The use of dosimetric data was determined by mining the statement of prescription dose, dose fractionation, target volume selection or arrangement and dose distribution. The consideration of the dosimetric data as covariates was based on the statement mentioned in the statistical analysis section. The significance of these covariates was extracted from the results section. Descriptive analyses were performed to determine their completeness and inclusion as covariates. A total of 174 studies were found to satisfy the inclusion criteria. Studies published ≥2010 showed increased use of dose distribution information (p = 0.07). 33% of studies did not include any dose features in the analysis of gene-toxicity associations. Only 29% included dose distribution features as covariates and reported the results. 59% of studies which included dose distribution features found significant associations to toxicity. A large proportion of studies on the correlation of genetic markers with radiotherapy-related side effects considered no dosimetric parameters. Significance of dose distribution features was found in more than half of the studies including these features, emphasizing their importance. Completeness of radiation-specific clinical data may have increased in recent years which may improve gene-toxicity association studies.
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Towards a taxonomy for integrated care: a mixed-methods study.
Valentijn, Pim P; Boesveld, Inge C; van der Klauw, Denise M; Ruwaard, Dirk; Struijs, Jeroen N; Molema, Johanna J W; Bruijnzeels, Marc A; Vrijhoef, Hubertus Jm
2015-01-01
Building integrated services in a primary care setting is considered an essential important strategy for establishing a high-quality and affordable health care system. The theoretical foundations of such integrated service models are described by the Rainbow Model of Integrated Care, which distinguishes six integration dimensions (clinical, professional, organisational, system, functional and normative integration). The aim of the present study is to refine the Rainbow Model of Integrated Care by developing a taxonomy that specifies the underlying key features of the six dimensions. First, a literature review was conducted to identify features for achieving integrated service delivery. Second, a thematic analysis method was used to develop a taxonomy of key features organised into the dimensions of the Rainbow Model of Integrated Care. Finally, the appropriateness of the key features was tested in a Delphi study among Dutch experts. The taxonomy consists of 59 key features distributed across the six integration dimensions of the Rainbow Model of Integrated Care. Key features associated with the clinical, professional, organisational and normative dimensions were considered appropriate by the experts. Key features linked to the functional and system dimensions were considered less appropriate. This study contributes to the ongoing debate of defining the concept and typology of integrated care. This taxonomy provides a development agenda for establishing an accepted scientific framework of integrated care from an end-user, professional, managerial and policy perspective.
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Zhu, Lianzhang; Chen, Leiming; Zhao, Dehai
2017-01-01
Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emotion classification methods. Five types of features are extracted from a speech sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate and short-term energy. By comparing statistical features with deep features extracted by a Deep Belief Network (DBN), we attempt to find the best features to identify the emotion status for speech. We propose a novel classification method that combines DBN and SVM (support vector machine) instead of using only one of them. In addition, a conjugate gradient method is applied to train DBN in order to speed up the training process. Gender-dependent experiments are conducted using an emotional speech database created by the Chinese Academy of Sciences. The results show that DBN features can reflect emotion status better than artificial features, and our new classification approach achieves an accuracy of 95.8%, which is higher than using either DBN or SVM separately. Results also show that DBN can work very well for small training databases if it is properly designed. PMID:28737705
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Bruegl, Amanda S.; Djordjevic, Bojana; Urbauer, Diana L.; Westin, Shannon N.; Soliman, Pamela T.; Lu, Karen H.; Luthra, Rajyalakshmi; Broaddus, Russell R.
2013-01-01
Clinical screening criteria, such as young age of endometrial cancer diagnosis and family history of signature cancers, have traditionally been used to identify women with Lynch Syndrome, which is caused by mutation of a DNA mismatch repair gene. Immunohistochemistry and microsatellite instability analysis have evolved as important screening tools to evaluate endometrial cancer patients for Lynch Syndrome. A complicating factor is that 15-20% of sporadic endometrial cancers have immunohistochemical loss of the DNA mismatch repair protein MLH1 and high levels of microsatellite instability due to methylation of MLH1. The PCR-based MLH1 methylation assay potentially resolves this issue, yet many clinical laboratories do not perform this assay. The objective of this study was to determine if clinical and pathologic features help to distinguish sporadic endometrial carcinomas with MLH1 loss secondary to MLH1 methylation from Lynch Syndrome-associated endometrial carcinomas with MLH1 loss and absence of MLH1 methylation. Of 337 endometrial carcinomas examined, 54 had immunohistochemical loss of MLH1. 40/54 had MLH1 methylation and were designated as sporadic, while 14/54 lacked MLH1 methylation and were designated as Lynch Syndrome. Diabetes and deep myometrial invasion were associated with Lynch Syndrome; no other clinical or pathological variable distinguished the 2 groups. Combining Society of Gynecologic Oncology screening criteria with these 2 features accurately captured all Lynch Syndrome cases, but with low specificity. In summary, no single clinical/pathologic feature or screening criteria tool accurately identified all Lynch Syndrome-associated endometrial carcinomas, highlighting the importance of the MLH1 methylation assay in the clinical evaluation of these patients. PMID:23888949
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Bruegl, Amanda S; Djordjevic, Bojana; Urbauer, Diana L; Westin, Shannon N; Soliman, Pamela T; Lu, Karen H; Luthra, Rajyalakshmi; Broaddus, Russell R
2014-01-01
Clinical screening criteria, such as young age of endometrial cancer diagnosis and family history of signature cancers, have traditionally been used to identify women with Lynch Syndrome, which is caused by mutation of a DNA mismatch repair gene. Immunohistochemistry and microsatellite instability analysis have evolved as important screening tools to evaluate endometrial cancer patients for Lynch Syndrome. A complicating factor is that 15-20% of sporadic endometrial cancers have immunohistochemical loss of the DNA mismatch repair protein MLH1 and high levels of microsatellite instability due to methylation of MLH1. The PCR-based MLH1 methylation assay potentially resolves this issue, yet many clinical laboratories do not perform this assay. The objective of this study was to determine if clinical and pathologic features help to distinguish sporadic endometrial carcinomas with MLH1 loss secondary to MLH1 methylation from Lynch Syndrome-associated endometrial carcinomas with MLH1 loss and absence of MLH1 methylation. Of 337 endometrial carcinomas examined, 54 had immunohistochemical loss of MLH1. 40/54 had MLH1 methylation and were designated as sporadic, while 14/54 lacked MLH1 methylation and were designated as Lynch Syndrome. Diabetes and deep myometrial invasion were associated with Lynch Syndrome; no other clinical or pathological variable distinguished the 2 groups. Combining Society of Gynecologic Oncology screening criteria with these 2 features accurately captured all Lynch Syndrome cases, but with low specificity. In summary, no single clinical/pathologic feature or screening criteria tool accurately identified all Lynch Syndrome-associated endometrial carcinomas, highlighting the importance of the MLH1 methylation assay in the clinical evaluation of these patients.
