NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available

Identifying the domains of context important to implementation science: a study protocol.

PubMed

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

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

PubMed Central

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

2017-01-01

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

The importance of internal facial features in learning new faces.

PubMed

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

2015-01-01

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

Identifying Trajectories of Borderline Personality Features in Adolescence

PubMed Central

Haltigan, John D.

2016-01-01

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

Identifying DNA Methylation Features that Underlie Prostate Cancer Disparities

DTIC Science & Technology

2016-10-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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

PubMed

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

2010-04-01

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

Method of identifying features in indexed data

DOEpatents

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

2001-06-26

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

Key clinical features to identify girls with CDKL5 mutations.

PubMed

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

2008-10-01

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

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

PubMed Central

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

2016-01-01

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

[Imported malaria in adults. Clinical, epidemiological and analytical features].

PubMed

Ramírez-Olivencia, G; Herrero, M D; Subirats, M; de Juanes, J R; Peña, J M; Puente, S

2012-01-01

Up to now, the epidemiological and clinical features of imported malaria in Spain have been described in small series from general hospitals. Almost all diagnosis had been made based on symptomatic patients. The aim of this study has been to determine the epidemiological, clinical and laboratorial characteristics of imported malaria in a Reference Unit for Tropical Diseases. We performed a cross-sectional, observational and retrospective study. The series consisted of patients diagnosed of malaria who had been attended at the Hospital Carlos III from January 1, 2002 to December 31, 2007. We identified 484 episodes of malaria, of which 398 cases were included in the analysis. Almost 50% of the patients were natives of endemic areas, while the rest were native-travelers or travelers. Most cases (88-98% according to the group) had not taken malaria chemoprophylaxis correctly when indicated. At the time of diagnosis, 30.4% of patients were asymptomatic and 28.1% of asymptomatic patients had anemia, 19.8% thrombocytopenia, 14% leukopenia, 5% hypocholesterolemia, 5% renal failure and 4.1% hypoglycemia. Low parasitemia was present in 97.5% of asymptomatic individuals compared to 80.5% of the symptomatic patients (P<0.001). Absence of chemoprophylaxis (or poor compliance) is the main reason for malaria in individuals traveling to endemic areas. Malaria must be ruled out in individuals coming from tropical countries with compatible symptoms, and it also should be suspected in certain groups of asymptomatic individuals with abnormal laboratorial parameters. Copyright © 2011 Elsevier España, S.L. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

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

PubMed Central

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Identifying the needs of elderly, hearing-impaired persons: the importance and utility of hearing aid attributes.

PubMed

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.

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

PubMed

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

2018-01-01

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

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

PubMed Central

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

2018-01-01

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

A Cross-Sectional Investigation of the Importance of Park Features for Promoting Regular Physical Activity in Parks.

PubMed

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.

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

PubMed

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

2017-01-30

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

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

PubMed

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

2004-11-01

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

Microbiota fingerprints lose individually identifying features over time.

PubMed

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

2017-01-09

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

Important features of Sustainable Aggregate Resource Management

USGS Publications Warehouse

Solar, Slavko V.; Shields, Deborah J.; Langer, William H.

2004-01-01

Every society, whether developed, developing or in a phase of renewal following governmental change, requires stable, adequate and secure supplies of natural resources. In the latter case, there could be significant need for construction materials for rebuilding infrastructure, industrial capacity, and housing. It is essential that these large-volume materials be provided in a rational manner that maximizes their societal contribution and minimizes environmental impacts. We describe an approach to resource management based on the principles of sustainable developed. Sustainable Aggregate Resource Management offers a way of addressing the conflicting needs and interests of environmental, economic, and social systems. Sustainability is an ethics based concept that utilizes science and democratic processes to reach acceptable agreements and tradeoffs among interests, while acknowledging the fundamental importance of the environment and social goods. We discuss the features of sustainable aggregate resource management.

Identifying 1st instar larvae for three forensically important blowfly species using "fingerprint" cuticular hydrocarbon analysis.

PubMed

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.

Classifying Imbalanced Data Streams via Dynamic Feature Group Weighting with Importance Sampling.

PubMed

Wu, Ke; Edwards, Andrea; Fan, Wei; Gao, Jing; Zhang, Kun

2014-04-01

Data stream classification and imbalanced data learning are two important areas of data mining research. Each has been well studied to date with many interesting algorithms developed. However, only a few approaches reported in literature address the intersection of these two fields due to their complex interplay. In this work, we proposed an importance sampling driven, dynamic feature group weighting framework (DFGW-IS) for classifying data streams of imbalanced distribution. Two components are tightly incorporated into the proposed approach to address the intrinsic characteristics of concept-drifting, imbalanced streaming data. Specifically, the ever-evolving concepts are tackled by a weighted ensemble trained on a set of feature groups with each sub-classifier (i.e. a single classifier or an ensemble) weighed by its discriminative power and stable level. The un-even class distribution, on the other hand, is typically battled by the sub-classifier built in a specific feature group with the underlying distribution rebalanced by the importance sampling technique. We derived the theoretical upper bound for the generalization error of the proposed algorithm. We also studied the empirical performance of our method on a set of benchmark synthetic and real world data, and significant improvement has been achieved over the competing algorithms in terms of standard evaluation metrics and parallel running time. Algorithm implementations and datasets are available upon request.

Important features of home-based support services for older Australians and their informal carers.

PubMed

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

PACS administrators' and radiologists' perspective on the importance of features for PACS selection.

PubMed

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.

Identifying marine Important Bird Areas using at-sea survey data

USGS Publications Warehouse

Smith, Melanie A.; Walker, Nathan J.; Free, Christopher M.; Kirchhoff, Matthew J.; Drew, Gary S.; Warnock, Nils; Stenhouse, Iain J.

2014-01-01

Effective marine bird conservation requires identification of at-sea locations used by populations for foraging, staging, and migration. Using an extensive database of at-sea survey data spanning over 30 years, we developed a standardized and data-driven spatial method for identifying globally significant marine Important Bird Areas in Alaska. To delineate these areas we developed a six-step process: binning data and accounting for unequal survey effort, filtering input data for persistence of species use, using a moving window analysis to produce maps representing a gradient from low to high abundance, drawing core area boundaries around major concentrations based on abundance thresholds, validating the results, and combining overlapping boundaries into important areas for multiple species. We identified 126 bird core areas which were merged into 59 pelagic sites important to 45 out of 57 species assessed. The final areas included approximately 34–38% of all marine birds in Alaska waters, within just 6% of the total area. We identified globally significant Important Bird Areas spanning 20 degrees of latitude and 56 degrees of longitude, in two different oceans, with climates ranging from temperate to polar. Although our maps did suffer from some data gaps, these gaps did not preclude us from identifying sites that incorporated 13% of the assessed continental waterbird population and 9% of the assessed global seabird population. The application of this technique over a large and productive region worked well for a wide range of birds, exhibiting a variety of foraging strategies and occupying a variety of ecosystem types.

Varied Rates of Implementation of Patient-Centered Medical Home Features and Residents' Perceptions of Their Importance Based on Practice Experience.

PubMed

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.

What vehicle features are considered important when buying an automobile? An examination of driver preferences by age and gender.

PubMed

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.

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

PubMed

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

2014-01-01

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

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

PubMed

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

2001-03-08

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

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

USDA-ARS?s Scientific Manuscript database

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

Role of Importance and Distinctiveness of Semantic Features in People with Aphasia: A Replication Study

ERIC Educational Resources Information Center

Mason-Baughman, Mary Beth; Wallace, Sarah E.

2014-01-01

Previous studies suggest that people with aphasia have incomplete lexical-semantic representations with decreased low-importance distinctive (LID) feature knowledge. In addition, decreased LID feature knowledge correlates with ability to discriminate among semantically related words. The current study seeks to replicate and extend previous…

Features of resilience

DOE PAGES

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

2017-02-20

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

Features of resilience

USGS Publications Warehouse

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

2017-01-01

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

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

PubMed

Tuo, Youlin; An, Ning; Zhang, Ming

2018-03-01

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

Familiarity and Within-Person Facial Variability: The Importance of the Internal and External Features.

PubMed

Kramer, Robin S S; Manesi, Zoi; Towler, Alice; Reynolds, Michael G; Burton, A Mike

2018-01-01

As faces become familiar, we come to rely more on their internal features for recognition and matching tasks. Here, we assess whether this same pattern is also observed for a card sorting task. Participants sorted photos showing either the full face, only the internal features, or only the external features into multiple piles, one pile per identity. In Experiments 1 and 2, we showed the standard advantage for familiar faces-sorting was more accurate and showed very few errors in comparison with unfamiliar faces. However, for both familiar and unfamiliar faces, sorting was less accurate for external features and equivalent for internal and full faces. In Experiment 3, we asked whether external features can ever be used to make an accurate sort. Using familiar faces and instructions on the number of identities present, we nevertheless found worse performance for the external in comparison with the internal features, suggesting that less identity information was available in the former. Taken together, we show that full faces and internal features are similarly informative with regard to identity. In comparison, external features contain less identity information and produce worse card sorting performance. This research extends current thinking on the shift in focus, both in attention and importance, toward the internal features and away from the external features as familiarity with a face increases.

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

DOEpatents

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

2014-06-17

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

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

PubMed

Vidyasagar, Mathukumalli

2015-01-01

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

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

DOEpatents

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

2014-12-23

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

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

PubMed

Mallik, Saurav; Bhadra, Tapas; Maulik, Ujjwal

2017-01-01

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

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

PubMed

Kurosumi, M; Mizukoshi, K

2018-05-01

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

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

PubMed

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

2016-10-01

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

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.

Identifying potential collapse features under highways : research implementation plan.

DOT National Transportation Integrated Search

2005-09-01

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

Identifying important nodes by adaptive LeaderRank

NASA Astrophysics Data System (ADS)

Xu, Shuang; Wang, Pei

2017-03-01

Spreading process is a common phenomenon in complex networks. Identifying important nodes in complex networks is of great significance in real-world applications. Based on the spreading process on networks, a lot of measures have been proposed to evaluate the importance of nodes. However, most of the existing measures are appropriate to static networks, which are fragile to topological perturbations. Many real-world complex networks are dynamic rather than static, meaning that the nodes and edges of such networks may change with time, which challenge numerous existing centrality measures. Based on a new weighted mechanism and the newly proposed H-index and LeaderRank (LR), this paper introduces a variant of the LR measure, called adaptive LeaderRank (ALR), which is a new member of the LR-family. Simulations on six real-world networks reveal that the new measure can well balance between prediction accuracy and robustness. More interestingly, the new measure can better adapt to the adjustment or local perturbations of network topologies, as compared with the existing measures. By discussing the detailed properties of the measures from the LR-family, we illustrate that the ALR has its competitive advantages over the other measures. The proposed algorithm enriches the measures to understand complex networks, and may have potential applications in social networks and biological systems.

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

PubMed

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

2013-04-01

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

Genes Important for Schizosaccharomyces pombe Meiosis Identified Through a Functional Genomics Screen

PubMed Central

Blyth, Julie; Makrantoni, Vasso; Barton, Rachael E.; Spanos, Christos; Rappsilber, Juri; Marston, Adele L.

2018-01-01

Meiosis is a specialized cell division that generates gametes, such as eggs and sperm. Errors in meiosis result in miscarriages and are the leading cause of birth defects; however, the molecular origins of these defects remain unknown. Studies in model organisms are beginning to identify the genes and pathways important for meiosis, but the parts list is still poorly defined. Here we present a comprehensive catalog of genes important for meiosis in the fission yeast, Schizosaccharomyces pombe. Our genome-wide functional screen surveyed all nonessential genes for roles in chromosome segregation and spore formation. Novel genes important at distinct stages of the meiotic chromosome segregation and differentiation program were identified. Preliminary characterization implicated three of these genes in centrosome/spindle pole body, centromere, and cohesion function. Our findings represent a near-complete parts list of genes important for meiosis in fission yeast, providing a valuable resource to advance our molecular understanding of meiosis. PMID:29259000

Phonological Feature Re-Assembly and the Importance of Phonetic Cues

ERIC Educational Resources Information Center

Archibald, John

2009-01-01

It is argued that new phonological features can be acquired in second languages, but that both feature acquisition and feature re-assembly are affected by the robustness of phonetic cues in the input.

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

PubMed

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

2018-05-01

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

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

PubMed

Haltigan, John D; Vaillancourt, Tracy

2016-03-01

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

STUDY TO IDENTIFY IMPORTANT PARAMETERS FOR CHARACTERIZING PESTICIDE RESIDUE TRANSFER EFFICIENCIES

EPA Science Inventory

To reduce the uncertainty associated with current estimates of children's exposure to pesticides by dermal contact and non-dietary ingestion, residue transfer data are required. Prior to conducting exhaustive studies, a screening study to identify the important parameters for...

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

PubMed

Wu, Feinan; Yao, Jie

2017-03-07

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

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

PubMed

Rudichenko, V M

2012-01-01

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

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

PubMed

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

2013-07-01

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

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

PubMed Central

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

2013-01-01

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

Designing attractive gamification features for collaborative storytelling websites.

PubMed

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

2013-06-01

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

Featured Image: Identifying Weird Galaxies

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2017-08-01

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

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

PubMed Central

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

2009-01-01

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

Identifying Outcomes that Are Important to Living Kidney Donors: A Nominal Group Technique Study.

PubMed

Hanson, Camilla S; Chapman, Jeremy R; Gill, John S; Kanellis, John; Wong, Germaine; Craig, Jonathan C; Teixeira-Pinto, Armando; Chadban, Steve J; Garg, Amit X; Ralph, Angelique F; Pinter, Jule; Lewis, Joshua R; Tong, Allison

2018-06-07

Living kidney donor candidates accept a range of risks and benefits when they decide to proceed with nephrectomy. Informed consent around this decision assumes they receive reliable data about outcomes they regard as critical to their decision making. We identified the outcomes most important to living kidney donors and described the reasons for their choices. Previous donors were purposively sampled from three transplant units in Australia (Sydney and Melbourne) and Canada (Vancouver). In focus groups using the nominal group technique, participants identified outcomes of donation, ranked them in order of importance, and discussed the reasons for their preferences. An importance score was calculated for each outcome. Qualitative data were analyzed thematically. Across 14 groups, 123 donors aged 27-78 years identified 35 outcomes. Across all participants, the ten highest ranked outcomes were kidney function (importance=0.40, scale 0-1), time to recovery (0.27), surgical complications (0.24), effect on family (0.22), donor-recipient relationship (0.21), life satisfaction (0.18), lifestyle restrictions (0.18), kidney failure (0.14), mortality (0.13), and acute pain/discomfort (0.12). Kidney function and kidney failure were more important to Canadian participants, compared with Australian donors. The themes identified included worthwhile sacrifice, insignificance of risks and harms, confidence and empowerment, unfulfilled expectations, and heightened susceptibility. Living kidney donors prioritized a range of outcomes, with the most important being kidney health and the surgical, lifestyle, functional, and psychosocial effects of donation. Donors also valued improvements to their family life and donor-recipient relationship. There were clear regional differences in the rankings. Copyright © 2018 by the American Society of Nephrology.

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

NASA Astrophysics Data System (ADS)

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

2009-12-01

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

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

PubMed

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

2018-04-12

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

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

NASA Astrophysics Data System (ADS)

Farsadnia, Farhad; Ghahreman, Bijan

2016-04-01

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

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

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

NASA Astrophysics Data System (ADS)

Podbielska, Halina; Bauer, Joanna

2005-09-01

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

Resampling procedures to identify important SNPs using a consensus approach.

PubMed

Pardy, Christopher; Motyer, Allan; Wilson, Susan

2011-11-29

Our goal is to identify common single-nucleotide polymorphisms (SNPs) (minor allele frequency > 1%) that add predictive accuracy above that gained by knowledge of easily measured clinical variables. We take an algorithmic approach to predict each phenotypic variable using a combination of phenotypic and genotypic predictors. We perform our procedure on the first simulated replicate and then validate against the others. Our procedure performs well when predicting Q1 but is less successful for the other outcomes. We use resampling procedures where possible to guard against false positives and to improve generalizability. The approach is based on finding a consensus regarding important SNPs by applying random forests and the least absolute shrinkage and selection operator (LASSO) on multiple subsamples. Random forests are used first to discard unimportant predictors, narrowing our focus to roughly 100 important SNPs. A cross-validation LASSO is then used to further select variables. We combine these procedures to guarantee that cross-validation can be used to choose a shrinkage parameter for the LASSO. If the clinical variables were unavailable, this prefiltering step would be essential. We perform the SNP-based analyses simultaneously rather than one at a time to estimate SNP effects in the presence of other causal variants. We analyzed the first simulated replicate of Genetic Analysis Workshop 17 without knowledge of the true model. Post-conference knowledge of the simulation parameters allowed us to investigate the limitations of our approach. We found that many of the false positives we identified were substantially correlated with genuine causal SNPs.

Crowding with conjunctions of simple features.

PubMed

Põder, Endel; Wagemans, Johan

2007-11-20

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

27 CFR 478.92 - How must licensed manufacturers and licensed importers identify firearms, armor piercing...

Code of Federal Regulations, 2010 CFR

2010-04-01

... Conduct of Business § 478.92 How must licensed manufacturers and licensed importers identify firearms... business; and (E) In the case of an imported firearm, the name of the country in which it was manufactured... place of business. For additional requirements relating to imported firearms, see Customs regulations at...

A Study for the Feature Selection to Identify GIEMSA-Stained Human Chromosomes Based on Artificial Neural Network

DTIC Science & Technology

2001-10-25

neural network (ANN) has been adopted for the human chromosome classification. It is important to select optimum features for training neural network...Many studies for computer-based chromosome analysis have shown that it is possible to classify chromosomes into 24 subgroups. In addition, artificial

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

PubMed Central

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

2013-01-01

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

Intelligence and Creativity in Problem Solving: The Importance of Test Features in Cognition Research

PubMed Central

Jaarsveld, Saskia; Lachmann, Thomas

2017-01-01

This paper discusses the importance of three features of psychometric tests for cognition research: construct definition, problem space, and knowledge domain. Definition of constructs, e.g., intelligence or creativity, forms the theoretical basis for test construction. Problem space, being well or ill-defined, is determined by the cognitive abilities considered to belong to the constructs, e.g., convergent thinking to intelligence, divergent thinking to creativity. Knowledge domain and the possibilities it offers cognition are reflected in test results. We argue that (a) comparing results of tests with different problem spaces is more informative when cognition operates in both tests on an identical knowledge domain, and (b) intertwining of abilities related to both constructs can only be expected in tests developed to instigate such a process. Test features should guarantee that abilities can contribute to self-generated and goal-directed processes bringing forth solutions that are both new and applicable. We propose and discuss a test example that was developed to address these issues. PMID:28220098

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Identifying hub stations and important lines of bus networks: A case study in Xiamen, China

NASA Astrophysics Data System (ADS)

Zhang, Hui; Zhuge, Chengxiang; Yu, Xiaohua

2018-07-01

Hub stations and important lines play key roles in transfers between stations. In this paper, a node failure model is proposed to identify hub stations. In the model, we introduce two new indicators called neighborhood degree ratio and transfer index to evaluate the importance of stations, which consider neighborhood stations' degree of station and the initial transfer times between stations. Moreover, line accessibility is developed to measure the importance of lines in the bus network. Xiamen bus network in 2016 is utilized to test the model. The results show that the two introduced indicators are more effective to identify hub stations compared with traditional complex network indicators such as degree, clustering coefficient and betweenness.

