Gradient Analysis and Classification of Carolina Bay Vegetation: A Framework for Bay Wetlands Conservation and Restoration

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

Diane De Steven,Ph.D.; Maureen Tone,PhD.

1997-10-01

This report address four project objectives: (1) Gradient model of Carolina bay vegetation on the SRS--The authors use ordination analyses to identify environmental and landscape factors that are correlated with vegetation composition. Significant factors can provide a framework for site-based conservation of existing diversity, and they may also be useful site predictors for potential vegetation in bay restorations. (2) Regional analysis of Carolina bay vegetation diversity--They expand the ordination analyses to assess the degree to which SRS bays encompass the range of vegetation diversity found in the regional landscape of South Carolina's western Upper Coastal Plain. Such comparisons can indicatemore » floristic status relative to regional potentials and identify missing species or community elements that might be re-introduced or restored. (3) Classification of vegetation communities in Upper Coastal Plain bays--They use cluster analysis to identify plant community-types at the regional scale, and explore how this classification may be functional with respect to significant environmental and landscape factors. An environmentally-based classification at the whole-bay level can provide a system of templates for managing bays as individual units and for restoring bays to desired plant communities. (4) Qualitative model for bay vegetation dynamics--They analyze present-day vegetation in relation to historic land uses and disturbances. The distinctive history of SRS bays provides the possibility of assessing pathways of post-disturbance succession. They attempt to develop a coarse-scale model of vegetation shifts in response to changing site factors; such qualitative models can provide a basis for suggesting management interventions that may be needed to maintain desired vegetation in protected or restored bays.« less

A naive Bayes algorithm for tissue origin diagnosis (TOD-Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system.

PubMed

Jiang, Weiqin; Shen, Yifei; Ding, Yongfeng; Ye, Chuyu; Zheng, Yi; Zhao, Peng; Liu, Lulu; Tong, Zhou; Zhou, Linfu; Sun, Shuo; Zhang, Xingchen; Teng, Lisong; Timko, Michael P; Fan, Longjiang; Fang, Weijia

2018-01-15

Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive Bayes algorithm (TOD-Bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and ∼1,000 feature genes were used to train and validate the TOD-Bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-Bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-Bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment. © 2017 UICC.

A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis.

PubMed

Bennet, Jaison; Ganaprakasam, Chilambuchelvan Arul; Arputharaj, Kannan

2014-01-01

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

Text Classification for Intelligent Portfolio Management

DTIC Science & Technology

2002-05-01

years including nearest neighbor classification [15], naive Bayes with EM (Ex- pectation Maximization) [11] [13], Winnow with active learning [10... Active Learning and Expectation Maximization (EM). In particular, active learning is used to actively select documents for labeling, then EM assigns...generalization with active learning . Machine Learning, 15(2):201–221, 1994. [3] I. Dagan and P. Engelson. Committee-based sampling for training

Naive Probability: A Mental Model Theory of Extensional Reasoning.

ERIC Educational Resources Information Center

Johnson-Laird, P. N.; Legrenzi, Paolo; Girotto, Vittorio; Legrenzi, Maria Sonino; Caverni, Jean-Paul

1999-01-01

Outlines a theory of naive probability in which individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an "extensional" way. The theory accommodates reasoning based on numerical premises, and explains how naive reasoners can infer posterior probabilities without relying on Bayes's theorem.…

Muscle categorization using PDF estimation and Naive Bayes classification.

PubMed

Adel, Tameem M; Smith, Benn E; Stashuk, Daniel W

2012-01-01

The structure of motor unit potentials (MUPs) and their times of occurrence provide information about the motor units (MUs) that created them. As such, electromyographic (EMG) data can be used to categorize muscles as normal or suffering from a neuromuscular disease. Using pattern discovery (PD) allows clinicians to understand the rationale underlying a certain muscle characterization; i.e. it is transparent. Discretization is required in PD, which leads to some loss in accuracy. In this work, characterization techniques that are based on estimating probability density functions (PDFs) for each muscle category are implemented. Characterization probabilities of each motor unit potential train (MUPT) are obtained from these PDFs and then Bayes rule is used to aggregate the MUPT characterization probabilities to calculate muscle level probabilities. Even though this technique is not as transparent as PD, its accuracy is higher than the discrete PD. Ultimately, the goal is to use a technique that is based on both PDFs and PD and make it as transparent and as efficient as possible, but first it was necessary to thoroughly assess how accurate a fully continuous approach can be. Using gaussian PDF estimation achieved improvements in muscle categorization accuracy over PD and further improvements resulted from using feature value histograms to choose more representative PDFs; for instance, using log-normal distribution to represent skewed histograms.

The diagnose of oil palm disease using Naive Bayes Method based on Expert System Technology

NASA Astrophysics Data System (ADS)

Nababan, Marlince; Laia, Yonata; Sitanggang, Delima; Sihombing, Oloan; Indra, Evta; Siregar, Saut; Purba, Windania; Mancur, Roy

2018-04-01

Expert system is dealt with system that used computer-based human intelligence to overcome particular problem which is commonly conducted by an expert. Frequent problem faced by the farmers of oil palm is the difficulty in defining the type of plant disease. As a result, the delay treatment of plant disease brings out the declining of farm products. An application system is needed to deal with the obstacles and diagnosing the type of oil palm plant disease. The researcher designed an intelligence-based application with input-output plan which is able to diagnose the type of oil palm plant disease by applying naive bayes method. Based on the research result by conducting bayes method with recognized symptom, diagnose of oil palm plant disease could be accomplished. The data of symptoms found are leaves turned yellow 0.4, dead leaves 0.4, black and brown color among the veins of leaves 0.5, young and old fruit with whole space 0.4, and decay of bunches is 0.3. The roots are tender in the amount of 0.5, and damage on sheath is 0.3. Through the chosen symptoms as mentioned above, the value of bayes is 80% with the type of disease is rotten bunch.

Automated Classification of Pathology Reports.

PubMed

Oleynik, Michel; Finger, Marcelo; Patrão, Diogo F C

2015-01-01

This work develops an automated classifier of pathology reports which infers the topography and the morphology classes of a tumor using codes from the International Classification of Diseases for Oncology (ICD-O). Data from 94,980 patients of the A.C. Camargo Cancer Center was used for training and validation of Naive Bayes classifiers, evaluated by the F1-score. Measures greater than 74% in the topographic group and 61% in the morphologic group are reported. Our work provides a successful baseline for future research for the classification of medical documents written in Portuguese and in other domains.

Investigations on classification categories for wetlands of Chesapeake Bay using remotely sensed data

NASA Technical Reports Server (NTRS)

Williamson, F. S. L.

1974-01-01

The use of remote sensors to determine the characteristics of the wetlands of the Chesapeake Bay and surrounding areas is discussed. The objectives of the program are stated as follows: (1) to use data and remote sensing techniques developed from studies of Rhode River, West River, and South River salt marshes to develop a wetland classification scheme useful in other regions of the Chesapeake Bay and to evaluate the classification system with respect to vegetation types, marsh physiography, man-induced perturbation, and salinity; and (2) to develop a program using remote sensing techniques, for the extension of the classification to Chesapeake Bay salt marshes and to coordinate this program with the goals of the Chesapeake Research Consortium and the states of Maryland and Virginia. Maps of the Chesapeake Bay areas are developed from aerial photographs to display the wetland structure and vegetation.

Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm.

PubMed

Chapman, Brian E; Lee, Sean; Kang, Hyunseok Peter; Chapman, Wendy W

2011-10-01

In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes' classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes' classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes' classifier using bigrams. Copyright © 2011 Elsevier Inc. All rights reserved.

A Novel Feature Selection Technique for Text Classification Using Naïve Bayes.

PubMed

Dey Sarkar, Subhajit; Goswami, Saptarsi; Agarwal, Aman; Aktar, Javed

2014-01-01

With the proliferation of unstructured data, text classification or text categorization has found many applications in topic classification, sentiment analysis, authorship identification, spam detection, and so on. There are many classification algorithms available. Naïve Bayes remains one of the oldest and most popular classifiers. On one hand, implementation of naïve Bayes is simple and, on the other hand, this also requires fewer amounts of training data. From the literature review, it is found that naïve Bayes performs poorly compared to other classifiers in text classification. As a result, this makes the naïve Bayes classifier unusable in spite of the simplicity and intuitiveness of the model. In this paper, we propose a two-step feature selection method based on firstly a univariate feature selection and then feature clustering, where we use the univariate feature selection method to reduce the search space and then apply clustering to select relatively independent feature sets. We demonstrate the effectiveness of our method by a thorough evaluation and comparison over 13 datasets. The performance improvement thus achieved makes naïve Bayes comparable or superior to other classifiers. The proposed algorithm is shown to outperform other traditional methods like greedy search based wrapper or CFS.

Relevance popularity: A term event model based feature selection scheme for text classification.

PubMed

Feng, Guozhong; An, Baiguo; Yang, Fengqin; Wang, Han; Zhang, Libiao

2017-01-01

Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

PubMed

Bokulich, Nicholas A; Kaehler, Benjamin D; Rideout, Jai Ram; Dillon, Matthew; Bolyen, Evan; Knight, Rob; Huttley, Gavin A; Gregory Caporaso, J

2018-05-17

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

PubMed Central

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

Bayes classification of interferometric TOPSAR data

NASA Technical Reports Server (NTRS)

Michel, T. R.; Rodriguez, E.; Houshmand, B.; Carande, R.

1995-01-01

We report the Bayes classification of terrain types at different sites using airborne interferometric synthetic aperture radar (INSAR) data. A Gaussian maximum likelihood classifier was applied on multidimensional observations derived from the SAR intensity, the terrain elevation model, and the magnitude of the interferometric correlation. Training sets for forested, urban, agricultural, or bare areas were obtained either by selecting samples with known ground truth, or by k-means clustering of random sets of samples uniformly distributed across all sites, and subsequent assignments of these clusters using ground truth. The accuracy of the classifier was used to optimize the discriminating efficiency of the set of features that was chosen. The most important features include the SAR intensity, a canopy penetration depth model, and the terrain slope. We demonstrate the classifier's performance across sites using a unique set of training classes for the four main terrain categories. The scenes examined include San Francisco (CA) (predominantly urban and water), Mount Adams (WA) (forested with clear cuts), Pasadena (CA) (urban with mountains), and Antioch Hills (CA) (water, swamps, fields). Issues related to the effects of image calibration and the robustness of the classification to calibration errors are explored. The relative performance of single polarization Interferometric data classification is contrasted against classification schemes based on polarimetric SAR data.

Using an Integrated Naive Bayes Calssifier for Crawling Relevent Data on the Web

NASA Astrophysics Data System (ADS)

Mihsra, A.

2015-12-01

In our experiments (at JPL, NASA) for DARPA Memex project, we wanted to crawl a large amount of data for various domains. A big challenge was data relevancy in the crawled data. More than 50% of the data was irrelevant to the domain at hand. One immediate solution was to use good seeds (seeds are the initial urls from where the program starts to crawl) and make sure that the crawl remains into the original host urls. This although a very efficient technique, fails under two conditions. One when you aim to reach deeper into the web; into new hosts (not in the seed list) and two when the website hosts myriad content types eg. a News website.The relevancy calculation used to be a post processing step i.e. once we had finished crawling, we trained a NaiveBayes Classifier and used it to find a rough relevancy of the web pages that we had. Integrating the relevancy into the crawling rather than after it was very important because crawling takes resources and time. To save both we needed to get an idea of relevancy of the whole crawl during run time and be able to steer its course accordingly. We use Apache Nutch as the crawler, which uses a plugin system to incorporate any new implementations and hence we built a plugin for Nutch.The Naive Bayes Parse Plugin works in the following way. It parses every page and decides, using a trained model (which is built in situ only once using the positive and negative examples given by the user in a very simple format), if it is relevant; If true, then it allows all the outlinks from that page to go to the next round of crawling; If not, then it gives the urls a second chance to prove themselves by checking some commonly expected words in the url relevant to that domain. This two tier system is very intuitive and efficient in focusing the crawl. In our initial test experiments over 100 seed urls, the results were astonishingly good with a recall of 98%.The same technique can be applied to geo-informatics. This will help scientists

A Pairwise Naïve Bayes Approach to Bayesian Classification.

PubMed

Asafu-Adjei, Josephine K; Betensky, Rebecca A

2015-10-01

Despite the relatively high accuracy of the naïve Bayes (NB) classifier, there may be several instances where it is not optimal, i.e. does not have the same classification performance as the Bayes classifier utilizing the joint distribution of the examined attributes. However, the Bayes classifier can be computationally intractable due to its required knowledge of the joint distribution. Therefore, we introduce a "pairwise naïve" Bayes (PNB) classifier that incorporates all pairwise relationships among the examined attributes, but does not require specification of the joint distribution. In this paper, we first describe the necessary and sufficient conditions under which the PNB classifier is optimal. We then discuss sufficient conditions for which the PNB classifier, and not NB, is optimal for normal attributes. Through simulation and actual studies, we evaluate the performance of our proposed classifier relative to the Bayes and NB classifiers, along with the HNB, AODE, LBR and TAN classifiers, using normal density and empirical estimation methods. Our applications show that the PNB classifier using normal density estimation yields the highest accuracy for data sets containing continuous attributes. We conclude that it offers a useful compromise between the Bayes and NB classifiers.

Data mining for dengue hemorrhagic fever (DHF) prediction with naive Bayes method

NASA Astrophysics Data System (ADS)

Arafiyah, Ria; Hermin, Fariani

2018-01-01

Handling of infectious diseases is determined by the accuracy and speed of diagnosis. Government through the Regulation of the Minister of Health of the Republic of Indonesia No. 82 of 2014 on the Control of Communicable Diseases establishes Dengue Hemorrhagic Fever (DHF) has made DHF prevention a national priority. Various attempts were made to overcome this misdiagnosis. The treatment and diagnosis of DHF using ANFIS has result an application program that can decide whether a patient has dengue fever or not [1]. An expert system of dengue prevention by using ANFIS has predict the weather and the number of sufferers [2]. The large number of data on DHF often cannot affect a person in making decisions. The use of data mining method, able to build data base support in decision makers diagnose DHF disease [3]. This study predicts DHF with the method of Naive Bayes. Parameter of The input variable is the patient’s medical data (temperature, spotting, bleeding, and tornuine test) and the output variable suffers from DBD or not while the system output is diagnosis of the patient suffering from DHF or not. Result of model test by using tools of Orange 3.4.5 obtained level of precision model is 77,3%.

A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.

PubMed

Wolfson, Julian; Bandyopadhyay, Sunayan; Elidrisi, Mohamed; Vazquez-Benitez, Gabriela; Vock, David M; Musgrove, Donald; Adomavicius, Gediminas; Johnson, Paul E; O'Connor, Patrick J

2015-09-20

Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system. Copyright © 2015 John Wiley & Sons, Ltd.

Machine learning approach to automatic exudate detection in retinal images from diabetic patients

NASA Astrophysics Data System (ADS)

Sopharak, Akara; Dailey, Matthew N.; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Tom; Thet Nwe, Khine; Aye Moe, Yin

2010-01-01

Exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early detection of exudates could improve patients' chances to avoid blindness. In this paper, we present a series of experiments on feature selection and exudates classification using naive Bayes and support vector machine (SVM) classifiers. We first fit the naive Bayes model to a training set consisting of 15 features extracted from each of 115,867 positive examples of exudate pixels and an equal number of negative examples. We then perform feature selection on the naive Bayes model, repeatedly removing features from the classifier, one by one, until classification performance stops improving. To find the best SVM, we begin with the best feature set from the naive Bayes classifier, and repeatedly add the previously-removed features to the classifier. For each combination of features, we perform a grid search to determine the best combination of hyperparameters ν (tolerance for training errors) and γ (radial basis function width). We compare the best naive Bayes and SVM classifiers to a baseline nearest neighbour (NN) classifier using the best feature sets from both classifiers. We find that the naive Bayes and SVM classifiers perform better than the NN classifier. The overall best sensitivity, specificity, precision, and accuracy are 92.28%, 98.52%, 53.05%, and 98.41%, respectively.

Know your data: understanding implicit usage versus explicit action in video content classification

NASA Astrophysics Data System (ADS)

Yew, Jude; Shamma, David A.

2011-02-01

In this paper, we present a method for video category classification using only social metadata from websites like YouTube. In place of content analysis, we utilize communicative and social contexts surrounding videos as a means to determine a categorical genre, e.g. Comedy, Music. We hypothesize that video clips belonging to different genre categories would have distinct signatures and patterns that are reflected in their collected metadata. In particular, we define and describe social metadata as usage or action to aid in classification. We trained a Naive Bayes classifier to predict categories from a sample of 1,740 YouTube videos representing the top five genre categories. Using just a small number of the available metadata features, we compare the classifications produced by our Naive Bayes classifier with those provided by the uploader of that particular video. Compared to random predictions with the YouTube data (21% accurate), our classifier attained a mediocre 33% accuracy in predicting video genres. However, we found that the accuracy of our classifier significantly improves by nominal factoring of the explicit data features. By factoring the ratings of the videos in the dataset, the classifier was able to accurately predict the genres of 75% of the videos. We argue that the patterns of social activity found in the metadata are not just meaningful in their own right, but are indicative of the meaning of the shared video content. The results presented by this project represents a first step in investigating the potential meaning and significance of social metadata and its relation to the media experience.

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

PubMed

Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya; Gomez-Beldarrain, Marian; Fernandez-Ruanova, Begonya; Garcia-Monco, Juan Carlos

2017-04-13

Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions. We select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms. When classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions). The proposed feature selection committee method improved the performance of migraine diagnosis

The impact of modeling the dependencies among patient findings on classification accuracy and calibration.

PubMed Central

Monti, S.; Cooper, G. F.

1998-01-01

We present a new Bayesian classifier for computer-aided diagnosis. The new classifier builds upon the naive-Bayes classifier, and models the dependencies among patient findings in an attempt to improve its performance, both in terms of classification accuracy and in terms of calibration of the estimated probabilities. This work finds motivation in the argument that highly calibrated probabilities are necessary for the clinician to be able to rely on the model's recommendations. Experimental results are presented, supporting the conclusion that modeling the dependencies among findings improves calibration. PMID:9929288

Naive scoring of human sleep based on a hidden Markov model of the electroencephalogram.

PubMed

Yaghouby, Farid; Modur, Pradeep; Sunderam, Sridhar

2014-01-01

Clinical sleep scoring involves tedious visual review of overnight polysomnograms by a human expert. Many attempts have been made to automate the process by training computer algorithms such as support vector machines and hidden Markov models (HMMs) to replicate human scoring. Such supervised classifiers are typically trained on scored data and then validated on scored out-of-sample data. Here we describe a methodology based on HMMs for scoring an overnight sleep recording without the benefit of a trained initial model. The number of states in the data is not known a priori and is optimized using a Bayes information criterion. When tested on a 22-subject database, this unsupervised classifier agreed well with human scores (mean of Cohen's kappa > 0.7). The HMM also outperformed other unsupervised classifiers (Gaussian mixture models, k-means, and linkage trees), that are capable of naive classification but do not model dynamics, by a significant margin (p < 0.05).

Using clustering and a modified classification algorithm for automatic text summarization

NASA Astrophysics Data System (ADS)

Aries, Abdelkrime; Oufaida, Houda; Nouali, Omar

2013-01-01

In this paper we describe a modified classification method destined for extractive summarization purpose. The classification in this method doesn't need a learning corpus; it uses the input text to do that. First, we cluster the document sentences to exploit the diversity of topics, then we use a learning algorithm (here we used Naive Bayes) on each cluster considering it as a class. After obtaining the classification model, we calculate the score of a sentence in each class, using a scoring model derived from classification algorithm. These scores are used, then, to reorder the sentences and extract the first ones as the output summary. We conducted some experiments using a corpus of scientific papers, and we have compared our results to another summarization system called UNIS.1 Also, we experiment the impact of clustering threshold tuning, on the resulted summary, as well as the impact of adding more features to the classifier. We found that this method is interesting, and gives good performance, and the addition of new features (which is simple using this method) can improve summary's accuracy.

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.

Hyperspectral Biofilm Classification Analysis for Carrying Capacity of Migratory Birds in the South Bay Salt Ponds

NASA Technical Reports Server (NTRS)

Hsu, Wei-Chen; Kuss, Amber Jean; Ketron, Tyler; Nguyen, Andrew; Remar, Alex Covello; Newcomer, Michelle; Fleming, Erich; Debout, Leslie; Debout, Brad; Detweiler, Angela;

A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data.

PubMed

Stephens, David; Diesing, Markus

2014-01-01

Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

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

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models tomore » curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.« less

Photometric Supernova Classification with Machine Learning

NASA Astrophysics Data System (ADS)

Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Opinion mining feature-level using Naive Bayes and feature extraction based analysis dependencies

NASA Astrophysics Data System (ADS)

Sanda, Regi; Baizal, Z. K. Abdurahman; Nhita, Fhira

2015-12-01

Development of internet and technology, has major impact and providing new business called e-commerce. Many e-commerce sites that provide convenience in transaction, and consumers can also provide reviews or opinions on products that purchased. These opinions can be used by consumers and producers. Consumers to know the advantages and disadvantages of particular feature of the product. Procuders can analyse own strengths and weaknesses as well as it's competitors products. Many opinions need a method that the reader can know the point of whole opinion. The idea emerged from review summarization that summarizes the overall opinion based on sentiment and features contain. In this study, the domain that become the main focus is about the digital camera. This research consisted of four steps 1) giving the knowledge to the system to recognize the semantic orientation of an opinion 2) indentify the features of product 3) indentify whether the opinion gives a positive or negative 4) summarizing the result. In this research discussed the methods such as Naï;ve Bayes for sentiment classification, and feature extraction algorithm based on Dependencies Analysis, which is one of the tools in Natural Language Processing (NLP) and knowledge based dictionary which is useful for handling implicit features. The end result of research is a summary that contains a bunch of reviews from consumers on the features and sentiment. With proposed method, accuration for sentiment classification giving 81.2 % for positive test data, 80.2 % for negative test data, and accuration for feature extraction reach 90.3 %.

Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy

NASA Astrophysics Data System (ADS)

Boschetto, Davide; Di Claudio, Gianluca; Mirzaei, Hadis; Leong, Rupert; Grisan, Enrico

2016-03-01

Celiac disease (CD) is an immune-mediated enteropathy triggered by exposure to gluten and similar proteins, affecting genetically susceptible persons, increasing their risk of different complications. Small bowels mucosa damage due to CD involves various degrees of endoscopically relevant lesions, which are not easily recognized: their overall sensitivity and positive predictive values are poor even when zoom-endoscopy is used. Confocal Laser Endomicroscopy (CLE) allows skilled and trained experts to qualitative evaluate mucosa alteration such as a decrease in goblet cells density, presence of villous atrophy or crypt hypertrophy. We present a method for automatically classifying CLE images into three different classes: normal regions, villous atrophy and crypt hypertrophy. This classification is performed after a features selection process, in which four features are extracted from each image, through the application of homomorphic filtering and border identification through Canny and Sobel operators. Three different classifiers have been tested on a dataset of 67 different images labeled by experts in three classes (normal, VA and CH): linear approach, Naive-Bayes quadratic approach and a standard quadratic analysis, all validated with a ten-fold cross validation. Linear classification achieves 82.09% accuracy (class accuracies: 90.32% for normal villi, 82.35% for VA and 68.42% for CH, sensitivity: 0.68, specificity 1.00), Naive Bayes analysis returns 83.58% accuracy (90.32% for normal villi, 70.59% for VA and 84.21% for CH, sensitivity: 0.84 specificity: 0.92), while the quadratic analysis achieves a final accuracy of 94.03% (96.77% accuracy for normal villi, 94.12% for VA and 89.47% for CH, sensitivity: 0.89, specificity: 0.98).

Implementation of mutual information and bayes theorem for classification microarray data

NASA Astrophysics Data System (ADS)

Dwifebri Purbolaksono, Mahendra; Widiastuti, Kurnia C.; Syahrul Mubarok, Mohamad; Adiwijaya; Aminy Ma’ruf, Firda

2018-03-01

Microarray Technology is one of technology which able to read the structure of gen. The analysis is important for this technology. It is for deciding which attribute is more important than the others. Microarray technology is able to get cancer information to diagnose a person’s gen. Preparation of microarray data is a huge problem and takes a long time. That is because microarray data contains high number of insignificant and irrelevant attributes. So, it needs a method to reduce the dimension of microarray data without eliminating important information in every attribute. This research uses Mutual Information to reduce dimension. System is built with Machine Learning approach specifically Bayes Theorem. This theorem uses a statistical and probability approach. By combining both methods, it will be powerful for Microarray Data Classification. The experiment results show that system is good to classify Microarray data with highest F1-score using Bayesian Network by 91.06%, and Naïve Bayes by 88.85%.

Comparisons and Selections of Features and Classifiers for Short Text Classification

NASA Astrophysics Data System (ADS)

Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

2017-10-01

Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

Linear dimension reduction and Bayes classification

NASA Technical Reports Server (NTRS)

Decell, H. P., Jr.; Odell, P. L.; Coberly, W. A.

1978-01-01

An explicit expression for a compression matrix T of smallest possible left dimension K consistent with preserving the n variate normal Bayes assignment of X to a given one of a finite number of populations and the K variate Bayes assignment of TX to that population was developed. The Bayes population assignment of X and TX were shown to be equivalent for a compression matrix T explicitly calculated as a function of the means and covariances of the given populations.

An Ant Colony Optimization Based Feature Selection for Web Page Classification

PubMed Central

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods. PMID:25136678

An ant colony optimization based feature selection for web page classification.

PubMed

Saraç, Esra; Özel, Selma Ayşe

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

Automatic classification of protein structures using physicochemical parameters.

PubMed

Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

2014-09-01

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

Classifying emotion in Twitter using Bayesian network

NASA Astrophysics Data System (ADS)

Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

2018-03-01

Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

Improving imbalanced scientific text classification using sampling strategies and dictionaries.

PubMed

Borrajo, L; Romero, R; Iglesias, E L; Redondo Marey, C M

2011-09-15

Many real applications have the imbalanced class distribution problem, where one of the classes is represented by a very small number of cases compared to the other classes. One of the systems affected are those related to the recovery and classification of scientific documentation. Sampling strategies such as Oversampling and Subsampling are popular in tackling the problem of class imbalance. In this work, we study their effects on three types of classifiers (Knn, SVM and Naive-Bayes) when they are applied to search on the PubMed scientific database. Another purpose of this paper is to study the use of dictionaries in the classification of biomedical texts. Experiments are conducted with three different dictionaries (BioCreative, NLPBA, and an ad-hoc subset of the UniProt database named Protein) using the mentioned classifiers and sampling strategies. Best results were obtained with NLPBA and Protein dictionaries and the SVM classifier using the Subsampling balancing technique. These results were compared with those obtained by other authors using the TREC Genomics 2005 public corpus. Copyright 2011 The Author(s). Published by Journal of Integrative Bioinformatics.

Study on bayes discriminant analysis of EEG data.

PubMed

Shi, Yuan; He, DanDan; Qin, Fang

2014-01-01

In this paper, we have done Bayes Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes Discriminant analysis to EEG data of six objects. Results In use of part of EEG data of 63 people, we have done Bayes Discriminant analysis, the electrode classification accuracy rates is 64.4%. Bayes Discriminant has higher prediction accuracy, EEG features (mainly αwave) extract more accurate. Bayes Discriminant would be better applied to the feature extraction and classification decisions of EEG data.

A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance

PubMed Central

2013-01-01

Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions. PMID:24302928

A neuro-fuzzy approach in the classification of students' academic performance.

PubMed

Do, Quang Hung; Chen, Jeng-Fung

2013-01-01

Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.

Diagnosis of combined faults in Rotary Machinery by Non-Naive Bayesian approach

NASA Astrophysics Data System (ADS)

Asr, Mahsa Yazdanian; Ettefagh, Mir Mohammad; Hassannejad, Reza; Razavi, Seyed Naser

2017-02-01

When combined faults happen in different parts of the rotating machines, their features are profoundly dependent. Experts are completely familiar with individuals faults characteristics and enough data are available from single faults but the problem arises, when the faults combined and the separation of characteristics becomes complex. Therefore, the experts cannot declare exact information about the symptoms of combined fault and its quality. In this paper to overcome this drawback, a novel method is proposed. The core idea of the method is about declaring combined fault without using combined fault features as training data set and just individual fault features are applied in training step. For this purpose, after data acquisition and resampling the obtained vibration signals, Empirical Mode Decomposition (EMD) is utilized to decompose multi component signals to Intrinsic Mode Functions (IMFs). With the use of correlation coefficient, proper IMFs for feature extraction are selected. In feature extraction step, Shannon energy entropy of IMFs was extracted as well as statistical features. It is obvious that most of extracted features are strongly dependent. To consider this matter, Non-Naive Bayesian Classifier (NNBC) is appointed, which release the fundamental assumption of Naive Bayesian, i.e., the independence among features. To demonstrate the superiority of NNBC, other counterpart methods, include Normal Naive Bayesian classifier, Kernel Naive Bayesian classifier and Back Propagation Neural Networks were applied and the classification results are compared. An experimental vibration signals, collected from automobile gearbox, were used to verify the effectiveness of the proposed method. During the classification process, only the features, related individually to healthy state, bearing failure and gear failures, were assigned for training the classifier. But, combined fault features (combined gear and bearing failures) were examined as test data. The achieved

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Uzbaş, Betül; Arslan, Ahmet

2018-04-01

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

Movement imagery classification in EMOTIV cap based system by Naïve Bayes.

PubMed

Stock, Vinicius N; Balbinot, Alexandre

2016-08-01

Brain-computer interfaces (BCI) provide means of communications and control, in assistive technology, which do not require motor activity from the user. The goal of this study is to promote classification of two types of imaginary movements, left and right hands, in an EMOTIV cap based system, using the Naïve Bayes classifier. A preliminary analysis with respect to results obtained by other experiments in this field is also conducted. Processing of the electroencephalography (EEG) signals is done applying Common Spatial Pattern filters. The EPOC electrodes cap is used for EEG acquisition, in two test subjects, for two distinct trial formats. The channels picked are FC5, FC6, P7 and P8 of the 10-20 system, and a discussion about the differences of using C3, C4, P3 and P4 positions is proposed. Dataset 3 of the BCI Competition II is also analyzed using the implemented algorithms. The maximum classification results for the proposed experiment and for the BCI Competition dataset were, respectively, 79% and 85% The conclusion of this study is that the picked positions for electrodes may be applied for BCI systems with satisfactory classification rates.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

Habitat Mapping and Classification of the Grand Bay National Estuarine Research Reserve using AISA Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Rose, K.

2012-12-01

Habitat mapping and classification provides essential information for land use planning and ecosystem research, monitoring and management. At the Grand Bay National Estuarine Research Reserve (GRDNERR), Mississippi, habitat characterization of the Grand Bay watershed will also be used to develop a decision-support tool for the NERR's managers and state and local partners. Grand Bay NERR habitat units were identified using a combination of remotely sensed imagery, aerial photography and elevation data. Airborne Imaging Spectrometer for Applications (AISA) hyperspectral data, acquired 5 and 6 May 2010, was analyzed and classified using ENVI v4.8 and v5.0 software. The AISA system was configured to return 63 bands of digital imagery data with a spectral range of 400 to 970 nm (VNIR), spectral resolution (bandwidth) at 8.76 nm, and 1 m spatial resolution. Minimum Noise Fraction (MNF) and Inverse Minimum Noise Fraction were applied to the data prior to using Spectral Angle Mapper ([SAM] supervised) and ISODATA (unsupervised) classification techniques. The resulting class image was exported to ArcGIS 10.0 and visually inspected and compared with the original imagery as well as auxiliary datasets to assist in the attribution of habitat characteristics to the spectral classes, including: National Agricultural Imagery Program (NAIP) aerial photography, Jackson County, MS, 2010; USFWS National Wetlands Inventory, 2007; an existing GRDNERR habitat map (2004), SAV (2009) and salt panne (2002-2003) GIS produced by GRDNERR; and USACE lidar topo-bathymetry, 2005. A field survey to validate the map's accuracy will take place during the 2012 summer season. ENVI's Random Sample generator was used to generate GIS points for a ground-truth survey. The broad range of coastal estuarine habitats and geomorphological features- many of which are transitional and vulnerable to environmental stressors- that have been identified within the GRDNERR point to the value of the Reserve for

Comparison of Hyperspectral and Multispectral Satellites for Forest Alliance Classification in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

Clark, M. L.

2016-12-01

The goal of this study was to assess multi-temporal, Hyperspectral Infrared Imager (HyspIRI) satellite imagery for improved forest class mapping relative to multispectral satellites. The study area was the western San Francisco Bay Area, California and forest alliances (e.g., forest communities defined by dominant or co-dominant trees) were defined using the U.S. National Vegetation Classification System. Simulated 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery were processed from image data acquired by NASA's AVIRIS airborne sensor in year 2015, with summer and multi-temporal (spring, summer, fall) data analyzed separately. HyspIRI reflectance was used to generate a suite of hyperspectral metrics that targeted key spectral features related to chemical and structural properties. The Random Forests classifier was applied to the simulated images and overall accuracies (OA) were compared to those from real Landsat 8 images. For each image group, broad land cover (e.g., Needle-leaf Trees, Broad-leaf Trees, Annual agriculture, Herbaceous, Built-up) was classified first, followed by a finer-detail forest alliance classification for pixels mapped as closed-canopy forest. There were 5 needle-leaf tree alliances and 16 broad-leaf tree alliances, including 7 Quercus (oak) alliance types. No forest alliance classification exceeded 50% OA, indicating that there was broad spectral similarity among alliances, most of which were not spectrally pure but rather a mix of tree species. In general, needle-leaf (Pine, Redwood, Douglas Fir) alliances had better class accuracies than broad-leaf alliances (Oaks, Madrone, Bay Laurel, Buckeye, etc). Multi-temporal data classifications all had 5-6% greater OA than with comparable summer data. For simulated data, HyspIRI metrics had 4-5% greater OA than Landsat 8 and Sentinel-2 multispectral imagery and 3-4% greater OA than HyspIRI reflectance. Finally, HyspIRI metrics had 8% greater OA than real Landsat 8 imagery. In conclusion, forest

Minimum Bayes risk image correlation

NASA Technical Reports Server (NTRS)

Minter, T. C., Jr.

1980-01-01

In this paper, the problem of designing a matched filter for image correlation will be treated as a statistical pattern recognition problem. It is shown that, by minimizing a suitable criterion, a matched filter can be estimated which approximates the optimum Bayes discriminant function in a least-squares sense. It is well known that the use of the Bayes discriminant function in target classification minimizes the Bayes risk, which in turn directly minimizes the probability of a false fix. A fast Fourier implementation of the minimum Bayes risk correlation procedure is described.

Fuzzy Naive Bayesian model for medical diagnostic decision support.

PubMed

Wagholikar, Kavishwar B; Vijayraghavan, Sundararajan; Deshpande, Ashok W

2009-01-01

This work relates to the development of computational algorithms to provide decision support to physicians. The authors propose a Fuzzy Naive Bayesian (FNB) model for medical diagnosis, which extends the Fuzzy Bayesian approach proposed by Okuda. A physician's interview based method is described to define a orthogonal fuzzy symptom information system, required to apply the model. For the purpose of elaboration and elicitation of characteristics, the algorithm is applied to a simple simulated dataset, and compared with conventional Naive Bayes (NB) approach. As a preliminary evaluation of FNB in real world scenario, the comparison is repeated on a real fuzzy dataset of 81 patients diagnosed with infectious diseases. The case study on simulated dataset elucidates that FNB can be optimal over NB for diagnosing patients with imprecise-fuzzy information, on account of the following characteristics - 1) it can model the information that, values of some attributes are semantically closer than values of other attributes, and 2) it offers a mechanism to temper exaggerations in patient information. Although the algorithm requires precise training data, its utility for fuzzy training data is argued for. This is supported by the case study on infectious disease dataset, which indicates optimality of FNB over NB for the infectious disease domain. Further case studies on large datasets are required to establish utility of FNB.

[Naïve Bayes classification for classifying injury-cause groups from Emergency Room data in the Friuli Venezia Giulia region (Northern Italy)].

PubMed

Valent, Francesca; Clagnan, Elena; Zanier, Loris

2014-01-01

to assess whether Naïve Bayes Classification could be used to classify injury causes from the Emergency Room (ER) database, because in the Friuli Venezia Giulia Region (Northern Italy) the electronic ER data have never been used to study the epidemiology of injuries, because the proportion of generic "accidental" causes is much higher than that of injuries with a specific cause. application of the Naïve Bayes Classification method to the regional ER database. sensitivity, specificity, positive and negative predictive values, agreement, and the kappa statistic were calculated for the train dataset and the distribution of causes of injury for the test dataset. on 22.248 records with known cause, the classifications assigned by the model agreed moderately (kappa =0.53) with those assigned by ER personnel. The model was then used on 76.660 unclassified cases. Although sensitivity and positive predictive value of the method were generally poor, mainly due to limitations in the ER data, it allowed to estimate for the first time the frequency of specific injury causes in the Region. the model was useful to provide the "big picture" of non-fatal injuries in the Region. To improve the collection of injury data at the ER, the options available for injury classification in the ER software are being revised to make categories exhaustive and mutually exclusive.

Use of the Coastal and Marine Ecological Classification Standard (CMECS) for Geological Studies in Glacier Bay, Alaska

NASA Astrophysics Data System (ADS)

Cochrane, G. R.; Hodson, T. O.; Allee, R.; Cicchetti, G.; Finkbeiner, M.; Goodin, K.; Handley, L.; Madden, C.; Mayer, G.; Shumchenia, E.

2012-12-01

The U S Geological Survey (USGS) is one of four primary organizations (along with the National Oceanographic and Atmospheric Administration, the Evironmental Protection Agency, and NatureServe) responsible for the development of the Coastal and Marine Ecological Classification Standard (CMECS) over the past decade. In June 2012 the Federal Geographic Data Committee approved CMECS as the first-ever comprehensive federal standard for classifying and describing coastal and marine ecosystems. The USGS has pioneered the application of CMECS in Glacier Bay, Alaska as part of its Seafloor Mapping and Benthic Habitat Studies Project. This presentation briefly describes the standard and its application as part of geological survey studies in the Western Arm of Glacier Bay. CMECS offers a simple, standard framework and common terminology for describing natural and human influenced ecosystems from the upper tidal reaches of estuaries to the deepest portions of the ocean. The framework is organized into two settings, biogeographic and aquatic, and four components, water column, geoform, substrate, and biotic. Each describes a separate aspect of the environment and biota. Settings and components can be used in combination or independently to describe ecosystem features. The hierarchical arrangement of units of the settings and components allows users to apply CMECS to the scale and specificity that best suits their needs. Modifiers allow users to customize the classification to meet specific needs. Biotopes can be described when there is a need for more detailed information on the biota and their environment. USGS efforts focused primarily on the substrate and geoform components. Previous research has demonstrated three classes of bottom type that can be derived from multibeam data that in part determine the distribution of benthic organisms: soft, flat bottom, mixed bottom including coarse sediment and low-relief rock with low to moderate rugosity, and rugose, hard bottom. The

The Analysis Performance Method Naive Bayes Andssvm Determine Pattern Groups of Disease

NASA Astrophysics Data System (ADS)

Sitanggang, Rianto; Tulus; Situmorang, Zakarias

2017-12-01

Information is a very important element and into the daily needs of the moment, to get a precise and accurate information is not easy, this research can help decision makers and make a comparison. Researchers perform data mining techniques to analyze the performance of methods and algorithms naïve Bayes methods Smooth Support Vector Machine (ssvm) in the grouping of the disease.The pattern of disease that is often suffered by people in the group can be in the detection area of the collection of information contained in the medical record. Medical records have infromasi disease by patients in coded according to standard WHO. Processing of medical record data to find patterns of this group of diseases that often occur in this community take the attribute address, sex, type of disease, and age. Determining the next analysis is grouping of four ersebut attribute. From the results of research conducted on the dataset fever diabete mellitus, naïve Bayes method produces an average value of 99% and an accuracy and SSVM method produces an average value of 93% accuracy

Geological sampling data and benthic biota classification: Buzzards Bay and Vineyard Sound, Massachusetts

USGS Publications Warehouse

Ackerman, Seth D.; Pappal, Adrienne L.; Huntley, Emily C.; Blackwood, Dann S.; Schwab, William C.

2015-01-01

Sea-floor sample collection is an important component of a statewide cooperative mapping effort between the U.S. Geological Survey (USGS) and the Massachusetts Office of Coastal Zone Management (CZM). Sediment grab samples, bottom photographs, and video transects were collected within Vineyard Sound and Buzzards Bay in 2010 aboard the research vesselConnecticut. This report contains sample data and related information, including analyses of surficial-sediment grab samples, locations and images of sea-floor photography, survey lines along which sea-floor video was collected, and a classification of benthic biota observed in sea-floor photographs and based on the Coastal and Marine Ecological Classification Standard (CMECS). These sample data and analyses information are used to verify interpretations of geophysical data and are an essential part of geologic maps of the sea floor. These data also provide a valuable inventory of benthic habitat and resources. Geographic information system (GIS) data, maps, and interpretations, produced through the USGS and CZM mapping cooperative, are intended to aid efforts to manage coastal and marine resources and to provide baseline information for research focused on coastal evolution and environmental change.

A SVM-based method for sentiment analysis in Persian language

NASA Astrophysics Data System (ADS)

Hajmohammadi, Mohammad Sadegh; Ibrahim, Roliana

2013-03-01

Persian language is the official language of Iran, Tajikistan and Afghanistan. Local online users often represent their opinions and experiences on the web with written Persian. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product. In this paper, standard machine learning techniques SVM and naive Bayes are incorporated into the domain of online Persian Movie reviews to automatically classify user reviews as positive or negative and performance of these two classifiers is compared with each other in this language. The effects of feature presentations on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The SVM classifier achieves as well as or better accuracy than naive Bayes in Persian movie. Unigrams are proved better features than bigrams and trigrams in capturing Persian sentiment orientation.

Classification of older adults with/without a fall history using machine learning methods.

PubMed

Lin Zhang; Ou Ma; Fabre, Jennifer M; Wood, Robert H; Garcia, Stephanie U; Ivey, Kayla M; McCann, Evan D

2015-01-01

Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.

Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.

PubMed

Das, D K; Maiti, A K; Chakraborty, C

2015-03-01

In this paper, we propose a comprehensive image characterization cum classification framework for malaria-infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three-image segmentation algorithms (namely, rule-based, Chan-Vese-based and marker-controlled watershed methods), marker-controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F-statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features' subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria-infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F-statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria-infected stage classification. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

PubMed

Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification

PubMed Central

Feng, Yang; Jiang, Jiancheng; Tong, Xin

2015-01-01

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing. PMID:27185970

Seafloor habitat mapping and classification in Glacier Bay, Alaska: Phase 1 & 2 1996-2004

USGS Publications Warehouse

Hooge, Philip N.; Carlson, Paul R.; Mondragon, Jennifer; Etherington, Lisa L.; Cochran, G.R.

2004-01-01

importance in the Park. Ocean floor bathymetry and sediment type are the building blocks of marine communities. Bottom type and shape affects the kinds of benthic communities that develop in a particular environment as well as the oceanographic conditions that communities are subject to. Accurate mapping of the ocean floor is essential for park manager’s understanding of existing marine communities and will be important in assessing human induced changes (e.g., vessel traffic and commercial fishing), biological change (e.g., rapid sea otter recolonization), and geological processes of change (e.g., deglaciation). Information on animal-habitat relationships, particularly within a marine reserve framework, will be valuable to agencies making decisions about critical habitats, marine reserve design, as well as fishery management. Identification and mapping of benthic habitat provides National Park Service mangers with tools to increase the effectiveness of resource management. The primary objective of this project is to investigate the geological characteristics of the biological habitats of halibut, Dungeness crab, king crab, and Tanner crab within Glacier Bay National Park. Additionally, habitat classification of shallow water regions of Glacier Bay will provide crucial information on the relationship between benthic habitat features and the abundance of benthic prey items for a variety of marine predators, including sea ducks, the rapidly increasing population of sea otters, and other marine mammals. 

Comparison of MSS and TM Data for Landcover Classification in the Chesapeake Bay Area: a Preliminary Report. [Taylor's Island, Maryland

NASA Technical Reports Server (NTRS)

Mulligan, P. J.; Gervin, J. C.; Lu, Y. C.