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The hip adductor muscle group in caviomorph rodents: anatomy and homology.
García-Esponda, César M; Candela, Adriana M
2015-06-01
Anatomical comparative studies including myological data of caviomorph rodents are relatively scarce, leading to a lack of use of muscular features in cladistic and morphofunctional analyses. In rodents, the hip adductor muscles constitute an important group of the hindlimb musculature, having an important function during the beginning of the stance phase. These muscles are subdivided in several distinct ways in the different clades of rodents, making the identification of their homologies hard to establish. In this contribution we provide a detailed description of the anatomical variation of the hip adductor muscle group of different genera of caviomorph rodents and identify the homologies of these muscles in the context of Rodentia. On this basis, we identify the characteristic pattern of the hip adductor muscles in Caviomorpha. Our results indicate that caviomorphs present a singular pattern of the hip adductor musculature that distinguishes them from other groups of rodents. They are characterized by having a single m. adductor brevis that includes solely its genicular part. This muscle, together with the m. gracilis, composes a muscular sheet that is medial to all other muscles of the hip adductor group. Both muscles probably have a synergistic action during locomotion, where the m. adductor brevis reinforces the multiple functions of the m. gracilis in caviomorphs. Mapping of analyzed myological characters in the context of Rodentia indicates that several features are recovered as potential synapomorphies of caviomorphs. Thus, analysis of the myological data described here adds to the current knowledge of caviomorph rodents from anatomical and functional points of view, indicating that this group has features that clearly differentiate them from other rodents. Copyright © 2015 Elsevier GmbH. All rights reserved.
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Klabjan, Diego; Jonnalagadda, Siddhartha Reddy
2016-01-01
Background Community-based question answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for Web-based health communities. Objective In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within Web-based health content that are good features in identifying valid answers. Methods Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. To rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. Results On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. Unified medical language system–based (health related) features used in the model enhance the algorithm’s performance by proximately 8%. A reasonably high rate of accuracy is obtained given that the data are considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus, and a number of overlapping health-related terms between questions. Conclusions Overall, our automated QA system based on historical QA pairs is shown to be effective according to the dataset in this case study. It is developed for general use in the health care domain, which can also be applied to other CQA sites. PMID:27485666
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Brar, Simren; Tsui, Clement K M; Dhillon, Braham; Bergeron, Marie-Josée; Joly, David L; Zambino, P J; El-Kassaby, Yousry A; Hamelin, Richard C
2015-01-01
White pine blister rust is caused by the fungal pathogen Cronartium ribicola J.C. Fisch (Basidiomycota, Pucciniales). This invasive alien pathogen was introduced into North America at the beginning of the 20th century on pine seedlings imported from Europe and has caused serious economic and ecological impacts. In this study, we applied a population and landscape genetics approach to understand the patterns of introduction and colonization as well as population structure and migration of C. ribicola. We characterized 1,292 samples of C. ribicola from 66 geographic locations in North America using single nucleotide polymorphisms (SNPs) and evaluated the effect of landscape features, host distribution, and colonization history on the structure of these pathogen populations. We identified eastern and western genetic populations in North America that are strongly differentiated. Genetic diversity is two to five times higher in eastern populations than in western ones, which can be explained by the repeated accidental introductions of the pathogen into northeastern North America compared with a single documented introduction into western North America. These distinct genetic populations are maintained by a barrier to gene flow that corresponds to a region where host connectivity is interrupted. Furthermore, additional cryptic spatial differentiation was identified in western populations. This differentiation corresponds to landscape features, such as mountain ranges, and also to host connectivity. We also detected genetic differentiation between the pathogen populations in natural stands and plantations, an indication that anthropogenic movement of this pathogen still takes place. These results highlight the importance of monitoring this invasive alien tree pathogen to prevent admixture of eastern and western populations where different pathogen races occur.
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Appolloni, L; Sandulli, R; Vetrano, G; Russo, G F
2018-05-15
Marine Protected Areas are considered key tools for conservation of coastal ecosystems. However, many reserves are characterized by several problems mainly related to inadequate zonings that often do not protect high biodiversity and propagule supply areas precluding, at the same time, economic important zones for local interests. The Gulf of Naples is here employed as a study area to assess the effects of inclusion of different conservation features and costs in reserve design process. In particular eight scenarios are developed using graph theory to identify propagule source patches and fishing and exploitation activities as costs-in-use for local population. Scenarios elaborated by MARXAN, software commonly used for marine conservation planning, are compared using multivariate analyses (MDS, PERMANOVA and PERMDISP) in order to assess input data having greatest effects on protected areas selection. MARXAN is heuristic software able to give a number of different correct results, all of them near to the best solution. Its outputs show that the most important areas to be protected, in order to ensure long-term habitat life and adequate propagule supply, are mainly located around the Gulf islands. In addition through statistical analyses it allowed us to prove that different choices on conservation features lead to statistically different scenarios. The presence of propagule supply patches forces MARXAN to select almost the same areas to protect decreasingly different MARXAN results and, thus, choices for reserves area selection. The multivariate analyses applied here to marine spatial planning proved to be very helpful allowing to identify i) how different scenario input data affect MARXAN and ii) what features have to be taken into account in study areas characterized by peculiar biological and economic interests. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Makishima, Hideki; Cazzolli, Heather; Szpurka, Hadrian; Dunbar, Andrew; Tiu, Ramon; Huh, Jungwon; Muramatsu, Hideki; O'Keefe, Christine; Hsi, Eric; Paquette, Ronald L.; Kojima, Seiji; List, Alan F.; Sekeres, Mikkael A.; McDevitt, Michael A.; Maciejewski, Jaroslaw P.