Important caves to be identified

NASA Astrophysics Data System (ADS)

Criteria to identify significant caves on federal land are being developed by the Interior Department's Bureau of Land Management and the Agriculture Department's Forest Service under requirements of the Federal Cave Resources Protection Act of 1988. The departments gave advance notice of proposed rulemaking March 3 and invited suggestions and comments from the public for 30 days.The law requires protection, to the extent practical, of significant caves on lands administered by the Secretaries of Agriculture and Interior and includes authority to issue and revoke permits for collection and removal of cave resources and special provisions for regulation of cave resources on Indian lands. Final regulations must be published by August 18, 1989.

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

PubMed

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

2017-01-01

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

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…

Statistical Analyses of Scatterplots to Identify Important Factors in Large-Scale Simulations

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

Kleijnen, J.P.C.; Helton, J.C.

1999-04-01

The robustness of procedures for identifying patterns in scatterplots generated in Monte Carlo sensitivity analyses is investigated. These procedures are based on attempts to detect increasingly complex patterns in the scatterplots under consideration and involve the identification of (1) linear relationships with correlation coefficients, (2) monotonic relationships with rank correlation coefficients, (3) trends in central tendency as defined by means, medians and the Kruskal-Wallis statistic, (4) trends in variability as defined by variances and interquartile ranges, and (5) deviations from randomness as defined by the chi-square statistic. The following two topics related to the robustness of these procedures are consideredmore » for a sequence of example analyses with a large model for two-phase fluid flow: the presence of Type I and Type II errors, and the stability of results obtained with independent Latin hypercube samples. Observations from analysis include: (1) Type I errors are unavoidable, (2) Type II errors can occur when inappropriate analysis procedures are used, (3) physical explanations should always be sought for why statistical procedures identify variables as being important, and (4) the identification of important variables tends to be stable for independent Latin hypercube samples.« less

Identifying important motivational factors for professionals in Greek hospitals

PubMed Central

Kontodimopoulos, Nick; Paleologou, Victoria; Niakas, Dimitris

2009-01-01

Background The purpose of this study was to identify important motivational factors according to the views of health-care professionals in Greek hospitals and particularly to determine if these might differ in the public and private sectors. Methods A previously developed -and validated- instrument addressing four work-related motivators (job attributes, remuneration, co-workers and achievements) was used. Three categories of health care professionals, doctors (N = 354), nurses (N = 581) and office workers (N = 418), working in public and private hospitals, participated and motivation was compared across socio-demographic and occupational variables. Results The range of reported motivational factors was mixed and Maslow's conclusions that lower level motivational factors must be met before ascending to the next level were not confirmed. The highest ranked motivator for the entire sample, and by professional subgroup, was achievements (P < 0.001). Within subgroups, motivators were similar, and only one significant difference was observed, namely between doctors and nurses in respect to co-workers (P < 0.05). Remuneration (and salary in particular) was reported as a significant incentive only for professionals in managerial positions. Health professionals in private hospitals were motivated by all factors significantly more than their public-hospital counterparts. Conclusion The results are in agreement with the literature which focuses attention to management approaches employing both monetary and non-monetary incentives to motivate health care workers. This study showed that intrinsic factors are particularly important and should become a target for effective employee motivation. PMID:19754968

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

PubMed Central

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

2014-01-01

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

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.

The relative importance of external and internal features of facial composites.

PubMed

Frowd, Charlie; Bruce, Vicki; McIntyre, Alex; Hancock, Peter

2007-02-01

Three experiments are reported that compare the quality of external with internal regions within a set of facial composites using two matching-type tasks. Composites are constructed with the aim of triggering recognition from people familiar with the targets, and past research suggests internal face features dominate representations of familiar faces in memory. However the experiments reported here show that the internal regions of composites are very poorly matched against the faces they purport to represent, while external feature regions alone were matched almost as well as complete composites. In Experiments 1 and 2 the composites used were constructed by participant-witnesses who were unfamiliar with the targets and therefore were predicted to demonstrate a bias towards the external parts of a face. In Experiment 3 we compared witnesses who were familiar or unfamiliar with the target items, but for both groups the external features were much better reproduced in the composites, suggesting it is the process of composite construction itself which is responsible for the poverty of the internal features. Practical implications of these results are discussed.

Mining featured biomarkers associated with prostatic carcinoma based on bioinformatics.

PubMed

Piao, Guanying; Wu, Jiarui

2013-11-01

To analyze the differentially expressed genes and identify featured biomarkers from prostatic carcinoma. The software "Significance Analysis of Microarray" (SAM) was used to identify the differentially coexpressed genes (DCGs). The DCGs existed in two datasets were analyzed by GO (Gene Ontology) functional annotation. A total of 389 DCGs were obtained. By GO analysis, we found these DCGs were closely related with the acinus development, TGF-β receptor and signal transduction pathways. Furthermore, five featured biomarkers were discovered by interaction analysis. These important signal pathways and oncogenes may provide potential therapeutic targets for prostatic carcinoma.

Intrinsic and contextual features in object recognition.

PubMed

Schlangen, Derrick; Barenholtz, Elan

2015-01-28

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

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

PubMed

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

2015-01-01

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

PTEN IDENTIFIED AS IMPORTANT RISK FACTOR OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE

PubMed Central

Hosgood, H Dean; Menashe, Idan; He, Xingzhou; Chanock, Stephen; Lan, Qing

2009-01-01

Common genetic variation may play an important role in altering chronic obstructive pulmonary disease (COPD) risk. In Xuanwei, China, the COPD rate is more than twice the Chinese national average, and COPD is strongly associated with in-home coal use. To identify genetic variation that may be associated with COPD in a population with substantial in-home coal smoke exposures, we evaluated 1,261 single nucleotide polymorphisms (SNPs) in 380 candidate genes potentially relevant for cancer and other human diseases in a population-based case-control study in Xuanwei (53 cases; 107 controls). PTEN was the most significantly associated gene with COPD in a minP analysis using 20,000 permutations (P = 0.00005). SNP-based analyses found that homozygote variant carriers of PTEN rs701848 (ORTT = 0.12, 95%CI = 0.03 - 0.47) had a significant decreased risk of COPD. PTEN, or phosphatase and tensin homolog, is an important regulator of cell cycle progression and cellular survival via the AKT signaling pathway. Our exploratory analysis suggests that genetic variation in PTEN may be an important risk factor of COPD in Xuanwei. However, due to the small sample size, additional studies are needed to evaluate these associations within Xuanwei and other populations with coal smoke exposures. PMID:19625176

Feature-based Morphometry

PubMed Central

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

2013-01-01

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

Combining Methods to Describe Important Marine Habitats for Top Predators: Application to Identify Biological Hotspots in Tropical Waters.

PubMed

Thiers, Laurie; Louzao, Maite; Ridoux, Vincent; Le Corre, Matthieu; Jaquemet, Sébastien; Weimerskirch, Henri

2014-01-01

In tropical waters resources are usually scarce and patchy, and predatory species generally show specific adaptations for foraging. Tropical seabirds often forage in association with sub-surface predators that create feeding opportunities by bringing prey close to the surface, and the birds often aggregate in large multispecific flocks. Here we hypothesize that frigatebirds, a tropical seabird adapted to foraging with low energetic costs, could be a good predictor of the distribution of their associated predatory species, including other seabirds (e.g. boobies, terns) and subsurface predators (e.g., dolphins, tunas). To test this hypothesis, we compared distribution patterns of marine predators in the Mozambique Channel based on a long-term dataset of both vessel- and aerial surveys, as well as tracking data of frigatebirds. By developing species distribution models (SDMs), we identified key marine areas for tropical predators in relation to contemporaneous oceanographic features to investigate multi-species spatial overlap areas and identify predator hotspots in the Mozambique Channel. SDMs reasonably matched observed patterns and both static (e.g. bathymetry) and dynamic (e.g. Chlorophyll a concentration and sea surface temperature) factors were important explaining predator distribution patterns. We found that the distribution of frigatebirds included the distributions of the associated species. The central part of the channel appeared to be the best habitat for the four groups of species considered in this study (frigatebirds, brown terns, boobies and sub-surface predators).

EFS: an ensemble feature selection tool implemented as R-package and web-application.

PubMed

Neumann, Ursula; Genze, Nikita; Heider, Dominik

2017-01-01

Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.

A Reduced Set of Features for Chronic Kidney Disease Prediction

PubMed Central

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

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.

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

PubMed Central

Parker, Karen J.; Maestripieri, Dario

2010-01-01

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

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

PubMed

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

2012-06-01

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

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…

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

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

PubMed Central

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

2016-01-01

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

Estimate variable importance for recurrent event outcomes with an application to identify hypoglycemia risk factors.

PubMed

Duan, Ran; Fu, Haoda

2015-08-30

Recurrent event data are an important data type for medical research. In particular, many safety endpoints are recurrent outcomes, such as hypoglycemic events. For such a situation, it is important to identify the factors causing these events and rank these factors by their importance. Traditional model selection methods are not able to provide variable importance in this context. Methods that are able to evaluate the variable importance, such as gradient boosting and random forest algorithms, cannot directly be applied to recurrent events data. In this paper, we propose a two-step method that enables us to evaluate the variable importance for recurrent events data. We evaluated the performance of our proposed method by simulations and applied it to a data set from a diabetes study. Copyright © 2015 John Wiley & Sons, Ltd.

[An outbreak of imported dengue fever from Myanmar to the border of China, with its viral molecular epidemiological features].

PubMed

Zhang, Hai-lin; Fu, Shi-hong; Deng, Zhang; Yuan, Jun; Jiang, Hong-yue; Li, Ming-hua; Gao, Xiao-yan; Wang, Jing-lin; Liu, Yong-hua; Yin, Zheng-liu; Yang, Wei-hong; Zhang, Yu-zhen; Feng, Yun; Wang, Huan-yu; Liang, Guo-dong

2013-05-01

To understand the epidemiologic characteristics of dengue fever, imported from Myanmar to the border of Yunnan province, China. Viral molecular epidemiologic features were also studied. Questionnaires were used on each diagnosed, suspected dengue fever, case or unknown cases with fever when coming from Myanmar entering the port and hospitals in Ruili city of Yunnan province. Serum samples of these patients were collected to detect IgM antibody against dengue virus and RT-PCR assay. Homology and phylogenetic tree based on the whole nucleotide sequence of PrM-C and NS5 gene of dengue virus were further analyzed. A total of 103 sera were collected from patients at acute stage in Ruili city in July to November 2008. Among them, 49 cases were confirmed for dengue fever according to IgM and nucleic acid testings. Except one, other 48 cases were all imported into Ruili, from Myanmar. Of those, 18 patients were residents from Mujie city of Myanmar and hospitalized in Ruili and the rest 30 patients were Chinese citizens who had finished business and returned from Myanmar. Two isolates of serum samples from the imported cases were identified and both homology and phylogenetic analysis were performed, using the nucleotide sequences of PrM and NS5 genes. They were divided into dengue type 1 (RLB61) and dengue type 3 (RLC31) and were closer to the dengue virus strains isolated from Southeast Asia countries. It is confirmed that an epidemic of dengue fever which was imported from Myanmar to Ruili city of Yunnan province, China. Evidence also showed that both type I and III epidemic strains of dengue virus did exist in Mujie city of Myanmar in 2008.

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

PubMed

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

2017-11-22

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

Prediction of lysine ubiquitylation with ensemble classifier and feature selection.

PubMed

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.

Prediction of interface residue based on the features of residue interaction network.

PubMed

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.

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.

How important is vehicle safety for older consumers in the vehicle purchase process?

PubMed

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

Clinical features that identify children with primary immunodeficiency diseases.

PubMed

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

2011-05-01

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

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,…

Robust extrema features for time-series data analysis.

PubMed

Vemulapalli, Pramod K; Monga, Vishal; Brennan, Sean N

2013-06-01

The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.

Print advertisements for Alzheimer's disease drugs: informational and transformational features.

PubMed

Gooblar, Jonathan; Carpenter, Brian D

2013-06-01

We examined print advertisements for Alzheimer's disease drugs published in journals and magazines between January 2008 and February 2012, using an informational versus transformational theoretical framework to identify objective and persuasive features. In 29 unique advertisements, we used qualitative methods to code and interpret identifying information, charts, benefit and side effect language, and persuasive appeals embedded in graphics and narratives. Most elements contained a mixture of informational and transformational features. Charts were used infrequently, but when they did appear the accompanying text often exaggerated the data. Benefit statements covered an array of symptoms, drug properties, and caregiver issues. Side effect statements often used positive persuasive appeals. Graphics and narrative features emphasized positive emotions and outcomes. We found subtle and sophisticated attempts both to educate and to persuade readers. It is important for consumers and prescribing physicians to read print advertisements critically so that they can make informed treatment choices.

SU-E-J-261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features

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

Zhang, L; Fried, D; Fave, X

Purpose: To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature’s differentiating capability. Methods: Twenty-seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre-treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre-treatment PET images (non-necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between twomore » groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2-tailed T-test was used to evaluate the differentiating capability of CT features on pre-treatment PT necrosis. Result: Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary-adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity

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.

TCGA study identifies genomic features of cervical cancer

Cancer.gov

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

Consumers' Preferences for Electronic Nicotine Delivery System Product Features: A Structured Content Analysis.

PubMed

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.

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

PubMed Central

Vassallo, Michael

2018-01-01

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

Identifying Elements of ICU Care That Families Report as Important But Unsatisfactory

PubMed Central

Osborn, Tristan R.; Curtis, J. Randall; Nielsen, Elizabeth L.; Back, Anthony L.; Shannon, Sarah E.

2012-01-01

Background: One in five deaths in the United States occurs in the ICU, and many of these deaths are experienced as less than optimal by families of dying people. The current study investigated the relationship between family satisfaction with ICU care and overall ratings of the quality of dying as a means of identifying targets for improving end-of-life experiences for patients and families. Methods: This multisite cross-sectional study surveyed families of patients who died in the ICU in one of 15 hospitals in western Washington State. Measures included the Family Satisfaction in the ICU (FS-ICU) and the Single-Item Quality of Dying (QOD-1) questionnaires. Associations between FS-ICU items and the QOD-1 were examined using multivariate linear regression controlling for patient and family demographics and hospital site. Results: Questionnaires were returned for 1,290 of 2,850 decedents (45%). Higher QOD-1 scores were significantly associated (all P < .05) with (1) perceived nursing skill and competence (β = 0.15), (2) support for family as decision-makers (β = 0.10), (3) family control over the patient’s care (β = 0.18), and (4) ICU atmosphere (β = 0.12). FS-ICU items that received low ratings and correlated with higher QOD-1 scores (ie, important items with room for improvement) were (1) support of family as decision-maker, (2) family control over patient’s care, and (3) ICU atmosphere. Conclusions: Increased support for families as decision-makers and for their desired level of control over patient care along with improvements in the ICU atmosphere were identified as aspects of the ICU experience that may be important targets for quality improvement. Trial registry: ClinicalTrials.gov; No.: NCT00685893; URL: www.clinicaltrials.gov. PMID:22661455

Mapping genomic features to functional traits through microbial whole genome sequences.

PubMed

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.

Feature selection and classification model construction on type 2 diabetic patients' data.

PubMed

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

Perceived Importance of Wellness Features at a Cancer Center: Patient and Staff Perspectives.

PubMed

Tinner, Michelle; Crovella, Paul; Rosenbaum, Paula F

2018-01-01

Determine the relative impact of 11 building wellness features on preference and on the ability to deliver/receive quality care for two groups: patients and caregivers. The impact of building features that promote wellness is of increasing interest to the building owners, designers, and occupants. This study performed a postoccupancy evaluation of two user groups at a healthcare facility with specific wellness features. Seventy-six staff and 62 patients of a cancer center were polled separately to determine their preferences in 11 categories. Results showed that all wellness features were viewed favorably by the two groups, with natural lighting, views of nature, and thermal comfort as top categories for both. The t-test comparisons were performed, and significant differences ( p < .05) between the two groups were found for three of the features (views of nature, art and murals, and indoor plants). Discussion of these differences and the interaction of competing design goals (thermal comfort, views of nature, natural light, and desire for privacy) are included. Designers and owners will want to consider the preferred use of roof gardens, art and murals, and indoor plants for patient spaces, where their relative value is greater. Access to private and quiet spaces is the top need for caregivers. Ease of movement, thermal comfort, and natural light were top needs for patients.

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

PubMed Central

2011-01-01

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

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

PubMed

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

2011-11-01

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

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.

Examining the design features of a communication-rich, problem-centred mathematics professional development

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.

Fatal methanol poisoning: features of liver histopathology.

PubMed

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.

Identifying pneumonia outbreaks of public health importance: can emergency department data assist in earlier identification?

PubMed

Hope, Kirsty; Durrheim, David N; Muscatello, David; Merritt, Tony; Zheng, Wei; Massey, Peter; Cashman, Patrick; Eastwood, Keith

2008-08-01

To retrospectively review the performance of a near real-time Emergency Department (ED) Syndromic Surveillance System operating in New South Wales for identifying pneumonia outbreaks of public health importance. Retrospective data was obtained from the NSW Emergency Department data collection for a rural hospital that has experienced a cluster of pneumonia diagnoses among teenage males in August 2006. ED standard reports were examined for signals in the overall count for each respiratory syndrome, and for elevated counts in individual subgroups including; age, sex and admission to hospital status. Using the current thresholds, the ED syndromic surveillance system would have trigged a signal for pneumonia syndrome in children aged 5-16 years four days earlier than the notification by a paediatrician and this signal was maintained for 14 days. If the ED syndromic surveillance system had been operating it could have identified the outbreak earlier than the paediatrician's notification. This may have permitted an earlier public health response. By understanding the behaviour of syndromes during outbreaks of public health importance, response protocols could be developed to facilitate earlier implementation of control measures.

On the importance of identifying, characterizing, and predicting fundamental phenomena towards microbial electrochemistry applications.

PubMed

Torres, César Iván

2014-06-01

The development of microbial electrochemistry research toward technological applications has increased significantly in the past years, leading to many process configurations. This short review focuses on the need to identify and characterize the fundamental phenomena that control the performance of microbial electrochemical cells (MXCs). Specifically, it discusses the importance of recent efforts to discover and characterize novel microorganisms for MXC applications, as well as recent developments to understand transport limitations in MXCs. As we increase our understanding of how MXCs operate, it is imperative to continue modeling efforts in order to effectively predict their performance, design efficient MXC technologies, and implement them commercially. Thus, the success of MXC technologies largely depends on the path of identifying, understanding, and predicting fundamental phenomena that determine MXC performance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Spatial features register: toward standardization of spatial features

USGS Publications Warehouse

Cascio, Janette

1994-01-01

As the need to share spatial data increases, more than agreement on a common format is needed to ensure that the data is meaningful to both the importer and the exporter. Effective data transfer also requires common definitions of spatial features. To achieve this, part 2 of the Spatial Data Transfer Standard (SDTS) provides a model for a spatial features data content specification and a glossary of features and attributes that fit this model. The model provides a foundation for standardizing spatial features. The glossary now contains only a limited subset of hydrographic and topographic features. For it to be useful, terms and definitions must be included for other categories, such as base cartographic, bathymetric, cadastral, cultural and demographic, geodetic, geologic, ground transportation, international boundaries, soils, vegetation, water, and wetlands, and the set of hydrographic and topographic features must be expanded. This paper will review the philosophy of the SDTS part 2 and the current plans for creating a national spatial features register as one mechanism for maintaining part 2.