1985-01-01

An area bordering the Eastern Shore of the Chesapeake Bay was selected for study and classified using unsupervised techniques applied to LANDSAT-2 MSS data and several band combinations of LANDSAT-4 TM data. The accuracies of these Level I land cover classifications were verified using the Taylor's Island USGS 7.5 minute topographic map which was photointerpreted, digitized and rasterized. The the Taylor's Island map, comparing the MSS and TM three band (2 3 4) classifications, the increased resolution of TM produced a small improvement in overall accuracy of 1% correct due primarily to a small improvement, and 1% and 3%, in areas such as water and woodland. This was expected as the MSS data typically produce high accuracies for categories which cover large contiguous areas. However, in the categories covering smaller areas within the map there was generally an improvement of at least 10%. Classification of the important residential category improved 12%, and wetlands were mapped with 11% greater accuracy.

Short-lived brain state after cued motor imagery in naive subjects.

PubMed

Pfurtscheller, G; Scherer, R; Müller-Putz, G R; Lopes da Silva, F H

2008-10-01

Multi-channel electroencephalography recordings have shown that a visual cue, indicating right hand, left hand or foot motor imagery, can induce a short-lived brain state in the order of about 500 ms. In the present study, 10 able-bodied subjects without any motor imagery experience (naive subjects) were asked to imagine the indicated limb movement for some seconds. Common spatial filtering and linear single-trial classification was applied to discriminate between two conditions (two brain states: right hand vs. left hand, left hand vs. foot and right hand vs. foot). The corresponding classification accuracies (mean +/- SD) were 80.0 +/- 10.6%, 83.3 +/- 10.2% and 83.6 +/- 8.8%, respectively. Inspection of central mu and beta rhythms revealed a short-lasting somatotopically specific event-related desynchronization (ERD) in the upper mu and/or beta bands starting approximately 300 ms after the cue onset and lasting for less than 1 s.

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

PubMed

Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

2016-11-01

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

Detection of Cardiovascular Disease Risk's Level for Adults Using Naive Bayes Classifier.

PubMed

Miranda, Eka; Irwansyah, Edy; Amelga, Alowisius Y; Maribondang, Marco M; Salim, Mulyadi

2016-07-01

The number of deaths caused by cardiovascular disease and stroke is predicted to reach 23.3 million in 2030. As a contribution to support prevention of this phenomenon, this paper proposes a mining model using a naïve Bayes classifier that could detect cardiovascular disease and identify its risk level for adults. The process of designing the method began by identifying the knowledge related to the cardiovascular disease profile and the level of cardiovascular disease risk factors for adults based on the medical record, and designing a mining technique model using a naïve Bayes classifier. Evaluation of this research employed two methods: accuracy, sensitivity, and specificity calculation as well as an evaluation session with cardiologists and internists. The characteristics of cardiovascular disease are identified by its primary risk factors. Those factors are diabetes mellitus, the level of lipids in the blood, coronary artery function, and kidney function. Class labels were assigned according to the values of these factors: risk level 1, risk level 2 and risk level 3. The evaluation of the classifier performance (accuracy, sensitivity, and specificity) in this research showed that the proposed model predicted the class label of tuples correctly (above 80%). More than eighty percent of respondents (including cardiologists and internists) who participated in the evaluation session agree till strongly agreed that this research followed medical procedures and that the result can support medical analysis related to cardiovascular disease. The research showed that the proposed model achieves good performance for risk level detection of cardiovascular disease.

Bayes Error Rate Estimation Using Classifier Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep

2003-01-01

The Bayes error rate gives a statistical lower bound on the error achievable for a given classification problem and the associated choice of features. By reliably estimating th is rate, one can assess the usefulness of the feature set that is being used for classification. Moreover, by comparing the accuracy achieved by a given classifier with the Bayes rate, one can quantify how effective that classifier is. Classical approaches for estimating or finding bounds for the Bayes error, in general, yield rather weak results for small sample sizes; unless the problem has some simple characteristics, such as Gaussian class-conditional likelihoods. This article shows how the outputs of a classifier ensemble can be used to provide reliable and easily obtainable estimates of the Bayes error with negligible extra computation. Three methods of varying sophistication are described. First, we present a framework that estimates the Bayes error when multiple classifiers, each providing an estimate of the a posteriori class probabilities, a recombined through averaging. Second, we bolster this approach by adding an information theoretic measure of output correlation to the estimate. Finally, we discuss a more general method that just looks at the class labels indicated by ensem ble members and provides error estimates based on the disagreements among classifiers. The methods are illustrated for artificial data, a difficult four-class problem involving underwater acoustic data, and two problems from the Problem benchmarks. For data sets with known Bayes error, the combiner-based methods introduced in this article outperform existing methods. The estimates obtained by the proposed methods also seem quite reliable for the real-life data sets for which the true Bayes rates are unknown.

Bayes-LQAS: classifying the prevalence of global acute malnutrition

PubMed Central

2010-01-01

Lot Quality Assurance Sampling (LQAS) applications in health have generally relied on frequentist interpretations for statistical validity. Yet health professionals often seek statements about the probability distribution of unknown parameters to answer questions of interest. The frequentist paradigm does not pretend to yield such information, although a Bayesian formulation might. This is the source of an error made in a recent paper published in this journal. Many applications lend themselves to a Bayesian treatment, and would benefit from such considerations in their design. We discuss Bayes-LQAS (B-LQAS), which allows for incorporation of prior information into the LQAS classification procedure, and thus shows how to correct the aforementioned error. Further, we pay special attention to the formulation of Bayes Operating Characteristic Curves and the use of prior information to improve survey designs. As a motivating example, we discuss the classification of Global Acute Malnutrition prevalence and draw parallels between the Bayes and classical classifications schemes. We also illustrate the impact of informative and non-informative priors on the survey design. Results indicate that using a Bayesian approach allows the incorporation of expert information and/or historical data and is thus potentially a valuable tool for making accurate and precise classifications. PMID:20534159

Bayes-LQAS: classifying the prevalence of global acute malnutrition.

PubMed

Olives, Casey; Pagano, Marcello

2010-06-09

Lot Quality Assurance Sampling (LQAS) applications in health have generally relied on frequentist interpretations for statistical validity. Yet health professionals often seek statements about the probability distribution of unknown parameters to answer questions of interest. The frequentist paradigm does not pretend to yield such information, although a Bayesian formulation might. This is the source of an error made in a recent paper published in this journal. Many applications lend themselves to a Bayesian treatment, and would benefit from such considerations in their design. We discuss Bayes-LQAS (B-LQAS), which allows for incorporation of prior information into the LQAS classification procedure, and thus shows how to correct the aforementioned error. Further, we pay special attention to the formulation of Bayes Operating Characteristic Curves and the use of prior information to improve survey designs. As a motivating example, we discuss the classification of Global Acute Malnutrition prevalence and draw parallels between the Bayes and classical classifications schemes. We also illustrate the impact of informative and non-informative priors on the survey design. Results indicate that using a Bayesian approach allows the incorporation of expert information and/or historical data and is thus potentially a valuable tool for making accurate and precise classifications.

[Study on the classification of dominant pathogens related to febrile respiratory syndrome, based on the method of Bayes discriminant analysis].

PubMed

Li, X C; Li, J S; Meng, L; Bai, Y N; Yu, D S; Liu, X N; Liu, X F; Jiang, X J; Ren, X W; Yang, X T; Shen, X P; Zhang, J W

2017-08-10

Objective: To understand the dominant pathogens of febrile respiratory syndrome (FRS) patients in Gansu province and to establish the Bayes discriminant function in order to identify the patients infected with the dominant pathogens. Methods: FRS patients were collected in various sentinel hospitals of Gansu province from 2009 to 2015 and the dominant pathogens were determined by describing the composition of pathogenic profile. Significant clinical variables were selected by stepwise discriminant analysis to establish the Bayes discriminant function. Results: In the detection of pathogens for FRS, both influenza virus and rhinovirus showed higher positive rates than those caused by other viruses (13.79%, 8.63%), that accounting for 54.38%, 13.73% of total viral positive patients. Most frequently detected bacteria would include Streptococcus pneumoniae , and haemophilus influenza (44.41%, 18.07%) that accounting for 66.21% and 24.55% among the bacterial positive patients. The original-validated rate of discriminant function, established by 11 clinical variables, was 73.1%, with the cross-validated rate as 70.6%. Conclusion: Influenza virus, Rhinovirus, Streptococcus pneumoniae and Haemophilus influenzae were the dominant pathogens of FRS in Gansu province. Results from the Bayes discriminant analysis showed both higher accuracy in the classification of dominant pathogens, and applicative value for FRS.

Evaluation of Interruption Behavior by Naive Encoders.

ERIC Educational Resources Information Center

Coon, Christine A.; Schwanenflugel, Paula J.

1996-01-01

Determines the characteristics of interactions that influence judgments of interruption behavior in naive observers. Asks subjects to decide whether an example of an interruption was an interruption and then rate it in terms of how "good" or "bad" it was. Finds that naive observers use some of the same features described in…

Risk of Erectile Dysfunction in Transfusion-naive Thalassemia Men

PubMed Central

Chen, Yu-Guang; Lin, Te-Yu; Lin, Cheng-Li; Dai, Ming-Shen; Ho, Ching-Liang; Kao, Chia-Hung

2015-01-01

Abstract Based on the mechanism of pathophysiology, thalassemia major or transfusion-dependent thalassemia patients may have an increased risk of developing organic erectile dysfunction resulting from hypogonadism. However, there have been few studies investigating the association between erectile dysfunction and transfusion-naive thalassemia populations. We constructed a population-based cohort study to elucidate the association between transfusion-naive thalassemia populations and organic erectile dysfunction This nationwide population-based cohort study involved analyzing data from 1998 to 2010 obtained from the Taiwanese National Health Insurance Research Database, with a follow-up period extending to the end of 2011. We identified men with transfusion-naive thalassemia and selected a comparison cohort that was frequency-matched with these according to age, and year of diagnosis thalassemia at a ratio of 1 thalassemia man to 4 control men. We analyzed the risks for transfusion-naive thalassemia men and organic erectile dysfunction by using Cox proportional hazards regression models. In this study, 588 transfusion-naive thalassemia men and 2337 controls were included. Total 12 patients were identified within the thalassaemia group and 10 within the control group. The overall risks for developing organic erectile dysfunction were 4.56-fold in patients with transfusion-naive thalassemia men compared with the comparison cohort after we adjusted for age and comorbidities. Our long-term cohort study results showed that in transfusion-naive thalassemia men, there was a higher risk for the development of organic erectile dysfunction, particularly in those patients with comorbidities. PMID:25837766

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features

NASA Astrophysics Data System (ADS)

Ma, Wenjuan; Ji, Yu; Qin, Zhuanping; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang

2018-02-01

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

Classification of earth terrain using polarimetric synthetic aperture radar images

NASA Technical Reports Server (NTRS)

Lim, H. H.; Swartz, A. A.; Yueh, H. A.; Kong, J. A.; Shin, R. T.; Van Zyl, J. J.

1989-01-01

Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.

Human Naive T Cells Express Functional CXCL8 and Promote Tumorigenesis.

PubMed

Crespo, Joel; Wu, Ke; Li, Wei; Kryczek, Ilona; Maj, Tomasz; Vatan, Linda; Wei, Shuang; Opipari, Anthony W; Zou, Weiping

2018-05-25

Naive T cells are thought to be functionally quiescent. In this study, we studied and compared the phenotype, cytokine profile, and potential function of human naive CD4 + T cells in umbilical cord and peripheral blood. We found that naive CD4 + T cells, but not memory T cells, expressed high levels of chemokine CXCL8. CXCL8 + naive T cells were preferentially enriched CD31 + T cells and did not express T cell activation markers or typical Th effector cytokines, including IFN-γ, IL-4, IL-17, and IL-22. In addition, upon activation, naive T cells retained high levels of CXCL8 expression. Furthermore, we showed that naive T cell-derived CXCL8 mediated neutrophil migration in the in vitro migration assay, supported tumor sphere formation, and promoted tumor growth in an in vivo human xenograft model. Thus, human naive T cells are phenotypically and functionally heterogeneous and can carry out active functions in immune responses. Copyright © 2018 by The American Association of Immunologists, Inc.

Wood identification of Dalbergia nigra (CITES Appendix I) using quantitative wood anatomy, principal components analysis and naïve Bayes classification

PubMed Central

Gasson, Peter; Miller, Regis; Stekel, Dov J.; Whinder, Frances; Ziemińska, Kasia

2010-01-01

Background and Aims Dalbergia nigra is one of the most valuable timber species of its genus, having been traded for over 300 years. Due to over-exploitation it is facing extinction and trade has been banned under CITES Appendix I since 1992. Current methods, primarily comparative wood anatomy, are inadequate for conclusive species identification. This study aims to find a set of anatomical characters that distinguish the wood of D. nigra from other commercially important species of Dalbergia from Latin America. Methods Qualitative and quantitative wood anatomy, principal components analysis and naïve Bayes classification were conducted on 43 specimens of Dalbergia, eight D. nigra and 35 from six other Latin American species. Key Results Dalbergia cearensis and D. miscolobium can be distinguished from D. nigra on the basis of vessel frequency for the former, and ray frequency for the latter. Principal components analysis was unable to provide any further basis for separating the species. Naïve Bayes classification using the four characters: minimum vessel diameter; frequency of solitary vessels; mean ray width; and frequency of axially fused rays, classified all eight D. nigra correctly with no false negatives, but there was a false positive rate of 36·36 %. Conclusions Wood anatomy alone cannot distinguish D. nigra from all other commercially important Dalbergia species likely to be encountered by customs officials, but can be used to reduce the number of specimens that would need further study. PMID:19884155

Mapping South San Francisco Bay's seabed diversity for use in wetland restoration planning

USGS Publications Warehouse

Fregoso, Theresa A.; Jaffe, B.; Rathwell, G.; Collins, W.; Rhynas, K.; Tomlin, V.; Sullivan, S.

2006-01-01

Data for an acoustic seabed classification were collected as a part of a California Coastal Conservancy funded bathymetric survey of South Bay in early 2005.  A QTC VIEW seabed classification system recorded echoes from a sungle bean 50 kHz echosounder.  Approximately 450,000 seabed classification records were generated from an are of of about 30 sq. miles.  Ten district acoustic classes were identified through an unsupervised classification system using principle component and cluster analyses.  One hundred and sixty-one grab samples and forty-five benthic community composition data samples collected in the study area shortly before and after the seabed classification survey, further refined the ten classes into groups based on grain size.  A preliminary map of surficial grain size of South Bay was developed from the combination of the seabed classification and the grab and benthic samples.  The initial seabed classification map, the grain size map, and locations of sediment samples will be displayed along with the methods of acousitc seabed classification.

Classification of postural profiles among mouth-breathing children by learning vector quantization.

PubMed

Mancini, F; Sousa, F S; Hummel, A D; Falcão, A E J; Yi, L C; Ortolani, C F; Sigulem, D; Pisa, I T

2011-01-01

Mouth breathing is a chronic syndrome that may bring about postural changes. Finding characteristic patterns of changes occurring in the complex musculoskeletal system of mouth-breathing children has been a challenge. Learning vector quantization (LVQ) is an artificial neural network model that can be applied for this purpose. The aim of the present study was to apply LVQ to determine the characteristic postural profiles shown by mouth-breathing children, in order to further understand abnormal posture among mouth breathers. Postural training data on 52 children (30 mouth breathers and 22 nose breathers) and postural validation data on 32 children (22 mouth breathers and 10 nose breathers) were used. The performance of LVQ and other classification models was compared in relation to self-organizing maps, back-propagation applied to multilayer perceptrons, Bayesian networks, naive Bayes, J48 decision trees, k, and k-nearest-neighbor classifiers. Classifier accuracy was assessed by means of leave-one-out cross-validation, area under ROC curve (AUC), and inter-rater agreement (Kappa statistics). By using the LVQ model, five postural profiles for mouth-breathing children could be determined. LVQ showed satisfactory results for mouth-breathing and nose-breathing classification: sensitivity and specificity rates of 0.90 and 0.95, respectively, when using the training dataset, and 0.95 and 0.90, respectively, when using the validation dataset. The five postural profiles for mouth-breathing children suggested by LVQ were incorporated into application software for classifying the severity of mouth breathers' abnormal posture.

Predicting the need for CT imaging in children with minor head injury using an ensemble of Naive Bayes classifiers.

PubMed

Klement, William; Wilk, Szymon; Michalowski, Wojtek; Farion, Ken J; Osmond, Martin H; Verter, Vedat

2012-03-01

Using an automatic data-driven approach, this paper develops a prediction model that achieves more balanced performance (in terms of sensitivity and specificity) than the Canadian Assessment of Tomography for Childhood Head Injury (CATCH) rule, when predicting the need for computed tomography (CT) imaging of children after a minor head injury. CT is widely considered an effective tool for evaluating patients with minor head trauma who have potentially suffered serious intracranial injury. However, its use poses possible harmful effects, particularly for children, due to exposure to radiation. Safety concerns, along with issues of cost and practice variability, have led to calls for the development of effective methods to decide when CT imaging is needed. Clinical decision rules represent such methods and are normally derived from the analysis of large prospectively collected patient data sets. The CATCH rule was created by a group of Canadian pediatric emergency physicians to support the decision of referring children with minor head injury to CT imaging. The goal of the CATCH rule was to maximize the sensitivity of predictions of potential intracranial lesion while keeping specificity at a reasonable level. After extensive analysis of the CATCH data set, characterized by severe class imbalance, and after a thorough evaluation of several data mining methods, we derived an ensemble of multiple Naive Bayes classifiers as the prediction model for CT imaging decisions. In the first phase of the experiment we compared the proposed ensemble model to other ensemble models employing rule-, tree- and instance-based member classifiers. Our prediction model demonstrated the best performance in terms of AUC, G-mean and sensitivity measures. In the second phase, using a bootstrapping experiment similar to that reported by the CATCH investigators, we showed that the proposed ensemble model achieved a more balanced predictive performance than the CATCH rule with an average

Derivation of novel human ground state naive pluripotent stem cells.

PubMed

Gafni, Ohad; Weinberger, Leehee; Mansour, Abed AlFatah; Manor, Yair S; Chomsky, Elad; Ben-Yosef, Dalit; Kalma, Yael; Viukov, Sergey; Maza, Itay; Zviran, Asaf; Rais, Yoach; Shipony, Zohar; Mukamel, Zohar; Krupalnik, Vladislav; Zerbib, Mirie; Geula, Shay; Caspi, Inbal; Schneir, Dan; Shwartz, Tamar; Gilad, Shlomit; Amann-Zalcenstein, Daniela; Benjamin, Sima; Amit, Ido; Tanay, Amos; Massarwa, Rada; Novershtern, Noa; Hanna, Jacob H

2013-12-12

Mouse embryonic stem (ES) cells are isolated from the inner cell mass of blastocysts, and can be preserved in vitro in a naive inner-cell-mass-like configuration by providing exogenous stimulation with leukaemia inhibitory factor (LIF) and small molecule inhibition of ERK1/ERK2 and GSK3β signalling (termed 2i/LIF conditions). Hallmarks of naive pluripotency include driving Oct4 (also known as Pou5f1) transcription by its distal enhancer, retaining a pre-inactivation X chromosome state, and global reduction in DNA methylation and in H3K27me3 repressive chromatin mark deposition on developmental regulatory gene promoters. Upon withdrawal of 2i/LIF, naive mouse ES cells can drift towards a primed pluripotent state resembling that of the post-implantation epiblast. Although human ES cells share several molecular features with naive mouse ES cells, they also share a variety of epigenetic properties with primed murine epiblast stem cells (EpiSCs). These include predominant use of the proximal enhancer element to maintain OCT4 expression, pronounced tendency for X chromosome inactivation in most female human ES cells, increase in DNA methylation and prominent deposition of H3K27me3 and bivalent domain acquisition on lineage regulatory genes. The feasibility of establishing human ground state naive pluripotency in vitro with equivalent molecular and functional features to those characterized in mouse ES cells remains to be defined. Here we establish defined conditions that facilitate the derivation of genetically unmodified human naive pluripotent stem cells from already established primed human ES cells, from somatic cells through induced pluripotent stem (iPS) cell reprogramming or directly from blastocysts. The novel naive pluripotent cells validated herein retain molecular characteristics and functional properties that are highly similar to mouse naive ES cells, and distinct from conventional primed human pluripotent cells. This includes competence in the generation

Comparative study of classification algorithms for damage classification in smart composite laminates

NASA Astrophysics Data System (ADS)

Khan, Asif; Ryoo, Chang-Kyung; Kim, Heung Soo

2017-04-01

This paper presents a comparative study of different classification algorithms for the classification of various types of inter-ply delaminations in smart composite laminates. Improved layerwise theory is used to model delamination at different interfaces along the thickness and longitudinal directions of the smart composite laminate. The input-output data obtained through surface bonded piezoelectric sensor and actuator is analyzed by the system identification algorithm to get the system parameters. The identified parameters for the healthy and delaminated structure are supplied as input data to the classification algorithms. The classification algorithms considered in this study are ZeroR, Classification via regression, Naïve Bayes, Multilayer Perceptron, Sequential Minimal Optimization, Multiclass-Classifier, and Decision tree (J48). The open source software of Waikato Environment for Knowledge Analysis (WEKA) is used to evaluate the classification performance of the classifiers mentioned above via 75-25 holdout and leave-one-sample-out cross-validation regarding classification accuracy, precision, recall, kappa statistic and ROC Area.

The Preference for Symmetry in Flower-Naive and Not-so-Naive Bumblebees

ERIC Educational Resources Information Center

Plowright, C. M. S.; Evans, S. A.; Leung, J. Chew; Collin, C. A.

2011-01-01

Truly flower-naive bumblebees, with no prior rewarded experience for visits on any visual patterns outside the colony, were tested for their choice of bilaterally symmetric over asymmetric patterns in a radial-arm maze. No preference for symmetry was found. Prior training with rewarded black and white disks did, however, lead to a significant…

Privacy-Preserving Evaluation of Generalization Error and Its Application to Model and Attribute Selection

NASA Astrophysics Data System (ADS)

Sakuma, Jun; Wright, Rebecca N.

Privacy-preserving classification is the task of learning or training a classifier on the union of privately distributed datasets without sharing the datasets. The emphasis of existing studies in privacy-preserving classification has primarily been put on the design of privacy-preserving versions of particular data mining algorithms, However, in classification problems, preprocessing and postprocessing— such as model selection or attribute selection—play a prominent role in achieving higher classification accuracy. In this paper, we show generalization error of classifiers in privacy-preserving classification can be securely evaluated without sharing prediction results. Our main technical contribution is a new generalized Hamming distance protocol that is universally applicable to preprocessing and postprocessing of various privacy-preserving classification problems, such as model selection in support vector machine and attribute selection in naive Bayes classification.