2009-01-01
Purpose Acquired somatic uniparental disomy (UPD) is commonly observed in myelodysplastic syndromes (MDS), myelodysplastic/myeloproliferative neoplasms (MDS/MPN), or secondary acute myelogenous leukemia (sAML) and may point toward genes harboring mutations. Recurrent UPD11q led to identification of homozygous mutations in c-Cbl, an E3 ubiquitin ligase involved in attenuation of proliferative signals transduced by activated receptor tyrosine kinases. We examined the role and frequency of Cbl gene family mutations in MPN and related conditions. Methods We applied high-density SNP-A karyotyping to identify loss of heterozygosity of 11q in 442 patients with MDS, MDS/MPN, MPN, sAML evolved from these conditions, and primary AML. We sequenced c-Cbl, Cbl-b, and Cbl-c in patients with or without corresponding UPD or deletions and correlated mutational status with clinical features and outcomes. Results We identified c-Cbl mutations in 5% and 9% of patients with chronic myelomonocytic leukemia (CMML) and sAML, and also in CML blast crisis and juvenile myelomonocytic leukemia (JMML). Most mutations were homozygous and affected c-Cbl; mutations in Cbl-b were also found in patients with similar clinical features. Patients with Cbl family mutations showed poor prognosis, with a median survival of 5 months. Pathomorphologic features included monocytosis, monocytoid blasts, aberrant expression of phosphoSTAT5, and c-kit overexpression. Serial studies showed acquisition of c-Cbl mutations during malignant evolution. Conclusion Mutations in the Cbl family RING finger domain or linker sequence constitute important pathogenic lesions associated with not only preleukemic CMML, JMML, and other MPN, but also progression to AML, suggesting that impairment of degradation of activated tyrosine kinases constitutes an important cancer mechanism. PMID:19901108
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Periglacial and glacial analogs for Martian landforms
NASA Technical Reports Server (NTRS)
Rossbacher, Lisa A.
1992-01-01
The list of useful terrestrial analogs for Martian landforms has been expanded to include: features developed by desiccation processes; catastrophic flood features associated with boulder-sized materials; and sorted ground developed at a density boundary. Quantitative analytical techniques developed for physical geography have been adapted and applied to planetary studies, including: quantification of the patterns of polygonally fractured ground to describe pattern randomness independent of pattern size, with possible correlation to the mechanism of origin and quantification of the relative area of a geomorphic feature or region in comparison to planetary scale. Information about Martian geomorphology studied in this project was presented at professional meetings world-wide, at seven colleges and universities, in two interactive televised courses, and as part of two books. Overall, this project has expanded the understanding of the range of terrestrial analogs for Martian landforms, including identifying several new analogs. The processes that created these terrestrial features are characterized by both cold temperatures and low humidity, and therefore both freeze-thaw and desiccation processes are important. All these results support the conclusion that water has played a significant role in the geomorphic history of Mars.
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A genome-wide methylation study on obesity: differential variability and differential methylation.
Xu, Xiaojing; Su, Shaoyong; Barnes, Vernon A; De Miguel, Carmen; Pollock, Jennifer; Ownby, Dennis; Shi, Hidong; Zhu, Haidong; Snieder, Harold; Wang, Xiaoling
2013-05-01
Besides differential methylation, DNA methylation variation has recently been proposed and demonstrated to be a potential contributing factor to cancer risk. Here we aim to examine whether differential variability in methylation is also an important feature of obesity, a typical non-malignant common complex disease. We analyzed genome-wide methylation profiles of over 470,000 CpGs in peripheral blood samples from 48 obese and 48 lean African-American youth aged 14-20 y old. A substantial number of differentially variable CpG sites (DVCs), using statistics based on variances, as well as a substantial number of differentially methylated CpG sites (DMCs), using statistics based on means, were identified. Similar to the findings in cancers, DVCs generally exhibited an outlier structure and were more variable in cases than in controls. By randomly splitting the current sample into a discovery and validation set, we observed that both the DVCs and DMCs identified from the first set could independently predict obesity status in the second set. Furthermore, both the genes harboring DMCs and the genes harboring DVCs showed significant enrichment of genes identified by genome-wide association studies on obesity and related diseases, such as hypertension, dyslipidemia, type 2 diabetes and certain types of cancers, supporting their roles in the etiology and pathogenesis of obesity. We generalized the recent finding on methylation variability in cancer research to obesity and demonstrated that differential variability is also an important feature of obesity-related methylation changes. Future studies on the epigenetics of obesity will benefit from both statistics based on means and statistics based on variances.
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Lu, Bin; Harley, Ronald G.; Du, Liang; Yang, Yi; Sharma, Santosh K.; Zambare, Prachi; Madane, Mayura A.
2014-06-17
A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.