Hypothesis testing for differentially correlated features.

PubMed

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

2016-10-01

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

Consumers’ Preferences for Electronic Nicotine Delivery System Product Features: A Structured Content Analysis

PubMed Central

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

Featured Image: Identifying a Glowing Shell

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2018-05-01

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

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

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

PubMed

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

2015-01-01

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

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.

Feature selection in feature network models: finding predictive subsets of features with the Positive Lasso.

PubMed

Frank, Laurence E; Heiser, Willem J

2008-05-01

A set of features is the basis for the network representation of proximity data achieved by feature network models (FNMs). Features are binary variables that characterize the objects in an experiment, with some measure of proximity as response variable. Sometimes features are provided by theory and play an important role in the construction of the experimental conditions. In some research settings, the features are not known a priori. This paper shows how to generate features in this situation and how to select an adequate subset of features that takes into account a good compromise between model fit and model complexity, using a new version of least angle regression that restricts coefficients to be non-negative, called the Positive Lasso. It will be shown that features can be generated efficiently with Gray codes that are naturally linked to the FNMs. The model selection strategy makes use of the fact that FNM can be considered as univariate multiple regression model. A simulation study shows that the proposed strategy leads to satisfactory results if the number of objects is less than or equal to 22. If the number of objects is larger than 22, the number of features selected by our method exceeds the true number of features in some conditions.

The Nature of Feedback: How Peer Feedback Features Affect Students' Implementation Rate and Quality of Revisions

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…

Global Sensitivity Analysis for Identifying Important Parameters of Nitrogen Nitrification and Denitrification under Model and Scenario Uncertainties

NASA Astrophysics Data System (ADS)

Ye, M.; Chen, Z.; Shi, L.; Zhu, Y.; Yang, J.

2017-12-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. While global sensitivity analysis is a vital tool for identifying the parameters important to nitrogen reactive transport, conventional global sensitivity analysis only considers parametric uncertainty. This may result in inaccurate selection of important parameters, because parameter importance may vary under different models and modeling scenarios. By using a recently developed variance-based global sensitivity analysis method, this paper identifies important parameters with simultaneous consideration of parametric uncertainty, model uncertainty, and scenario uncertainty. In a numerical example of nitrogen reactive transport modeling, a combination of three scenarios of soil temperature and two scenarios of soil moisture leads to a total of six scenarios. Four alternative models are used to evaluate reduction functions used for calculating actual rates of nitrification and denitrification. The model uncertainty is tangled with scenario uncertainty, as the reduction functions depend on soil temperature and moisture content. The results of sensitivity analysis show that parameter importance varies substantially between different models and modeling scenarios, which may lead to inaccurate selection of important parameters if model and scenario uncertainties are not considered. This problem is avoided by using the new method of sensitivity analysis in the context of model averaging and scenario averaging. The new method of sensitivity analysis can be applied to other problems of contaminant transport modeling when model uncertainty and/or scenario uncertainty are present.

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

PubMed Central

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

Barcode Identifiers as a Practical Tool for Reliable Species Assignment of Medically Important Black Yeast Species

PubMed Central

Heinrichs, Guido; de Hoog, G. Sybren

2012-01-01

Herpotrichiellaceous black yeasts and relatives comprise severe pathogens flanked by nonpathogenic environmental siblings. Reliable identification by conventional methods is notoriously difficult. Molecular identification is hampered by the sequence variability in the internal transcribed spacer (ITS) domain caused by difficult-to-sequence homopolymeric regions and by poor taxonomic attribution of sequences deposited in GenBank. Here, we present a potential solution using short barcode identifiers (27 to 50 bp) based on ITS2 ribosomal DNA (rDNA), which allows unambiguous definition of species-specific fragments. Starting from proven sequences of ex-type and authentic strains, we were able to describe 103 identifiers. Multiple BLAST searches of these proposed barcode identifiers in GenBank revealed uniqueness for 100 taxonomic entities, whereas the three remaining identifiers each matched with two entities, but the species of these identifiers could easily be discriminated by differences in the remaining ITS regions. Using the proposed barcode identifiers, a 4.1-fold increase of 100% matches in GenBank was achieved in comparison to the classical approach using the complete ITS sequences. The proposed barcode identifiers will be made accessible for the diagnostic laboratory in a permanently updated online database, thereby providing a highly practical, reliable, and cost-effective tool for identification of clinically important black yeasts and relatives. PMID:22785187

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

NASA Astrophysics Data System (ADS)

Zhang, Xuexia; Wu, Pengfei

2009-10-01

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

A Features Selection for Crops Classification

NASA Astrophysics Data System (ADS)

Liu, Yifan; Shao, Luyi; Yin, Qiang; Hong, Wen

2016-08-01

The components of the polarimetric target decomposition reflect the differences of target since they linked with the scattering properties of the target and can be imported into SVM as the classification features. The result of decomposition usually concentrate on part of the components. Selecting a combination of components can reduce the features that importing into the SVM. The features reduction can lead to less calculation and targeted classification of one target when we classify a multi-class area. In this research, we import different combinations of features into the SVM and find a better combination for classification with a data of AGRISAR.

Print Advertisements for Alzheimer’s Disease Drugs: Informational and Transformational Features

PubMed Central

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

Report: EPA Can Better Reduce Risks From Illegal Pesticides by Effectively Identifying Imports for Inspection and Sampling

EPA Pesticide Factsheets

Report #17-P-0412, September 28, 2017. Low rates of inspections and sampling can create a risk that the EPA may not be identifying and deterring the import of pesticides harmful to people or the environment.

Analysis of geometric moments as features for firearm identification.

PubMed

Md Ghani, Nor Azura; Liong, Choong-Yeun; Jemain, Abdul Aziz

2010-05-20

The task of identifying firearms from forensic ballistics specimens is exacting in crime investigation since the last two decades. Every firearm, regardless of its size, make and model, has its own unique 'fingerprint'. These fingerprints transfer when a firearm is fired to the fired bullet and cartridge case. The components that are involved in producing these unique characteristics are the firing chamber, breech face, firing pin, ejector, extractor and the rifling of the barrel. These unique characteristics are the critical features in identifying firearms. It allows investigators to decide on which particular firearm that has fired the bullet. Traditionally the comparison of ballistic evidence has been a tedious and time-consuming process requiring highly skilled examiners. Therefore, the main objective of this study is the extraction and identification of suitable features from firing pin impression of cartridge case images for firearm recognition. Some previous studies have shown that firing pin impression of cartridge case is one of the most important characteristics used for identifying an individual firearm. In this study, data are gathered using 747 cartridge case images captured from five different pistols of type 9mm Parabellum Vektor SP1, made in South Africa. All the images of the cartridge cases are then segmented into three regions, forming three different set of images, i.e. firing pin impression image, centre of firing pin impression image and ring of firing pin impression image. Then geometric moments up to the sixth order were generated from each part of the images to form a set of numerical features. These 48 features were found to be significantly different using the MANOVA test. This high dimension of features is then reduced into only 11 significant features using correlation analysis. Classification results using cross-validation under discriminant analysis show that 96.7% of the images were classified correctly. These results demonstrate

Visual search for feature and conjunction targets with an attention deficit.

PubMed

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.

The importance of service-users' perspectives: A systematic review of qualitative evidence reveals overlooked critical features of weight management programmes.

PubMed

Sutcliffe, Katy; Melendez-Torres, G J; Burchett, Helen E D; Richardson, Michelle; Rees, Rebecca; Thomas, James

2018-03-14

Extensive research effort shows that weight management programmes (WMPs) targeting both diet and exercise are broadly effective. However, the critical features of WMPs remain unclear. To develop a deeper understanding of WMPs critical features, we undertook a systematic review of qualitative evidence. We sought to understand from a service-user perspective how programmes are experienced, and may be effective, on the ground. We identified qualitative studies from existing reviews and updated the searches of one review. We included UK studies capturing the views of adult WMP users. Thematic analysis was used inductively to code and synthesize the evidence. Service users were emphatic that supportive relationships, with service providers or WMP peers, are the most critical aspect of WMPs. Supportive relationships were described as providing an extrinsic motivator or "hook" which helped to overcome barriers such as scepticism about dietary advice or a lack confidence to engage in physical activity. The evidence revealed that service-users' understandings of the critical features of WMPs differ from the focus of health promotion guidance or descriptions of evaluated programmes which largely emphasize educational or goal setting aspects of WMPs. Existing programme guidance may not therefore fully address the needs of service users. The study illustrates that the perspectives of service users can reveal unanticipated intervention mechanisms or underemphasized critical features and underscores the value of a holistic understanding about "what happens" in complex psychosocial interventions such as WMPs. © 2017 The Authors Health Expectations published by John Wiley & Sons Ltd.

Epilepsy with auditory features

PubMed Central

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

Using the Developmental Gene Bicoid to Identify Species of Forensically Important Blowflies (Diptera: Calliphoridae)

PubMed Central

Park, Seong Hwan; Park, Chung Hyun; Zhang, Yong; Piao, Huguo; Chung, Ukhee; Kim, Seong Yoon; Ko, Kwang Soo; Yi, Cheong-Ho; Jo, Tae-Ho; Hwang, Juck-Joon

2013-01-01

Identifying species of insects used to estimate postmortem interval (PMI) is a major subject in forensic entomology. Because forensic insect specimens are morphologically uniform and are obtained at various developmental stages, DNA markers are greatly needed. To develop new autosomal DNA markers to identify species, partial genomic sequences of the bicoid (bcd) genes, containing the homeobox and its flanking sequences, from 12 blowfly species (Aldrichina grahami, Calliphora vicina, Calliphora lata, Triceratopyga calliphoroides, Chrysomya megacephala, Chrysomya pinguis, Phormia regina, Lucilia ampullacea, Lucilia caesar, Lucilia illustris, Hemipyrellia ligurriens and Lucilia sericata; Calliphoridae: Diptera) were determined and analyzed. This study first sequenced the ten blowfly species other than C. vicina and L. sericata. Based on the bcd sequences of these 12 blowfly species, a phylogenetic tree was constructed that discriminates the subfamilies of Calliphoridae (Luciliinae, Chrysomyinae, and Calliphorinae) and most blowfly species. Even partial genomic sequences of about 500 bp can distinguish most blowfly species. The short intron 2 and coding sequences downstream of the bcd homeobox in exon 3 could be utilized to develop DNA markers for forensic applications. These gene sequences are important in the evolution of insect developmental biology and are potentially useful for identifying insect species in forensic science. PMID:23586044

Acoustic features of objects matched by an echolocating bottlenose dolphin.

PubMed

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.

American Bird conservancy's approach to the U.S. Important Bird Area Program - identifying the top 500 global sites

Treesearch

Robert M. Chipley

2005-01-01

The idea for the Important Bird Area Program originated in a series of studies in the early 1980s conducted by BirdLife International. Recognizing that these studies could become a powerful tool for conservation, BirdLife International began an effort to identify and gather data regarding the most important areas for birds in Europe and to make this information...

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

PubMed Central

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

2015-01-01

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

Gene expression profiling identifies inflammation and angiogenesis as distinguishing features of canine hemangiosarcoma.

PubMed

Tamburini, Beth A; Phang, Tzu L; Fosmire, Susan P; Scott, Milcah C; Trapp, Susan C; Duckett, Megan M; Robinson, Sally R; Slansky, Jill E; Sharkey, Leslie C; Cutter, Gary R; Wojcieszyn, John W; Bellgrau, Donald; Gemmill, Robert M; Hunter, Lawrence E; Modiano, Jaime F

2010-11-09

The etiology of hemangiosarcoma remains incompletely understood. Its common occurrence in dogs suggests predisposing factors favor its development in this species. These factors could represent a constellation of heritable characteristics that promote transformation events and/or facilitate the establishment of a microenvironment that is conducive for survival of malignant blood vessel-forming cells. The hypothesis for this study was that characteristic molecular features distinguish hemangiosarcoma from non-malignant endothelial cells, and that such features are informative for the etiology of this disease. We first investigated mutations of VHL and Ras family genes that might drive hemangiosarcoma by sequencing tumor DNA and mRNA (cDNA). Protein expression was examined using immunostaining. Next, we evaluated genome-wide gene expression profiling using the Affymetrix Canine 2.0 platform as a global approach to test the hypothesis. Data were evaluated using routine bioinformatics and validation was done using quantitative real time RT-PCR. Each of 10 tumor and four non-tumor samples analyzed had wild type sequences for these genes. At the genome wide level, hemangiosarcoma cells clustered separately from non-malignant endothelial cells based on a robust signature that included genes involved in inflammation, angiogenesis, adhesion, invasion, metabolism, cell cycle, signaling, and patterning. This signature did not simply reflect a cancer-associated angiogenic phenotype, as it also distinguished hemangiosarcoma from non-endothelial, moderately to highly angiogenic bone marrow-derived tumors (lymphoma, leukemia, osteosarcoma). The data show that inflammation and angiogenesis are important processes in the pathogenesis of vascular tumors, but a definitive ontogeny of the cells that give rise to these tumors remains to be established. The data do not yet distinguish whether functional or ontogenetic plasticity creates this phenotype, although they suggest that cells

Gene expression profiling identifies inflammation and angiogenesis as distinguishing features of canine hemangiosarcoma

PubMed Central

2010-01-01

Background The etiology of hemangiosarcoma remains incompletely understood. Its common occurrence in dogs suggests predisposing factors favor its development in this species. These factors could represent a constellation of heritable characteristics that promote transformation events and/or facilitate the establishment of a microenvironment that is conducive for survival of malignant blood vessel-forming cells. The hypothesis for this study was that characteristic molecular features distinguish hemangiosarcoma from non-malignant endothelial cells, and that such features are informative for the etiology of this disease. Methods We first investigated mutations of VHL and Ras family genes that might drive hemangiosarcoma by sequencing tumor DNA and mRNA (cDNA). Protein expression was examined using immunostaining. Next, we evaluated genome-wide gene expression profiling using the Affymetrix Canine 2.0 platform as a global approach to test the hypothesis. Data were evaluated using routine bioinformatics and validation was done using quantitative real time RT-PCR. Results Each of 10 tumor and four non-tumor samples analyzed had wild type sequences for these genes. At the genome wide level, hemangiosarcoma cells clustered separately from non-malignant endothelial cells based on a robust signature that included genes involved in inflammation, angiogenesis, adhesion, invasion, metabolism, cell cycle, signaling, and patterning. This signature did not simply reflect a cancer-associated angiogenic phenotype, as it also distinguished hemangiosarcoma from non-endothelial, moderately to highly angiogenic bone marrow-derived tumors (lymphoma, leukemia, osteosarcoma). Conclusions The data show that inflammation and angiogenesis are important processes in the pathogenesis of vascular tumors, but a definitive ontogeny of the cells that give rise to these tumors remains to be established. The data do not yet distinguish whether functional or ontogenetic plasticity creates this

Mutations in Epilepsy and Intellectual Disability Genes in Patients with Features of Rett Syndrome

PubMed Central

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

Epidemiology and clinical features of imported malaria in East London.

PubMed

Francis, Benjamin C; Gonzalo, Ximena; Duggineni, Sirisha; Thomas, Janice M; NicFhogartaigh, Caoimhe; Babiker, Zahir Osman Eltahir

2016-06-01

Malaria is the most common imported tropical disease in the United Kingdom (UK). The overall mortality is low but inter-regional differences have been observed. We conducted a 2-year retrospective review of clinical and laboratory records of patients with malaria attending three acute hospitals in East London from 1 April 2013 through 31 March 2015. Epidemiological and clinical characteristics of imported malaria were described and risk factors associated with severe falciparum malaria were explored. In total, 133 patients with laboratory-confirmed malaria were identified including three requiring critical care admission but no deaths. The median age at presentation was 41 years (IQR 30-50). The majority of patients were males (64.7%, 86/133) and had Black or Black British ethnicity (67.5%, 79/117). West Africa was the most frequent region of travel (70.4%, 76/108). Chemoprophylaxis use was poor (25.3%, 20/79). The interval between arriving in the UK and presenting to hospital was short (median 10 days; IQR 5-15.5, n = 84). July-September was the peak season of presentation (34.6%, 46/133). Plasmodium falciparum was the commonest species (76.7%, 102/133) and 31.4% (32/102) of these patients had parasitaemia >2%. Severe falciparum malaria was documented in 36.3% (37/102) of patients and the October-March season presentation was associated with an increased risk of severity (OR 3.00; 95% CI 1.30-6.93). Black patients appeared to have reduced risk of severe falciparum malaria (OR 0.46; 95% CI 0.16-1.35) but this was not statistically significant. HIV sero-status was determined in only 27.1% (36/133) of cases. Only 8.5% (10/117) of all malaria patients were treated as outpatients. Clinicians need to raise awareness on malaria prevention strategies, improve rates of HIV testing in tropical travellers, and familiarise themselves with ambulatory management of malaria. The relationship between season of presentation, ethnicity and severity of falciparum malaria

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

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.

Constrained clusters of gene expression profiles with pathological features.

PubMed

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.

Features of Coping with Disease in Iranian Multiple Sclerosis Patients: a Qualitative Study.

PubMed

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.

Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.

PubMed

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.

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations.

PubMed

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 the distinct features of geometric structures for hole trapping to generate radicals on rutile TiO₂(110) in photooxidation using density functional theory calculations with hybrid functional.

PubMed

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.

Biologically important conformational features of DNA as interpreted by quantum mechanics and molecular mechanics computations of its simple fragments.

PubMed

Poltev, V; Anisimov, V M; Dominguez, V; Gonzalez, E; Deriabina, A; Garcia, D; Rivas, F; Polteva, N A

2018-02-01

Deciphering the mechanism of functioning of DNA as the carrier of genetic information requires identifying inherent factors determining its structure and function. Following this path, our previous DFT studies attributed the origin of unique conformational characteristics of right-handed Watson-Crick duplexes (WCDs) to the conformational profile of deoxydinucleoside monophosphates (dDMPs) serving as the minimal repeating units of DNA strand. According to those findings, the directionality of the sugar-phosphate chain and the characteristic ranges of dihedral angles of energy minima combined with the geometric differences between purines and pyrimidines determine the dependence on base sequence of the three-dimensional (3D) structure of WCDs. This work extends our computational study to complementary deoxydinucleotide-monophosphates (cdDMPs) of non-standard conformation, including those of Z-family, Hoogsteen duplexes, parallel-stranded structures, and duplexes with mispaired bases. For most of these systems, except Z-conformation, computations closely reproduce experimental data within the tolerance of characteristic limits of dihedral parameters for each conformation family. Computation of cdDMPs with Z-conformation reveals that their experimental structures do not correspond to the internal energy minimum. This finding establishes the leading role of external factors in formation of the Z-conformation. Energy minima of cdDMPs of non-Watson-Crick duplexes demonstrate different sequence-dependence features than those known for WCDs. The obtained results provide evidence that the biologically important regularities of 3D structure distinguish WCDs from duplexes having non-Watson-Crick nucleotide pairing.

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.

PubMed

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.

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation

PubMed Central

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

A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque

PubMed Central

Hassani, S. A.; Oemisch, M.; Balcarras, M.; Westendorff, S.; Ardid, S.; van der Meer, M. A.; Tiesinga, P.; Womelsdorf, T.

2017-01-01

Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework. PMID:28091572

Radiological features for the approach in trans-sphenoidal pituitary surgery.

PubMed

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.

Trajectory analysis via a geometric feature space approach

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

Rintoul, Mark D.; Wilson, Andrew T.