A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions.

PubMed

Gao, Xiang; Lin, Huaiying; Dong, Qunfeng

2017-01-01

Dysbiosis of microbial communities is associated with various human diseases, raising the possibility of using microbial compositions as biomarkers for disease diagnosis. We have developed a Bayes classifier by modeling microbial compositions with Dirichlet-multinomial distributions, which are widely used to model multicategorical count data with extra variation. The parameters of the Dirichlet-multinomial distributions are estimated from training microbiome data sets based on maximum likelihood. The posterior probability of a microbiome sample belonging to a disease or healthy category is calculated based on Bayes' theorem, using the likelihood values computed from the estimated Dirichlet-multinomial distribution, as well as a prior probability estimated from the training microbiome data set or previously published information on disease prevalence. When tested on real-world microbiome data sets, our method, called DMBC (for Dirichlet-multinomial Bayes classifier), shows better classification accuracy than the only existing Bayesian microbiome classifier based on a Dirichlet-multinomial mixture model and the popular random forest method. The advantage of DMBC is its built-in automatic feature selection, capable of identifying a subset of microbial taxa with the best classification accuracy between different classes of samples based on cross-validation. This unique ability enables DMBC to maintain and even improve its accuracy at modeling species-level taxa. The R package for DMBC is freely available at https://github.com/qunfengdong/DMBC. IMPORTANCE By incorporating prior information on disease prevalence, Bayes classifiers have the potential to estimate disease probability better than other common machine-learning methods. Thus, it is important to develop Bayes classifiers specifically tailored for microbiome data. Our method shows higher classification accuracy than the only existing Bayesian classifier and the popular random forest method, and thus provides an

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

PubMed

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

2018-04-01

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

The comprehensive health care orientation process indicators explain hospital organisation's attractiveness: a Bayesian analysis of newly hired nurse and physician survey data.

PubMed

Peltokoski, Jaana; Vehviläinen-Julkunen, Katri; Pitkäaho, Taina; Mikkonen, Santtu; Miettinen, Merja

2015-10-01

To examine the relationship of a comprehensive health care orientation process with a hospital's attractiveness. Little is known about indicators of the employee orientation process that most likely explain a hospital organisation's attractiveness. Empirical data collected from registered nurses (n = 145) and physicians (n = 37) working in two specialised hospital districts. A Naive Bayes Classification was applied to examine the comprehensive orientation process indicators that predict hospital's attractiveness. The model was composed of five orientation process indicators: the contribution of the orientation process to nurses' and physicians' intention to stay; the defined responsibilities of the orientation process; interaction between newcomer and colleagues; responsibilities that are adapted for tasks; and newcomers' baseline knowledge assessment that should be done before the orientation phase. The Naive Bayes Classification was used to explore employee orientation process and related indicators. The model constructed provides insight that can be used in designing and implementing the orientation process to promote the hospital organisation's attractiveness. Managers should focus on developing fluently organised orientation practices based on the indicators that predict the hospital's attractiveness. For the purpose of personalised orientation, employees' baseline knowledge and competence level should be assessed before the orientation phase. © 2014 John Wiley & Sons Ltd.

Hydrologic Landscape Classification to Estimate Bristol Bay Watershed Hydrology

EPA Science Inventory

The use of hydrologic landscapes has proven to be a useful tool for broad scale assessment and classification of landscapes across the United States. These classification systems help organize larger geographical areas into areas of similar hydrologic characteristics based on cl...

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

NASA Astrophysics Data System (ADS)

Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

The nearest neighbor and the bayes error rates.

PubMed

Loizou, G; Maybank, S J

1987-02-01

The (k, l) nearest neighbor method of pattern classification is compared to the Bayes method. If the two acceptance rates are equal then the asymptotic error rates satisfy the inequalities Ek,l + 1 ¿ E*(¿) ¿ Ek,l dE*(¿), where d is a function of k, l, and the number of pattern classes, and ¿ is the reject threshold for the Bayes method. An explicit expression for d is given which is optimal in the sense that for some probability distributions Ek,l and dE* (¿) are equal.

Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Jamaluddin; Siringoringo, Rimbun

2017-12-01

Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%

Do the Naive Know Best? The Predictive Power of Naive Ratings of Couple Interactions

ERIC Educational Resources Information Center

Baucom, Katherine J. W.; Baucom, Brian R.; Christensen, Andrew

2012-01-01

We examined the utility of naive ratings of communication patterns and relationship quality in a large sample of distressed couples. Untrained raters assessed 10-min videotaped interactions from 134 distressed couples who participated in both problem-solving and social support discussions at each of 3 time points (pre-therapy, post-therapy, and…

Blocking the recruitment of naive CD4+ T cells reverses immunosuppression in breast cancer

PubMed Central

Su, Shicheng; Liao, Jianyou; Liu, Jiang; Huang, Di; He, Chonghua; Chen, Fei; Yang, LinBing; Wu, Wei; Chen, Jianing; Lin, Ling; Zeng, Yunjie; Ouyang, Nengtai; Cui, Xiuying; Yao, Herui; Su, Fengxi; Huang, Jian-dong; Lieberman, Judy; Liu, Qiang; Song, Erwei

2017-01-01

The origin of tumor-infiltrating Tregs, critical mediators of tumor immunosuppression, is unclear. Here, we show that tumor-infiltrating naive CD4+ T cells and Tregs in human breast cancer have overlapping TCR repertoires, while hardly overlap with circulating Tregs, suggesting that intratumoral Tregs mainly develop from naive T cells in situ rather than from recruited Tregs. Furthermore, the abundance of naive CD4+ T cells and Tregs is closely correlated, both indicating poor prognosis for breast cancer patients. Naive CD4+ T cells adhere to tumor slices in proportion to the abundance of CCL18-producing macrophages. Moreover, adoptively transferred human naive CD4+ T cells infiltrate human breast cancer orthotopic xenografts in a CCL18-dependent manner. In human breast cancer xenografts in humanized mice, blocking the recruitment of naive CD4+ T cells into tumor by knocking down the expression of PITPNM3, a CCL18 receptor, significantly reduces intratumoral Tregs and inhibits tumor progression. These findings suggest that breast tumor-infiltrating Tregs arise from chemotaxis of circulating naive CD4+ T cells that differentiate into Tregs in situ. Inhibiting naive CD4+ T cell recruitment into tumors by interfering with PITPNM3 recognition of CCL18 may be an attractive strategy for anticancer immunotherapy. PMID:28290464

Naive Juveniles Are More Likely to Become Breeders after Witnessing Predator Mobbing.

PubMed

Griesser, Michael; Suzuki, Toshitaka N

2017-01-01

Responding appropriately during the first predatory attack in life is often critical for survival. In many social species, naive juveniles acquire this skill from conspecifics, but its fitness consequences remain virtually unknown. Here we experimentally demonstrate how naive juvenile Siberian jays (Perisoreus infaustus) derive a long-term fitness benefit from witnessing knowledgeable adults mobbing their principal predator, the goshawk (Accipiter gentilis). Siberian jays live in family groups of two to six individuals that also can include unrelated nonbreeders. Field observations showed that Siberian jays encounter predators only rarely, and, indeed, naive juveniles do not respond to predator models when on their own but do when observing other individuals mobbing them. Predator exposure experiments demonstrated that naive juveniles had a substantially higher first-winter survival after observing knowledgeable group members mobbing a goshawk model, increasing their likelihood of acquiring a breeding position later in life. Previous research showed that naive individuals may learn from others how to respond to predators, care for offspring, or choose mates, generally assuming that social learning has long-term fitness consequences without empirical evidence. Our results demonstrate a long-term fitness benefit of vertical social learning for naive individuals in the wild, emphasizing its evolutionary importance in animals, including humans.

Comparative analysis of tree classification models for detecting fusarium oxysporum f. sp cubense (TR4) based on multi soil sensor parameters

NASA Astrophysics Data System (ADS)

Estuar, Maria Regina Justina; Victorino, John Noel; Coronel, Andrei; Co, Jerelyn; Tiausas, Francis; Señires, Chiara Veronica

2017-09-01

Use of wireless sensor networks and smartphone integration design to monitor environmental parameters surrounding plantations is made possible because of readily available and affordable sensors. Providing low cost monitoring devices would be beneficial, especially to small farm owners, in a developing country like the Philippines, where agriculture covers a significant amount of the labor market. This study discusses the integration of wireless soil sensor devices and smartphones to create an application that will use multidimensional analysis to detect the presence or absence of plant disease. Specifically, soil sensors are designed to collect soil quality parameters in a sink node from which the smartphone collects data from via Bluetooth. Given these, there is a need to develop a classification model on the mobile phone that will report infection status of a soil. Though tree classification is the most appropriate approach for continuous parameter-based datasets, there is a need to determine whether tree models will result to coherent results or not. Soil sensor data that resides on the phone is modeled using several variations of decision tree, namely: decision tree (DT), best-fit (BF) decision tree, functional tree (FT), Naive Bayes (NB) decision tree, J48, J48graft and LAD tree, where decision tree approaches the problem by considering all sensor nodes as one. Results show that there are significant differences among soil sensor parameters indicating that there are variances in scores between the infected and uninfected sites. Furthermore, analysis of variance in accuracy, recall, precision and F1 measure scores from tree classification models homogeneity among NBTree, J48graft and J48 tree classification models.

Chinese Sentence Classification Based on Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

Analysis of calibrated seafloor backscatter for habitat classification methodology and case study of 158 spots in the Bay of Biscay and Celtic Sea

NASA Astrophysics Data System (ADS)

Fezzani, Ridha; Berger, Laurent

2018-06-01

An automated signal-based method was developed in order to analyse the seafloor backscatter data logged by calibrated multibeam echosounder. The processing consists first in the clustering of each survey sub-area into a small number of homogeneous sediment types, based on the backscatter average level at one or several incidence angles. Second, it uses their local average angular response to extract discriminant descriptors, obtained by fitting the field data to the Generic Seafloor Acoustic Backscatter parametric model. Third, the descriptors are used for seafloor type classification. The method was tested on the multi-year data recorded by a calibrated 90-kHz Simrad ME70 multibeam sonar operated in the Bay of Biscay, France and Celtic Sea, Ireland. It was applied for seafloor-type classification into 12 classes, to a dataset of 158 spots surveyed for demersal and benthic fauna study and monitoring. Qualitative analyses and classified clusters using extracted parameters show a good discriminatory potential, indicating the robustness of this approach.

Two separate defects affecting true naive or virtual memory T cell precursors combine to reduce naive T cell responses with aging.

PubMed

Renkema, Kristin R; Li, Gang; Wu, Angela; Smithey, Megan J; Nikolich-Žugich, Janko

2014-01-01

Naive T cell responses are eroded with aging. We and others have recently shown that unimmunized old mice lose ≥ 70% of Ag-specific CD8 T cell precursors and that many of the remaining precursors acquire a virtual (central) memory (VM; CD44(hi)CD62L(hi)) phenotype. In this study, we demonstrate that unimmunized TCR transgenic (TCRTg) mice also undergo massive VM conversion with age, exhibiting rapid effector function upon both TCR and cytokine triggering. Age-related VM conversion in TCRTg mice directly depended on replacement of the original TCRTg specificity by endogenous TCRα rearrangements, indicating that TCR signals must be critical in VM conversion. Importantly, we found that VM conversion had adverse functional effects in both old wild-type and old TCRTg mice; that is, old VM, but not old true naive, T cells exhibited blunted TCR-mediated, but not IL-15-mediated, proliferation. This selective proliferative senescence correlated with increased apoptosis in old VM cells in response to peptide, but decreased apoptosis in response to homeostatic cytokines IL-7 and IL-15. Our results identify TCR as the key factor in differential maintenance and function of Ag-specific precursors in unimmunized mice with aging, and they demonstrate that two separate age-related defects--drastic reduction in true naive T cell precursors and impaired proliferative capacity of their VM cousins--combine to reduce naive T cell responses with aging.

Naive Theories of Social Groups

ERIC Educational Resources Information Center

Rhodes, Marjorie

2012-01-01

Four studies examined children's (ages 3-10, Total N = 235) naive theories of social groups, in particular, their expectations about how group memberships constrain social interactions. After introduction to novel groups of people, preschoolers (ages 3-5) reliably expected agents from one group to harm members of the other group (rather than…

Mapping online transportation service quality and multiclass classification problem solving priorities

NASA Astrophysics Data System (ADS)

Alamsyah, Andry; Rachmadiansyah, Imam

2018-03-01

Online transportation service is known for its accessibility, transparency, and tariff affordability. These points make online transportation have advantages over the existing conventional transportation service. Online transportation service is an example of disruptive technology that change the relationship between customers and companies. In Indonesia, there are high competition among online transportation provider, hence the companies must maintain and monitor their service level. To understand their position, we apply both sentiment analysis and multiclass classification to understand customer opinions. From negative sentiments, we can identify problems and establish problem-solving priorities. As a case study, we use the most popular online transportation provider in Indonesia: Gojek and Grab. Since many customers are actively give compliment and complain about company’s service level on Twitter, therefore we collect 61,721 tweets in Bahasa during one month observations. We apply Naive Bayes and Support Vector Machine methods to see which model perform best for our data. The result reveal Gojek has better service quality with 19.76% positive and 80.23% negative sentiments than Grab with 9.2% positive and 90.8% negative. The Gojek highest problem-solving priority is regarding application problems, while Grab is about unusable promos. The overall result shows general problems of both case study are related to accessibility dimension which indicate lack of capability to provide good digital access to the end users.

Classification of iRBD and Parkinson's disease patients based on eye movements during sleep.

PubMed

Christensen, Julie A E; Koch, Henriette; Frandsen, Rune; Kempfner, Jacob; Arvastson, Lars; Christensen, Soren R; Sorensen, Helge B D; Jennum, Poul

2013-01-01

Patients suffering from the sleep disorder idiopathic rapid-eye-movement sleep behavior disorder (iRBD) have been observed to be in high risk of developing Parkinson's disease (PD). This makes it essential to analyze them in the search for PD biomarkers. This study aims at classifying patients suffering from iRBD or PD based on features reflecting eye movements (EMs) during sleep. A Latent Dirichlet Allocation (LDA) topic model was developed based on features extracted from two electrooculographic (EOG) signals measured as parts in full night polysomnographic (PSG) recordings from ten control subjects. The trained model was tested on ten other control subjects, ten iRBD patients and ten PD patients, obtaining a EM topic mixture diagram for each subject in the test dataset. Three features were extracted from the topic mixture diagrams, reflecting "certainty", "fragmentation" and "stability" in the timely distribution of the EM topics. Using a Naive Bayes (NB) classifier and the features "certainty" and "stability" yielded the best classification result and the subjects were classified with a sensitivity of 95 %, a specificity of 80% and an accuracy of 90 %. This study demonstrates in a data-driven approach, that iRBD and PD patients may exhibit abnorm form and/or timely distribution of EMs during sleep.

Comparing K-mer based methods for improved classification of 16S sequences.

PubMed

Vinje, Hilde; Liland, Kristian Hovde; Almøy, Trygve; Snipen, Lars

2015-07-01

The need for precise and stable taxonomic classification is highly relevant in modern microbiology. Parallel to the explosion in the amount of sequence data accessible, there has also been a shift in focus for classification methods. Previously, alignment-based methods were the most applicable tools. Now, methods based on counting K-mers by sliding windows are the most interesting classification approach with respect to both speed and accuracy. Here, we present a systematic comparison on five different K-mer based classification methods for the 16S rRNA gene. The methods differ from each other both in data usage and modelling strategies. We have based our study on the commonly known and well-used naïve Bayes classifier from the RDP project, and four other methods were implemented and tested on two different data sets, on full-length sequences as well as fragments of typical read-length. The difference in classification error obtained by the methods seemed to be small, but they were stable and for both data sets tested. The Preprocessed nearest-neighbour (PLSNN) method performed best for full-length 16S rRNA sequences, significantly better than the naïve Bayes RDP method. On fragmented sequences the naïve Bayes Multinomial method performed best, significantly better than all other methods. For both data sets explored, and on both full-length and fragmented sequences, all the five methods reached an error-plateau. We conclude that no K-mer based method is universally best for classifying both full-length sequences and fragments (reads). All methods approach an error plateau indicating improved training data is needed to improve classification from here. Classification errors occur most frequent for genera with few sequences present. For improving the taxonomy and testing new classification methods, the need for a better and more universal and robust training data set is crucial.

Document-Level Classification of CT Pulmonary Angiography Reports based on an Extension of the ConText Algorithm

PubMed Central

Chapman, Brian E.; Lee, Sean; Kang, Hyunseok Peter; Chapman, Wendy W.

2011-01-01

In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes’ classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes’ classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes’ classifier using bigrams. PMID:21459155

Remote Sensing Image Classification Applied to the First National Geographical Information Census of China

NASA Astrophysics Data System (ADS)

Yu, Xin; Wen, Zongyong; Zhu, Zhaorong; Xia, Qiang; Shun, Lan

2016-06-01

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

Naive Theory of Biology: The Pre-School Child's Explanation of Death

ERIC Educational Resources Information Center

Vlok, Milandre; de Witt, Marike W.

2012-01-01

This article explains the naive theory of biology that the pre-school child uses to explain the cause of death. The empirical investigation showed that the young participants do use a naive theory of biology to explain function and do make reference to "vitalistic causality" in explaining organ function. Furthermore, most of these…

Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

DTIC Science & Technology

2015-03-19

learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but much more...classifier, Generative learning, Discriminative learning, Naïve Bayes, Feature selection, Logistic regression , higher order attribute independence 16...discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but

Automatic Fault Characterization via Abnormality-Enhanced Classification

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

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

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

Calcium-mediated shaping of naive CD4 T-cell phenotype and function

PubMed Central

Guichard, Vincent; Bonilla, Nelly; Durand, Aurélie; Audemard-Verger, Alexandra; Guilbert, Thomas; Martin, Bruno

2017-01-01

Continuous contact with self-major histocompatibility complex ligands is essential for the survival of naive CD4 T cells. We have previously shown that the resulting tonic TCR signaling also influences their fate upon activation by increasing their ability to differentiate into induced/peripheral regulatory T cells. To decipher the molecular mechanisms governing this process, we here focus on the TCR signaling cascade and demonstrate that a rise in intracellular calcium levels is sufficient to modulate the phenotype of mouse naive CD4 T cells and to increase their sensitivity to regulatory T-cell polarization signals, both processes relying on calcineurin activation. Accordingly, in vivo calcineurin inhibition leads the most self-reactive naive CD4 T cells to adopt the phenotype of their less self-reactive cell-counterparts. Collectively, our findings demonstrate that calcium-mediated activation of the calcineurin pathway acts as a rheostat to shape both the phenotype and effector potential of naive CD4 T cells in the steady-state. PMID:29239722

Construction accident narrative classification: An evaluation of text mining techniques.

PubMed

Goh, Yang Miang; Ubeynarayana, C U

2017-11-01

Learning from past accidents is fundamental to accident prevention. Thus, accident and near miss reporting are encouraged by organizations and regulators. However, for organizations managing large safety databases, the time taken to accurately classify accident and near miss narratives will be very significant. This study aims to evaluate the utility of various text mining classification techniques in classifying 1000 publicly available construction accident narratives obtained from the US OSHA website. The study evaluated six machine learning algorithms, including support vector machine (SVM), linear regression (LR), random forest (RF), k-nearest neighbor (KNN), decision tree (DT) and Naive Bayes (NB), and found that SVM produced the best performance in classifying the test set of 251 cases. Further experimentation with tokenization of the processed text and non-linear SVM were also conducted. In addition, a grid search was conducted on the hyperparameters of the SVM models. It was found that the best performing classifiers were linear SVM with unigram tokenization and radial basis function (RBF) SVM with uni-gram tokenization. In view of its relative simplicity, the linear SVM is recommended. Across the 11 labels of accident causes or types, the precision of the linear SVM ranged from 0.5 to 1, recall ranged from 0.36 to 0.9 and F1 score was between 0.45 and 0.92. The reasons for misclassification were discussed and suggestions on ways to improve the performance were provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

LORETA functional imaging in antipsychotic-naive and olanzapine-, clozapine- and risperidone-treated patients with schizophrenia.

PubMed

Tislerova, Barbora; Brunovsky, Martin; Horacek, Jiri; Novak, Tomas; Kopecek, Miloslav; Mohr, Pavel; Krajca, Vladimír

2008-01-01

The aim of our study was to detect changes in the distribution of electrical brain activity in schizophrenic patients who were antipsychotic naive and those who received treatment with clozapine, olanzapine or risperidone. We included 41 subjects with schizophrenia (antipsychotic naive = 11; clozapine = 8; olanzapine = 10; risperidone = 12) and 20 healthy controls. Low-resolution brain electromagnetic tomography was computed from 19-channel electroencephalography for the frequency bands delta, theta, alpha-1, alpha-2, beta-1, beta-2 and beta-3. We compared antipsychotic-naive subjects with healthy controls and medicated patients. (1) Comparing antipsychotic-naive subjects and controls we found a general increase in the slow delta and theta frequencies over the fronto-temporo-occipital cortex, particularly in the temporolimbic structures, an increase in alpha-1 and alpha-2 in the temporal cortex and an increase in beta-1 and beta-2 in the temporo-occipital and posterior limbic structures. (2) Comparing patients who received clozapine and those who were antipsychotic naive, we found an increase in delta and theta frequencies in the anterior cingulate and medial frontal cortex, and a decrease in alpha-1 and beta-2 in the occipital structures. (3) Comparing patients taking olanzapine with those who were antipsychotic naive, there was an increase in theta frequencies in the anterior cingulum, a decrease in alpha-1, beta-2 and beta-3 in the occipital cortex and posterior limbic structures, and a decrease in beta-3 in the frontotemporal cortex and anterior cingulum. (4) In patients taking risperidone, we found no significant changes from those who were antipsychotic naive. Our results in antipsychotic-naive patients are in agreement with existing functional findings. Changes in those taking clozapine and olanzapine versus those who were antipsychotic naive suggest a compensatory mechanism in the neurobiological substrate for schizophrenia. The lack of difference in

Unravelling core microbial metabolisms in the hypersaline microbial mats of Shark Bay using high-throughput metagenomics

PubMed Central

Ruvindy, Rendy; White III, Richard Allen; Neilan, Brett Anthony; Burns, Brendan Paul

2016-01-01

Modern microbial mats are potential analogues of some of Earth's earliest ecosystems. Excellent examples can be found in Shark Bay, Australia, with mats of various morphologies. To further our understanding of the functional genetic potential of these complex microbial ecosystems, we conducted for the first time shotgun metagenomic analyses. We assembled metagenomic next-generation sequencing data to classify the taxonomic and metabolic potential across diverse morphologies of marine mats in Shark Bay. The microbial community across taxonomic classifications using protein-coding and small subunit rRNA genes directly extracted from the metagenomes suggests that three phyla Proteobacteria, Cyanobacteria and Bacteriodetes dominate all marine mats. However, the microbial community structure between Shark Bay and Highbourne Cay (Bahamas) marine systems appears to be distinct from each other. The metabolic potential (based on SEED subsystem classifications) of the Shark Bay and Highbourne Cay microbial communities were also distinct. Shark Bay metagenomes have a metabolic pathway profile consisting of both heterotrophic and photosynthetic pathways, whereas Highbourne Cay appears to be dominated almost exclusively by photosynthetic pathways. Alternative non-rubisco-based carbon metabolism including reductive TCA cycle and 3-hydroxypropionate/4-hydroxybutyrate pathways is highly represented in Shark Bay metagenomes while not represented in Highbourne Cay microbial mats or any other mat forming ecosystems investigated to date. Potentially novel aspects of nitrogen cycling were also observed, as well as putative heavy metal cycling (arsenic, mercury, copper and cadmium). Finally, archaea are highly represented in Shark Bay and may have critical roles in overall ecosystem function in these modern microbial mats. PMID:26023869

The Persistence of "Solid" and "Liquid" Naive Conceptions: A Reaction Time Study

ERIC Educational Resources Information Center

Babai, Reuven; Amsterdamer, Anat

2008-01-01

The study explores whether the naive concepts of "solid" and "liquid" persist in adolescence. Accuracy of responses and reaction times where measured while 41 ninth graders classified different solids (rigid, non-rigid and powders) and different liquids (runny, dense) into solid or liquid. The results show that these naive conceptions affect…

Using Loss Functions for DIF Detection: An Empirical Bayes Approach.