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Prowse, Rebecca L; Dalbeth, Nicola; Kavanaugh, Arthur; Adebajo, Adewale O; Gaffo, Angelo L; Terkeltaub, Robert; Mandell, Brian F; Suryana, Bagus P P; Goldenstein-Schainberg, Claudia; Diaz-Torne, Cèsar; Khanna, Dinesh; Lioté, Frederic; Mccarthy, Geraldine; Kerr, Gail S; Yamanaka, Hisashi; Janssens, Hein; Baraf, Herbert F; Chen, Jiunn-Horng; Vazquez-Mellado, Janitzia; Harrold, Leslie R; Stamp, Lisa K; Van De Laar, Mart A; Janssen, Matthijs; Doherty, Michael; Boers, Maarten; Edwards, N Lawrence; Gow, Peter; Chapman, Peter; Khanna, Puja; Helliwell, Philip S; Grainger, Rebecca; Schumacher, H Ralph; Neogi, Tuhina; Jansen, Tim L; Louthrenoo, Worawit; Sivera, Francisca; Taylor, William J; Alten, Rieke
2013-04-01
To identify a comprehensive list of features that might discriminate between gout and other rheumatic musculoskeletal conditions, to be used subsequently for a case-control study to develop and test new classification criteria for gout. Two Delphi exercises were conducted using Web-based questionnaires: one with physicians from several countries who had an interest in gout and one with patients from New Zealand who had gout. Physicians rated a list of potentially discriminating features that were identified by literature review and expert opinion, and patients rated a list of features that they generated themselves. Agreement was defined by the RAND/UCLA disagreement index. Forty-four experienced physicians and 9 patients responded to all iterations. For physicians, 71 items were identified by literature review and 15 more were suggested by physicians. The physician survey showed agreement for 26 discriminatory features and 15 as not discriminatory. The patients identified 46 features of gout, for which there was agreement on 25 items as being discriminatory and 7 items as not discriminatory. Patients and physicians agreed upon several key features of gout. Physicians emphasized objective findings, imaging, and patterns of symptoms, whereas patients emphasized severity, functional results, and idiographic perception of symptoms.
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Extending the McDonald Observatory Serendipitous Survey of UV/Blue Asteroid Spectra
NASA Technical Reports Server (NTRS)
Vilas, Faith; Cochran, A. L.
1999-01-01
Moderate resolution asteroid spectra in the 350 - 650 nm spectral range acquired randomly over many years (Cochran and Vilas, Icarus v 127, 121, 1997) identified absorption features in spectra of some of the asteroids. A feature centered at 430 nm was identified in the spectra of some low-albedo asteroids (C class and subclass), similar to the feature identified by Vilas et al. (Icarus, v. 102, 225,1993) in other low-albedo asteroid spectra and attributed to a ferric iron spin-forbidden transition in iron alteration minerals such as jarosite. Features at 505 nm and 430 nm were identified in the spectrum of 4 Vesta. The 505-nm feature is highly diagnostic of the amount and form of calcium in pyroxenes. This suggested further research on the sharpness and spectral placement of this feature in the spectra of Vesta and Vestoids (e.g., Cochran and Vilas, Icarus v. 134, 207, 1998). In 1997 and 1998, additional UV/blue spectra were obtained at the 2.7-m Harlan J. Smith telescope with a facility cassegrain spectrograph. These included spectra of low-albedo asteroids, the R-class asteroid 349 Dembowska, and the M-class asteroid 135 Hertha. These spectra will be presented and identified features will be discussed.
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Classification of clinically useful sentences in clinical evidence resources.
Morid, Mohammad Amin; Fiszman, Marcelo; Raja, Kalpana; Jonnalagadda, Siddhartha R; Del Fiol, Guilherme
2016-04-01
Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care. To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs. We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure. The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62). The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier's model and features used for UpToDate generalized well to Medline abstracts. Copyright © 2016 Elsevier Inc. All rights reserved.
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McKibbon, K A
1998-01-01
Evidence-based practice (EBP) is spreading in popularity in many health care disciplines. One of its main features is the reliance on the partnership among hard scientific evidence, clinical expertise, and individual patient needs and choices. Librarians play an important role in the spread of EBP because of the importance of identifying and retrieving appropriate literature from various sources for use in making health care decisions. This article gives an overview of how to search for therapy, diagnosis, etiology, and prognosis both for original studies and secondary publications such as systematic reviews, meta-analyses, and clinical practice guidelines. Understanding how this research is done, how it is indexed, and how to retrieve the clinical evidence are an important set of skills that librarians can provide for clinicians interested in EBP. PMID:9681176
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Apoptosis and other immune biomarkers predict influenza vaccine responsiveness.
Furman, David; Jojic, Vladimir; Kidd, Brian; Shen-Orr, Shai; Price, Jordan; Jarrell, Justin; Tse, Tiffany; Huang, Huang; Lund, Peder; Maecker, Holden T; Utz, Paul J; Dekker, Cornelia L; Koller, Daphne; Davis, Mark M
2013-04-16
Despite the importance of the immune system in many diseases, there are currently no objective benchmarks of immunological health. In an effort to identifying such markers, we used influenza vaccination in 30 young (20-30 years) and 59 older subjects (60 to >89 years) as models for strong and weak immune responses, respectively, and assayed their serological responses to influenza strains as well as a wide variety of other parameters, including gene expression, antibodies to hemagglutinin peptides, serum cytokines, cell subset phenotypes and in vitro cytokine stimulation. Using machine learning, we identified nine variables that predict the antibody response with 84% accuracy. Two of these variables are involved in apoptosis, which positively associated with the response to vaccination and was confirmed to be a contributor to vaccine responsiveness in mice. The identification of these biomarkers provides new insights into what immune features may be most important for immune health.
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Sex differences in prenatal epigenetic programming of stress pathways.
Bale, Tracy L
2011-07-01
Maternal stress experience is associated with neurodevelopmental disorders including schizophrenia and autism. Recent studies have examined mechanisms by which changes in the maternal milieu may be transmitted to the developing embryo and potentially translated into programming of the epigenome. Animal models of prenatal stress have identified important sex- and temporal-specific effects on offspring stress responsivity. As dysregulation of stress pathways is a common feature in most neuropsychiatric diseases, molecular and epigenetic analyses at the maternal-embryo interface, especially in the placenta, may provide unique insight into identifying much-needed predictive biomarkers. In addition, as most neurodevelopmental disorders present with a sex bias, examination of sex differences in the inheritance of phenotypic outcomes may pinpoint gene targets and specific windows of vulnerability in neurodevelopment, which have been disrupted. This review discusses the association and possible contributing mechanisms of prenatal stress in programming offspring stress pathway dysregulation and the importance of sex.