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

Trajectory analysis via a geometric feature space approach

DOE PAGES

Rintoul, Mark D.; Wilson, Andrew T.

2015-10-05

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

Permafrost features on Earth and Mars: Similarities, differences

NASA Technical Reports Server (NTRS)

Joens, H. P.

1985-01-01

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

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.

Scanning genomic areas under selection sweep and association mapping as tools to identify horticultural important genes in watermelon

USDA-ARS?s Scientific Manuscript database

Watermelon (Citrullus lanatus var. lanatus) contains 88% water, sugars, and several important health-related compounds, including lycopene, citrulline, arginine, and glutathione. The current genetic diversity study uses microsatellites with known map positions to identify genomic regions that under...

[Imported malaria and HIV infection in Madrid. Clinical and epidemiological features].

PubMed

Ramírez-Olivencia, G; Herrero, M D; Subirats, M; de Juanes, J R; Peña, J M; Puente, S

2012-01-01

Few data are available in Spain data on human immunodeficiency virus (HIV) patients coinfected with malaria. This study has aimed to determine the epidemiological and clinical characteristics of imported malaria in patients coinfected with HIV. A case-series retrospective study was performed using the patient's medical records. The study population consisted on patients diagnosed with malaria attended in our center from january 1, 2002 to december 31, 2007. A total of 484 episodes of malaria, 398 of which were included in this study, were identified. Co-infection with HIV was described in 32 cases. All of them occurred in individuals presumably with some degree of semi-immunity. In the coinfected group, there were 13 cases (40.6%) asymptomatic, whereas this event occurred in 99 cases of patients not coinfected (37.2%) (P=0.707). The greater presence of anemia in co-infected patients (62.5% vs 32.3% in non-coinfected [P=0.001]) stands out. In present study, the clinical presentation forms were similar, regardless of the presence or absence of HIV infection. Although the study population does not reflect all possible scenarios of malaria and HIV coinfection, our results indicate the reality of patients attended in the Autonomous Community of Madrid. Copyright © 2011 Elsevier España, S.L. All rights reserved.

A New Scheme to Characterize and Identify Protein Ubiquitination Sites.

PubMed

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.

Seasonal spatial patterns in seabird and marine mammal distribution in the eastern Chukchi and western Beaufort seas: Identifying biologically important pelagic areas

NASA Astrophysics Data System (ADS)

Kuletz, Kathy J.; Ferguson, Megan C.; Hurley, Brendan; Gall, Adrian E.; Labunski, Elizabeth A.; Morgan, Tawna C.

2015-08-01

The Chukchi and Beaufort seas are undergoing rapid climate change and increased human activity. Conservation efforts for upper trophic level predators such as seabirds and marine mammals require information on species' distributions and identification of important marine areas. Here we describe broad-scale distributions of seabirds and marine mammals. We examined spatial patterns of relative abundance of seabirds and marine mammals in the eastern Chukchi and western Beaufort seas during summer (15 June-31 August) and fall (1 September-20 November) from 2007 to 2012. We summarized 49,206 km of shipboard surveys for seabirds and 183,157 km of aerial surveys for marine mammals into a grid of 40-km × 40-km cells. We used Getis-Ord Gi∗ hotspot analysis to test for cells with higher relative abundance than expected when compared to all cells within the study area. We identified cells representing single species and taxonomic group hotspots, cells representing hotspots for multiple species, and cells representing hotspots for both seabirds and marine mammals. The locations of hotspots varied among species but often were located near underwater canyons or over continental shelf features and slopes. Hotspots for seabirds, walrus, and gray whales occurred primarily in the Chukchi Sea. Hotspots for bowhead whales and other pinnipeds (i.e., seals) occurred near Barrow Canyon and along the Beaufort Sea shelf and slope. Hotspots for belugas occurred in both the Chukchi and Beaufort seas. There were three hotspots shared by both seabirds and marine mammals in summer: off Wainwright in the eastern Chukchi Sea, south of Hanna Shoal, and at the mouth of Barrow Canyon. In fall, the only identified shared hotspot occurred at the mouth of Barrow Canyon. Shared hotspots are characterized by strong fronts caused by upwelling and currents, and these areas can have high densities of euphausiids in summer and fall. Due to the high relative abundance of animals and diversity of taxa

An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

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.

Mutations in epilepsy and intellectual disability genes in patients with features of Rett syndrome.

PubMed

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.

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

Recognizing human activities using appearance metric feature and kinematics feature

NASA Astrophysics Data System (ADS)

Qian, Huimin; Zhou, Jun; Lu, Xinbiao; Wu, Xinye

2017-05-01

The problem of automatically recognizing human activities from videos through the fusion of the two most important cues, appearance metric feature and kinematics feature, is considered. And a system of two-dimensional (2-D) Poisson equations is introduced to extract the more discriminative appearance metric feature. Specifically, the moving human blobs are first detected out from the video by background subtraction technique to form a binary image sequence, from which the appearance feature designated as the motion accumulation image and the kinematics feature termed as centroid instantaneous velocity are extracted. Second, 2-D discrete Poisson equations are employed to reinterpret the motion accumulation image to produce a more differentiated Poisson silhouette image, from which the appearance feature vector is created through the dimension reduction technique called bidirectional 2-D principal component analysis, considering the balance between classification accuracy and time consumption. Finally, a cascaded classifier based on the nearest neighbor classifier and two directed acyclic graph support vector machine classifiers, integrated with the fusion of the appearance feature vector and centroid instantaneous velocity vector, is applied to recognize the human activities. Experimental results on the open databases and a homemade one confirm the recognition performance of the proposed algorithm.

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.

Multimodal emotional state recognition using sequence-dependent deep hierarchical features.

PubMed

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.

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

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

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

2015-06-15

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

Individual differences in using geometric and featural cues to maintain spatial orientation: cue quantity and cue ambiguity are more important than cue type.

PubMed

Kelly, Jonathan W; McNamara, Timothy P; Bodenheimer, Bobby; Carr, Thomas H; Rieser, John J

2009-02-01

Two experiments explored the role of environmental cues in maintaining spatial orientation (sense of self-location and direction) during locomotion. Of particular interest was the importance of geometric cues (provided by environmental surfaces) and featural cues (nongeometric properties provided by striped walls) in maintaining spatial orientation. Participants performed a spatial updating task within virtual environments containing geometric or featural cues that were ambiguous or unambiguous indicators of self-location and direction. Cue type (geometric or featural) did not affect performance, but the number and ambiguity of environmental cues did. Gender differences, interpreted as a proxy for individual differences in spatial ability and/or experience, highlight the interaction between cue quantity and ambiguity. When environmental cues were ambiguous, men stayed oriented with either one or two cues, whereas women stayed oriented only with two. When environmental cues were unambiguous, women stayed oriented with one cue.

Feature-based attention elicits surround suppression in feature space.

PubMed

Störmer, Viola S; Alvarez, George A

2014-09-08

It is known that focusing attention on a particular feature (e.g., the color red) facilitates the processing of all objects in the visual field containing that feature [1-7]. Here, we show that such feature-based attention not only facilitates processing but also actively inhibits processing of similar, but not identical, features globally across the visual field. We combined behavior and electrophysiological recordings of frequency-tagged potentials in human observers to measure this inhibitory surround in feature space. We found that sensory signals of an attended color (e.g., red) were enhanced, whereas sensory signals of colors similar to the target color (e.g., orange) were suppressed relative to colors more distinct from the target color (e.g., yellow). Importantly, this inhibitory effect spreads globally across the visual field, thus operating independently of location. These findings suggest that feature-based attention comprises an excitatory peak surrounded by a narrow inhibitory zone in color space to attenuate the most distracting and potentially confusable stimuli during visual perception. This selection profile is akin to what has been reported for location-based attention [8-10] and thus suggests that such center-surround mechanisms are an overarching principle of attention across different domains in the human brain. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identifying and Aligning Expectations in a Mentoring Relationship

PubMed Central

Huskins, W. Charles; Silet, Karin; Weber‐Main, Anne Marie; Begg, Melissa D.; Fowler, Jr, Vance G.; Hamilton, John; Fleming, Michael

2011-01-01

Abstract The mentoring relationship between a scholar and their primary mentor is a core feature of research training. Anecdotal evidence suggests this relationship is adversely affected when scholar and mentor expectations are not aligned. We examined three questions: (1) What is the value in assuring that the expectations of scholars and mentors are mutually identified and aligned? (2) What types of programmatic interventions facilitate this process? (3) What types of expectations are important to identify and align? We addressed these questions through a systematic literature review, focus group interviews of mentors and scholars, a survey of Clinical and Translational Science Award (CTSA) KL2 program directors, and review of formal programmatic mechanisms used by KL2 programs. We found broad support for the importance of identifying and aligning the expectations of scholars and mentors and evidence that mentoring contracts, agreements, and training programs facilitate this process. These tools focus on aligning expectations with respect to the scholar’s research, education, professional development and career advancement as well as support, communication, and personal conduct and interpersonal relations. Research is needed to assess test the efficacy of formal alignment activities. Clin Trans Sci 2011; Volume 4: 439–447 PMID:22212226

Triangulating Principal Effectiveness: How Perspectives of Parents, Teachers, and Assistant Principals Identify the Central Importance of Managerial Skills

ERIC Educational Resources Information Center

Grissom, Jason A.; Loeb, Susanna

2011-01-01

While the importance of effective principals is undisputed, few studies have identified specific skills that principals need to promote school success. This study draws on unique data combining survey responses from principals, assistant principals, teachers, and parents with rich administrative data to determine which principal skills correlate…

Identification of four class emotion from Indonesian spoken language using acoustic and lexical features

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.

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

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.

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.

The future of primordial features with large-scale structure surveys

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

The future of primordial features with large-scale structure surveys

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

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

2016-11-01

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

Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome.

PubMed

Higuera, Clara; Gardiner, Katheleen J; Cios, Krzysztof J

2015-01-01

Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome

PubMed Central

Higuera, Clara; Gardiner, Katheleen J.; Cios, Krzysztof J.

2015-01-01

Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets. PMID:26111164

Feature Extraction Assessment Study.

DTIC Science & Technology

1984-11-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Infrared vehicle recognition using unsupervised feature learning based on K-feature

NASA Astrophysics Data System (ADS)

Lin, Jin; Tan, Yihua; Xia, Haijiao; Tian, Jinwen

2018-02-01

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

PubMed Central

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

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

PubMed

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

2018-05-21

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

Genome-Wide Association Study Identifying Candidate Genes Influencing Important Agronomic Traits of Flax (Linum usitatissimum L.) Using SLAF-seq

PubMed Central

Xie, Dongwei; Dai, Zhigang; Yang, Zemao; Sun, Jian; Zhao, Debao; Yang, Xue; Zhang, Liguo; Tang, Qing; Su, Jianguang

2018-01-01

Flax (Linum usitatissimum L.) is an important cash crop, and its agronomic traits directly affect yield and quality. Molecular studies on flax remain inadequate because relatively few flax genes have been associated with agronomic traits or have been identified as having potential applications. To identify markers and candidate genes that can potentially be used for genetic improvement of crucial agronomic traits, we examined 224 specimens of core flax germplasm; specifically, phenotypic data for key traits, including plant height, technical length, number of branches, number of fruits, and 1000-grain weight were investigated under three environmental conditions before specific-locus amplified fragment sequencing (SLAF-seq) was employed to perform a genome-wide association study (GWAS) for these five agronomic traits. Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci and candidate genes that exhibited a significant correlation with the important agronomic traits. Our analyses identified a total of 42 SNP loci that showed significant correlations with the five important agronomic flax traits. Next, candidate genes were screened in the 10 kb zone of each of the 42 SNP loci. These SNP loci were then analyzed by a more stringent screening via co-identification using both a general linear model (GLM) and a mixed linear model (MLM) as well as co-occurrences in at least two of the three environments, whereby 15 final candidate genes were obtained. Based on these results, we determined that UGT and PL are candidate genes for plant height, GRAS and XTH are candidate genes for the number of branches, Contig1437 and LU0019C12 are candidate genes for the number of fruits, and PHO1 is a candidate gene for the 1000-seed weight. We propose that the identified SNP loci and corresponding candidate genes might serve as a biological basis for improving crucial agronomic flax traits. PMID:29375606

Genome-Wide Association Study Identifying Candidate Genes Influencing Important Agronomic Traits of Flax (Linum usitatissimum L.) Using SLAF-seq.

PubMed

Xie, Dongwei; Dai, Zhigang; Yang, Zemao; Sun, Jian; Zhao, Debao; Yang, Xue; Zhang, Liguo; Tang, Qing; Su, Jianguang

2017-01-01

Flax ( Linum usitatissimum L.) is an important cash crop, and its agronomic traits directly affect yield and quality. Molecular studies on flax remain inadequate because relatively few flax genes have been associated with agronomic traits or have been identified as having potential applications. To identify markers and candidate genes that can potentially be used for genetic improvement of crucial agronomic traits, we examined 224 specimens of core flax germplasm; specifically, phenotypic data for key traits, including plant height, technical length, number of branches, number of fruits, and 1000-grain weight were investigated under three environmental conditions before specific-locus amplified fragment sequencing (SLAF-seq) was employed to perform a genome-wide association study (GWAS) for these five agronomic traits. Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci and candidate genes that exhibited a significant correlation with the important agronomic traits. Our analyses identified a total of 42 SNP loci that showed significant correlations with the five important agronomic flax traits. Next, candidate genes were screened in the 10 kb zone of each of the 42 SNP loci. These SNP loci were then analyzed by a more stringent screening via co-identification using both a general linear model (GLM) and a mixed linear model (MLM) as well as co-occurrences in at least two of the three environments, whereby 15 final candidate genes were obtained. Based on these results, we determined that UGT and PL are candidate genes for plant height, GRAS and XTH are candidate genes for the number of branches, Contig1437 and LU0019C12 are candidate genes for the number of fruits, and PHO1 is a candidate gene for the 1000-seed weight. We propose that the identified SNP loci and corresponding candidate genes might serve as a biological basis for improving crucial agronomic flax traits.

Crowd-sourced Ontology for Photoleukocoria: Identifying Common Internet Search Terms for a Potentially Important Pediatric Ophthalmic Sign.

PubMed

Staffieri, Sandra E; Kearns, Lisa S; Sanfilippo, Paul G; Craig, Jamie E; Mackey, David A; Hewitt, Alex W

2018-02-01

Leukocoria is the most common presenting sign for pediatric eye disease including retinoblastoma and cataract, with worse outcomes if diagnosis is delayed. We investigated whether individuals could identify leukocoria in photographs (photoleukocoria) and examined their subsequent Internet search behavior. Using a web-based questionnaire, in this cross-sectional study we invited adults aged over 18 years to view two photographs of a child with photoleukocoria, and then search the Internet to determine a possible diagnosis and action plan. The most commonly used search terms and websites accessed were recorded. The questionnaire was completed by 1639 individuals. Facebook advertisement was the most effective recruitment strategy. The mean age of all respondents was 38.95 ± 14.59 years (range, 18-83), 94% were female, and 59.3% had children. An abnormality in the images presented was identified by 1613 (98.4%) participants. The most commonly used search terms were: "white," "pupil," "photo," and "eye" reaching a variety of appropriate websites or links to print or social media articles. Different words or phrases were used to describe the same observation of photoleukocoria leading to a range of websites. Variations in the description of observed signs and search words influenced the sites reached, information obtained, and subsequent help-seeking intentions. Identifying the most commonly used search terms for photoleukocoria is an important step for search engine optimization. Being directed to the most appropriate websites informing of the significance of photoleukocoria and the appropriate actions to take could improve delays in diagnosis of important pediatric eye disease such as retinoblastoma or cataract.

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

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

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

PubMed

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

2017-05-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Cosmetics as a feature of the extended human phenotype: modulation of the perception of biologically important facial signals.

PubMed

Etcoff, Nancy L; Stock, Shannon; Haley, Lauren E; Vickery, Sarah A; House, David M

2011-01-01

Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first

Cosmetics as a Feature of the Extended Human Phenotype: Modulation of the Perception of Biologically Important Facial Signals

PubMed Central

Etcoff, Nancy L.; Stock, Shannon; Haley, Lauren E.; Vickery, Sarah A.; House, David M.

2011-01-01

Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first

Learning about the internal structure of categories through classification and feature inference.

PubMed

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.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-31

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

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

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-01

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

Histopathological features of Proteus syndrome.

PubMed

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

2008-05-01

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

Maternal employment and Mexican school-age children overweight in 2012: the importance of households features.

PubMed

Espinosa, Alejandro Martínez

2018-01-01

International evidence regarding the relationship between maternal employment and school-age children overweight and obesity shows divergent results. In Mexico, this relationship has not been confirmed by national data sets analysis. Consequently, the objective of this article was to evaluate the role of the mothers' participation in labor force related to excess body weight in Mexican school-age children (aged 5-11 years). A cross-sectional study was conducted on a sample of 17,418 individuals from the National Health and Nutrition Survey 2012, applying binomial logistic regression models. After controlling for individual, maternal and contextual features, the mothers' participation in labor force was associated with children body composition. However, when the household features (living arrangements, household ethnicity, size, food security and socioeconomic status) were incorporated, maternal employment was no longer statically significant. Household features are crucial factors for understanding the overweight and obesity prevalence levels in Mexican school-age children, despite the mother having a paid job. Copyright: © 2018 Permanyer.

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.

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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

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.

MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins

PubMed Central

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

MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.

PubMed

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.

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

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

PubMed Central

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

2015-01-01

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

GATOR: Requirements capturing of telephony features

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

Linguistic feature analysis for protein interaction extraction

PubMed Central

2009-01-01

Background The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels. Results Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared. Conclusion Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches. PMID:19909518

Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.

PubMed

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.

Infrastructure features outperform environmental variables explaining rabbit abundance around motorways.

PubMed

Planillo, Aimara; Malo, Juan E

2018-01-01

Human disturbance is widespread across landscapes in the form of roads that alter wildlife populations. Knowing which road features are responsible for the species response and their relevance in comparison with environmental variables will provide useful information for effective conservation measures. We sampled relative abundance of European rabbits, a very widespread species, in motorway verges at regional scale, in an area with large variability in environmental and infrastructure conditions. Environmental variables included vegetation structure, plant productivity, distance to water sources, and altitude. Infrastructure characteristics were the type of vegetation in verges, verge width, traffic volume, and the presence of embankments. We performed a variance partitioning analysis to determine the relative importance of two sets of variables on rabbit abundance. Additionally, we identified the most important variables and their effects model averaging after model selection by AICc on hypothesis-based models. As a group, infrastructure features explained four times more variability in rabbit abundance than environmental variables, being the effects of the former critical in motorway stretches located in altered landscapes with no available habitat for rabbits, such as agricultural fields. Model selection and Akaike weights showed that verge width and traffic volume are the most important variables explaining rabbit abundance index, with positive and negative effects, respectively. In the light of these results, the response of species to the infrastructure can be modulated through the modification of motorway features, being some of them manageable in the design phase. The identification of such features leads to suggestions for improvement through low-cost corrective measures and conservation plans. As a general indication, keeping motorway verges less than 10 m wide will prevent high densities of rabbits and avoid the unwanted effects that rabbit populations

Modifiable Neighborhood Features Associated With Adolescent Homicide

PubMed Central

Culyba, Alison J.; Jacoby, Sara F.; Richmond, Therese S.; Fein, Joel A.; Hohl, Bernadette C.; Branas, Charles C.