ERIC Educational Resources Information Center

Zwick, Rebecca; Thayer, Dorothy; Lewis, Charles

2000-01-01

Studied a method for flagging differential item functioning (DIF) based on loss functions. Builds on earlier research that led to the development of an empirical Bayes enhancement to the Mantel-Haenszel DIF analysis. Tested the method through simulation and found its performance better than some commonly used DIF classification systems. (SLD)

Producing a satellite-derived map and modelling Spartina alterniflora expansion for Willapa Bay in Washington State

NASA Astrophysics Data System (ADS)

Berlin, Cynthia Jane

1998-12-01

This research addresses the identification of the areal extent of the intertidal wetlands of Willapa Bay, Washington, and the evaluation of the potential for exotic Spartina alterniflora (smooth cordgrass) expansion in the bay using a spatial geographic approach. It is hoped that the results will address not only the management needs of the study area but provide a research design that may be applied to studies of other coastal wetlands. Four satellite images, three Landsat Multi-Spectral (MSS) and one Thematic Mapper (TM), are used to derive a map showing areas of water, low, middle and high intertidal, and upland. Two multi-date remote sensing mapping techniques are assessed: a supervised classification using density-slicing and an unsupervised classification using an ISODATA algorithm. Statistical comparisons are made between the resultant derived maps and the U.S.G.S. topographic maps for the Willapa Bay area. The potential for Spartina expansion in the bay is assessed using a sigmoidal (logistic) growth model and a spatial modelling procedure for four possible growth scenarios: without management controls (Business-as-Usual), with moderate management controls (e.g. harvesting to eliminate seed setting), under a hypothetical increase in the growth rate that may reflect favorable environmental changes, and under a hypothetical decrease in the growth rate that may reflect aggressive management controls. Comparisons for the statistics of the two mapping techniques suggest that although the unsupervised classification method performed satisfactorily, the supervised classification (density-slicing) method provided more satisfactory results. Results from the modelling of potential Spartina expansion suggest that Spartina expansion will proceed rapidly for the Business-as-Usual and hypothetical increase in the growth rate scenario, and at a slower rate for the elimination of seed setting and hypothetical decrease in the growth rate scenarios, until all potential

A Machine Learning Concept for DTN Routing

NASA Technical Reports Server (NTRS)

Dudukovich, Rachel; Hylton, Alan; Papachristou, Christos

2017-01-01

This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.

Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes.

PubMed

Mikulich-Gilbertson, Susan K; Wagner, Brandie D; Grunwald, Gary K; Riggs, Paula D; Zerbe, Gary O

2018-01-01

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes

What Fits into a Mirror: Naive Beliefs about the Field of View

ERIC Educational Resources Information Center

Bianchi, Ivana; Savardi, Ugo

2012-01-01

Research on naive physics and naive optics have shown that people hold surprising beliefs about everyday phenomena that are in contrast with what they see. In this article, we investigated what adults expect to be the field of view of a mirror from various viewpoints. The studies presented here confirm that humans have difficulty dealing with the…

Impaired processing speed and attention in first-episode drug naive schizophrenia with deficit syndrome.

PubMed

Chen, Ce; Jiang, Wenhui; Zhong, Na; Wu, Jin; Jiang, Haifeng; Du, Jiang; Li, Ye; Ma, Xiancang; Zhao, Min; Hashimoto, Kenji; Gao, Chengge

2014-11-01

Although first-episode drug naive patients with schizophrenia are known to show cognitive impairment, the cognitive performances of these patients, who suffer deficit syndrome, compared with those who suffer non-deficit syndrome is undetermined. The aim of this study was to compare cognitive performances in first-episode drug-naive schizophrenia with deficit syndrome or non-deficit syndrome. First-episode drug naive patients (n=49) and medicated patients (n=108) with schizophrenia, and age, sex, and education matched healthy controls (n=57 for the first-episode group, and n=128 for the medicated group) were enrolled. Patients were divided into deficit or non-deficit syndrome groups, using the Schedule for Deficit Syndrome. Cognitive performance was assessed using the CogState computerized cognitive battery. All cognitive domains in first-episode drug naive and medicated patients showed significant impairment compared with their respective control groups. Furthermore, cognitive performance in first-episode drug naive patients was significantly worse than in medicated patients. Interestingly, the cognitive performance markers of processing speed and attention, in first-episode drug naive patients with deficit syndrome, were both significantly worse than in equivalent patients without deficit syndrome. In contrast, no differences in cognitive performance were found between the two groups of medicated patients. In conclusion, this study found that first-episode drug naive schizophrenia with deficit syndrome showed significantly impaired processing speed and attention, compared with patients with non-deficit syndrome. These findings highlight processing speed and attention as potential targets for pharmacological and psychosocial interventions in first-episode schizophrenia with deficit syndrome, since these domains are associated with social outcomes. Copyright © 2014 Elsevier B.V. All rights reserved.

'Educated' dendritic cells act as messengers from memory to naive T helper cells.

PubMed

Alpan, Oral; Bachelder, Eric; Isil, Eda; Arnheiter, Heinz; Matzinger, Polly

2004-06-01

Ingested antigens lead to the generation of effector T cells that secrete interleukin 4 (IL-4) rather than interferon-gamma (IFN-gamma) and are capable of influencing naive T cells in their immediate environment to do the same. Using chimeric mice generated by aggregation of two genotypically different embryos, we found that the conversion of a naive T cell occurs only if it can interact with the same antigen-presenting cell, although not necessarily the same antigen, as the effector T cell. Using a two-step culture system in vitro, we found that antigen-presenting dendritic cells can act as 'temporal bridges' to relay information from orally immunized memory CD4 T cells to naive CD4 T cells. The orally immunized T cells use IL-4 and IL-10 (but not CD40 ligand) to 'educate' dendritic cells, which in turn induce naive T cells to produce the same cytokines as those produced by the orally immunized memory T cells.

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.

PubMed

Zhang, Hui; Cao, Zhi-Xing; Li, Meng; Li, Yu-Zhi; Peng, Cheng

2016-11-01

The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (M W ), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development. Copyright © 2016 Elsevier Ltd. All rights reserved.

Admiralty Bay Benthos Diversity—A census of a complex polar ecosystem

NASA Astrophysics Data System (ADS)

Siciński, Jacek; Jażdżewski, Krzysztof; Broyer, Claude De; Presler, Piotr; Ligowski, Ryszard; Nonato, Edmundo F.; Corbisier, Thais N.; Petti, Monica A. V.; Brito, Tania A. S.; Lavrado, Helena P.; BŁażewicz-Paszkowycz, Magdalena; Pabis, Krzysztof; Jażdżewska, Anna; Campos, Lucia S.

2011-03-01

A thorough census of Admiralty Bay benthic biodiversity was completed through the synthesis of data, acquired from more than 30 years of observations. Most of the available records arise from successive Polish and Brazilian Antarctic expeditions organized since 1977 and 1982, respectively, but also include new data from joint collecting efforts during the International Polar Year (2007-2009). Geological and hydrological characteristics of Admiralty Bay and a comprehensive species checklist with detailed data on the distribution and nature of the benthic communities are provided. Approximately 1300 species of benthic organisms (excluding bacteria, fungi and parasites) were recorded from the bay's entire depth range (0-500 m). Generalized classifications and the descriptions of soft-bottom and hard-bottom invertebrate communities are presented. A time-series analysis showed seasonal and interannual changes in the shallow benthic communities, likely to be related to ice formation and ice melt within the bay. As one of the best studied regions in the maritime Antarctic Admiralty Bay represents a legacy site, where continued, systematically integrated data sampling can evaluate the effects of climate change on marine life. Both high species richness and high assemblage diversity of the Admiralty Bay shelf benthic community have been documented against the background of habitat heterogeneity.

Top predators affect the composition of naive protist communities, but only in their early-successional stage.

PubMed

Zander, Axel; Gravel, Dominique; Bersier, Louis-Félix; Gray, Sarah M

2016-02-01

Introduced top predators have the potential to disrupt community dynamics when prey species are naive to predation. The impact of introduced predators may also vary depending on the stage of community development. Early-succession communities are likely to have small-bodied and fast-growing species, but are not necessarily good at defending against predators. In contrast, late-succession communities are typically composed of larger-bodied species that are more predator resistant relative to small-bodied species. Yet, these aspects are greatly neglected in invasion studies. We therefore tested the effect of top predator presence on early- and late-succession communities that were either naive or non-naive to top predators. We used the aquatic community held within the leaves of Sarracenia purpurea. In North America, communities have experienced the S. purpurea top predator and are therefore non-naive. In Europe, this predator is not present and its niche has not been filled, making these communities top-predator naive. We collected early- and late-succession communities from two non-naive and two naive sites, which are climatically similar. We then conducted a common-garden experiment, with and without the presence of the top predator, in which we recorded changes in community composition, body size spectra, bacterial density, and respiration. We found that the top predator had no statistical effect on global measures of community structure and functioning. However, it significantly altered protist composition, but only in naive, early-succession communities, highlighting that the state of community development is important for understanding the impact of invasion.

Children and Adolescents' Understandings of Family Resemblance: A Study of Naive Inheritance Concepts

ERIC Educational Resources Information Center

Williams, Joanne M.

2012-01-01

This paper aims to provide developmental data on two connected naive inheritance concepts and to explore the coherence of children's naive biology knowledge. Two tasks examined children and adolescents' (4, 7, 10, and 14 years) conceptions of phenotypic resemblance across kin (in physical characteristics, disabilities, and personality traits). The…

Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.

PubMed

Fang, Jiansong; Yang, Ranyao; Gao, Li; Zhou, Dan; Yang, Shengqian; Liu, Ai-Lin; Du, Guan-hua

2013-11-25

Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.

Early Remission Is a Realistic Target in a Majority of Patients with DMARD-naive Rheumatoid Arthritis.

PubMed

Rannio, Tuomas; Asikainen, Juha; Kokko, Arto; Hannonen, Pekka; Sokka, Tuulikki

2016-04-01

We analyzed remission rates at 3 and 12 months in patients with rheumatoid arthritis (RA) who were naive for disease-modifying antirheumatic drugs (DMARD) and who were treated in a Finnish rheumatology clinic from 2008 to 2011. We compared remission rates and drug treatments between patients with RA and patients with undifferentiated arthritis (UA). Data from all DMARD-naive RA and UA patients from the healthcare district were collected using software that includes demographic and clinical characteristics, disease activity, medications, and patient-reported outcomes. Our rheumatology clinic applies the treat-to-target principle, electronic monitoring of patients, and multidisciplinary care. Out of 409 patients, 406 had data for classification by the 2010 RA criteria of the American College of Rheumatology/European League Against Rheumatism. A total of 68% were female, and mean age (SD) was 58 (16) years. Respectively, 56%, 60%, and 68% were positive for anticyclic citrullinated peptide antibodies (anti-CCP), rheumatoid factor (RF), and RF/anti-CCP, and 19% had erosive disease. The median (interquartile range) duration of symptoms was 6 (4-12) months. A total of 310 were classified as RA and 96 as UA. The patients with UA were younger, had better functional status and lower disease activity, and were more often seronegative than the patients with RA. The 28-joint Disease Activity Score (3 variables) remission rates of RA and UA patients at 3 months were 67% and 58% (p = 0.13), and at 12 months, 71% and 79%, respectively (p = 0.16). Sustained remission was observed in 57%/56% of RA/UA patients. Patients with RA used more conventional synthetic DMARD combinations than did patients with UA. None used biological DMARD at 3 months, and only 2.7%/1.1% of the patients (RA/UA) used them at 12 months (p = 0.36). Remarkably high remission rates are achievable in real-world DMARD-naive patients with RA or UA.

Supervised DNA Barcodes species classification: analysis, comparisons and results

PubMed Central

2014-01-01

Background Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. Methods In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the classification results are compared with respect to ad-hoc and well-established DNA Barcode classification methods. Results A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the classification procedure. The analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable classification model. Rule-based methods have slightly inferior classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior classification performances with respect to the traditional DNA Barcode

IL-7-Induced Proliferation of Human Naive CD4 T-Cells Relies on Continued Thymic Activity.

PubMed

Silva, Susana L; Albuquerque, Adriana S; Matoso, Paula; Charmeteau-de-Muylder, Bénédicte; Cheynier, Rémi; Ligeiro, Dário; Abecasis, Miguel; Anjos, Rui; Barata, João T; Victorino, Rui M M; Sousa, Ana E

2017-01-01

Naive CD4 T-cell maintenance is critical for immune competence. We investigated here the fine-tuning of homeostatic mechanisms of the naive compartment to counteract the loss of de novo CD4 T-cell generation. Adults thymectomized in early childhood during corrective cardiac surgery were grouped based on presence or absence of thymopoiesis and compared with age-matched controls. We found that the preservation of the CD31 - subset was independent of the thymus and that its size is tightly controlled by peripheral mechanisms, including prolonged cell survival as attested by Bcl-2 levels. Conversely, a significant contraction of the CD31 + naive subset was observed in the absence of thymic activity. This was associated with impaired responses of purified naive CD4 T-cells to IL-7, namely, in vitro proliferation and upregulation of CD31 expression, which likely potentiated the decline in recent thymic emigrants. Additionally, we found no apparent constraint in the differentiation of naive cells into the memory compartment in individuals completely lacking thymic activity despite upregulation of DUSP6 , a phosphatase associated with increased TCR threshold. Of note, thymectomized individuals featuring some degree of thymopoiesis were able to preserve the size and diversity of the naive CD4 compartment, further arguing against complete thymectomy in infancy. Overall, our data suggest that robust peripheral mechanisms ensure the homeostasis of CD31 - naive CD4 pool and point to the requirement of continuous thymic activity to the maintenance of IL-7-driven homeostatic proliferation of CD31 + naive CD4 T-cells, which is essential to secure T-cell diversity throughout life.

Risk of erectile dysfunction in transfusion-naive thalassemia men: a nationwide population-based retrospective cohort study.

PubMed

Chen, Yu-Guang; Lin, Te-Yu; Lin, Cheng-Li; Dai, Ming-Shen; Ho, Ching-Liang; Kao, Chia-Hung

2015-04-01

Based on the mechanism of pathophysiology, thalassemia major or transfusion-dependent thalassemia patients may have an increased risk of developing organic erectile dysfunction resulting from hypogonadism. However, there have been few studies investigating the association between erectile dysfunction and transfusion-naive thalassemia populations. We constructed a population-based cohort study to elucidate the association between transfusion-naive thalassemia populations and organic erectile dysfunction. This nationwide population-based cohort study involved analyzing data from 1998 to 2010 obtained from the Taiwanese National Health Insurance Research Database, with a follow-up period extending to the end of 2011. We identified men with transfusion-naive thalassemia and selected a comparison cohort that was frequency-matched with these according to age, and year of diagnosis thalassemia at a ratio of 1 thalassemia man to 4 control men. We analyzed the risks for transfusion-naive thalassemia men and organic erectile dysfunction by using Cox proportional hazards regression models. In this study, 588 transfusion-naive thalassemia men and 2337 controls were included. Total 12 patients were identified within the thalassaemia group and 10 within the control group. The overall risks for developing organic erectile dysfunction were 4.56-fold in patients with transfusion-naive thalassemia men compared with the comparison cohort after we adjusted for age and comorbidities. Our long-term cohort study results showed that in transfusion-naive thalassemia men, there was a higher risk for the development of organic erectile dysfunction, particularly in those patients with comorbidities.

Evaluation of the impact of chitosan/DNA nanoparticles on the differentiation of human naive CD4+ T cells

NASA Astrophysics Data System (ADS)

Liu, Lanxia; Bai, Yuanyuan; Zhu, Dunwan; Song, Liping; Wang, Hai; Dong, Xia; Zhang, Hailing; Leng, Xigang

2011-06-01

Chitosan (CS) is one of the most widely studied polymers in non-viral gene delivery since it is a cationic polysaccharide that forms nanoparticles with DNA and hence protects the DNA against digestion by DNase. However, the impact of CS/DNA nanoparticle on the immune system still remains poorly understood. Previous investigations did not found CS/DNA nanoparticles had any significant impact on the function of human and murine macrophages. To date, little is known about the interaction between CS/DNA nanoparticles and naive CD4+ T cells. This study was designed to investigate whether CS/DNA nanoparticles affect the initial differentiation direction of human naive CD4+ T cells. The indirect impact of CS/DNA nanoparticles on naive CD4+ T cell differentiation was investigated by incubating the nanoparticles with human macrophage THP-1 cells in one chamber of a transwell co-incubation system, with the enriched human naive CD4+ T cells being placed in the other chamber of the transwell. The nanoparticles were also co-incubated with the naive CD4+ T cells to explore their direct impact on naive CD4+ T cell differentiation by measuring the release of IL-4 and IFN-γ from the cells. It was demonstrated that CS/DNA nanoparticles induced slightly elevated production of IL-12 by THP-1 cells, possibly owing to the presence of CpG motifs in the plasmid. However, this macrophage stimulating activity was much less significant as compared with lipopolysaccharide and did not impact on the differentiation of the naive CD4+ T cells. It was also demonstrated that, when directly exposed to the naive CD4+ T cells, the nanoparticles induced neither the activation of the naive CD4+ T cells in the absence of recombinant cytokines (recombinant human IL-4 or IFN-γ) that induce naive CD4+ T cell polarization, nor any changes in the differentiation direction of naive CD4+ T cells in the presence of the corresponding cytokines.

75 FR 29891 - Special Local Regulation; Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-28

...-AA08 Special Local Regulation; Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay... Lighthouse Dock, Fire Island, NY due to the annual Maggie Fischer Memorial Great South Bay Cross Bay Swim..., Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, NY, in the Federal Register (74...

Multinomial mixture model with heterogeneous classification probabilities

USGS Publications Warehouse

Holland, M.D.; Gray, B.R.

2011-01-01

Royle and Link (Ecology 86(9):2505-2512, 2005) proposed an analytical method that allowed estimation of multinomial distribution parameters and classification probabilities from categorical data measured with error. While useful, we demonstrate algebraically and by simulations that this method yields biased multinomial parameter estimates when the probabilities of correct category classifications vary among sampling units. We address this shortcoming by treating these probabilities as logit-normal random variables within a Bayesian framework. We use Markov chain Monte Carlo to compute Bayes estimates from a simulated sample from the posterior distribution. Based on simulations, this elaborated Royle-Link model yields nearly unbiased estimates of multinomial and correct classification probability estimates when classification probabilities are allowed to vary according to the normal distribution on the logit scale or according to the Beta distribution. The method is illustrated using categorical submersed aquatic vegetation data. ?? 2010 Springer Science+Business Media, LLC.

Prediction of outcome in multiorgan resections for cancer using a bayes-network.

PubMed

Udelnow, Andrej; Leinung, Steffen; Grochola, Lukasz Filipp; Henne-Bruns, Doris; Wfcrl, Peter

2013-01-01

The long-term success of multivisceral resections for cancer is difficult to forecast due to the complexity of factors influencing the prognosis. The aim of our study was to assess the predictivity of a Bayes network for the postoperative outcome and survival. We included each oncologic patient undergoing resection of 4 or more organs from 2002 till 2005 at the Ulm university hospital. Preoperative data were assessed as well as the tumour classification, the resected organs, intra- and postoperative complications and overall survival. Using the Genie 2.0 software we developed a Bayes network. Multivisceral tumour resections were performed in 22 patients. The receiver operating curve areas of the variables "survival >12 months" and "hospitalisation >28 days" as predicted by the Bayes network were 0.81 and 0.77 and differed significantly from 0.5 (p: 0.019 and 0.028, respectively). The positive predictive values of the Bayes network for these variables were 1 and 0.8 and the negative ones 0.71 and 0.88, respectively. Bayes networks are useful for the prognosis estimation of individual patients and can help to decide whether to perform a multivisceral resection for cancer.

Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity.