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PACS administrators' and radiologists' perspective on the importance of features for PACS selection.
Joshi, Vivek; Narra, Vamsi R; Joshi, Kailash; Lee, Kyootai; Melson, David
2014-08-01
Picture archiving and communication systems (PACS) play a critical role in radiology. This paper presents the criteria important to PACS administrators for selecting a PACS. A set of criteria are identified and organized into an integrative hierarchical framework. Survey responses from 48 administrators are used to identify the relative weights of these criteria through an analytical hierarchy process. The five main dimensions for PACS selection in order of importance are system continuity and functionality, system performance and architecture, user interface for workflow management, user interface for image manipulation, and display quality. Among the subdimensions, the highest weights were assessed for security, backup, and continuity; tools for continuous performance monitoring; support for multispecialty images; and voice recognition/transcription. PACS administrators' preferences were generally in line with that of previously reported results for radiologists. Both groups assigned the highest priority to ensuring business continuity and preventing loss of data through features such as security, backup, downtime prevention, and tools for continuous PACS performance monitoring. PACS administrators' next high priorities were support for multispecialty images, image retrieval speeds from short-term and long-term storage, real-time monitoring, and architectural issues of compatibility and integration with other products. Thus, next to ensuring business continuity, administrators' focus was on issues that impact their ability to deliver services and support. On the other hand, radiologists gave high priorities to voice recognition, transcription, and reporting; structured reporting; and convenience and responsiveness in manipulation of images. Thus, radiologists' focus appears to be on issues that may impact their productivity, effort, and accuracy.
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NASA Astrophysics Data System (ADS)
Farag, A. Z. A.; Sultan, M.; Elkadiri, R.; Abdelhalim, A.
2014-12-01
An integrated approach using remote sensing, landscape analysis and statistical methods was conducted to assess the role of groundwater sapping in shaping the Saharan landscape. A GIS-based logistic regression model was constructed to automatically delineate the spatial distribution of the sapping features over areas occupied by the Nubian Sandstone Aquifer System (NSAS): (1) an inventory was compiled of known locations of sapping features identified either in the field or from satellite datasets (e.g. Orbview-3 and Google Earth Digital Globe imagery); (2) spatial analyses were conducted in a GIS environment and seven geomorphological and geological predisposing factors (i.e. slope, stream density, cross-sectional and profile curvature, minimum and maximum curvature, and lithology) were identified; (3) a binary logistic regression model was constructed, optimized and validated to describe the relationship between the sapping locations and the set of controlling factors and (4) the generated model (prediction accuracy: 90.1%) was used to produce a regional sapping map over the NSAS. Model outputs indicate: (1) groundwater discharge and structural control played an important role in excavating the Saharan natural depressions as evidenced by the wide distribution of sapping features (areal extent: 1180 km2) along the fault-controlled escarpments of the Libyan Plateau; (2) proximity of mapped sapping features to reported paleolake and tufa deposits suggesting a causal effect. Our preliminary observations (from satellite imagery) and statistical analyses together with previous studies in the North Western Sahara Aquifer System (North Africa), Sinai Peninsula, Negev Desert, and The Plateau of Najd (Saudi Arabia) indicate extensive occurrence of sapping features along the escarpments bordering the northern margins of the Saharan-Arabian Desert; these areas share similar hydrologic settings with the NSAS domains and they too witnessed wet climatic periods in the Mid-Late Quaternary.
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[Mixed depressions: clinical and neurophysiological biomarkers].
Micoulaud Franchi, J-A; Geoffroy, P-A; Vion-Dury, J; Balzani, C; Belzeaux, R; Maurel, M; Cermolacce, M; Fakra, E; Azorin, J-M
2013-12-01
Epidemiological studies of major depressive episodes (MDE) highlighted the frequent association of symptoms or signs of mania or hypomania with depressive syndrome. Beyond the strict definition of DSM-IV, epidemiological recognition of a subset of MDE characterized by the presence of symptoms or signs of the opposite polarity is clinically important because it is associated with pejorative prognosis and therapeutic response compared to the subgroup of "typical MDE". The development of DSM-5 took into account the epidemiological data. DSM-5 opted for a more dimensional perspective in implementing the concept of "mixed features" from an "episode" to a "specification" of mood disorder. As outlined in the DSM-5: "Mixed features associated with a major depressive episode have been found to be a significant risk factor for the development of bipolar I and II disorder. As a result, it is clinically useful to note the presence of this specifier for treatment planning and monitoring of response to therapeutic". However, the mixed features are sometimes difficult to identify, and neurophysiological biomarkers would be useful to make a more specific diagnosis. Two neurophysiological models make it possible to better understand MDE with mixed features : i) the emotional regulation model that highlights a tendency to hyper-reactive and unstable emotion response, and ii) the vigilance regulation model that highlights, through EEG recording, a tendency to unstable vigilance. Further research is required to better understand relationships between these two models. These models provide the opportunity of a neurophysiological framework to better understand the mixed features associated with MDE and to identify potential neurophysiological biomarkers to guide therapeutic strategies. Copyright © 2013 L’Encéphale. Published by Elsevier Masson SAS.. All rights reserved.