2016-01-01

IMPORTANCE Homicide is a leading cause of adolescent mortality. To our knowledge, relatively little has been studied in terms of the association between environmental neighborhood features, such as streets, buildings, and natural surroundings, and severe violent injury among youth. OBJECTIVE To assess associations between environmental neighborhood features and adolescent homicide in order to identify targets for future place-based interventions. DESIGN, SETTING, AND PARTICIPANTS Population-based case-control study conducted in Philadelphia, Pennsylvania, from April 15, 2008, to March 31, 2014. We identified adolescents who died by homicide at 13 to 20 years of age from 2010 to 2012 while residing in Philadelphia. We used incidence-density sampling and random-digit dialing to recruit control participants ages 13 to 20 years matched on sex and indoor-outdoor location at the time of each index case participant’s homicide. EXPOSURES To obtain environmental data about modifiable features that were present in the immediate surroundings of our case and control participants, blinded field researchers used standardized techniques to photograph case and control participant outdoor locations. Photographic data were stitched together to create 360° panoramic images that were coded for 60 elements of the visible environment. MAIN OUTCOME AND MEASURE Adolescent homicide. RESULTS We enrolled 143 homicide case participants (mean [SD] age, 18.4 [1.5] years) and 155 matched control participants (mean [SD] age, 17.2 [2.1] years) who were both outdoors at the time of the homicide. In adjusted analyses, multiple features of Philadelphia streets, buildings, and natural surroundings were associated with adolescent homicide. The presence of street lighting (odds ratio [OR], 0.24; 95% CI, 0.09-0.70), illuminated walk/don’t walk signs (OR, 0.16; 95% CI, 0.03-0.92), painted marked crosswalks (OR, 0.17; 95% CI, 0.04-0.63), public transportation (OR, 0.13; 95% CI, 0.03-0.49), parks (OR

Targeted Feature Detection for Data-Dependent Shotgun Proteomics.

PubMed

Weisser, Hendrik; Choudhary, Jyoti S

2017-08-04

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

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

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

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

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

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

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

Which ante mortem clinical features predict progressive supranuclear palsy pathology?

PubMed

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

2017-07-01

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

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

PubMed Central

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

2012-01-01

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

Sensor feature fusion for detecting buried objects

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

Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

1993-04-01

Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in amore » two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.« less

Depth estimation of features in video frames with improved feature matching technique using Kinect sensor

NASA Astrophysics Data System (ADS)

Sharma, Kajal; Moon, Inkyu; Kim, Sung Gaun

2012-10-01

Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.

Systematic reviews identify important methodological flaws in stroke rehabilitation therapy primary studies: review of reviews.

PubMed

Santaguida, Pasqualina; Oremus, Mark; Walker, Kathryn; Wishart, Laurie R; Siegel, Karen Lohmann; Raina, Parminder

2012-04-01

A "review of reviews" was undertaken to assess methodological issues in studies evaluating nondrug rehabilitation interventions in stroke patients. MEDLINE, CINAHL, PsycINFO, and the Cochrane Database of Systematic Reviews were searched from January 2000 to January 2008 within the stroke rehabilitation setting. Electronic searches were supplemented by reviews of reference lists and citations identified by experts. Eligible studies were systematic reviews; excluded citations were narrative reviews or reviews of reviews. Review characteristics and criteria for assessing methodological quality of primary studies within them were extracted. The search yielded 949 English-language citations. We included a final set of 38 systematic reviews. Cochrane reviews, which have a standardized methodology, were generally of higher methodological quality than non-Cochrane reviews. Most systematic reviews used standardized quality assessment criteria for primary studies, but not all were comprehensive. Reviews showed that primary studies had problems with randomization, allocation concealment, and blinding. Baseline comparability, adverse events, and co-intervention or contamination were not consistently assessed. Blinding of patients and providers was often not feasible and was not evaluated as a source of bias. The eligible systematic reviews identified important methodological flaws in the evaluated primary studies, suggesting the need for improvement of research methods and reporting. Copyright © 2012 Elsevier Inc. All rights reserved.

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

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

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

An Unrecognized Rash Progressing to Lyme Carditis: Important Features and Recommendations Regarding Lyme Disease.

PubMed

Lee, Shawn; Singla, Montish

2016-01-01

We present a case report of 46-year-old man with no medical history, who complained of extreme fatigue, near-syncope, and palpitations. He initially presented in complete heart block. A transvenous pacemaker was placed in the emergency department, and he was started empirically on Ceftriaxone for Lyme disease. He was admitted and over the course of the next few days, his rhythm regressed to Mobitz type I first-degree atrioventricular block and then to normal sinus rhythm. This case report highlights some important features regarding Lyme carditis, a rare presentation of early disseminated Lyme disease (seen in a few weeks to months after the initial tick bite). In 25%-30% of patients, the characteristic targetoid rash may not be seen, a likely culprit of the disease not being detected early and progressing to disseminated disease. The most common cardiac complaint of Lyme disease is palpitations, occurring in 6.6% of patients, which may not accurately reflect progression into disseminated Lyme disease because it is a nonspecific finding. Conduction abnormality, occurring in 1.8% of patients, is a more specific finding of Borrelia invading cardiac tissue. Finally, this case report highlights a recommendation that patients with confirmed Lyme disease or those presenting with cardiac abnormalities or symptoms who have an atypical profile for a cardiac event should be screened with a 12-lead electrocardiogram, Lyme serology, and be considered for antibiotic therapy with the possibility of temporary pacing.

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

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.

System Complexity Reduction via Feature Selection

ERIC Educational Resources Information Center

Deng, Houtao

2011-01-01

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree…

High Dimensional Classification Using Features Annealed Independence Rules.

PubMed

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

Clinical features distinguishing grief from depressive episodes: A qualitative analysis.

PubMed

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.

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

PubMed Central

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

2015-01-01

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

Feature-Based Retinal Image Registration Using D-Saddle Feature

PubMed Central

Hasikin, Khairunnisa; A. Karim, Noor Khairiah; Ahmedy, Fatimah

2017-01-01

Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle. PMID:29204257

Identifying important comorbidity among cancer populations using administrative data: Prevalence and impact on survival.

PubMed

Sarfati, Diana; Gurney, Jason; Lim, Bee Teng; Bagheri, Nasser; Simpson, Andrew; Koea, Jonathan; Dennett, Elizabeth

2016-03-01

Our study sought to optimize the identification and investigate the impact of comorbidity in cancer patients using routinely collected hospitalization data. We undertook an iterative process of classification of important clinical conditions involving evaluation of relevant literature and consultation with clinicians. Patients diagnosed with colon, rectal, breast, ovarian, uterine, stomach, liver, renal or bladder cancers (n = 14,096) between 2006 and 2008 were identified from the New Zealand Cancer Registry. Conditions were identified using data on diagnoses from hospital admissions for 5 years prior to cancer diagnosis. Patients were followed up until end of 2009 using routine mortality data. Prevalence estimates for each condition by site were calculated. All-cause mortality impact of common conditions was investigated using Cox regression models adjusted for age and stage at diagnosis. Patients with liver and stomach cancers tended to have higher comorbidity and those with breast cancer, lower comorbidity than other cancer patients. Of the 50 conditions, the most common were hypertension (prevalence 8.0-20.9%), cardiac conditions (2.1-13.5%) and diabetes with (2.3-13.3%) and without (2.9-12.9%) complications. Comorbidity was associated with higher all-cause mortality but the impact varied by condition and across cancer site, with impact less for cancers with poor prognoses. Conditions most consistently associated with adverse outcomes across all cancer sites were renal disease, coagulopathies and congestive heart failure. Comorbidity is highly prevalent in cancer populations, but prevalence and impact of conditions differ markedly by cancer type. © 2013 Wiley Publishing Asia Pty Ltd.

Targeted Feature Detection for Data-Dependent Shotgun Proteomics

PubMed Central

2017-01-01

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

Redd Site Selection and Spawning Habitat Use by Fall Chinook Salmon: The Importance of Geomorphic Features in Large Rivers

PubMed

Geist; Dauble

1998-09-01

/ Knowledge of the three-dimensional connectivity between rivers and groundwater within the hyporheic zone can be used to improve the definition of fall chinook salmon (Oncorhynchus tshawytscha) spawning habitat. Information exists on the microhabitat characteristics that define suitable salmon spawning habitat. However, traditional spawning habitat models that use these characteristics to predict available spawning habitat are restricted because they can not account for the heterogeneous nature of rivers. We present a conceptual spawning habitat model for fall chinook salmon that describes how geomorphic features of river channels create hydraulic processes, including hyporheic flows, that influence where salmon spawn in unconstrained reaches of large mainstem alluvial rivers. Two case studies based on empirical data from fall chinook salmon spawning areas in the Hanford Reach of the Columbia River are presented to illustrate important aspects of our conceptual model. We suggest that traditional habitat models and our conceptual model be combined to predict the limits of suitable fall chinook salmon spawning habitat. This approach can incorporate quantitative measures of river channel morphology, including general descriptors of geomorphic features at different spatial scales, in order to understand the processes influencing redd site selection and spawning habitat use. This information is needed in order to protect existing salmon spawning habitat in large rivers, as well as to recover habitat already lost.KEY WORDS: Hyporheic zone; Geomorphology; Spawning habitat; Large rivers; Fall chinook salmon; Habitat management

The building blocks of a 'Liveable Neighbourhood': Identifying the key performance indicators for walking of an operational planning policy in Perth, Western Australia.

PubMed

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.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

DOE PAGES

Dai, Heng; Ye, Ming; Walker, Anthony P.; ...

2017-03-28

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

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

Dai, Heng; Ye, Ming; Walker, Anthony P.

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Global sensitivity analysis for identifying important parameters of nitrogen nitrification and denitrification under model uncertainty and scenario uncertainty

NASA Astrophysics Data System (ADS)

Chen, Zhuowei; Shi, Liangsheng; Ye, Ming; Zhu, Yan; Yang, Jinzhong

2018-06-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. By using a new variance-based global sensitivity analysis method, this paper identifies important parameters for nitrogen reactive transport with simultaneous consideration of these three uncertainties. A combination of three scenarios of soil temperature and two scenarios of soil moisture creates a total of six scenarios. Four alternative models describing the effect of soil temperature and moisture content are used to evaluate the reduction functions used for calculating actual reaction rates. The results show that for nitrogen reactive transport problem, parameter importance varies substantially among different models and scenarios. Denitrification and nitrification process is sensitive to soil moisture content status rather than to the moisture function parameter. Nitrification process becomes more important at low moisture content and low temperature. However, the changing importance of nitrification activity with respect to temperature change highly relies on the selected model. Model-averaging is suggested to assess the nitrification (or denitrification) contribution by reducing the possible model error. Despite the introduction of biochemical heterogeneity or not, fairly consistent parameter importance rank is obtained in this study: optimal denitrification rate (Kden) is the most important parameter; reference temperature (Tr) is more important than temperature coefficient (Q10); empirical constant in moisture response function (m) is the least important one. Vertical distribution of soil moisture but not temperature plays predominant role controlling nitrogen reaction. This study provides insight into the nitrogen reactive transport modeling and demonstrates an effective strategy of selecting the important parameters when future temperature and soil moisture carry uncertainties or when modelers face with multiple ways of establishing nitrogen

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.

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

ERIC Educational Resources Information Center

Kajiyama, Tomoko; Echizen, Isao

2015-01-01

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

Dynamics of feature categorization.

PubMed

Martí, Daniel; Rinzel, John

2013-01-01

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

Premotor and non-motor features of Parkinson’s disease

PubMed Central

Goldman, Jennifer G.; Postuma, Ron

2014-01-01

Purpose of review This review highlights recent advances in premotor and non-motor features in Parkinson’s disease, focusing on these issues in the context of prodromal and early stage Parkinson’s disease. Recent findings While Parkinson’s disease patients experience a wide range of non-motor symptoms throughout the disease course, studies demonstrate that non-motor features are not solely a late manifestation. Indeed, disturbances of smell, sleep, mood, and gastrointestinal function may herald Parkinson’s disease or related synucleinopathies and precede these neurodegenerative conditions by 5 or more years. In addition, other non-motor symptoms such as cognitive impairment are now recognized in incident or de novo Parkinson’s disease cohorts. Many of these non-motor features reflect disturbances in non-dopaminergic systems and early involvement of peripheral and central nervous systems including olfactory, enteric, and brainstem neurons as in Braak’s proposed pathological staging of Parkinson’s disease. Current research focuses on identifying potential biomarkers that may detect persons at risk for Parkinson’s disease and permit early intervention with neuroprotective or disease-modifying therapeutics. Summary Recent studies provide new insights on the frequency, pathophysiology, and importance of non-motor features in Parkinson’s disease as well as the recognition that these non-motor symptoms occur in premotor, early, and later phases of Parkinson’s disease. PMID:24978368

Advanced alerting features: displaying new relevant data and retracting alerts.

PubMed Central

Kuperman, G. J.; Hiltz, F. L.; Teich, J. M.

1997-01-01

We added two advanced features to our automated alerting system. The first feature identifies and displays, at the time an alert is reviewed, relevant data filed between the login time of a specimen leading to an alerting result and the time the alert is reviewed. Relevant data is defined as data of the same kind as generated the alert. The other feature retracts alerts when the alerting value is edited and no longer satisfies the alerting criteria. We evaluated the two features for a 14-week period (new relevant data) and a 6-week period (retraction). Of a total of 1104 alerts in the 14-week evaluation, 286 (25.9%) had new relevant data displayed at alert review time. Of the 286, 75.2% were due to additions of comments to the original piece of alerting data; 24.1% were due to new or pending laboratory results of the same type that generated the alert. Two alerts (out of 490) were retracted in a 6 week period. We conclude that in our system, new clinically relevant data is often added between the time of specimen login and the time that an alerting result from that specimen is reviewed. Retractions occur rarely but are important to detect and communicate. PMID:9357625

Observations on morphology of immature Lucilia porphyrina (Diptera: Calliphoridae), a fly species of forensic importance.

PubMed

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.

Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

PubMed Central

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

2016-01-01

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

Hadoop neural network for parallel and distributed feature selection.

PubMed

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

2016-06-01

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

Learning and Improving in Quality Improvement Collaboratives: Which Collaborative Features Do Participants Value Most?

PubMed Central

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

Organizational contextual features that influence the implementation of evidence-based practices across healthcare settings: a systematic integrative review.

PubMed

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

Ability of Slovakian Pupils to Identify Birds

ERIC Educational Resources Information Center

Prokop, Pavol; Rodak, Rastislav

2009-01-01

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

qFeature

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

2015-09-14

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

Clinical features, proximate causes, and consequences of active convulsive epilepsy in Africa

PubMed Central

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

Features of the Correlation Structure of Price Indices

PubMed Central

Gao, Xiangyun; An, Haizhong; Zhong, Weiqiong

2013-01-01

What are the features of the correlation structure of price indices? To answer this question, 5 types of price indices, including 195 specific price indices from 2003 to 2011, were selected as sample data. To build a weighted network of price indices each price index is represented by a vertex, and a positive correlation between two price indices is represented by an edge. We studied the features of the weighted network structure by applying economic theory to the analysis of complex network parameters. We found that the frequency of the price indices follows a normal distribution by counting the weighted degrees of the nodes, and we identified the price indices which have an important impact on the network's structure. We found out small groups in the weighted network by the methods of k-core and k-plex. We discovered structure holes in the network by calculating the hierarchy of the nodes. Finally, we found that the price indices weighted network has a small-world effect by calculating the shortest path. These results provide a scientific basis for macroeconomic control policies. PMID:23593399

Image feature based GPS trace filtering for road network generation and road segmentation

DOE PAGES

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

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

Identifying specific beliefs to target to improve restaurant employees' intentions for performing three important food safety behaviors.

PubMed

Pilling, Valerie K; Brannon, Laura A; Shanklin, Carol W; Howells, Amber D; Roberts, Kevin R

2008-06-01

Current national food safety training programs appear ineffective at improving food safety practices in foodservice operations, given the substantial number of Americans affected by foodborne illnesses after eating in restaurants each year. The Theory of Planned Behavior (TpB) was used to identify important beliefs that may be targeted to improve foodservice employees' intentions for three food safety behaviors that have the most substantial affect on public health: hand washing, using thermometers, and proper handling of food contact surfaces. In a cross-sectional design, foodservice employees (n=190) across three midwestern states completed a survey assessing TpB components and knowledge for the three food safety behaviors. Multiple regression analyses were performed on the TpB components for each behavior. Independent-samples t tests identified TpB beliefs that discriminated between participants who absolutely intend to perform the behaviors and those with lower intention. Employees' attitudes were the one consistent predictor of intentions for performing all three behaviors. However, a unique combination of important predictors existed for each separate behavior. Interventions for improving employees' behavioral intentions for food safety should focus on TpB components that predict intentions for each behavior and should bring all employees' beliefs in line with those of the employees who already intend to perform the food safety behaviors. Registered dietitians; dietetic technicians, registered; and foodservice managers can use these results to enhance training sessions and motivational programs to improve employees' food safety behaviors. Results also assist these professionals in recognizing their responsibility for enforcing and providing adequate resources for proper food safety behaviors.

Imported Wines: Identifying and Removing Wines Contaminated with Diethylene Glycol.

DTIC Science & Technology

1986-03-01

at controlling health risks, BATF has used its labeling authority to prohibit the marketing of alcoholic beverages that are mislabeled by virtue of...or beverages contaminated with harmful substances into the U.S. market . DEG, a toxic substance, would be such a contaminant. The BATF’S authority in...representing a significant risk to health are identified and removed from k the market . BATF did not conduct a risk assessment or seek an assess- ment from

Dermoscopic features of nail psoriasis treated with biologics.

PubMed

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

2017-05-01

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

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

PubMed

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

2014-01-01

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

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

PubMed Central

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

2014-01-01

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

Online feature selection with streaming features.

PubMed

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

2013-05-01

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

Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method.

PubMed

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.

Exploring KM Features of High-Performance Companies

NASA Astrophysics Data System (ADS)

Wu, Wei-Wen

2007-12-01

For reacting to an increasingly rival business environment, many companies emphasize the importance of knowledge management (KM). It is a favorable way to explore and learn KM features of high-performance companies. However, finding out the critical KM features of high-performance companies is a qualitative analysis problem. To handle this kind of problem, the rough set approach is suitable because it is based on data-mining techniques to discover knowledge without rigorous statistical assumptions. Thus, this paper explored KM features of high-performance companies by using the rough set approach. The results show that high-performance companies stress the importance on both tacit and explicit knowledge, and consider that incentives and evaluations are the essentials to implementing KM.

Inherited Fe and Ti electron transition spectroscopic features in altered ultramafic-carbonatite intrusives

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

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

PubMed Central

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

2009-01-01

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

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

PubMed

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

2009-05-01

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

Controllable Edge Feature Sharpening for Dental Applications

PubMed Central

2014-01-01

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

Controllable edge feature sharpening for dental applications.

PubMed

Fan, Ran; Jin, Xiaogang

2014-01-01

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

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

NASA Astrophysics Data System (ADS)

Czapiga, M. J.; Parker, G.

2017-12-01

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

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.

Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

DOE PAGES

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

Feature Selection and Pedestrian Detection Based on Sparse Representation.

PubMed

Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei

2015-01-01

Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony.

Feature selection using probabilistic prediction of support vector regression.

PubMed

Yang, Jian-Bo; Ong, Chong-Jin

2011-06-01

This paper presents a new wrapper-based feature selection method for support vector regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiments show that the proposed method generally performs better than, or at least as well as, the existing methods, with notable advantage when the dataset is sparse.