PubMed

Zhang, Hui; Kang, Yan-Li; Zhu, Yuan-Yuan; Zhao, Kai-Xia; Liang, Jun-Yu; Ding, Lan; Zhang, Teng-Guo; Zhang, Ji

2017-06-01

Prediction of drug candidates for mutagenicity is a regulatory requirement since mutagenic compounds could pose a toxic risk to humans. The aim of this investigation was to develop a novel prediction model of mutagenicity by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test sets. For comparison, the recursive partitioning classifier prediction model was also established and other various reported prediction models of mutagenicity were collected. Among these methods, the prediction performance of naïve Bayes classifier established here displayed very well and stable, which yielded average overall prediction accuracies for the internal 5-fold cross validation of the training set and external test set I set were 89.1±0.4% and 77.3±1.5%, respectively. The concordance of the external test set II with 446 marketed drugs was 90.9±0.3%. In addition, four simple molecular descriptors (e.g., Apol, No. of H donors, Num-Rings and Wiener) related to mutagenicity and five representative substructures of mutagens (e.g., aromatic nitro, hydroxyl amine, nitroso, aromatic amine and N-methyl-N-methylenemethanaminum) produced by ECFP_14 fingerprints were identified. We hope the established naïve Bayes prediction model can be applied to risk assessment processes; and the obtained important information of mutagenic chemicals can guide the design of chemical libraries for hit and lead optimization. Copyright © 2017 Elsevier B.V. All rights reserved.

A comparison of acoustic and observed sediment classifications as predictor variables for modelling biotope distributions in Galway Bay, Ireland

NASA Astrophysics Data System (ADS)

O'Carroll, Jack P. J.; Kennedy, Robert; Ren, Lei; Nash, Stephen; Hartnett, Michael; Brown, Colin

2017-10-01

The INFOMAR (Integrated Mapping For the Sustainable Development of Ireland's Marine Resource) initiative has acoustically mapped and classified a significant proportion of Ireland's Exclusive Economic Zone (EEZ), and is likely to be an important tool in Ireland's efforts to meet the criteria of the MSFD. In this study, open source and relic data were used in combination with new grab survey data to model EUNIS level 4 biotope distributions in Galway Bay, Ireland. The correct prediction rates of two artificial neural networks (ANNs) were compared to assess the effectiveness of acoustic sediment classifications versus sediments that were visually classified by an expert in the field as predictor variables. To test for autocorrelation between predictor variables the RELATE routine with Spearman rank correlation method was used. Optimal models were derived by iteratively removing predictor variables and comparing the correct prediction rates of each model. The models with the highest correct prediction rates were chosen as optimal. The optimal models each used a combination of salinity (binary; 0 = polyhaline and 1 = euhaline), proximity to reef (binary; 0 = within 50 m and 1 = outside 50 m), depth (continuous; metres) and a sediment descriptor (acoustic or observed) as predictor variables. As the status of benthic habitats is required to be assessed under the MSFD the Ecological Status (ES) of the subtidal sediments of Galway Bay was also assessed using the Infaunal Quality Index. The ANN that used observed sediment classes as predictor variables could correctly predict the distribution of biotopes 67% of the time, compared to 63% for the ANN using acoustic sediment classes. Acoustic sediment ANN predictions were affected by local sediment heterogeneity, and the lack of a mixed sediment class. The all-round poor performance of ANNs is likely to be a result of the temporally variable and sparsely distributed data within the study area.

Identifying Wrist Fracture Patients with High Accuracy by Automatic Categorization of X-ray Reports

PubMed Central

de Bruijn, Berry; Cranney, Ann; O’Donnell, Siobhan; Martin, Joel D.; Forster, Alan J.

2006-01-01

The authors performed this study to determine the accuracy of several text classification methods to categorize wrist x-ray reports. We randomly sampled 751 textual wrist x-ray reports. Two expert reviewers rated the presence (n = 301) or absence (n = 450) of an acute fracture of wrist. We developed two information retrieval (IR) text classification methods and a machine learning method using a support vector machine (TC-1). In cross-validation on the derivation set (n = 493), TC-1 outperformed the two IR based methods and six benchmark classifiers, including Naive Bayes and a Neural Network. In the validation set (n = 258), TC-1 demonstrated consistent performance with 93.8% accuracy; 95.5% sensitivity; 92.9% specificity; and 87.5% positive predictive value. TC-1 was easy to implement and superior in performance to the other classification methods. PMID:16929046

33 CFR 100.124 - Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, New York.

Code of Federal Regulations, 2011 CFR

2011-07-01

... South Bay Cross Bay Swim, Great South Bay, New York. 100.124 Section 100.124 Navigation and Navigable... NAVIGABLE WATERS § 100.124 Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, New York. (a) Regulated area. All navigable waters of Great South Bay, NY within a 100 yard radius of each...

The Hayflick Limit May Determine the Effective Clonal Diversity of Naive T Cells.

PubMed

Ndifon, Wilfred; Dushoff, Jonathan

2016-06-15

Having a large number of sufficiently abundant T cell clones is important for adequate protection against diseases. However, as shown in this paper and elsewhere, between young adulthood and >70 y of age the effective clonal diversity of naive CD4/CD8 T cells found in human blood declines by a factor of >10. (Effective clonal diversity accounts for both the number and the abundance of T cell clones.) The causes of this observation are incompletely understood. A previous study proposed that it might result from the emergence of certain rare, replication-enhancing mutations in T cells. In this paper, we propose an even simpler explanation: that it results from the loss of T cells that have attained replicative senescence (i.e., the Hayflick limit). Stochastic numerical simulations of naive T cell population dynamics, based on experimental parameters, show that the rate of homeostatic T cell proliferation increases after the age of ∼60 y because naive T cells collectively approach replicative senescence. This leads to a sharp decline of effective clonal diversity after ∼70 y, in agreement with empirical data. A mathematical analysis predicts that, without an increase in the naive T cell proliferation rate, this decline will occur >50 yr later than empirically observed. These results are consistent with a model in which exhaustion of the proliferative capacity of naive T cells causes a sharp decline of their effective clonal diversity and imply that therapeutic potentiation of thymopoiesis might either prevent or reverse this outcome. Copyright © 2016 by The American Association of Immunologists, Inc.

Identifying patients in target customer segments using a two-stage clustering-classification approach: a hospital-based assessment.

PubMed

Chen, You-Shyang; Cheng, Ching-Hsue; Lai, Chien-Jung; Hsu, Cheng-Yi; Syu, Han-Jhou

2012-02-01

Identifying patients in a Target Customer Segment (TCS) is important to determine the demand for, and to appropriately allocate resources for, health care services. The purpose of this study is to propose a two-stage clustering-classification model through (1) initially integrating the RFM attribute and K-means algorithm for clustering the TCS patients and (2) then integrating the global discretization method and the rough set theory for classifying hospitalized departments and optimizing health care services. To assess the performance of the proposed model, a dataset was used from a representative hospital (termed Hospital-A) that was extracted from a database from an empirical study in Taiwan comprised of 183,947 samples that were characterized by 44 attributes during 2008. The proposed model was compared with three techniques, Decision Tree, Naive Bayes, and Multilayer Perceptron, and the empirical results showed significant promise of its accuracy. The generated knowledge-based rules provide useful information to maximize resource utilization and support the development of a strategy for decision-making in hospitals. From the findings, 75 patients in the TCS, three hospital departments, and specific diagnostic items were discovered in the data for Hospital-A. A potential determinant for gender differences was found, and the age attribute was not significant to the hospital departments. Copyright © 2011 Elsevier Ltd. All rights reserved.

Naive B cells generate regulatory T cells in the presence of a mature immunologic synapse.

PubMed

Reichardt, Peter; Dornbach, Bastian; Rong, Song; Beissert, Stefan; Gueler, Faikah; Loser, Karin; Gunzer, Matthias

2007-09-01

Naive B cells are ineffective antigen-presenting cells and are considered unable to activate naive T cells. However, antigen-specific contact of these cells leads to stable cell pairs that remain associated over hours in vivo. The physiologic role of such pairs has not been evaluated. We show here that antigen-specific conjugates between naive B cells and naive T cells display a mature immunologic synapse in the contact zone that is absent in T-cell-dendritic-cell (DC) pairs. B cells induce substantial proliferation but, contrary to DCs, no loss of L-selectin in T cells. Surprisingly, while DC-triggered T cells develop into normal effector cells, B-cell stimulation over 72 hours induces regulatory T cells inhibiting priming of fresh T cells in a contact-dependent manner in vitro. In vivo, the regulatory T cells home to lymph nodes where they potently suppress immune responses such as in cutaneous hypersensitivity and ectopic allogeneic heart transplant rejection. Our finding might help to explain old observations on tolerance induction by B cells, identify the mature immunologic synapse as a central functional module of this process, and suggest the use of naive B-cell-primed regulatory T cells, "bTregs," as a useful approach for therapeutic intervention in adverse adaptive immune responses.

Single-accelerometer-based daily physical activity classification.

PubMed

Long, Xi; Yin, Bin; Aarts, Ronald M

2009-01-01

In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of approximately 80%, which was comparable with that obtained by a Decision Tree classifier.

Automatic topic identification of health-related messages in online health community using text classification.

PubMed

Lu, Yingjie

2013-01-01

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

33 CFR 167.103 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 2 2011-07-01 2011-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach. 167.103 Section 167.103 Navigation and Navigable... the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach. (a) A separation...

33 CFR 167.102 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 2 2012-07-01 2012-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach. 167.102 Section 167.102 Navigation and....102 In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach. (a) A...

33 CFR 167.103 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 2 2012-07-01 2012-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach. 167.103 Section 167.103 Navigation and Navigable... the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach. (a) A separation...

33 CFR 167.103 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach. 167.103 Section 167.103 Navigation and Navigable... the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach. (a) A separation...

33 CFR 167.102 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach. 167.102 Section 167.102 Navigation and....102 In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach. (a) A...

33 CFR 167.102 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach. 167.102 Section 167.102 Navigation and....102 In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach. (a) A...

33 CFR 167.103 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach. 167.103 Section 167.103 Navigation and Navigable... the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Buzzards Bay approach. (a) A separation...

33 CFR 167.102 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 2 2011-07-01 2011-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach. 167.102 Section 167.102 Navigation and....102 In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Narragansett Bay approach. (a) A...

Color Shaded-Relief and Surface-Classification Maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

USGS Publications Warehouse

Mars, John L.; Garrity, Christopher P.; Houseknecht, David W.; Amoroso, Lee; Meares, Donald C.

2007-01-01

-relief and surface-classification data (sheet 3). Remote sensing datasets that were used to compile the maps include Landsat 7 Enhanced Thematic Mapper+ (ETM+), and interferometric synthetic aperture radar (IFSAR) data. In addition, a 1:250,000-scale geologic map of the Harrison Bay quadrangle, Alaska (Carter and Galloway, 1985, 2005) was used in conjunction with ETM+ and IFSAR data.

Geologic characteristics of benthic habitats in Glacier Bay, southeast Alaska

USGS Publications Warehouse

Harney, Jodi N.; Cochrane, Guy R.; Etherington, Lisa L.; Dartnell, Pete; Golden, Nadine E.; Chezar, Hank

2006-01-01

In April 2004, more than 40 hours of georeferenced submarine digital video was collected in water depths of 15-370 m in Glacier Bay to (1) ground-truth existing geophysical data (bathymetry and acoustic reflectance), (2) examine and record geologic characteristics of the sea floor, and (3) investigate the relation between substrate types and benthic communities, and (4) construct predictive maps of seafloor geomorphology and habitat distribution. Common substrates observed include rock, boulders, cobbles, rippled sand, bioturbated mud, and extensive beds of living horse mussels and scallops. Four principal sea-floor geomorphic types are distinguished by using video observations. Their distribution in lower and central Glacier Bay is predicted using a supervised, hierarchical decision-tree statistical classification of geophysical data.

In silico prediction of drug-induced myelotoxicity by using Naïve Bayes method.

PubMed

Zhang, Hui; Yu, Peng; Zhang, Teng-Guo; Kang, Yan-Li; Zhao, Xiao; Li, Yuan-Yuan; He, Jia-Hui; Zhang, Ji

2015-11-01

Drug-induced myelotoxicity usually leads to decrease the production of platelets, red cells, and white cells. Thus, early identification and characterization of myelotoxicity hazard in drug development is very necessary. The purpose of this investigation was to develop a prediction model of drug-induced myelotoxicity by using a Naïve Bayes classifier. For comparison, other prediction models based on support vector machine and single-hidden-layer feed-forward neural network  methods were also established. Among all the prediction models, the Naïve Bayes classification model showed the best prediction performance, which offered an average overall prediction accuracy of [Formula: see text] for the training set and [Formula: see text] for the external test set. The significant contributions of this study are that we first developed a Naïve Bayes classification model of drug-induced myelotoxicity adverse effect using a larger scale dataset, which could be employed for the prediction of drug-induced myelotoxicity. In addition, several important molecular descriptors and substructures of myelotoxic compounds have been identified, which should be taken into consideration in the design of new candidate compounds to produce safer and more effective drugs, ultimately reducing the attrition rate in later stages of drug development.

Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse.

PubMed

Garrard, Peter; Rentoumi, Vassiliki; Gesierich, Benno; Miller, Bruce; Gorno-Tempini, Maria Luisa

2014-06-01

Advances in automatic text classification have been necessitated by the rapid increase in the availability of digital documents. Machine learning (ML) algorithms can 'learn' from data: for instance a ML system can be trained on a set of features derived from written texts belonging to known categories, and learn to distinguish between them. Such a trained system can then be used to classify unseen texts. In this paper, we explore the potential of the technique to classify transcribed speech samples along clinical dimensions, using vocabulary data alone. We report the accuracy with which two related ML algorithms [naive Bayes Gaussian (NBG) and naive Bayes multinomial (NBM)] categorized picture descriptions produced by: 32 semantic dementia (SD) patients versus 10 healthy, age-matched controls; and SD patients with left- (n = 21) versus right-predominant (n = 11) patterns of temporal lobe atrophy. We used information gain (IG) to identify the vocabulary features that were most informative to each of these two distinctions. In the SD versus control classification task, both algorithms achieved accuracies of greater than 90%. In the right- versus left-temporal lobe predominant classification, NBM achieved a high level of accuracy (88%), but this was achieved by both NBM and NBG when the features used in the training set were restricted to those with high values of IG. The most informative features for the patient versus control task were low frequency content words, generic terms and components of metanarrative statements. For the right versus left task the number of informative lexical features was too small to support any specific inferences. An enriched feature set, including values derived from Quantitative Production Analysis (QPA) may shed further light on this little understood distinction. Copyright © 2013 Elsevier Ltd. All rights reserved.

33 CFR 100.124 - Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, New York.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, New York. 100.124 Section 100.124 Navigation and Navigable... NAVIGABLE WATERS § 100.124 Maggie Fischer Memorial Great South Bay Cross Bay Swim, Great South Bay, New York...

Naive T-cell receptor transgenic T cells help memory B cells produce antibody

PubMed Central

Duffy, Darragh; Yang, Chun-Ping; Heath, Andrew; Garside, Paul; Bell, Eric B

2006-01-01

Injection of the same antigen following primary immunization induces a classic secondary response characterized by a large quantity of high-affinity antibody of an immunoglobulin G class produced more rapidly than in the initial response – the products of memory B cells are qualitatively distinct from that of the original naive B lymphocytes. Very little is known of the help provided by the CD4 T cells that stimulate memory B cells. Using antigen-specific T-cell receptor transgenic CD4 T cells (DO11.10) as a source of help, we found that naive transgenic T cells stimulated memory B cells almost as well (in terms of quantity and speed) as transgenic T cells that had been recently primed. There was a direct correlation between serum antibody levels and the number of naive transgenic T cells transferred. Using T cells from transgenic interleukin-2-deficient mice we showed that interleukin-2 was not required for a secondary response, although it was necessary for a primary response. The results suggested that the signals delivered by CD4 T cells and required by memory B cells for their activation were common to both antigen-primed and naive CD4 T cells. PMID:17067314

IL-21 sustains CD28 expression on IL-15-activated human naive CD8+ T cells.

PubMed

Alves, Nuno L; Arosa, Fernando A; van Lier, René A W

2005-07-15

Human naive CD8+ T cells are able to respond in an Ag-independent manner to IL-7 and IL-15. Whereas IL-7 largely maintains CD8+ T cells in a naive phenotype, IL-15 drives these cells to an effector phenotype characterized, among other features, by down-regulation of the costimulatory molecule CD28. We evaluated the influence of the CD4+ Th cell-derived common gamma-chain cytokine IL-21 on cytokine-induced naive CD8+ T cell activation. Stimulation with IL-21 did not induce division and only slightly increased IL-15-induced proliferation of naive CD8+ T cells. Strikingly, however, IL-15-induced down-modulation of CD28 was completely prevented by IL-21 at the protein and transcriptional level. Subsequent stimulation via combined TCR/CD3 and CD28 triggering led to a markedly higher production of IL-2 and IFN-gamma in IL-15/IL-21-stimulated cells compared with IL-15-stimulated T cells. Our data show that IL-21 modulates the phenotype of naive CD8+ T cells that have undergone IL-15 induced homeostatic proliferation and preserves their responsiveness to CD28 ligands.

Left Ventricular Strain in Chemotherapy-Naive and Radiotherapy-Naive Patients With Cancer.

PubMed

Tadic, Marijana; Genger, Martin; Baudisch, Ana; Kelle, Sebastian; Cuspidi, Cesare; Belyavskiy, Evgeny; Burkhardt, Franziska; Venneri, Lucia; Attanasio, Philipp; Pieske, Burkert

2018-03-01

We sought to investigate left ventricular (LV) function and mechanics in patients with cancer before they received chemotherapy or radiotherapy, as well as the relationship between cancer and reduced LV multidirectional strain in the whole study population. The retrospective study involved 122 chemotherapy- and radiotherapy-naive patients with cancer and 45 age- and sex-matched controls with a cardiovascular risk profile similar to that of the patients with cancer. All the patients underwent echocardiographic examination before introduction of chemotherapy or radiotherapy. LV longitudinal (-19.1% ± 2.1% vs -17.8% ± 3.5%; P = 0.022), circumferential (-22.9% ± 3.5% vs -20.1% ± 4.1%; P < 0.001), and radial (40.5% ± 8.8% vs 35.2% ± 10.7%; P = 0.004) strain was significantly lower in the patients with cancer than in the control group. Endocardial and midmyocardial longitudinal LV strain was significantly reduced in the patients with cancer compared with the controls, whereas epicardial longitudinal strain was similar between these groups. Endocardial, midmyocardial, and epicardial circumferential strain was significantly lower in the chemotherapy- or radiotherapy-naive patients with cancer than in the controls. Cancer was associated with reduced longitudinal (odds ratio [OR], 9.0; 95% confidence interval [CI], 2.20-23.50; P < 0.001), reduced circumferential (OR, 7.1; 95% CI, 3.80-20.40; P < 0.001), and reduced radial strain (OR, 7.2; 95% CI, 3.41-25.10; P < 0.001) independent of age, sex, body mass index, diabetes, and hypertension. LV mechanics was impaired in the patients with cancer compared with the controls even before initiation of chemotherapy and radiotherapy. Cancer and hypertension were associated with reduced LV multidirectional strain independent of other clinical parameters. The present results indicate that cancer itself potentially induces cardiac remodelling independent of chemotherapy and radiotherapy. Copyright © 2017 Canadian

Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests

ERIC Educational Resources Information Center

Rudner, Lawrence

2016-01-01

In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows…

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

PubMed

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case

From genus to phylum: large-subunit and internal transcribed spacer rRNA operon regions show similar classification accuracies influenced by database composition.

PubMed

Porras-Alfaro, Andrea; Liu, Kuan-Liang; Kuske, Cheryl R; Xie, Gary

2014-02-01

We compared the classification accuracy of two sections of the fungal internal transcribed spacer (ITS) region, individually and combined, and the 5' section (about 600 bp) of the large-subunit rRNA (LSU), using a naive Bayesian classifier and BLASTN. A hand-curated ITS-LSU training set of 1,091 sequences and a larger training set of 8,967 ITS region sequences were used. Of the factors evaluated, database composition and quality had the largest effect on classification accuracy, followed by fragment size and use of a bootstrap cutoff to improve classification confidence. The naive Bayesian classifier and BLASTN gave similar results at higher taxonomic levels, but the classifier was faster and more accurate at the genus level when a bootstrap cutoff was used. All of the ITS and LSU sections performed well (>97.7% accuracy) at higher taxonomic ranks from kingdom to family, and differences between them were small at the genus level (within 0.66 to 1.23%). When full-length sequence sections were used, the LSU outperformed the ITS1 and ITS2 fragments at the genus level, but the ITS1 and ITS2 showed higher accuracy when smaller fragment sizes of the same length and a 50% bootstrap cutoff were used. In a comparison using the larger ITS training set, ITS1 and ITS2 had very similar accuracy classification for fragments between 100 and 200 bp. Collectively, the results show that any of the ITS or LSU sections we tested provided comparable classification accuracy to the genus level and underscore the need for larger and more diverse classification training sets.

From Genus to Phylum: Large-Subunit and Internal Transcribed Spacer rRNA Operon Regions Show Similar Classification Accuracies Influenced by Database Composition

PubMed Central

Liu, Kuan-Liang; Kuske, Cheryl R.

2014-01-01

We compared the classification accuracy of two sections of the fungal internal transcribed spacer (ITS) region, individually and combined, and the 5′ section (about 600 bp) of the large-subunit rRNA (LSU), using a naive Bayesian classifier and BLASTN. A hand-curated ITS-LSU training set of 1,091 sequences and a larger training set of 8,967 ITS region sequences were used. Of the factors evaluated, database composition and quality had the largest effect on classification accuracy, followed by fragment size and use of a bootstrap cutoff to improve classification confidence. The naive Bayesian classifier and BLASTN gave similar results at higher taxonomic levels, but the classifier was faster and more accurate at the genus level when a bootstrap cutoff was used. All of the ITS and LSU sections performed well (>97.7% accuracy) at higher taxonomic ranks from kingdom to family, and differences between them were small at the genus level (within 0.66 to 1.23%). When full-length sequence sections were used, the LSU outperformed the ITS1 and ITS2 fragments at the genus level, but the ITS1 and ITS2 showed higher accuracy when smaller fragment sizes of the same length and a 50% bootstrap cutoff were used. In a comparison using the larger ITS training set, ITS1 and ITS2 had very similar accuracy classification for fragments between 100 and 200 bp. Collectively, the results show that any of the ITS or LSU sections we tested provided comparable classification accuracy to the genus level and underscore the need for larger and more diverse classification training sets. PMID:24242255

Classification of patients by severity grades during triage in the emergency department using data mining methods.