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iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties
Feng, Peng-Mian; Ding, Chen; Zuo, Yong-Chun; Chou, Kuo-Chen
2012-01-01
Nucleosome positioning has important roles in key cellular processes. Although intensive efforts have been made in this area, the rules defining nucleosome positioning is still elusive and debated. In this study, we carried out a systematic comparison among the profiles of twelve DNA physicochemical features between the nucleosomal and linker sequences in the Saccharomyces cerevisiae genome. We found that nucleosomal sequences have some position-specific physicochemical features, which can be used for in-depth studying nucleosomes. Meanwhile, a new predictor, called iNuc-PhysChem, was developed for identification of nucleosomal sequences by incorporating these physicochemical properties into a 1788-D (dimensional) feature vector, which was further reduced to a 884-D vector via the IFS (incremental feature selection) procedure to optimize the feature set. It was observed by a cross-validation test on a benchmark dataset that the overall success rate achieved by iNuc-PhysChem was over 96% in identifying nucleosomal or linker sequences. As a web-server, iNuc-PhysChem is freely accessible to the public at http://lin.uestc.edu.cn/server/iNuc-PhysChem. For the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented just for the integrity in developing the predictor. Meanwhile, for those who prefer to run predictions in their own computers, the predictor's code can be easily downloaded from the web-server. It is anticipated that iNuc-PhysChem may become a useful high throughput tool for both basic research and drug design. PMID:23144709
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Ma, Xin; Guo, Jing; Sun, Xiao
2016-01-01
DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.
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Sockeye: A 3D Environment for Comparative Genomics
Montgomery, Stephen B.; Astakhova, Tamara; Bilenky, Mikhail; Birney, Ewan; Fu, Tony; Hassel, Maik; Melsopp, Craig; Rak, Marcin; Robertson, A. Gordon; Sleumer, Monica; Siddiqui, Asim S.; Jones, Steven J.M.
2004-01-01
Comparative genomics techniques are used in bioinformatics analyses to identify the structural and functional properties of DNA sequences. As the amount of available sequence data steadily increases, the ability to perform large-scale comparative analyses has become increasingly relevant. In addition, the growing complexity of genomic feature annotation means that new approaches to genomic visualization need to be explored. We have developed a Java-based application called Sockeye that uses three-dimensional (3D) graphics technology to facilitate the visualization of annotation and conservation across multiple sequences. This software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization. PMID:15123592
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Common respiratory conditions of the newborn
Gallacher, David J.; Hart, Kylie
2016-01-01
Key points Respiratory distress is a common presenting feature among newborn infants. Prompt investigation to ascertain the underlying diagnosis and appropriate subsequent management is important to improve outcomes. Many of the underlying causes of respiratory distress in a newborn are unique to this age group. A chest radiograph is crucial to assist in diagnosis of an underlying cause. Educational aims To inform readers of the common respiratory problems encountered in neonatology and the evidence-based management of these conditions. To enable readers to develop a framework for diagnosis of an infant with respiratory distress. The first hours and days of life are of crucial importance for the newborn infant as the infant adapts to the extra-uterine environment. The newborn infant is vulnerable to a range of respiratory diseases, many unique to this period of early life as the developing fluid-filled fetal lungs adapt to the extrauterine environment. The clinical signs of respiratory distress are important to recognise and further investigate, to identify the underlying cause. The epidemiology, diagnostic features and management of common neonatal respiratory conditions are covered in this review article aimed at all healthcare professionals who come into contact with newborn infants. PMID:27064402
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Chimaeric sounds reveal dichotomies in auditory perception
Smith, Zachary M.; Delgutte, Bertrand; Oxenham, Andrew J.
2008-01-01
By Fourier's theorem1, signals can be decomposed into a sum of sinusoids of different frequencies. This is especially relevant for hearing, because the inner ear performs a form of mechanical Fourier transform by mapping frequencies along the length of the cochlear partition. An alternative signal decomposition, originated by Hilbert2, is to factor a signal into the product of a slowly varying envelope and a rapidly varying fine time structure. Neurons in the auditory brainstem3–6 sensitive to these features have been found in mammalian physiological studies. To investigate the relative perceptual importance of envelope and fine structure, we synthesized stimuli that we call ‘auditory chimaeras’, which have the envelope of one sound and the fine structure of another. Here we show that the envelope is most important for speech reception, and the fine structure is most important for pitch perception and sound localization. When the two features are in conflict, the sound of speech is heard at a location determined by the fine structure, but the words are identified according to the envelope. This finding reveals a possible acoustic basis for the hypothesized ‘what’ and ‘where’ pathways in the auditory cortex7–10. PMID:11882898
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'I love my work, but ... ': the 'best' and the 'worst' in nurse educators' working life in Finland.
Harri, M
1996-06-01
The aim of this study was to investigate nurse educators' perceptions of the quality of their working life. Questionnaires were sent to 706 Finnish nurse educators and their spouses or other adult living with them. The results concerning the structured part of the questionnaire have been described in detail in an earlier paper (Harri 1995). This paper describes the data collected by means of semi-structured questions from 477 (68%) educators and 409 (58%) spouses) and open-ended questions (309 nurse educators and 167 spouses), analysed using content analysis. The seven categories that were identified as the best features of work were, in descending order of prevalence: students, freedom, challenges, teaching, miscellaneous, colleagues, and physical environment. Among the worst features of work, workload ranked first, followed by inadequacy of personal resources, administrative issues, changes, interpersonal relationships, miscellaneous, and physical environment. In the open-ended comments the nurse educators used 400 expressions to describe positive features of their working life, while they used 597 expressions to describe negative features. The spouses made 90 positive comments about the working life of their teacher spouse, and 425 negative comments. The importance of this kind of study was remarked on quite frequently both by nurse educators (22%) and their spouses (12%). The study provides important clues to understanding the present reality of nurse educators' working life. It is hoped that the findings of the study will help focus attention on ways of developing the quality of nurse educators' working life.
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A distance constrained synaptic plasticity model of C. elegans neuronal network
NASA Astrophysics Data System (ADS)
Badhwar, Rahul; Bagler, Ganesh
2017-03-01
Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.