Feature Masking in Computer Game Promotes Visual Imagery

ERIC Educational Resources Information Center

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

2007-01-01

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

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.

PubMed

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.

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

PubMed Central

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

Kinematic Features of Jaw and Lips Distinguish Symptomatic from Presymptomatic Stages of Bulbar Decline in Amyotrophic Lateral Sclerosis

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…

Genomic Analyses Yield Markers for Identifying Agronomically Important Genes in Potato

USDA-ARS?s Scientific Manuscript database

This study explores the genetic architecture underling the potato evolution through a comprehensive assessment of wild and cultivated potato species based on the re-sequencing of 201 accessions of Solanum section Petota with >12 × genome coverage. We identified 450 domesticated genes, which showed e...

Feature-based attentional modulation increases with stimulus separation in divided-attention tasks.

PubMed

Sally, Sharon L; Vidnyánsky, Zoltán; Papathomas, Thomas V

2009-01-01

Attention modifies our visual experience by selecting certain aspects of a scene for further processing. It is therefore important to understand factors that govern the deployment of selective attention over the visual field. Both location and feature-specific mechanisms of attention have been identified and their modulatory effects can interact at a neural level (Treue and Martinez-Trujillo, 1999). The effects of spatial parameters on feature-based attentional modulation were examined for the feature dimensions of orientation, motion and color using three divided-attention tasks. Subjects performed concurrent discriminations of two briefly presented targets (Gabor patches) to the left and right of a central fixation point at eccentricities of +/-2.5 degrees , 5 degrees , 10 degrees and 15 degrees in the horizontal plane. Gabors were size-scaled to maintain consistent single-task performance across eccentricities. For all feature dimensions, the data show a linear increase in the attentional effects with target separation. In a control experiment, Gabors were presented on an isoeccentric viewing arc at 10 degrees and 15 degrees at the closest spatial separation (+/-2.5 degrees ) of the main experiment. Under these conditions, the effects of feature-based attentional effects were largely eliminated. Our results are consistent with the hypothesis that feature-based attention prioritizes the processing of attended features. Feature-based attentional mechanisms may have helped direct the attentional focus to the appropriate target locations at greater separations, whereas similar assistance may not have been necessary at closer target spacings. The results of the present study specify conditions under which dual-task performance benefits from sharing similar target features and may therefore help elucidate the processes by which feature-based attention operates.

Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers

PubMed Central

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

Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach.

PubMed

Núñez-Vivanco, Gabriel; Valdés-Jiménez, Alejandro; Besoaín, Felipe; Reyes-Parada, Miguel

2016-01-01

Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D patterns such as allosteric sites, enzyme-cofactor interaction motifs, etc. This makes necessary the development of new ligand-independent methods to search and compare 3D patterns in all available protein structures. Here we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility

Features selection and classification to estimate elbow movements

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

Plaque echodensity and textural features are associated with histologic carotid plaque instability.

PubMed

Doonan, Robert J; Gorgui, Jessica; Veinot, Jean P; Lai, Chi; Kyriacou, Efthyvoulos; Corriveau, Marc M; Steinmetz, Oren K; Daskalopoulou, Stella S

2016-09-01

Carotid plaque echodensity and texture features predict cerebrovascular symptomatology. Our purpose was to determine the association of echodensity and textural features obtained from a digital image analysis (DIA) program with histologic features of plaque instability as well as to identify the specific morphologic characteristics of unstable plaques. Patients scheduled to undergo carotid endarterectomy were recruited and underwent carotid ultrasound imaging. DIA was performed to extract echodensity and textural features using Plaque Texture Analysis software (LifeQ Medical Ltd, Nicosia, Cyprus). Carotid plaque surgical specimens were obtained and analyzed histologically. Principal component analysis (PCA) was performed to reduce imaging variables. Logistic regression models were used to determine if PCA variables and individual imaging variables predicted histologic features of plaque instability. Image analysis data from 160 patients were analyzed. Individual imaging features of plaque echolucency and homogeneity were associated with a more unstable plaque phenotype on histology. These results were independent of age, sex, and degree of carotid stenosis. PCA reduced 39 individual imaging variables to five PCA variables. PCA1 and PCA2 were significantly associated with overall plaque instability on histology (both P = .02), whereas PCA3 did not achieve statistical significance (P = .07). DIA features of carotid plaques are associated with histologic plaque instability as assessed by multiple histologic features. Importantly, unstable plaques on histology appear more echolucent and homogeneous on ultrasound imaging. These results are independent of stenosis, suggesting that image analysis may have a role in refining the selection of patients who undergo carotid endarterectomy. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Global map of eolian features on Mars.

USGS Publications Warehouse

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

1985-01-01

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

SU-E-J-270: Repeated 18F-FDG PET/CTs Based Feature Analysis for the Predication of Anal Cancer Recurrence

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

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

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection.

PubMed

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.

Modeling crash injury severity by road feature to improve safety.

PubMed

Penmetsa, Praveena; Pulugurtha, Srinivas S

2018-01-02

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

Identifying selectively important amino acid positions associated with alternative habitat environments in fish mitochondrial genomes.

PubMed

Xia, Jun Hong; Li, Hong Lian; Zhang, Yong; Meng, Zi Ning; Lin, Hao Ran

2018-05-01

Fish species inhabitating seawater (SW) or freshwater (FW) habitats have to develop genetic adaptations to alternative environment factors, especially salinity. Functional consequences of the protein variations associated with habitat environments in fish mitochondrial genomes have not yet received much attention. We analyzed 829 complete fish mitochondrial genomes and compared the amino acid differences of 13 mitochondrial protein families between FW and SW fish groups. We identified 47 specificity determining sites (SDS) that associated with FW or SW environments from 12 mitochondrial protein families. Thirty-two (68%) of the SDS sites are hydrophobic, 13 (28%) are neutral, and the remaining sites are acidic or basic. Seven of those SDS from ND1, ND2 and ND5 were scored as probably damaging to the protein structures. Furthermore, phylogenetic tree based Bayes Empirical Bayes analysis also detected 63 positive sites associated with alternative habitat environments across ten mtDNA proteins. These signatures could be important for studying mitochondrial genetic variation relevant to fish physiology and ecology.

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

PubMed

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

2010-09-15

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

Triangulating Principal Effectiveness: How Perspectives of Parents, Teachers, and Assistant Principals Identify the Central Importance of Managerial Skills. Working Paper 35

ERIC Educational Resources Information Center

Grissom, Jason A.; Loeb, Susanna

2009-01-01

While the importance of effective principals is undisputed, few studies have addressed what specific skills principals need to promote school success. This study draws on unique data combining survey responses from principals, assistant principals, teachers and parents with rich administrative data to identify which principal skills matter most…

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

PubMed

Kawamoto, Kensaku; Lobach, David F

2003-01-01

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

Patterns of Dysmorphic Features in Schizophrenia

PubMed Central

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

2011-01-01

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

Highlighting impact: Do editors' selections identify influential papers?

NASA Astrophysics Data System (ADS)

Antonoyiannakis, Manolis

A recent trend in scientific publishing is that journal editors highlight each week a select set among the papers published (usually) in their respective journals. The highlighted papers are deemed of higher quality, importance, or interest than the 'average' paper and feature prominently in the publishers' websites. We perform a citation analysis of the highlighted papers for a number of journals from various publishers in physics. By comparing the performance of highlighted papers relative to (a) typical papers and (b) highly cited papers in their source journals and in other journals in the field, we explore whether, and to what extent, the selection process at the time of publication identifies papers that will turn out to be influential. We discuss the broader implications for research assessment.

The Relative Importance of Family History, Gender, Mode of Onset, and Age at Onsetin Predicting Clinical Features of First-Episode Psychotic Disorders.

PubMed

Compton, Michael T; Berez, Chantal; Walker, Elaine F

Family history of psychosis, gender, mode of onset, and age at onset are considered prognostic factors important to clinicians evaluating first-episode psychosis; yet, clinicians have little guidance as to how these four factors differentially predict early-course substance abuse, symptomatology, and functioning. We conducted a "head-to-head comparison" of these four factors regarding their associations with key clinical features at initial hospitalization. We also assessed potential interactions between gender and family history with regard to age at onset of psychosis and symptom severity. Consecutively admitted first-episode patients (n=334) were evaluated in two studies that rigorously assessed a number of early-course variables. Associations among variables of interest were examined using Pearson correlations, χ 2 tests, Student's t-tests, and 2×2 factorial analyses of variance. Substance (nicotine, alcohol, and cannabis) abuse and positive symptom severity were predicted only by male gender. Negative symptom severity and global functioning impairments were predicted by earlier age at onset of psychosis. General psychopathology symptom severity was predicted by both mode of onset and age at onset. Interaction effects were not observed with regard to gender and family history in predicting age at onset or symptom severity. The four prognostic features have differential associations with substance abuse, domains of symptom severity, and global functioning. Gender and age at onset of psychosis appear to be more predictive of clinical features at the time of initial evaluation (and thus presumably longer term outcomes) than the presence of a family history of psychosis and a more gradual mode of onset.

MIIB: A Metric to Identify Top Influential Bloggers in a Community.

PubMed

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.

A statistical-textural-features based approach for classification of solid drugs using surface microscopic images.

PubMed

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.

Automated Quantification of Gradient Defined Features

DTIC Science & Technology

2008-09-01

defined features in submarine environments. The technique utilizes MATLAB scripts to convert bathymetry data into a gradient dataset, produce gradient...maps, and most importantly, automate the process of defining and characterizing gradient defined features such as flows, faults, landslide scarps, folds...convergent plate margin hosts a series of large serpentinite mud volcanoes (Fig. 1). One of the largest of these active mud volcanoes is Big Blue

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

ERIC Educational Resources Information Center

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

2012-01-01

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

Reliability of resting-state microstate features in electroencephalography.

PubMed

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

2014-01-01

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

Feature instructions improve face-matching accuracy

PubMed Central

Bindemann, Markus

2018-01-01

Identity comparisons of photographs of unfamiliar faces are prone to error but important for applied settings, such as person identification at passport control. Finding techniques to improve face-matching accuracy is therefore an important contemporary research topic. This study investigated whether matching accuracy can be improved by instruction to attend to specific facial features. Experiment 1 showed that instruction to attend to the eyebrows enhanced matching accuracy for optimized same-day same-race face pairs but not for other-race faces. By contrast, accuracy was unaffected by instruction to attend to the eyes, and declined with instruction to attend to ears. Experiment 2 replicated the eyebrow-instruction improvement with a different set of same-race faces, comprising both optimized same-day and more challenging different-day face pairs. These findings suggest that instruction to attend to specific features can enhance face-matching accuracy, but feature selection is crucial and generalization across face sets may be limited. PMID:29543822

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

PubMed

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

2010-05-01

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

SU-E-I-01: Iterative CBCT Reconstruction with a Feature-Preserving Penalty

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

Lyu, Q; Li, B; Southern Medical University, Guangzhou

2015-06-15

Purpose: Low-dose CBCT is desired in various clinical applications. Iterative image reconstruction algorithms have shown advantages in suppressing noise in low-dose CBCT. However, due to the smoothness constraint enforced during the reconstruction process, edges may be blurred and image features may lose in the reconstructed image. In this work, we proposed a new penalty design to preserve image features in the image reconstructed by iterative algorithms. Methods: Low-dose CBCT is reconstructed by minimizing the penalized weighted least-squares (PWLS) objective function. Binary Robust Independent Elementary Features (BRIEF) of the image were integrated into the penalty of PWLS. BRIEF is a generalmore » purpose point descriptor that can be used to identify important features of an image. In this work, BRIEF distance of two neighboring pixels was used to weigh the smoothing parameter in PWLS. For pixels of large BRIEF distance, weaker smooth constraint will be enforced. Image features will be better preserved through such a design. The performance of the PWLS algorithm with BRIEF penalty was evaluated by a CatPhan 600 phantom. Results: The image quality reconstructed by the proposed PWLS-BRIEF algorithm is superior to that by the conventional PWLS method and the standard FDK method. At matched noise level, edges in PWLS-BRIEF reconstructed image are better preserved. Conclusion: This study demonstrated that the proposed PWLS-BRIEF algorithm has great potential on preserving image features in low-dose CBCT.« less

Using Manipulated Photographs to Identify Features of Streetscapes That May Encourage Older Adults to Walk for Transport

PubMed Central

Van Cauwenberg, Jelle; Van Holle, Veerle; De Bourdeaudhuij, Ilse; Clarys, Peter; Nasar, Jack; Salmon, Jo; Goubert, Liesbet; Deforche, Benedicte

2014-01-01

Experimental evidence of environmental features important for physical activity is challenging to procure in real world settings. The current study aimed to investigate the causal effects of environmental modifications on a photographed street's appeal for older adults' walking for transport. Secondly, we examined whether these effects differed according to gender, functional limitations, and current level of walking for transport. Thirdly, we examined whether different environmental modifications interacted with each other. Qualitative responses were also reported to gain deeper insight into the observed quantitative relationships. Two sets of 16 panoramic photographs of a streetscape were created, in which six environmental factors were manipulated (sidewalk evenness, traffic level, general upkeep, vegetation, separation from traffic, and benches). Sixty older adults sorted these photographs on appeal for walking for transport on a 7-point scale and reported qualitative information on the reasons for their rankings. Sidewalk evenness appeared to have the strongest influence on a street's appeal for transport-related walking. The effect of sidewalk evenness was even stronger when the street's overall upkeep was good and when traffic was absent. Absence of traffic, presence of vegetation, and separation from traffic also increased a street's appeal for walking for transport. There were no moderating effects by gender or functional limitations. The presence of benches increased the streetscape's appeal among participants who already walked for transport at least an hour/week. The protocols and methods used in the current study carry the potential to further our understanding of environment-PA relationships. Our findings indicated sidewalk evenness as the most important environmental factor influencing a street's appeal for walking for transport among older adults. However, future research in larger samples and in real-life settings is needed to confirm current

Using manipulated photographs to identify features of streetscapes that may encourage older adults to walk for transport.

PubMed

Van Cauwenberg, Jelle; Van Holle, Veerle; De Bourdeaudhuij, Ilse; Clarys, Peter; Nasar, Jack; Salmon, Jo; Goubert, Liesbet; Deforche, Benedicte

2014-01-01

Experimental evidence of environmental features important for physical activity is challenging to procure in real world settings. The current study aimed to investigate the causal effects of environmental modifications on a photographed street's appeal for older adults' walking for transport. Secondly, we examined whether these effects differed according to gender, functional limitations, and current level of walking for transport. Thirdly, we examined whether different environmental modifications interacted with each other. Qualitative responses were also reported to gain deeper insight into the observed quantitative relationships. Two sets of 16 panoramic photographs of a streetscape were created, in which six environmental factors were manipulated (sidewalk evenness, traffic level, general upkeep, vegetation, separation from traffic, and benches). Sixty older adults sorted these photographs on appeal for walking for transport on a 7-point scale and reported qualitative information on the reasons for their rankings. Sidewalk evenness appeared to have the strongest influence on a street's appeal for transport-related walking. The effect of sidewalk evenness was even stronger when the street's overall upkeep was good and when traffic was absent. Absence of traffic, presence of vegetation, and separation from traffic also increased a street's appeal for walking for transport. There were no moderating effects by gender or functional limitations. The presence of benches increased the streetscape's appeal among participants who already walked for transport at least an hour/week. The protocols and methods used in the current study carry the potential to further our understanding of environment-PA relationships. Our findings indicated sidewalk evenness as the most important environmental factor influencing a street's appeal for walking for transport among older adults. However, future research in larger samples and in real-life settings is needed to confirm current

Higher criticism thresholding: Optimal feature selection when useful features are rare and weak.

PubMed

Donoho, David; Jin, Jiashun

2008-09-30

In important application fields today-genomics and proteomics are examples-selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, ..., p, let pi(i) denote the two-sided P-value associated with the ith feature Z-score and pi((i)) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p - pi((i)))/sqrt{i/p(1-i/p)}. We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT.

Higher criticism thresholding: Optimal feature selection when useful features are rare and weak

PubMed Central

Donoho, David; Jin, Jiashun

2008-01-01

In important application fields today—genomics and proteomics are examples—selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, …, p, let πi denote the two-sided P-value associated with the ith feature Z-score and π(i) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p − π(i))/i/p(1−i/p). We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT. PMID:18815365

Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

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

PubMed Central

Alsufyani, NA; Lam, EWN

2011-01-01

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

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

PubMed

Alsufyani, N A; Lam, E W N

2011-03-01

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

Consumer-identified barriers and strategies for optimizing technology use in the workplace.

PubMed

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.

The Mysterious 6565 Å Absorption Feature of the Galactic Halo

NASA Astrophysics Data System (ADS)

Sethi, Shiv K.; Shchekinov, Yuri; Nath, Biman B.

2017-12-01

We consider various possible scenarios to explain the recent observation of what has been called a broad Hα absorption in our Galactic halo, with peak optical depth τ ≃ 0.01 and equivalent width W≃ 0.17 \\mathringA . We show that the absorbed feature cannot arise from the circumgalactic and ISM Hα absorption. As the observed absorption feature is quite broad ({{Δ }}λ ≃ 30 \\mathringA ), we also consider CNO lines that lie close to Hα as possible alternatives to explain the feature. We show that such lines could also not account for the observed feature. Instead, we suggest that it could arise from diffuse interstellar bands (DIBs) carriers or polyaromatic hydrocarbons (PAHs) absorption. While we identify several such lines close to the Hα transition, we are unable to determine the molecule responsible for the observed feature, partly because of selection effects that prevent us from identifying DIBs/PAHs features close to Hα using local observations. Deep integration of a few extragalactic sources with high spectral resolution might allow us to distinguish between different possible explanations.

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

PubMed

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

2017-08-18

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

Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations

PubMed Central

Gurunathan, Rajalakshmi; Van Emden, Bernard; Panchanathan, Sethuraman; Kumar, Sudhir

2004-01-01

Background Modern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns. Results The effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches. Conclusions We conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns

Clinical features, proximate causes, and consequences of active convulsive epilepsy in Africa.

PubMed

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

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature

Informative Feature Selection for Object Recognition via Sparse PCA

DTIC Science & Technology

2011-04-07

constraint on images collected from low-power camera net- works instead of high-end photography is that establishing wide-baseline feature correspondence of...variable selection tool for selecting informative features in the object images captured from low-resolution cam- era sensor networks. Firstly, we...More examples can be found in Figure 4 later. 3. Identifying Informative Features Classical PCA is a well established tool for the analysis of high

Sensor-oriented feature usability evaluation in fingerprint segmentation

NASA Astrophysics Data System (ADS)

Li, Ying; Yin, Yilong; Yang, Gongping

2013-06-01

Existing fingerprint segmentation methods usually process fingerprint images captured by different sensors with the same feature or feature set. We propose to improve the fingerprint segmentation result in view of an important fact that images from different sensors have different characteristics for segmentation. Feature usability evaluation, which means to evaluate the usability of features to find the personalized feature or feature set for different sensors to improve the performance of segmentation. The need for feature usability evaluation for fingerprint segmentation is raised and analyzed as a new issue. To address this issue, we present a decision-tree-based feature-usability evaluation method, which utilizes a C4.5 decision tree algorithm to evaluate and pick the best suitable feature or feature set for fingerprint segmentation from a typical candidate feature set. We apply the novel method on the FVC2002 database of fingerprint images, which are acquired by four different respective sensors and technologies. Experimental results show that the accuracy of segmentation is improved, and time consumption for feature extraction is dramatically reduced with selected feature(s).