PubMed

Zmiri, Dror; Shahar, Yuval; Taieb-Maimon, Meirav

2012-04-01

To test the feasibility of classifying emergency department patients into severity grades using data mining methods. Emergency department records of 402 patients were classified into five severity grades by two expert physicians. The Naïve Bayes and C4.5 algorithms were applied to produce classifiers from patient data into severity grades. The classifiers' results over several subsets of the data were compared with the physicians' assessments, with a random classifier, and with a classifier that selects the maximal-prevalence class. Positive predictive value, multiple-class extensions of sensitivity and specificity combinations, and entropy change. The mean accuracy of the data mining classifiers was 52.94 ± 5.89%, significantly better (P < 0.05) than the mean accuracy of a random classifier (34.60 ± 2.40%). The entropy of the input data sets was reduced through classification by a mean of 10.1%. Allowing for classification deviations of one severity grade led to mean accuracy of 85.42 ± 1.42%. The classifiers' accuracy in that case was similar to the physicians' consensus rate. Learning from consensus records led to better performance. Reducing the number of severity grades improved results in certain cases. The performance of the Naïve Bayes and C4.5 algorithms was similar; in unbalanced data sets, Naïve Bayes performed better. It is possible to produce a computerized classification model for the severity grade of triage patients, using data mining methods. Learning from patient records regarding which there is a consensus of several physicians is preferable to learning from each physician's patients. Either Naïve Bayes or C4.5 can be used; Naïve Bayes is preferable for unbalanced data sets. An ambiguity in the intermediate severity grades seems to hamper both the physicians' agreement and the classifiers' accuracy. © 2010 Blackwell Publishing Ltd.

33 CFR 110.78 - Sturgeon Bay, Sturgeon Bay, Wis.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false Sturgeon Bay, Sturgeon Bay, Wis. 110.78 Section 110.78 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.78 Sturgeon Bay, Sturgeon Bay, Wis. (a) Area 1...

33 CFR 110.78 - Sturgeon Bay, Sturgeon Bay, Wis.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Sturgeon Bay, Sturgeon Bay, Wis. 110.78 Section 110.78 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.78 Sturgeon Bay, Sturgeon Bay, Wis. (a) Area 1...

33 CFR 110.78 - Sturgeon Bay, Sturgeon Bay, Wis.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false Sturgeon Bay, Sturgeon Bay, Wis. 110.78 Section 110.78 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.78 Sturgeon Bay, Sturgeon Bay, Wis. (a) Area 1...

33 CFR 110.78 - Sturgeon Bay, Sturgeon Bay, Wis.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Sturgeon Bay, Sturgeon Bay, Wis. 110.78 Section 110.78 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY ANCHORAGES ANCHORAGE REGULATIONS Special Anchorage Areas § 110.78 Sturgeon Bay, Sturgeon Bay, Wis. (a) Area 1...

Children's Conceptions of Mental Illness: A Naive Theory Approach

ERIC Educational Resources Information Center

Fox, Claudine; Buchanan-Barrow, Eithne; Barrett, Martyn

2010-01-01

This paper reports two studies that investigated children's conceptions of mental illness using a naive theory approach, drawing upon a conceptual framework for analysing illness representations which distinguishes between the identity, causes, consequences, curability, and timeline of an illness. The studies utilized semi-structured interviewing…

Mathematical Model of Naive T Cell Division and Survival IL-7 Thresholds.

PubMed

Reynolds, Joseph; Coles, Mark; Lythe, Grant; Molina-París, Carmen

2013-01-01

We develop a mathematical model of the peripheral naive T cell population to study the change in human naive T cell numbers from birth to adulthood, incorporating thymic output and the availability of interleukin-7 (IL-7). The model is formulated as three ordinary differential equations: two describe T cell numbers, in a resting state and progressing through the cell cycle. The third is introduced to describe changes in IL-7 availability. Thymic output is a decreasing function of time, representative of the thymic atrophy observed in aging humans. Each T cell is assumed to possess two interleukin-7 receptor (IL-7R) signaling thresholds: a survival threshold and a second, higher, proliferation threshold. If the IL-7R signaling strength is below its survival threshold, a cell may undergo apoptosis. When the signaling strength is above the survival threshold, but below the proliferation threshold, the cell survives but does not divide. Signaling strength above the proliferation threshold enables entry into cell cycle. Assuming that individual cell thresholds are log-normally distributed, we derive population-average rates for apoptosis and entry into cell cycle. We have analyzed the adiabatic change in homeostasis as thymic output decreases. With a parameter set representative of a healthy individual, the model predicts a unique equilibrium number of T cells. In a parameter range representative of persistent viral or bacterial infection, where naive T cell cycle progression is impaired, a decrease in thymic output may result in the collapse of the naive T cell repertoire.

Highly efficient gene transfer in naive human T cells with a murine leukemia virus-based vector.

PubMed

Dardalhon, V; Jaleco, S; Rebouissou, C; Ferrand, C; Skander, N; Swainson, L; Tiberghien, P; Spits, H; Noraz, N; Taylor, N

2000-08-01

Retroviral vectors based on the Moloney murine leukemia virus (MuLV) have become the primary tool for gene delivery into hematopoietic cells, but clinical trials have been hampered by low transduction efficiencies. Recently, we and others have shown that gene transfer of MuLV-based vectors into T cells can be significantly augmented using a fibronectin-facilitated protocol. Nevertheless, the relative abilities of naive (CD45RA(+)) and memory (CD45RO(+)) lymphocyte subsets to be transduced has not been assessed. Although naive T cells demonstrate a restricted cytokine profile following antigen stimulation and a decreased susceptibility to infection with human immunodeficiency virus, it was not clear whether they could be efficiently infected with a MuLV vector. This study describes conditions that permitted gene transfer of an enhanced green fluorescent protein-expressing retroviral vector in more than 50% of naive umbilical cord (UC) blood and peripheral blood (PB) T cells following CD3/CD28 ligation. Moreover, treatment of naive T cells with interleukin-7 resulted in the maintenance of a CD45RA phenotype and gene transfer levels approached 20%. Finally, it was determined that parameters for optimal transduction of CD45RA(+) T cells isolated from PB and UC blood differed: transduction of the UC cells was significantly increased by the presence of autologous mononuclear cells (24.5% versus 56.5%). Because naive T cells harbor a receptor repertoire that allows them to respond to novel antigens, the development of protocols targeting their transduction is crucial for gene therapy applications. This approach will also allow the functions of exogenous genes to be evaluated in primary nontransformed naive T cells.

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Naive vs. Sophisticated Methods of Forecasting Public Library Circulations.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1984-01-01

Two sophisticated--autoregressive integrated moving average (ARIMA), straight-line regression--and two naive--simple average, monthly average--forecasting techniques were used to forecast monthly circulation totals of 34 public libraries. Comparisons of forecasts and actual totals revealed that ARIMA and monthly average methods had smallest mean…

Details: Elevation of Plate Typical Bay, SectionThrough Plate Typical Bay, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Details: Elevation of Plate Typical Bay, Section-Through Plate Typical Bay, Section-Through Plate Center Bay, Elevation of Plate Center Bay - Contoocook Covered Bridge, Spanning Contoocook River, Hopkinton, Merrimack County, NH

33 CFR 80.1114 - San Pedro Bay-Anaheim Bay, CA.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false San Pedro Bay-Anaheim Bay, CA. 80... INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Pacific Coast § 80.1114 San Pedro Bay—Anaheim Bay, CA. (a) A line drawn across the seaward extremities of the Anaheim Bay Entrance Jetties; thence to Long...

33 CFR 80.1114 - San Pedro Bay-Anaheim Bay, CA.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false San Pedro Bay-Anaheim Bay, CA. 80... INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Pacific Coast § 80.1114 San Pedro Bay—Anaheim Bay, CA. (a) A line drawn across the seaward extremities of the Anaheim Bay Entrance Jetties; thence to Long...

33 CFR 80.1114 - San Pedro Bay-Anaheim Bay, CA.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false San Pedro Bay-Anaheim Bay, CA. 80... INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Pacific Coast § 80.1114 San Pedro Bay—Anaheim Bay, CA. (a) A line drawn across the seaward extremities of the Anaheim Bay Entrance Jetties; thence to Long...

33 CFR 80.1114 - San Pedro Bay-Anaheim Bay, CA.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false San Pedro Bay-Anaheim Bay, CA. 80... INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Pacific Coast § 80.1114 San Pedro Bay—Anaheim Bay, CA. (a) A line drawn across the seaward extremities of the Anaheim Bay Entrance Jetties; thence to Long...

33 CFR 80.1114 - San Pedro Bay-Anaheim Bay, CA.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false San Pedro Bay-Anaheim Bay, CA. 80... INTERNATIONAL NAVIGATION RULES COLREGS DEMARCATION LINES Pacific Coast § 80.1114 San Pedro Bay—Anaheim Bay, CA. (a) A line drawn across the seaward extremities of the Anaheim Bay Entrance Jetties; thence to Long...

33 CFR 100.911 - Bay City Airshow, Bay City, MI.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false Bay City Airshow, Bay City, MI. 100.911 Section 100.911 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY REGATTAS AND MARINE PARADES SAFETY OF LIFE ON NAVIGABLE WATERS § 100.911 Bay City Airshow, Bay City, MI. (a...

33 CFR 100.911 - Bay City Airshow, Bay City, MI.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Bay City Airshow, Bay City, MI. 100.911 Section 100.911 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY REGATTAS AND MARINE PARADES SAFETY OF LIFE ON NAVIGABLE WATERS § 100.911 Bay City Airshow, Bay City, MI. (a...

33 CFR 100.911 - Bay City Airshow, Bay City, MI.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false Bay City Airshow, Bay City, MI. 100.911 Section 100.911 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY REGATTAS AND MARINE PARADES SAFETY OF LIFE ON NAVIGABLE WATERS § 100.911 Bay City Airshow, Bay City, MI. (a...

33 CFR 100.911 - Bay City Airshow, Bay City, MI.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Bay City Airshow, Bay City, MI. 100.911 Section 100.911 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY REGATTAS AND MARINE PARADES SAFETY OF LIFE ON NAVIGABLE WATERS § 100.911 Bay City Airshow, Bay City, MI. (a...

33 CFR 100.911 - Bay City Airshow, Bay City, MI.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Bay City Airshow, Bay City, MI. 100.911 Section 100.911 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY REGATTAS AND MARINE PARADES SAFETY OF LIFE ON NAVIGABLE WATERS § 100.911 Bay City Airshow, Bay City, MI. (a...

Wavelet Packet Entropy for Heart Murmurs Classification

PubMed Central

Safara, Fatemeh; Doraisamy, Shyamala; Azman, Azreen; Jantan, Azrul; Ranga, Sri

2012-01-01

Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification. PMID:23227043

Telomerase Is Involved in IL-7-Mediated Differential Survival of Naive and Memory CD4+ T Cells1

PubMed Central

Yang, Yinhua; An, Jie; Weng, Nan-ping

2008-01-01

IL-7 plays an essential role in T cell maintenance and survival. The survival effect of IL-7 is thought to be mediated through regulation of Bcl2 family proteins. After a comparative analysis of IL-7-induced growth and cell death of human naive and memory CD4+ T cells, we observed that more memory CD4+ T cells underwent cell division and proceeded to apoptosis than naive cells in response to IL-7. However, IL-7-induced expressions of Bcl2 family members (Bcl2, Bcl-xL, Bax, and Bad) were similar between naive and memory cells. Instead, we found that IL-7 induced higher levels of telomerase activity in naive cells than in memory cells, and the levels of IL-7-induced telomerase activity had a significant inverse correlation with cell death in CD4+ T cells. Furthermore, we showed that reducing expression of telomerase reverse transcriptase and telomerase activity significantly increased cell death of IL-7-cultured CD4+ T cells. Together, these findings demonstrate that telomerase is involved in IL-7-mediated differential survival of naive and memory CD4+ T cells. PMID:18322183

Identification of Phragmites australis and Spartina alterniflora in the Yangtze Estuary between Bayes and BP neural network using hyper-spectral data

NASA Astrophysics Data System (ADS)

Liu, Pudong; Zhou, Jiayuan; Shi, Runhe; Zhang, Chao; Liu, Chaoshun; Sun, Zhibin; Gao, Wei

2016-09-01

The aim of this work was to identify the coastal wetland plants between Bayes and BP neural network using hyperspectral data in order to optimize the classification method. For this purpose, we chose two dominant plants (invasive S. alterniflora and native P. australis) in the Yangtze Estuary, the leaf spectral reflectance of P. australis and S. alterniflora were measured by ASD field spectral machine. We tested the Bayes method and BP neural network for the identification of these two species. Results showed that three different bands (i.e., 555 nm 711 nm and 920 nm) could be identified as the sensitive bands for the input parameters for the two methods. Bayes method and BP neural network prediction model both performed well (Bayes prediction for 88.57% accuracy, BP neural network model prediction for about 80% accuracy), but Bayes theorem method could give higher accuracy and stability.

Application of Bayes' to the prediction of referral decisions made by specialist optometrists in relation to chronic open angle glaucoma.

PubMed

Gurney, J C; Ansari, E; Harle, D; O'Kane, N; Sagar, R V; Dunne, M C M

2018-02-09

To determine the accuracy of a Bayesian learning scheme (Bayes') applied to the prediction of clinical decisions made by specialist optometrists in relation to the referral refinement of chronic open angle glaucoma. This cross-sectional observational study involved collection of data from the worst affected or right eyes of a consecutive sample of cases (n = 1,006) referred into the West Kent Clinical Commissioning Group Community Ophthalmology Team (COT) by high street optometrists. Multilevel classification of each case was based on race, sex, age, family history of chronic open angle glaucoma, reason for referral, Goldmann Applanation Tonometry (intraocular pressure and interocular asymmetry), optic nerve head assessment (vertical size, cup disc ratio and interocular asymmetry), central corneal thickness and visual field analysis (Hodapp-Parrish-Anderson classification). Randomised stratified tenfold cross-validation was applied to determine the accuracy of Bayes' by comparing its output to the clinical decisions of three COT specialist optometrists; namely, the decision to discharge, follow-up or refer each case. Outcomes of cross-validation, expressed as means and standard deviations, showed that the accuracy of Bayes' was high (95%, 2.0%) but that it falsely discharged (3.4%, 1.6%) or referred (3.1%, 1.5%) some cases. The results indicate that Bayes' has the potential to augment the decisions of specialist optometrists.

Hyperspectral analysis of seagrass in Redfish Bay, Texas

NASA Astrophysics Data System (ADS)

Wood, John S.

Remote sensing using multi- and hyperspectral imaging and analysis has been used in resource management for quite some time, and for a variety of purposes. In the studies to follow, hyperspectral imagery of Redfish Bay is used to discriminate between species of seagrasses found below the water surface. Water attenuates and reflects light and energy from the electromagnetic spectrum, and as a result, subsurface analysis can be more complex than that performed in the terrestrial world. In the following studies, an iterative process is developed, using ENVI image processing software and ArcGIS software. Band selection was based on recommendations developed empirically in conjunction with ongoing research into depth corrections, which were applied to the imagery bands (a default depth of 65 cm was used). Polygons generated, classified and aggregated within ENVI are reclassified in ArcGIS using field site data that was randomly selected for that purpose. After the first iteration, polygons that remain classified as 'Mixed' are subjected to another iteration of classification in ENVI, then brought into ArcGIS and reclassified. Finally, when that classification scheme is exhausted, a supervised classification is performed, using a 'Maximum Likelihood' classification technique, which assigned the remaining polygons to the classification that was most like the training polygons, by digital number value. Producer's Accuracy by classification ranged from 23.33 % for the 'MixedMono' class to 66.67% for the 'Bare' class; User's Accuracy by classification ranged from 22.58% for the 'MixedMono' class to 69.57% for the 'Bare' classification. An overall accuracy of 37.93% was achieved. Producers and Users Accuracies for Halodule were 29% and 39%, respectively; for Thalassia, they were 46% and 40%. Cohen's Kappa Coefficient was calculated at .2988. We then returned to the field and collected spectral signatures of monotypic stands of seagrass at varying depths and at three sensor

Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

PubMed

Maity, Maitreya; Dhane, Dhiraj; Mungle, Tushar; Maiti, A K; Chakraborty, Chandan

2017-10-26

Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance. The proposed methodology for automated evaluation of parasites includes pre-processing of blood smear microscopic images followed by erythrocytes segmentation. To differentiate between different parasites; a total of 138 quantitative features characterising colour, morphology, and texture are extracted from segmented erythrocytes. An integrated pattern classification framework is designed where four feature selection methods viz. Correlation-based Feature Selection (CFS), Chi-square, Information Gain, and RELIEF are employed with three different classifiers i.e. Naive Bayes', C4.5, and Instance-Based Learning (IB1) individually. Optimal features subset with the best classifier is selected for achieving maximum diagnostic precision. It is seen that the proposed method achieved with 99.2% sensitivity and 99.6% specificity by combining CFS and C4.5 in comparison with other methods. Moreover, the web-based tool is entirely designed using open standards like Java for a web application, ImageJ for image processing, and WEKA for data mining considering its feasibility in rural places with minimal health care facilities.

33 CFR 165.1182 - Safety/Security Zone: San Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., Carquinez Strait, and Suisun Bay, CA. (a) Regulated area. The following area is established as a moving... Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA. 165.1182 Section 165.1182 Navigation and... vessels transit from a line drawn between San Francisco Main Ship Channel buoys 7 and 8 (LLNR 4190 & 4195...

33 CFR 165.1182 - Safety/Security Zone: San Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA.

Code of Federal Regulations, 2011 CFR

2011-07-01

..., Carquinez Strait, and Suisun Bay, CA. (a) Regulated area. The following area is established as a moving... Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA. 165.1182 Section 165.1182 Navigation and... vessels transit from a line drawn between San Francisco Main Ship Channel buoys 7 and 8 (LLNR 4190 & 4195...

33 CFR 165.1182 - Safety/Security Zone: San Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., Carquinez Strait, and Suisun Bay, CA. (a) Regulated area. The following area is established as a moving... Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA. 165.1182 Section 165.1182 Navigation and... vessels transit from a line drawn between San Francisco Main Ship Channel buoys 7 and 8 (LLNR 4190 & 4195...

33 CFR 165.1182 - Safety/Security Zone: San Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., Carquinez Strait, and Suisun Bay, CA. (a) Regulated area. The following area is established as a moving... Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA. 165.1182 Section 165.1182 Navigation and... vessels transit from a line drawn between San Francisco Main Ship Channel buoys 7 and 8 (LLNR 4190 & 4195...

33 CFR 165.1182 - Safety/Security Zone: San Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., Carquinez Strait, and Suisun Bay, CA. (a) Regulated area. The following area is established as a moving... Francisco Bay, San Pablo Bay, Carquinez Strait, and Suisun Bay, CA. 165.1182 Section 165.1182 Navigation and... vessels transit from a line drawn between San Francisco Main Ship Channel buoys 7 and 8 (LLNR 4190 & 4195...

Ensemble of Chaotic and Naive Approaches for Performance Enhancement in Video Encryption.

PubMed

Chandrasekaran, Jeyamala; Thiruvengadam, S J

2015-01-01

Owing to the growth of high performance network technologies, multimedia applications over the Internet are increasing exponentially. Applications like video conferencing, video-on-demand, and pay-per-view depend upon encryption algorithms for providing confidentiality. Video communication is characterized by distinct features such as large volume, high redundancy between adjacent frames, video codec compliance, syntax compliance, and application specific requirements. Naive approaches for video encryption encrypt the entire video stream with conventional text based cryptographic algorithms. Although naive approaches are the most secure for video encryption, the computational cost associated with them is very high. This research work aims at enhancing the speed of naive approaches through chaos based S-box design. Chaotic equations are popularly known for randomness, extreme sensitivity to initial conditions, and ergodicity. The proposed methodology employs two-dimensional discrete Henon map for (i) generation of dynamic and key-dependent S-box that could be integrated with symmetric algorithms like Blowfish and Data Encryption Standard (DES) and (ii) generation of one-time keys for simple substitution ciphers. The proposed design is tested for randomness, nonlinearity, avalanche effect, bit independence criterion, and key sensitivity. Experimental results confirm that chaos based S-box design and key generation significantly reduce the computational cost of video encryption with no compromise in security.

Naive (commonsense) geography and geobrowser usability after ten years of Google Earth

NASA Astrophysics Data System (ADS)

Hamerlinck, J. D.

2016-04-01

In 1995, the concept of ‘naive geography’ was formally introduced as an area of cognitive geographic information science representing ‘the body of knowledge that people have about the surrounding geographic world’ and reflecting ‘the way people think and reason about geographic space and time, both consciously and subconsciously’. The need to incorporate such commonsense knowledge and reasoning into design of geospatial technologies was identified but faced challenges in formalizing these relationships and processes in software implementation. Ten years later, the Google Earth geobrowser was released, marking the beginning of a new era of open access to, and application of, geographic data and information in society. Fast-forward to today, and the opportunity presents itself to take stock of twenty years of naive geography and a decade of the ubiquitous virtual globe. This paper introduces an ongoing research effort to explore the integration of naive (or commonsense) geography concepts in the Google Earth geobrowser virtual globe and their possible impact on Google Earth's usability, utility, and usefulness. A multi-phase methodology is described, combining usability reviews and usability testing with use-case scenarios involving the U.S.-Canadian Yellowstone to Yukon Initiative. Initial progress on a usability review combining cognitive walkthroughs and heuristics evaluation is presented.

Ensemble of Chaotic and Naive Approaches for Performance Enhancement in Video Encryption

PubMed Central

Chandrasekaran, Jeyamala; Thiruvengadam, S. J.

2015-01-01

Owing to the growth of high performance network technologies, multimedia applications over the Internet are increasing exponentially. Applications like video conferencing, video-on-demand, and pay-per-view depend upon encryption algorithms for providing confidentiality. Video communication is characterized by distinct features such as large volume, high redundancy between adjacent frames, video codec compliance, syntax compliance, and application specific requirements. Naive approaches for video encryption encrypt the entire video stream with conventional text based cryptographic algorithms. Although naive approaches are the most secure for video encryption, the computational cost associated with them is very high. This research work aims at enhancing the speed of naive approaches through chaos based S-box design. Chaotic equations are popularly known for randomness, extreme sensitivity to initial conditions, and ergodicity. The proposed methodology employs two-dimensional discrete Henon map for (i) generation of dynamic and key-dependent S-box that could be integrated with symmetric algorithms like Blowfish and Data Encryption Standard (DES) and (ii) generation of one-time keys for simple substitution ciphers. The proposed design is tested for randomness, nonlinearity, avalanche effect, bit independence criterion, and key sensitivity. Experimental results confirm that chaos based S-box design and key generation significantly reduce the computational cost of video encryption with no compromise in security. PMID:26550603

Three Naive Questions: Addressed to the Modern Educational Optimism

ERIC Educational Resources Information Center

Krstic, Predrag

2016-01-01

This paper aims to question anew the popular and supposedly self-evident affirmation of education, in its modern incarnation as in its historical notion. The "naive" questions suggest that we have recently taken for granted that education ought to be for the masses, that it ought to be upbringing, and that it is better than ignorance.…

Intra-articular clearance of labeled dextrans from naive and arthritic rat knee joints.