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Kruse, Arnold-Jan; Croce, Sabrina; Kruitwagen, Roy F P M; Riedl, Robert G; Slangen, Brigitte F M; Van Gorp, Toon; Van de Vijver, Koen K
2014-11-01
Although the World Health Organization (WHO) in 2003 defined endometrial stromal sarcomas (ESSs) in general have a good prognosis, considerable differences in clinical behavior and prognosis may exist between different patients with ESS. The ESSs of the type associated with YWHAE-NUTM2 (previously named YWHAE-FAM22) fusion have a more aggressive clinical behavior and poorer prognosis than conventional ESS. Recently, the WHO 2014 classification recognizes this subset of ESS as a separate entity and classifies these as high-grade ESSs. Recognition of this subset has therefore an important clinical impact. We performed a review of the literature to delineate the clinicopathologic features of ESS patients with an YWHAE-NUTM2 rearrangement, with the goal to recognize this subset of ESS. We report a case of a woman with WHO 2014-defined high-grade ESS. Furthermore, published English literature was reviewed for YWHAE-FAM22 ESS and uterus. Twenty patients were identified, with a median age of 50 (range, 28-67) years. There were no clinical features able to recognize YWHAE-NUTM2 ESS. However, they characteristically contain specific histopathological features. Furthermore, YWHAE-NUTM2 ESSs are strongly cyclin D1 positive in contrast to conventional low-grade ESSs. YWHAE-NUTM2 ESSs represent a subset of ESSs with an aggressive clinical behavior and poor prognosis. Specific histopathological features may indicate the presence of YWHAE-NUTM2 rearrangement, which subsequently can be confirmed by cyclin D1 immunostaining.
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Forman, Jane; Creswell, John W; Damschroder, Laura; Kowalski, Christine P; Krein, Sarah L
2008-12-01
Infection control professionals and hospital epidemiologists are accustomed to using quantitative research. Although quantitative studies are extremely important in the field of infection control and prevention, often they cannot help us explain why certain factors affect the use of infection control practices and identify the underlying mechanisms through which they do so. Qualitative research methods, which use open-ended techniques, such as interviews, to collect data and nonstatistical techniques to analyze it, provide detailed, diverse insights of individuals, useful quotes that bring a realism to applied research, and information about how different health care settings operate. Qualitative research can illuminate the processes underlying statistical correlations, inform the development of interventions, and show how interventions work to produce observed outcomes. This article describes the key features of qualitative research and the advantages that such features add to existing quantitative research approaches in the study of infection control. We address the goal of qualitative research, the nature of the research process, sampling, data collection and analysis, validity, generalizability of findings, and presentation of findings. Health services researchers are increasingly using qualitative methods to address practical problems by uncovering interacting influences in complex health care environments. Qualitative research methods, applied with expertise and rigor, can contribute important insights to infection prevention efforts.
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Blue Marble Matches: Using Earth for Planetary Comparisons
NASA Technical Reports Server (NTRS)
Graff, Paige Valderrama
2009-01-01
Goal: This activity is designed to introduce students to geologic processes on Earth and model how scientists use Earth to gain a better understanding of other planetary bodies in the solar system. Objectives: Students will: 1. Identify common descriptor characteristics used by scientists to describe geologic features in images. 2. Identify geologic features and how they form on Earth. 3. Create a list of defining/distinguishing characteristics of geologic features 4. Identify geologic features in images of other planetary bodies. 5. List observations and interpretations about planetary body comparisons. 6. Create summary statements about planetary body comparisons.
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Which ante mortem clinical features predict progressive supranuclear palsy pathology?
Respondek, Gesine; Kurz, Carolin; Arzberger, Thomas; Compta, Yaroslau; Englund, Elisabet; Ferguson, Leslie W; Gelpi, Ellen; Giese, Armin; Irwin, David J; Meissner, Wassilios G; Nilsson, Christer; Pantelyat, Alexander; Rajput, Alex; van Swieten, John C; Troakes, Claire; Josephs, Keith A; Lang, Anthony E; Mollenhauer, Brit; Müller, Ulrich; Whitwell, Jennifer L; Antonini, Angelo; Bhatia, Kailash P; Bordelon, Yvette; Corvol, Jean-Christophe; Colosimo, Carlo; Dodel, Richard; Grossman, Murray; Kassubek, Jan; Krismer, Florian; Levin, Johannes; Lorenzl, Stefan; Morris, Huw; Nestor, Peter; Oertel, Wolfgang H; Rabinovici, Gil D; Rowe, James B; van Eimeren, Thilo; Wenning, Gregor K; Boxer, Adam; Golbe, Lawrence I; Litvan, Irene; Stamelou, Maria; Höglinger, Günter U
2017-07-01
Progressive supranuclear palsy (PSP) is a neuropathologically defined disease presenting with a broad spectrum of clinical phenotypes. To identify clinical features and investigations that predict or exclude PSP pathology during life, aiming at an optimization of the clinical diagnostic criteria for PSP. We performed a systematic review of the literature published since 1996 to identify clinical features and investigations that may predict or exclude PSP pathology. We then extracted standardized data from clinical charts of patients with pathologically diagnosed PSP and relevant disease controls and calculated the sensitivity, specificity, and positive predictive value of key clinical features for PSP in this cohort. Of 4166 articles identified by the database inquiry, 269 met predefined standards. The literature review identified clinical features predictive of PSP, including features of the following 4 functional domains: ocular motor dysfunction, postural instability, akinesia, and cognitive dysfunction. No biomarker or genetic feature was found reliably validated to predict definite PSP. High-quality original natural history data were available from 206 patients with pathologically diagnosed PSP and from 231 pathologically diagnosed disease controls (54 corticobasal degeneration, 51 multiple system atrophy with predominant parkinsonism, 53 Parkinson's disease, 73 behavioral variant frontotemporal dementia). We identified clinical features that predicted PSP pathology, including phenotypes other than Richardson's syndrome, with varying sensitivity and specificity. Our results highlight the clinical variability of PSP and the high prevalence of phenotypes other than Richardson's syndrome. The features of variant phenotypes with high specificity and sensitivity should serve to optimize clinical diagnosis of PSP. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.
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Barber, Andrew J; Lawson, David D A; Field, E Anne
2009-04-01
The following case reports describe the clinical features, diagnosis and management of two patients who presented to their general dental practitioner with a complaint of orofacial paraesthesia. After appropriate investigations, both patients were diagnosed as having benign intracranial tumours and were managed by a neurosurgeon. These cases illustrate the important role the general dental practitioner has in the early recognition of potentially life-threatening conditions.