Feature-Based Morphometry: Discovering Group-related Anatomical Patterns

PubMed Central

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

2015-01-01

This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1). PMID:19853047

Feature engineering for drug name recognition in biomedical texts: feature conjunction and feature selection.

PubMed

Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong; Fan, Xiaoming

2015-01-01

Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.

Windblown Features on Venus and Geological Mapping

NASA Technical Reports Server (NTRS)

Greeley, Ronald

1999-01-01

The objectives of this study were to: 1) develop a global data base of aeolian features by searching Magellan coverage for possible time-variable wind streaks, 2) analyze the data base to characterize aeolian features and processes on Venus, 3) apply the analysis to assessments of wind patterns near the surface and for comparisons with atmospheric circulation models, 4) analyze shuttle radar data acquired for aeolian features on Earth to determine their radar characteristics, and 5) conduct geological mapping of two quadrangles. Wind, or aeolian, features are observed on Venus and aeolian processes play a role in modifying its surface. Analysis of features resulting from aeolian processes provides insight into characteristics of both the atmosphere and the surface. Wind related features identified on Venus include erosional landforms (yardangs), depositional dune fields, and features resulting from the interaction of the atmosphere and crater ejecta at the time of impact. The most abundant aeolian features are various wind streaks. Their discovery on Venus afforded the opportunity to learn about the interaction of the atmosphere and surface, both for the identification of sediments and in mapping near-surface winds.

Quantitative DNA Methylation Analysis Identifies a Single CpG Dinucleotide Important for ZAP-70 Expression and Predictive of Prognosis in Chronic Lymphocytic Leukemia

PubMed Central

Claus, Rainer; Lucas, David M.; Stilgenbauer, Stephan; Ruppert, Amy S.; Yu, Lianbo; Zucknick, Manuela; Mertens, Daniel; Bühler, Andreas; Oakes, Christopher C.; Larson, Richard A.; Kay, Neil E.; Jelinek, Diane F.; Kipps, Thomas J.; Rassenti, Laura Z.; Gribben, John G.; Döhner, Hartmut; Heerema, Nyla A.; Marcucci, Guido; Plass, Christoph; Byrd, John C.

2012-01-01

Purpose Increased ZAP-70 expression predicts poor prognosis in chronic lymphocytic leukemia (CLL). Current methods for accurately measuring ZAP-70 expression are problematic, preventing widespread application of these tests in clinical decision making. We therefore used comprehensive DNA methylation profiling of the ZAP-70 regulatory region to identify sites important for transcriptional control. Patients and Methods High-resolution quantitative DNA methylation analysis of the entire ZAP-70 gene regulatory regions was conducted on 247 samples from patients with CLL from four independent clinical studies. Results Through this comprehensive analysis, we identified a small area in the 5′ regulatory region of ZAP-70 that showed large variability in methylation in CLL samples but was universally methylated in normal B cells. High correlation with mRNA and protein expression, as well as activity in promoter reporter assays, revealed that within this differentially methylated region, a single CpG dinucleotide and neighboring nucleotides are particularly important in ZAP-70 transcriptional regulation. Furthermore, by using clustering approaches, we identified a prognostic role for this site in four independent data sets of patients with CLL using time to treatment, progression-free survival, and overall survival as clinical end points. Conclusion Comprehensive quantitative DNA methylation analysis of the ZAP-70 gene in CLL identified important regions responsible for transcriptional regulation. In addition, loss of methylation at a specific single CpG dinucleotide in the ZAP-70 5′ regulatory sequence is a highly predictive and reproducible biomarker of poor prognosis in this disease. This work demonstrates the feasibility of using quantitative specific ZAP-70 methylation analysis as a relevant clinically applicable prognostic test in CLL. PMID:22564988

Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

PubMed Central

Zhao, Yu-Xiang; Chou, Chien-Hsing

2016-01-01

In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

PubMed

Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

2014-08-01

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature

Selective attention to temporal features on nested time scales.

PubMed

Henry, Molly J; Herrmann, Björn; Obleser, Jonas

2015-02-01

Meaningful auditory stimuli such as speech and music often vary simultaneously along multiple time scales. Thus, listeners must selectively attend to, and selectively ignore, separate but intertwined temporal features. The current study aimed to identify and characterize the neural network specifically involved in this feature-selective attention to time. We used a novel paradigm where listeners judged either the duration or modulation rate of auditory stimuli, and in which the stimulation, working memory demands, response requirements, and task difficulty were held constant. A first analysis identified all brain regions where individual brain activation patterns were correlated with individual behavioral performance patterns, which thus supported temporal judgments generically. A second analysis then isolated those brain regions that specifically regulated selective attention to temporal features: Neural responses in a bilateral fronto-parietal network including insular cortex and basal ganglia decreased with degree of change of the attended temporal feature. Critically, response patterns in these regions were inverted when the task required selectively ignoring this feature. The results demonstrate how the neural analysis of complex acoustic stimuli with multiple temporal features depends on a fronto-parietal network that simultaneously regulates the selective gain for attended and ignored temporal features. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Random forest feature selection approach for image segmentation

NASA Astrophysics Data System (ADS)

Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina; Vaida, Mircea Florin

2017-03-01

In the field of image segmentation, discriminative models have shown promising performance. Generally, every such model begins with the extraction of numerous features from annotated images. Most authors create their discriminative model by using many features without using any selection criteria. A more reliable model can be built by using a framework that selects the important variables, from the point of view of the classification, and eliminates the unimportant once. In this article we present a framework for feature selection and data dimensionality reduction. The methodology is built around the random forest (RF) algorithm and its variable importance evaluation. In order to deal with datasets so large as to be practically unmanageable, we propose an algorithm based on RF that reduces the dimension of the database by eliminating irrelevant features. Furthermore, this framework is applied to optimize our discriminative model for brain tumor segmentation.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

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

Dai, Heng; Ye, Ming; Walker, Anthony P.

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averagingmore » methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

PubMed

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

Identifying the Most Important 21st Century Workforce Competencies: An Analysis of the Occupational Information Network (O*NET). Research Report. ETS RR-13-21

ERIC Educational Resources Information Center

Burrus, Jeremy; Jackson, Teresa; Xi, Nuo; Steinberg, Jonathan

2013-01-01

To identify the most important competencies for college graduates to succeed in the 21st century workforce, we conducted an analysis of the Occupational Information Network (O*NET) database. O*NET is a large job analysis operated and maintained by the U.S. Department of Labor. We specifically analyzed ratings of the importance of abilities (52…

Feature extraction via KPCA for classification of gait patterns.

PubMed

Wu, Jianning; Wang, Jue; Liu, Li

2007-06-01

Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.

The Importance of Capturing Topographic Features for Modeling Groundwater Flow and Transport in Mountainous Watersheds

NASA Astrophysics Data System (ADS)

Wang, C.; Gomez-Velez, J. D.; Wilson, J. L.

2017-12-01

Groundwater plays a key role in runoff generation and stream water chemistry from reach to watershed scales. The spatial distribution of ridges and streams can influence the spatial patterns of groundwater recharge and drainage, specially in mountainous terrains where these features are more prominent. However, typical modeling efforts simplify or ignore some of these features due to computational limitations without a systematic investigation of the implications for flow and transport within the watershed. In this study, we investigate the effect of capturing key topographic features on modeled groundwater flow and transport characteristics in a mountainous watershed. We build model scenarios of different topographic complexity levels (TCLs) to capture different levels of representation of streams and ridges in the model. Modeled baseflow and groundwater mean residence time (MRT) are used to quantify the differences among TCLs. Our results show that capturing the streams and ridges has a significant influence on simulated groundwater flow and transport patterns. Topographic complexity controls the proportion of baseflow generated from local, intermediate, and regional flow paths, thus influencing the amount and MRT of basefow flowing into streams of different Horton-Strahler orders. We further simulate the concentration of solute exported into streams from subsurface chemical weathering. The concentration of chemical weathering products in streams is less sensitive to model TCL due to the thermodynamic constraint on the equilibrium concentration of the chemical weathering. We also tested the influence of geology on the effect of TCL. The effect of TCL is consistent under different geological conditions; however, it is enhanced in models with low hydraulic conductivity because more of the flow is forced into shallow and local flow paths. All of these changes can affect our ability to interpret environmental tracer data and predict bio- and geo-chemical evolution of

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

PubMed

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

2016-12-01

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

Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

PubMed Central

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

2017-01-01

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone. PMID:29123329

Preferred features of urban parks and forests

Treesearch

Herbert W. Schroeder

1982-01-01

To make the most efficient use of scarce recreation resources, urban forest managers need to know what features of recreation sites are the most important for creating high-quality recreation environments. In this study, observers viewed photographs of urban forest sites in the Chicago area and described the features of the sites that they liked and disliked. Natural...

Community Perceptions of Specific Skin Features of Possible Melanoma

ERIC Educational Resources Information Center

Baade, Peter D.; Balanda, Kevin P.; Stanton, Warren R.; Lowe, John B.; Del Mar, Chris B.

2004-01-01

Background: Melanoma can be curable if detected early. One component of detecting melanoma is an awareness of the important features of the disease. It is currently not clear which features the community view as indicative of melanoma. Objective: To investigate which features of the skin members of an urban community believe may indicate skin…

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

PubMed Central

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

2008-01-01

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

Identification of informative features for predicting proinflammatory potentials of engine exhausts.

PubMed

Wang, Chia-Chi; Lin, Ying-Chi; Lin, Yuan-Chung; Jhang, Syu-Ruei; Tung, Chun-Wei

2017-08-18

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment. To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm. A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively. The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

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

PubMed

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

2017-01-01

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

Biologically active ligands for yersinia outer protein H (YopH): feature based pharmacophore screening, docking and molecular dynamics studies.

PubMed

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.

Isolated severe tricuspid regurgitation: the importance of identifying underlying mechanism.

PubMed

Poh, Kian Keong; Solis, Jorge; Hung, Judy

2008-07-21

An 88-year-old woman presented with right heart failure, history of diarrhoea, abdominal pain, weight lost, dyspnoea over several weeks and a new pan-systolic murmur. Echocardiography showed retracted tricuspid leaflets with incomplete coaptation resulting in severe regurgitation. Subcostal view showed an adjacent hepatic cyst leading to biopsy, which revealed neoplastic neuroendocrine cells. Her 24-hour urinary 5-hydroxyindoleacetic acid level was elevated. The unifying diagnosis was carcinoid syndrome for which she was treated. Echocardiography is an important tool for diagnosis, management and prognosis of carcinoid heart disease.

Perceived cultural importance and actual self-importance of values in cultural identification.

PubMed

Wan, Ching; Chiu, Chi-yue; Tam, Kim-pong; Lee, Sau-lai; Lau, Ivy Yee-man; Peng, Siqing

2007-02-01

Cross-cultural psychologists assume that core cultural values define to a large extent what a culture is. Typically, core values are identified through an actual self-importance approach, in which core values are those that members of the culture as a group strongly endorse. In this article, the authors propose a perceived cultural importance approach to identifying core values, in which core values are values that members of the culture as a group generally believe to be important in the culture. In 5 studies, the authors examine the utility of the perceived cultural importance approach. Results consistently showed that, compared with values of high actual self-importance, values of high perceived cultural importance play a more important role in cultural identification. These findings have important implications for conceptualizing and measuring cultures. ((c) 2007 APA, all rights reserved).

Influenza-associated Encephalitis/Encephalopathy Identified by the Australian Childhood Encephalitis Study 2013-2015.

PubMed

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.

A formal theory of feature binding in object perception.

PubMed

Ashby, F G; Prinzmetal, W; Ivry, R; Maddox, W T

1996-01-01

Visual objects are perceived correctly only if their features are identified and then bound together. Illusory conjunctions result when feature identification is correct but an error occurs during feature binding. A new model is proposed that assumes feature binding errors occur because of uncertainty about the location of visual features. This model accounted for data from 2 new experiments better than a model derived from A. M. Treisman and H. Schmidt's (1982) feature integration theory. The traditional method for detecting the occurrence of true illusory conjunctions is shown to be fundamentally flawed. A reexamination of 2 previous studies provided new insights into the role of attention and location information in object perception and a reinterpretation of the deficits in patients who exhibit attentional disorders.

Solid-like features in dense vapors near the fluid critical point

NASA Astrophysics Data System (ADS)

Ruppeiner, George; Dyjack, Nathan; McAloon, Abigail; Stoops, Jerry

2017-06-01

The phase diagram (pressure versus temperature) of the pure fluid is typically envisioned as being featureless apart from the presence of the liquid-vapor coexistence curve terminating at the critical point. However, a number of recent authors have proposed that this simple picture misses important features, such as the Widom line, the Fisher-Widom line, and the Frenkel line. In our paper, we discuss another way of augmenting the pure fluid phase diagram, lines of zero thermodynamic curvature R = 0 separating regimes of fluid solid-like behavior (R > 0) from gas-like or liquid-like behavior (R < 0). We systematically evaluate R for the 121 pure fluids in the NIST/REFPROP (version 9.1) fluid database near the saturated vapor line from the triple point to the critical point. Our specific goal was to identify regions of positive R abutting the saturated vapor line ("feature D"). We found the following: (i) 97/121 of the NIST/REFPROP fluids have feature D. (ii) The presence and character of feature D correlates with molecular complexity, taken to be the number of atoms Q per molecule. (iii) The solid-like properties of feature D might be attributable to a mesoscopic model based on correlations among coordinated spinning molecules, a model that might be testable with computer simulations. (iv) There are a number of correlations between thermodynamic quantities, including the acentric factor ω , but we found little explicit correlation between ω and the shape of a molecule. (v) Feature D seriously constrains the size of the asymptotic fluid critical point regime, possibly resolving a long-standing mystery about why these are so small. (vi) Feature D correlates roughly with regimes of anomalous sound propagation.

Discriminant Features and Temporal Structure of Nonmanuals in American Sign Language

PubMed Central

Benitez-Quiroz, C. Fabian; Gökgöz, Kadir; Wilbur, Ronnie B.; Martinez, Aleix M.

2014-01-01

To fully define the grammar of American Sign Language (ASL), a linguistic model of its nonmanuals needs to be constructed. While significant progress has been made to understand the features defining ASL manuals, after years of research, much still needs to be done to uncover the discriminant nonmanual components. The major barrier to achieving this goal is the difficulty in correlating facial features and linguistic features, especially since these correlations may be temporally defined. For example, a facial feature (e.g., head moves down) occurring at the end of the movement of another facial feature (e.g., brows moves up), may specify a Hypothetical conditional, but only if this time relationship is maintained. In other instances, the single occurrence of a movement (e.g., brows move up) can be indicative of the same grammatical construction. In the present paper, we introduce a linguistic–computational approach to efficiently carry out this analysis. First, a linguistic model of the face is used to manually annotate a very large set of 2,347 videos of ASL nonmanuals (including tens of thousands of frames). Second, a computational approach is used to determine which features of the linguistic model are more informative of the grammatical rules under study. We used the proposed approach to study five types of sentences – Hypothetical conditionals, Yes/no questions, Wh-questions, Wh-questions postposed, and Assertions – plus their polarities – positive and negative. Our results verify several components of the standard model of ASL nonmanuals and, most importantly, identify several previously unreported features and their temporal relationship. Notably, our results uncovered a complex interaction between head position and mouth shape. These findings define some temporal structures of ASL nonmanuals not previously detected by other approaches. PMID:24516528

Structural Features of Algebraic Quantum Notations

ERIC Educational Resources Information Center

Gire, Elizabeth; Price, Edward

2015-01-01

The formalism of quantum mechanics includes a rich collection of representations for describing quantum systems, including functions, graphs, matrices, histograms of probabilities, and Dirac notation. The varied features of these representations affect how computations are performed. For example, identifying probabilities of measurement outcomes…

Selective processing of multiple features in the human brain: effects of feature type and salience.

PubMed

McGinnis, E Menton; Keil, Andreas

2011-02-09

Identifying targets in a stream of items at a given constant spatial location relies on selection of aspects such as color, shape, or texture. Such attended (target) features of a stimulus elicit a negative-going event-related brain potential (ERP), termed Selection Negativity (SN), which has been used as an index of selective feature processing. In two experiments, participants viewed a series of Gabor patches in which targets were defined as a specific combination of color, orientation, and shape. Distracters were composed of different combinations of color, orientation, and shape of the target stimulus. This design allows comparisons of items with and without specific target features. Consistent with previous ERP research, SN deflections extended between 160-300 ms. Data from the subsequent P3 component (300-450 ms post-stimulus) were also examined, and were regarded as an index of target processing. In Experiment A, predominant effects of target color on SN and P3 amplitudes were found, along with smaller ERP differences in response to variations of orientation and shape. Manipulating color to be less salient while enhancing the saliency of the orientation of the Gabor patch (Experiment B) led to delayed color selection and enhanced orientation selection. Topographical analyses suggested that the location of SN on the scalp reliably varies with the nature of the to-be-attended feature. No interference of non-target features on the SN was observed. These results suggest that target feature selection operates by means of electrocortical facilitation of feature-specific sensory processes, and that selective electrocortical facilitation is more effective when stimulus saliency is heightened.

Carboxylator: incorporating solvent-accessible surface area for identifying protein carboxylation sites

NASA Astrophysics Data System (ADS)

Lu, Cheng-Tsung; Chen, Shu-An; Bretaña, Neil Arvin; Cheng, Tzu-Hsiu; Lee, Tzong-Yi

2011-10-01

In proteins, glutamate (Glu) residues are transformed into γ-carboxyglutamate (Gla) residues in a process called carboxylation. The process of protein carboxylation catalyzed by γ-glutamyl carboxylase is deemed to be important due to its involvement in biological processes such as blood clotting cascade and bone growth. There is an increasing interest within the scientific community to identify protein carboxylation sites. However, experimental identification of carboxylation sites via mass spectrometry-based methods is observed to be expensive, time-consuming, and labor-intensive. Thus, we were motivated to design a computational method for identifying protein carboxylation sites. This work aims to investigate the protein carboxylation by considering the composition of amino acids that surround modification sites. With the implication of a modified residue prefers to be accessible on the surface of a protein, the solvent-accessible surface area (ASA) around carboxylation sites is also investigated. Radial basis function network is then employed to build a predictive model using various features for identifying carboxylation sites. Based on a five-fold cross-validation evaluation, a predictive model trained using the combined features of amino acid sequence (AA20D), amino acid composition, and ASA, yields the highest accuracy at 0.874. Furthermore, an independent test done involving data not included in the cross-validation process indicates that in silico identification is a feasible means of preliminary analysis. Additionally, the predictive method presented in this work is implemented as Carboxylator (http://csb.cse.yzu.edu.tw/Carboxylator/), a web-based tool for identifying carboxylated proteins with modification sites in order to help users in investigating γ-glutamyl carboxylation.

Features of Online Health Communities for Adolescents With Type 1 Diabetes

PubMed Central

Ho, Yun-Xian; O’Connor, Brendan H.; Mulvaney, Shelagh A.

2014-01-01

The aim of this exploratory study was to examine diabetes online health communities (OHCs) available to adolescents with type 1 diabetes (T1D). We sought to identify and classify site features and relate them to evidence-based processes for improving self-management. We reviewed 18 OHCs and identified the following five feature categories: social learning and networking, information, guidance, engagement, and personal health data sharing. While features that have been associated with improved self-management were present, such as social learning, results suggest that more guidance or structure would be helpful to ensure that those processes were focused on promoting positive beliefs and behaviors. Enhancing guidance-related features and structure to existing OHCs could provide greater opportunity for effective diabetes self-management support. To support clinical recommendations, more research is needed to quantitatively relate features and participation in OHCs to patient outcomes. PMID:24473058

Vertical Feature Mask Feature Classification Flag Extraction

Atmospheric Science Data Center

2013-03-28

  Vertical Feature Mask Feature Classification Flag Extraction This routine demonstrates extraction of the ... in a CALIPSO Lidar Level 2 Vertical Feature Mask feature classification flag value. It is written in Interactive Data Language (IDL) ...

Engineering nanoscale surface features to sustain microparticle rolling in flow.

PubMed

Kalasin, Surachate; Santore, Maria M

2015-05-26

Nanoscopic features of channel walls are often engineered to facilitate microfluidic transport, for instance when surface charge enables electro-osmosis or when grooves drive mixing. The dynamic or rolling adhesion of flowing microparticles on a channel wall holds potential to accomplish particle sorting or to selectively transfer reactive species or signals between the wall and flowing particles. Inspired by cell rolling under the direction of adhesion molecules called selectins, we present an engineered platform in which the rolling of flowing microparticles is sustained through the incorporation of entirely synthetic, discrete, nanoscale, attractive features into the nonadhesive (electrostatically repulsive) surface of a flow channel. Focusing on one example or type of nanoscale feature and probing the impact of broad systematic variations in surface feature loading and processing parameters, this study demonstrates how relatively flat, weakly adhesive nanoscale features, positioned with average spacings on the order of tens of nanometers, can produce sustained microparticle rolling. We further demonstrate how the rolling velocity and travel distance depend on flow and surface design. We identify classes of related surfaces that fail to support rolling and present a state space that identifies combinations of surface and processing variables corresponding to transitions between rolling, free particle motion, and arrest. Finally we identify combinations of parameters (surface length scales, particle size, flow rates) where particles can be manipulated with size-selectivity.

Combined rule extraction and feature elimination in supervised classification.

PubMed

Liu, Sheng; Patel, Ronak Y; Daga, Pankaj R; Liu, Haining; Fu, Gang; Doerksen, Robert J; Chen, Yixin; Wilkins, Dawn E

2012-09-01

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Combined Rule Extraction and Feature Elimination (CRF), based on 1-norm regularized random forests. CRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied CRF to several drug activity prediction and microarray data sets. CRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.

The Features of Female Managers' Personality Traits in Organization

ERIC Educational Resources Information Center

Gabdreeva, Guzel Sh.; Khalfieva, Alisa R.

2016-01-01

The relevance of the "female" management features study is driven by the active penetration of women to management in various fields and the emergence of a new social category "Business-women". The article contains the results of a study aimed to identify the features of personal properties and structure of low-level,…

Accounts of bullying on Twitter in relation to dentofacial features and orthodontic treatment.

PubMed

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.

Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments

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.

The effects of variations in parameters and algorithm choices on calculated radiomics feature values: initial investigations and comparisons to feature variability across CT image acquisition conditions

NASA Astrophysics Data System (ADS)

Emaminejad, Nastaran; Wahi-Anwar, Muhammad; Hoffman, John; Kim, Grace H.; Brown, Matthew S.; McNitt-Gray, Michael

2018-02-01

Translation of radiomics into clinical practice requires confidence in its interpretations. This may be obtained via understanding and overcoming the limitations in current radiomic approaches. Currently there is a lack of standardization in radiomic feature extraction. In this study we examined a few factors that are potential sources of inconsistency in characterizing lung nodules, such as 1)different choices of parameters and algorithms in feature calculation, 2)two CT image dose levels, 3)different CT reconstruction algorithms (WFBP, denoised WFBP, and Iterative). We investigated the effect of variation of these factors on entropy textural feature of lung nodules. CT images of 19 lung nodules identified from our lung cancer screening program were identified by a CAD tool and contours provided. The radiomics features were extracted by calculating 36 GLCM based and 4 histogram based entropy features in addition to 2 intensity based features. A robustness index was calculated across different image acquisition parameters to illustrate the reproducibility of features. Most GLCM based and all histogram based entropy features were robust across two CT image dose levels. Denoising of images slightly improved robustness of some entropy features at WFBP. Iterative reconstruction resulted in improvement of robustness in a fewer times and caused more variation in entropy feature values and their robustness. Within different choices of parameters and algorithms texture features showed a wide range of variation, as much as 75% for individual nodules. Results indicate the need for harmonization of feature calculations and identification of optimum parameters and algorithms in a radiomics study.

Simulation study and guidelines to generate Laser-induced Surface Acoustic Waves for human skin feature detection

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.

Features of a Mobile Support App for Patients With Chronic Obstructive Pulmonary Disease: Literature Review and Current Applications

PubMed Central

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

Identifying Important Career Indicators of Undergraduate Geoscience Students Upon Completion of Their Degree

NASA Astrophysics Data System (ADS)

Wilson, C. E.; Keane, C. M.; Houlton, H. R.

2012-12-01

The American Geosciences Institute (AGI) decided to create the National Geoscience Student Exit Survey in order to identify the initial pathways into the workforce for these graduating students, as well as assess their preparedness for entering the workforce upon graduation. The creation of this survey stemmed from a combination of experiences with the AGI/AGU Survey of Doctorates and discussions at the following Science Education Research Center (SERC) workshops: "Developing Pathways to Strong Programs for the Future", "Strengthening Your Geoscience Program", and "Assessing Geoscience Programs". These events identified distinct gaps in understanding the experiences and perspectives of geoscience students during one of their most profound professional transitions. Therefore, the idea for the survey arose as a way to evaluate how the discipline is preparing and educating students, as well as identifying the students' desired career paths. The discussions at the workshops solidified the need for this survey and created the initial framework for the first pilot of the survey. The purpose of this assessment tool is to evaluate student preparedness for entering the geosciences workforce; identify student decision points for entering geosciences fields and remaining in the geosciences workforce; identify geosciences fields that students pursue in undergraduate and graduate school; collect information on students' expected career trajectories and geosciences professions; identify geosciences career sectors that are hiring new graduates; collect information about salary projections; overall effectiveness of geosciences departments regionally and nationally; demonstrate the value of geosciences degrees to future students, the institutions, and employers; and establish a benchmark to perform longitudinal studies of geosciences graduates to understand their career pathways and impacts of their educational experiences on these decisions. AGI's Student Exit Survey went through

Sentiment analysis of feature ranking methods for classification accuracy

NASA Astrophysics Data System (ADS)

Joseph, Shashank; Mugauri, Calvin; Sumathy, S.

2017-11-01

Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.

Experimental assessment of the importance of amino acid positions identified by an entropy-based correlation analysis of multiple-sequence alignments.

PubMed

Dietrich, Susanne; Borst, Nadine; Schlee, Sandra; Schneider, Daniel; Janda, Jan-Oliver; Sterner, Reinhard; Merkl, Rainer

2012-07-17

The analysis of a multiple-sequence alignment (MSA) with correlation methods identifies pairs of residue positions whose occupation with amino acids changes in a concerted manner. It is plausible to assume that positions that are part of many such correlation pairs are important for protein function or stability. We have used the algorithm H2r to identify positions k in the MSAs of the enzymes anthranilate phosphoribosyl transferase (AnPRT) and indole-3-glycerol phosphate synthase (IGPS) that show a high conn(k) value, i.e., a large number of significant correlations in which k is involved. The importance of the identified residues was experimentally validated by performing mutagenesis studies with sAnPRT and sIGPS from the archaeon Sulfolobus solfataricus. For sAnPRT, five H2r mutant proteins were generated by replacing nonconserved residues with alanine or the prevalent residue of the MSA. As a control, five residues with conn(k) values of zero were chosen randomly and replaced with alanine. The catalytic activities and conformational stabilities of the H2r and control mutant proteins were analyzed by steady-state enzyme kinetics and thermal unfolding studies. Compared to wild-type sAnPRT, the catalytic efficiencies (k(cat)/K(M)) were largely unaltered. In contrast, the apparent thermal unfolding temperature (T(M)(app)) was lowered in most proteins. Remarkably, the strongest observed destabilization (ΔT(M)(app) = 14 °C) was caused by the V284A exchange, which pertains to the position with the highest correlation signal [conn(k) = 11]. For sIGPS, six H2r mutant and four control proteins with alanine exchanges were generated and characterized. The k(cat)/K(M) values of four H2r mutant proteins were reduced between 13- and 120-fold, and their T(M)(app) values were decreased by up to 5 °C. For the sIGPS control proteins, the observed activity and stability decreases were much less severe. Our findings demonstrate that positions with high conn(k) values have an

Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

PubMed Central

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

2017-01-01

Context: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Aims: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. Materials and Methods: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. Statistical Analysis Used: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. Results: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Conclusions: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic

Interactions between space-based and feature-based attention.

PubMed

Leonard, Carly J; Balestreri, Angela; Luck, Steven J

2015-02-01

Although early research suggested that attention to nonspatial features (i.e., red) was confined to stimuli appearing at an attended spatial location, more recent research has emphasized the global nature of feature-based attention. For example, a distractor sharing a target feature may capture attention even if it occurs at a task-irrelevant location. Such findings have been used to argue that feature-based attention operates independently of spatial attention. However, feature-based attention may nonetheless interact with spatial attention, yielding larger feature-based effects at attended locations than at unattended locations. The present study tested this possibility. In 2 experiments, participants viewed a rapid serial visual presentation (RSVP) stream and identified a target letter defined by its color. Target-colored distractors were presented at various task-irrelevant locations during the RSVP stream. We found that feature-driven attentional capture effects were largest when the target-colored distractor was closer to the attended location. These results demonstrate that spatial attention modulates the strength of feature-based attention capture, calling into question the prior evidence that feature-based attention operates in a global manner that is independent of spatial attention.

A landscape ecology approach identifies important drivers of urban biodiversity.

PubMed

Turrini, Tabea; Knop, Eva

2015-04-01

Cities are growing rapidly worldwide, yet a mechanistic understanding of the impact of urbanization on biodiversity is lacking. We assessed the impact of urbanization on arthropod diversity (species richness and evenness) and abundance in a study of six cities and nearby intensively managed agricultural areas. Within the urban ecosystem, we disentangled the relative importance of two key landscape factors affecting biodiversity, namely the amount of vegetated area and patch isolation. To do so, we a priori selected sites that independently varied in the amount of vegetated area in the surrounding landscape at the 500-m scale and patch isolation at the 100-m scale, and we hold local patch characteristics constant. As indicator groups, we used bugs, beetles, leafhoppers, and spiders. Compared to intensively managed agricultural ecosystems, urban ecosystems supported a higher abundance of most indicator groups, a higher number of bug species, and a lower evenness of bug and beetle species. Within cities, a high amount of vegetated area increased species richness and abundance of most arthropod groups, whereas evenness showed no clear pattern. Patch isolation played only a limited role in urban ecosystems, which contrasts findings from agro-ecological studies. Our results show that urban areas can harbor a similar arthropod diversity and abundance compared to intensively managed agricultural ecosystems. Further, negative consequences of urbanization on arthropod diversity can be mitigated by providing sufficient vegetated space in the urban area, while patch connectivity is less important in an urban context. This highlights the need for applying a landscape ecological approach to understand the mechanisms shaping urban biodiversity and underlines the potential of appropriate urban planning for mitigating biodiversity loss. © 2015 John Wiley & Sons Ltd.

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

PubMed

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.

Why replication is important in landscape genetics: American black bear in the Rocky Mountains

USGS Publications Warehouse

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.

Childhood Precursors of Adult Borderline Personality Disorder Features: A Longitudinal Study.

PubMed

Cramer, Phebe

2016-07-01

This study identifies childhood personality traits that are precursors of adult Borderline Personality Disorder (BPD) features. In a longitudinal study, childhood personality traits were assessed at age 11 (N = 100) using the California Child Q-set (CCQ: Block and Block, 1980). A number of these Q-items were found to be significantly correlated (p < 0.001) with a prototype-based measure of BPD features at age 23. Factor analysis of these Q-items suggested that they could be characterized by two underlying personality dimensions: Impulsivity and Nonconformity/Aggression. The findings thus provide evidence that childhood personality traits predict adult BPD features. Identifying such childhood precursors provides an opportunity for early intervention.

Automated Extraction of Secondary Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne M.; Haimes, Robert

2005-01-01

The use of Computational Fluid Dynamics (CFD) has become standard practice in the design and development of the major components used for air and space propulsion. To aid in the post-processing and analysis phase of CFD many researchers now use automated feature extraction utilities. These tools can be used to detect the existence of such features as shocks, vortex cores and separation and re-attachment lines. The existence of secondary flow is another feature of significant importance to CFD engineers. Although the concept of secondary flow is relatively understood there is no commonly accepted mathematical definition for secondary flow. This paper will present a definition for secondary flow and one approach for automatically detecting and visualizing secondary flow.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Feature-level sentiment analysis by using comparative domain corpora

NASA Astrophysics Data System (ADS)

Quan, Changqin; Ren, Fuji

2016-06-01

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

Metacatalog of Planetary Surface Features for Multicriteria Evaluation of Surface Evolution: the Integrated Planetary Feature Database

NASA Astrophysics Data System (ADS)

Hargitai, Henrik

2016-10-01

We have created a metacatalog, or catalog or catalogs, of surface features of Mars that also includes the actual data in the catalogs listed. The goal is to make mesoscale surface feature databases available in one place, in a GIS-ready format. The databases can be directly imported to ArcGIS or other GIS platforms, like Google Mars. Some of the catalogs in our database are also ingested into the JMARS platform.All catalogs have been previously published in a peer-reviewed journal, but they may contain updates of the published catalogs. Many of the catalogs are "integrated", i.e. they merge databases or information from various papers on the same topic, including references to each individual features listed.Where available, we have included shapefiles with polygon or linear features, however, most of the catalogs only contain point data of their center points and morphological data.One of the unexpected results of the planetary feature metacatalog is that some features have been described by several papers, using different, i.e., conflicting designations. This shows the need for the development of an identification system suitable for mesoscale (100s m to km sized) features that tracks papers and thus prevents multiple naming of the same feature.The feature database can be used for multicriteria analysis of a terrain, thus enables easy distribution pattern analysis and the correlation of the distribution of different landforms and features on Mars. Such catalog makes a scientific evaluation of potential landing sites easier and more effective during the selection process and also supports automated landing site selections.The catalog is accessible at https://planetarydatabase.wordpress.com/.

Human listeners provide insights into echo features used by dolphins (Tursiops truncatus) to discriminate among objects.

PubMed

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.

Essential Features of Tier 2 Social-Behavioral Interventions

ERIC Educational Resources Information Center

Yong, Minglee; Cheney, Douglas A.

2013-01-01

The purpose of this study is to identify the essential features of Tier 2 interventions conducted within multitier systems of behavior support in schools. A systematic literature search identified 12 empirical studies that were coded and scored according to a list of Tier 2 specific RE-AIM criteria, related to the Reach, Effectiveness, Adoption,…

Early Readers and Electronic Texts: CD-ROM Storybook Features That Influence Reading Behaviors

ERIC Educational Resources Information Center

Lefever-Davis, Shirley; Pearman, Cathy

2005-01-01

This research explores the impact of CD-ROM storybook features on the reading behaviors of 6- and 7-year-old students with limited exposure to CD-ROM storybooks. Six categories of behaviors were identified: tracking, electronic feature dependency, distractibility, spectator stance, electronic feature limitations, and electronic features as tools.…

Acoustic Features Influence Musical Choices Across Multiple Genres.

PubMed

Barone, Michael D; Bansal, Jotthi; Woolhouse, Matthew H

2017-01-01

Based on a large behavioral dataset of music downloads, two analyses investigate whether the acoustic features of listeners' preferred musical genres influence their choice of tracks within non-preferred, secondary musical styles. Analysis 1 identifies feature distributions for pairs of genre-defined subgroups that are distinct. Using correlation analysis, these distributions are used to test the degree of similarity between subgroups' main genres and the other music within their download collections. Analysis 2 explores the issue of main-to-secondary genre influence through the production of 10 feature-influence matrices, one per acoustic feature, in which cell values indicate the percentage change in features for genres and subgroups compared to overall population averages. In total, 10 acoustic features and 10 genre-defined subgroups are explored within the two analyses. Results strongly indicate that the acoustic features of people's main genres influence the tracks they download within non-preferred, secondary musical styles. The nature of this influence and its possible actuating mechanisms are discussed with respect to research on musical preference, personality, and statistical learning.

Impact of feature saliency on visual category learning.

PubMed

Hammer, Rubi

2015-01-01

People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the 'essence' of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies.

Impact of feature saliency on visual category learning

PubMed Central

Hammer, Rubi

2015-01-01

People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the ‘essence’ of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies. PMID:25954220

Acoustic Features Influence Musical Choices Across Multiple Genres

PubMed Central

Barone, Michael D.; Bansal, Jotthi; Woolhouse, Matthew H.

2017-01-01

Based on a large behavioral dataset of music downloads, two analyses investigate whether the acoustic features of listeners' preferred musical genres influence their choice of tracks within non-preferred, secondary musical styles. Analysis 1 identifies feature distributions for pairs of genre-defined subgroups that are distinct. Using correlation analysis, these distributions are used to test the degree of similarity between subgroups' main genres and the other music within their download collections. Analysis 2 explores the issue of main-to-secondary genre influence through the production of 10 feature-influence matrices, one per acoustic feature, in which cell values indicate the percentage change in features for genres and subgroups compared to overall population averages. In total, 10 acoustic features and 10 genre-defined subgroups are explored within the two analyses. Results strongly indicate that the acoustic features of people's main genres influence the tracks they download within non-preferred, secondary musical styles. The nature of this influence and its possible actuating mechanisms are discussed with respect to research on musical preference, personality, and statistical learning. PMID:28725200

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

PubMed Central

Chan, Thomas Y. K.

2016-01-01

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

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

PubMed

Chan, Thomas Y K

2016-04-01

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

The PRC2-binding long non-coding RNAs in human and mouse genomes are associated with predictive sequence features

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.

Complete Genome Sequence of the Cystic Fibrosis Pathogen Achromobacter xylosoxidans NH44784-1996 Complies with Important Pathogenic Phenotypes

PubMed Central

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

Using ancestry-informative markers to identify fine structure across 15 populations of European origin.

PubMed

Huckins, Laura M; Boraska, Vesna; Franklin, Christopher S; Floyd, James A B; Southam, Lorraine; Sullivan, Patrick F; Bulik, Cynthia M; Collier, David A; Tyler-Smith, Chris; Zeggini, Eleftheria; Tachmazidou, Ioanna

2014-10-01

The Wellcome Trust Case Control Consortium 3 anorexia nervosa genome-wide association scan includes 2907 cases from 15 different populations of European origin genotyped on the Illumina 670K chip. We compared methods for identifying population stratification, and suggest list of markers that may help to counter this problem. It is usual to identify population structure in such studies using only common variants with minor allele frequency (MAF) >5%; we find that this may result in highly informative SNPs being discarded, and suggest that instead all SNPs with MAF >1% may be used. We established informative axes of variation identified via principal component analysis and highlight important features of the genetic structure of diverse European-descent populations, some studied for the first time at this scale. Finally, we investigated the substructure within each of these 15 populations and identified SNPs that help capture hidden stratification. This work can provide information regarding the designing and interpretation of association results in the International Consortia.