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Wide Body Aircraft Demand Potential at Washington National Airport,
1977-09-01
the city-pair markets. Probably the most important feature of FA-7 is the fact that it allows for investigation of the behavior of airlines to changes...FINANCIAL INFORMfATION YLIGHTS BY AIRCRAFT TPE ~.4/J \\ FUEL COSUMED PASSENGERS UARRIED BY TOA IR F FLIGHITS TOTAL AIRCRAFT USAGE coded data. Sample...the various levels of operations. Similar behavior can be identified in the simultaneous increase of both types of aircraft at Dulles. Tables lAthrough
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Carty, Sally E; Doherty, Gerard M; Inabnet, William B; Pasieka, Janice L; Randolph, Gregory W; Shaha, Ashok R; Terris, David J; Tufano, Ralph P; Tuttle, R Michael
2012-04-01
Thyroid cancer specialists require specific perioperative information to develop a management plan for patients with thyroid cancer, but there is not yet a model for effective interdisciplinary data communication. The American Thyroid Association Surgical Affairs Committee was asked to define a suggested essential perioperative dataset representing the critical information that should be readily available to participating members of the treatment team. To identify and agree upon a multidisciplinary set of critical perioperative findings requiring communication, we examined diverse best-practice documents relating to thyroidectomy and extracted common features felt to enhance precise, direct communication with nonsurgical caregivers. Suggested essential datasets for the preoperative, intraoperative, and immediate postoperative findings and management of patients undergoing surgery for thyroid cancer were identified and are presented. For operative reporting, the essential features of both a dictated narrative format and a synoptic computer format are modeled in detail. The importance of interdisciplinary communication is discussed with regard to the extent of required resection, the final pathology findings, surgical complications, and other factors that may influence risk stratification, adjuvant treatment, and surveillance. Accurate communication of the important findings and sequelae of thyroidectomy for cancer is critical to individualized risk stratification as well as to the clinical issues of thyroid cancer care that are often jointly managed in the postoperative setting. True interdisciplinary care is essential to providing optimal care and surveillance.
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The Co-Existence of Myasthenia Gravis in Patients with Myositis: A Case Series
Paik, Julie J.; Corse, Andrea M.; Mammen, Andrew L.
2014-01-01
Objective Myositis and myasthenia gravis (MG) are both autoimmune disorders presenting with muscle weakness. Rarely, they occur simultaneously in the same patient. Since the management of myasthenia gravis differs from that of myositis, it is important to recognize when patients have both diseases. We reviewed the cases of 6 patients with both myositis and MG to identify clinical features that suggest the possibility of co-existing MG in myositis patients. Methods We identified 6 patients with dermatomyositis or polymyositis and MG. We reviewed their medical records to assess their clinical presentations, laboratory findings, and electrophysiological features. Results All 6 patients had definite dermatomyositis or polymyositis by the criteria of Bohan and Peter as well as electrophysiologic and/or serologic confirmation of MG. Among overlap patients, 5/6 (83%) had bulbar weakness, 2/6 (33%) had ptosis, and 1/6 (17%) had diplopia. Fatigable weakness was noted by 5/6 (83%) patients. Treatment with pyridostigmine improved symptoms in 5/6 (83%). High dose steroids were associated with worsening weakness in 2/6 (33%) patients. Conclusions Prominent bulbar symptoms, ptosis, diplopia, and fatigable weakness should suggest the possibility of MG in patients with myositis. A suspicion of MG may be confirmed through appropriate electrophysiologic and laboratory testing. In those with myositis-MG overlap, high dose steroids may exacerbate symptoms and pryidostigmine may play an important therapeutic role. PMID:24412588
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NASA Astrophysics Data System (ADS)
McGowan, H. A.; Neil, D.
2005-12-01
The identification of sources of water on Mars will be critical to the successful exploration of the planet and the establishment of a permanent presence by humans. While the Martian polar ice caps contain up to 70% water by mass, the extreme climate of these regions means that they may not be suitable for habitation. As a result, other sites must be identified where access to water is possible. Recent evidence has emerged that suggests sand dunes on Mars may contain 40-50% water by mass (Bourke 2005). In this paper, we present niveo-aeolian features observed in the sand dunes of the Victoria Valley, Antarctica, which have long been considered an Earth analogue for those on Mars (Morris et al. 1972). These features include cornices of permafrosted sand in dune-crest deflation hollows, exposed erosion resistant frozen water and sand lenses, wet sand flows and seeps. We also report on the morphological characteristics of sand sink holes which form in chains above layers of buried, melting and/or sublimating snow. This process is apparently reliant on the melting of inter-grain ice bonds and subsequent formation of a dry mobile sand layer on the dune surface. These micro-morphological features associated with summertime denivation of the Victoria Valley sand dunes, which are 5 to 10 m high and several hundred meters in crest length, are too small to identify on air photographs, satellite imagery and LIDAR DEMS of these transverse barchanoid ridges. However, on Mars where sand dunes are 1 to 2 orders of magnitude larger, these features may be identifiable if solid water exists within them, as suggested by Bourke (2005). Perhaps of greater importance, they may indicate the presence of buried palaeo-snow layers which have been preserved beneath the erosion resistant permafrosted sand dunes on Mars. We believe that the formation and subsequent exposure of these snow layers is the primary cause of the denivation features present in the polar dunes of the Victoria Valley, Antarctica. References: Bourke, M.C. 2005: Water on Mars. The Halstead Lecture, British Association for the Advancement of Science, Trinity College, Dublin, September 2005. Morris, E.C., Mutch, T.A. and Holt, H.E. 1972: Atlas of geologic features in the Dry Valleys of South Victoria Land, Antarctica: Possible analogs of Martian surface features. Interagency report: Astrogeology 52. Prepared under NASA contract L-9718 by the Geological Survey.