PubMed

Mwangi, Timothy K; Berke, Ian M; Nieves, Eduardo H; Bell, Richard D; Adams, Samuel B; Setton, Lori A

2018-05-26

Determine the effects of arthritis on the trans-synovial clearance of small and large model compounds following local delivery to the knee joint in a rat model. Intra-articular delivery was studied in rat knee joints in an osteoarthritis model of joint instability (medial collateral ligament and meniscus transection model or MMT). Fluorescently-labeled 10 kDa or 500 kDa dextran was injected in the arthritic or unoperated control (naive) joints 3 weeks after surgical destabilization, and the temporal clearance pattern was evaluated via in vivo regional fluorescence imaging, dextran concentrations in plasma and draining lymph nodes, and by quantification of fluorescence in histological synovium sections. Together these data were used to evaluate the effect of osteoarthritis and solute size on the rate of drug clearance from the joint. Clearance of 10 kDa dextran from the joint space quantified using in vivo fluorescence imaging of the knee joint region was not significantly different between naive and MMT joints. In contrast, clearance of 500 kDa dextran was significantly reduced for MMT joints when compared to naive joints by fluorescence in vivo imaging. Drug accumulation in lymph nodes and plasma were lower for the 500 kDa dextran as compared to 10 kDa dextran, and lymph node levels were further reduced with the presence of osteoarthritis. Furthermore, synovium was significantly thicker in MMT joints than in naive joints and image analysis of joint tissue sections revealed different trans-synovial distributions of 10 and 500 kDa dextran. Large macromolecules were retained in the arthritic joint longer than in the healthy joint, while smaller molecules were cleared similarly in healthy and arthritic joints. In vivo fluorescence imaging, plasma and lymph node concentrations, and spatial distributions of drug fluorescence identified differences in higher molecular weight clearance between naive and arthritic disease states. Findings may relate to a

[Diversity and antimicrobial activities of cultivable bacteria isolated from Jiaozhou Bay].

PubMed

Wang, Yiting; Zhang, Chuanbo; Qi, Lin; Jia, Xiaoqiang; Lu, Wenyu

2016-12-04

Marine microorganisms have a great potential in producing biologically active secondary metabolites. In order to study the diversity and antimicrobial activity, we explored 9 sediment samples in different observation sites of Jiaozhou bay. We used YPD and Z2216E culture medium to isolate bacteria from the sediments; 16S rRNA was sequenced for classification and identification of the isolates. Then, we used Oxford cup method to detect antimicrobial activities of the isolated bacteria against 7 test strains. Lastly, we selected 16 representatives to detect secondary-metabolite biosynthesis genes:PKSI, NRPS, CYP, PhzE, dTGD by PCR specific amplification. A total of 76 bacterial strains were isolated from Jiaozhou bay; according to the 16S rRNA gene sequence analysis. These strains could be sorted into 11 genera belonging to 8 different families:Aneurinibacillus, Brevibacillus, Microbacterium, Oceanisphae, Bacillus, Marinomonas, Staphylococcus, Kocuria, Arthrobacters, Micrococcus and Pseudoalteromonas. Of them 34 strains showed antimicrobial activity against at least one of the tested strains. All 16 strains had at least one function genes, 5 strains possessed more than three function genes. Jiaozhou bay area is rich in microbial resources with potential in providing useful secondary metabolites.

BayesMotif: de novo protein sorting motif discovery from impure datasets.

PubMed

Hu, Jianjun; Zhang, Fan

2010-01-18

Protein sorting is the process that newly synthesized proteins are transported to their target locations within or outside of the cell. This process is precisely regulated by protein sorting signals in different forms. A major category of sorting signals are amino acid sub-sequences usually located at the N-terminals or C-terminals of protein sequences. Genome-wide experimental identification of protein sorting signals is extremely time-consuming and costly. Effective computational algorithms for de novo discovery of protein sorting signals is needed to improve the understanding of protein sorting mechanisms. We formulated the protein sorting motif discovery problem as a classification problem and proposed a Bayesian classifier based algorithm (BayesMotif) for de novo identification of a common type of protein sorting motifs in which a highly conserved anchor is present along with a less conserved motif regions. A false positive removal procedure is developed to iteratively remove sequences that are unlikely to contain true motifs so that the algorithm can identify motifs from impure input sequences. Experiments on both implanted motif datasets and real-world datasets showed that the enhanced BayesMotif algorithm can identify anchored sorting motifs from pure or impure protein sequence dataset. It also shows that the false positive removal procedure can help to identify true motifs even when there is only 20% of the input sequences containing true motif instances. We proposed BayesMotif, a novel Bayesian classification based algorithm for de novo discovery of a special category of anchored protein sorting motifs from impure datasets. Compared to conventional motif discovery algorithms such as MEME, our algorithm can find less-conserved motifs with short highly conserved anchors. Our algorithm also has the advantage of easy incorporation of additional meta-sequence features such as hydrophobicity or charge of the motifs which may help to overcome the limitations of

Gender classification from video under challenging operating conditions

NASA Astrophysics Data System (ADS)

Mendoza-Schrock, Olga; Dong, Guozhu

2014-06-01

The literature is abundant with papers on gender classification research. However the majority of such research is based on the assumption that there is enough resolution so that the subject's face can be resolved. Hence the majority of the research is actually in the face recognition and facial feature area. A gap exists for gender classification under challenging operating conditions—different seasonal conditions, different clothing, etc.—and when the subject's face cannot be resolved due to lack of resolution. The Seasonal Weather and Gender (SWAG) Database is a novel database that contains subjects walking through a scene under operating conditions that span a calendar year. This paper exploits a subset of that database—the SWAG One dataset—using data mining techniques, traditional classifiers (ex. Naïve Bayes, Support Vector Machine, etc.) and traditional (canny edge detection, etc.) and non-traditional (height/width ratios, etc.) feature extractors to achieve high correct gender classification rates (greater than 85%). Another novelty includes exploiting frame differentials.

Left-Turn Bays

DOT National Transportation Integrated Search

1996-05-01

The topic of left-turn bays (left-turn lanes) involves the following three issues: 1.) Warrants; 2.) Bay Length and 3.) Design Details. This discussion paper deals with warrants and bay length -- including queue storage at signalized and unsignalized...

Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model

ERIC Educational Resources Information Center

de la Torre, Jimmy; Hong, Yuan; Deng, Weiling

2010-01-01

To better understand the statistical properties of the deterministic inputs, noisy "and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the…

Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides.

PubMed

Panwar, Bharat; Raghava, Gajendra P S

2015-04-01

The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting residues in proteins, but limited efforts have been made for the identification of protein-interacting nucleotides in RNAs. In order to discriminate protein-interacting and non-interacting nucleotides, we used various classifiers (NaiveBayes, NaiveBayesMultinomial, BayesNet, ComplementNaiveBayes, MultilayerPerceptron, J48, SMO, RandomForest, SMO and SVM(light)) for prediction model development using various features and achieved highest 83.92% sensitivity, 84.82 specificity, 84.62% accuracy and 0.62 Matthew's correlation coefficient by SVM(light) based models. We observed that certain tri-nucleotides like ACA, ACC, AGA, CAC, CCA, GAG, UGA, and UUU preferred in protein-interaction. All the models have been developed using a non-redundant dataset and are evaluated using five-fold cross validation technique. A web-server called RNApin has been developed for the scientific community (http://crdd.osdd.net/raghava/rnapin/). Copyright © 2015 Elsevier Inc. All rights reserved.

Bayes multiple decision functions.

PubMed

Wu, Wensong; Peña, Edsel A

2013-01-01

This paper deals with the problem of simultaneously making many ( M ) binary decisions based on one realization of a random data matrix X . M is typically large and X will usually have M rows associated with each of the M decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business. A Bayesian decision-theoretic approach to this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the quality of decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix X are assumed to be stochastically independent, we allow a dependent data structure with the associations obtained through a class of frailty-induced Archimedean copulas. In particular, non-Gaussian dependent data structure, which is typical with failure-time data, can be entertained. The numerical implementation of the determination of the Bayes optimal action is facilitated through sequential Monte Carlo techniques. The theory developed could also be extended to the problem of multiple hypotheses testing, multiple classification and prediction, and high-dimensional variable selection. The proposed procedure is illustrated for the simple versus simple hypotheses setting and for the composite hypotheses setting

33 CFR 100.919 - International Bay City River Roar, Bay City, MI.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false International Bay City River Roar, Bay City, MI. 100.919 Section 100.919 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF... Bay City River Roar, Bay City, MI. (a) Regulated Area. A regulated area is established to include all...

33 CFR 100.919 - International Bay City River Roar, Bay City, MI.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false International Bay City River Roar, Bay City, MI. 100.919 Section 100.919 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF... Bay City River Roar, Bay City, MI. (a) Regulated Area. A regulated area is established to include all...

Expert and Naive Raters Using the PAG: Does it Matter?

ERIC Educational Resources Information Center

Cornelius, Edwin T.; And Others

1984-01-01

Questions the observed correlation between job experts and naive raters using the Position Analysis Questionnaire (PAQ); and conducts a replication of the Smith and Hakel study (1979) with college students (N=39). Concluded that PAQ ratings from job experts and college students are not equivalent and therefore are not interchangeable. (LLL)

LDA boost classification: boosting by topics

NASA Astrophysics Data System (ADS)

Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

2012-12-01

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

Right lateralized white matter abnormalities in first-episode, drug-naive paranoid schizophrenia.

PubMed

Guo, Wenbin; Liu, Feng; Liu, Zhening; Gao, Keming; Xiao, Changqing; Chen, Huafu; Zhao, Jingping

2012-11-30

Numerous studies in first-episode schizophrenia suggest the involvement of white matter (WM) abnormalities in multiple regions underlying the pathogenesis of this condition. However, there has never been a neuroimaging study in patients with first-episode, drug-naive paranoid schizophrenia by using tract-based spatial statistics (TBSS) method. Here, we used diffusion tensor imaging (DTI) with TBSS method to investigate the brain WM integrity in patients with first-episode, drug-naive paranoid schizophrenia. Twenty patients with first-episode, drug-naive paranoid schizophrenia and 26 healthy subjects matched with age, gender, and education level were scanned with DTI. An automated TBSS approach was employed to analyze the data. Voxel-wise statistics revealed that patients with paranoid schizophrenia had decreased fractional anisotropy (FA) values in the right superior longitudinal fasciculus (SLF) II, the right fornix, the right internal capsule, and the right external capsule compared to healthy subjects. Patients did not have increased FA values in any brain regions compared to healthy subjects. There was no correlation between the FA values in any brain regions and patient demographics and the severity of illness. Our findings suggest right-sided alterations of WM integrity in the WM tracts of cortical and subcortical regions may play an important role in the pathogenesis of paranoid schizophrenia. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.

PubMed

Griffis, Joseph C; Allendorfer, Jane B; Szaflarski, Jerzy P

2016-01-15

Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. Our method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations. Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatial patterning of water quality in Biscayne Bay, Florida as a function of land use and water management.

PubMed

Caccia, Valentina G; Boyer, Joseph N

2005-11-01

An objective classification analysis was performed on a water quality data set from 25 sites collected monthly during 1994-2003. The water quality parameters measured included: TN, TON, DIN, NH4+, NO3-, NO2-, TP, SRP, TN:TP ratio, TOC, DO, CHL A, turbidity, salinity and temperature. Based on this spatial analysis, Biscayne Bay was divided into five zones having similar water quality characteristics. A robust nutrient gradient, driven mostly by dissolved inorganic nitrogen, from alongshore to offshore in the main Bay, was a large determinant in the spatial clustering. Two of these zones (Alongshore and Inshore) were heavily influenced by freshwater input from four canals which drain the South Dade agricultural area, Black Point Landfill, and sewage treatment plant. The North Bay zone, with high turbidity, phytoplankton biomass, total phosphorus, and low DO, was affected by runoff from five canals, the Munisport Landfill, and the urban landscape. The South Bay zone, an embayment surrounded by mangrove wetlands with little urban development, was high in dissolved organic constituents but low in inorganic nutrients. The Main Bay was the area most influenced by water exchange with the Atlantic Ocean and showed the lowest nutrient concentrations. The water quality in Biscayne Bay is therefore highly dependent of the land use and influence from the watershed.

Automating document classification for the Immune Epitope Database

PubMed Central

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

2007-01-01

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

Does the cost function matter in Bayes decision rule?

PubMed

Schlü ter, Ralf; Nussbaum-Thom, Markus; Ney, Hermann

2012-02-01

In many tasks in pattern recognition, such as automatic speech recognition (ASR), optical character recognition (OCR), part-of-speech (POS) tagging, and other string recognition tasks, we are faced with a well-known inconsistency: The Bayes decision rule is usually used to minimize string (symbol sequence) error, whereas, in practice, we want to minimize symbol (word, character, tag, etc.) error. When comparing different recognition systems, we do indeed use symbol error rate as an evaluation measure. The topic of this work is to analyze the relation between string (i.e., 0-1) and symbol error (i.e., metric, integer valued) cost functions in the Bayes decision rule, for which fundamental analytic results are derived. Simple conditions are derived for which the Bayes decision rule with integer-valued metric cost function and with 0-1 cost gives the same decisions or leads to classes with limited cost. The corresponding conditions can be tested with complexity linear in the number of classes. The results obtained do not make any assumption w.r.t. the structure of the underlying distributions or the classification problem. Nevertheless, the general analytic results are analyzed via simulations of string recognition problems with Levenshtein (edit) distance cost function. The results support earlier findings that considerable improvements are to be expected when initial error rates are high.

Thinking Process of Naive Problem Solvers to Solve Mathematical Problems

ERIC Educational Resources Information Center

Mairing, Jackson Pasini

2017-01-01

Solving problems is not only a goal of mathematical learning. Students acquire ways of thinking, habits of persistence and curiosity, and confidence in unfamiliar situations by learning to solve problems. In fact, there were students who had difficulty in solving problems. The students were naive problem solvers. This research aimed to describe…

The Profession of Psychology Scale: Sophisticated and Naive Students' Responses

ERIC Educational Resources Information Center

Rosenthal, Gary T.; Soper, Barlow; Rachal, Chris; McKnight, Richard R.; Price, A. W.

2004-01-01

The Profession of Psychology Scale (Rosenthal, McKnight & Price, 2001) was used to investigate whether taking more psychology courses results in a more accurate understanding of what is required to become a psychologist. Data indicate that though misconceptions exist in both Naive students (those who had not completed any psychology courses) and…

IL-15 induces antigen-independent expansion and differentiation of human naive CD8+ T cells in vitro.

PubMed

Alves, Nuno L; Hooibrink, Berend; Arosa, Fernando A; van Lier, René A W

2003-10-01

Recent studies in mice have shown that although interleukin 15 (IL-15) plays an important role in regulating homeostasis of memory CD8+ T cells, it has no apparent function in controlling homeostatic proliferation of naive T cells. We here assessed the influence of IL-15 on antigen-independent expansion and differentiation of human CD8+ T cells. Both naive and primed human T cells divided in response to IL-15. In this process, naive CD8+ T cells successively down-regulated CD45RA and CD28 but maintained CD27 expression. Concomitant with these phenotypic changes, naive cells acquired the ability to produce interferon gamma (IFN-gamma) and tumor necrosis factor alpha (TNF-alpha), expressed perforin and granzyme B, and acquired cytotoxic properties. Primed CD8+ T cells, from both noncytotoxic (CD45RA-CD27+) and cytotoxic (CD45RA+CD27-) subsets, responded to IL-15 and yielded ample numbers of cytokine-secreting and cytotoxic effector cells. In summary, all human CD8+ T-cell subsets had the ability to respond to IL-15, which suggests a generic influence of this cytokine on CD8+ T-cell homeostasis in man.

Naive and effector B-cell subtypes are increased in chronic rhinosinusitis with polyps.

PubMed

Miljkovic, Dijana; Psaltis, Alkis; Wormald, Peter-John; Vreugde, Sarah

2018-01-01

Recent studies demonstrated that B cells and their chemoattractants are elevated in the nasal mucosa of patients with chronic rhinosinusitis (CRS) with nasal polyposis (CRSwNP). However, the presence of naive B cells and of plasmablasts and memory B-cell subsets in the mucosa and periphery of the same patient with CRS is yet to be characterized. Here we sought to quantify naive, plasmablasts, and memory B cells in mucosal tissue and peripheral blood of patients with CRSwNP, patients with CRS without nasal polyps (CRSsNP), and control patients. Polyps, mucosa, and peripheral blood samples were prospectively collected from the patients with CRS and from the non-CRS controls. We used flow cytometry to distinguish among naive, plasmablast, and memory B cells in sinus tissue and peripheral blood. A total of 45 patients were recruited for the study. The patients with CRSwNP had significantly increased mucosal B-cell numbers versus the controls (3.39 ± 4.05% versus 0.39 ± 1.05% of live cells; p < 0.01, Kruskal-Wallis test), which included naive B cells (0.61 ± 0.94 versus 0.11 ± 0.24% of live cells; p < 0.03, Kruskal-Wallis test), plasmablasts (0.06 ± 0.26 versus 0.00 ± 0.00% of live cells; p < 0.055, Kruskal-Wallis test), and memory B cells (0.62 ± 1.26 versus 0.05 ± 0.15% of live cells; p < 0.02, Kruskal-Wallis test). Our study identified increased frequencies of different B-cell subtypes in the mucosa of patients with CRSwNP but not in the peripheral blood. We also found that patients with CRSwNP had significantly increased B-cell subtypes compared with the patients with CRSsNP and the controls. These results implied a potential role for mucosal B cells in the ongoing inflammation in patients with CRSwNP.

Learning accurate very fast decision trees from uncertain data streams

NASA Astrophysics Data System (ADS)

Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo

2015-12-01

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.

77 FR 70891 - Safety Zone; Bay Bridge Construction, San Francisco Bay, San Francisco, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-28

...-AA00 Safety Zone; Bay Bridge Construction, San Francisco Bay, San Francisco, CA AGENCY: Coast Guard... the navigable waters of the San Francisco Bay near Yerba Buena Island, CA in support of the Bay Bridge... construction of the Bay Bridge, the safety zone is necessary to provide for the safety of mariners transiting...

33 CFR 100.112 - Swim the Bay, Narragansett Bay, Narragansett, RI.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Swim the Bay, Narragansett Bay, Narragansett, RI. 100.112 Section 100.112 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY REGATTAS AND MARINE PARADES SAFETY OF LIFE ON NAVIGABLE WATERS § 100.112 Swim the Bay...

A Workshop for High School Students on Naive Set Theory

ERIC Educational Resources Information Center

Wegner, Sven-Ake

2014-01-01

In this article we present the prototype of a workshop on naive set theory designed for high school students in or around the seventh year of primary education. Our concept is based on two events which the author organized in 2006 and 2010 for students of elementary school and high school, respectively. The article also includes a practice report…

Marine benthic habitat mapping of the West Arm, Glacier Bay National Park and Preserve, Alaska

USGS Publications Warehouse

Hodson, Timothy O.; Cochrane, Guy R.; Powell, Ross D.

2013-01-01

Seafloor geology and potential benthic habitats were mapped in West Arm, Glacier Bay National Park and Preserve, Alaska, using multibeam sonar, groundtruthed observations, and geological interpretations. The West Arm of Glacier Bay is a recently deglaciated fjord system under the influence of glacial and paraglacial marine processes. High glacially derived sediment and meltwater fluxes, slope instabilities, and variable bathymetry result in a highly dynamic estuarine environment and benthic ecosystem. We characterize the fjord seafloor and potential benthic habitats using the recently developed Coastal and Marine Ecological Classification Standard (CMECS) by the National Oceanic and Atmospheric Administration (NOAA) and NatureServe. Due to the high flux of glacially sourced fines, mud is the dominant substrate within the West Arm. Water-column characteristics are addressed using a combination of CTD and circulation model results. We also present sediment accumulation data derived from differential bathymetry. These data show the West Arm is divided into two contrasting environments: a dynamic upper fjord and a relatively static lower fjord. The results of these analyses serve as a test of the CMECS classification scheme and as a baseline for ongoing and future mapping efforts and correlations between seafloor substrate, benthic habitats, and glacimarine processes.

Association of serum brain derived neurotropic factor with duration of drug-naive period and positive-negative symptom scores in drug naive schizophrenia.

PubMed

Bakirhan, Abdurrahim; Yalcin Sahiner, Safak; Sahiner, Ismail Volkan; Safak, Yasir; Goka, Erol

2017-01-01

The aim of this study was to compare the serum brain derived neurotropic factor (BNDF) levels of patients with schizophrenia who had never received an antipsychotic treatment with those of a control group. Also, to analyze the relationship between the Positive and Negative Symptom Scale (PANSS) scores and BDNF levels of the patients during the period they were drug-naive. The sample of the study comprised patients who presentedto the Psychiatry Clinic and were admitted after a distinctive schizophrenia diagnosis was made in accordance with the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) diagnosis classification and who were not using and never had any antipsychotic medicine. A total of 160 participants were included in the study, 80 of whom had schizophrenia patients and 80 constituted the age- and sex-matched healthy control group. Before the start of the treatment, the serum samples to be checked for the BDNF levels were collected from the patients. The difference between the average BDNF levels of the groups were statistically significant (t = -5.25; p˂.001). An analysis as to whether there was a relation between the BDNF levels and the drug-naïve duration indicated no correlations. An examination of the relationship between PANSS scores and BDNF levels of the patients yielded no correlations. Serum BDNF levels seem to be one of the indicators of schizophrenia and its progress; nevertheless, we still do not have sufficient information about this neurotropic factor. In light of our study, the neurodevelopmental changes that occur at disease onset of the illness prominently affect the progress of the illness, which highlights the importance of the treatment in the early stages.

A HYBRID HIGH RESOLUTION IMAGE CLASSIFICATION METHOD FOR MAPPING EELGRASS DISTRIBUTIONS IN YAQUINA BAY ESTUARY, OREGON

EPA Science Inventory

False-color infrared aerial photography of the Yaquina Bay Estuary, Oregon was acquired at extreme low tides and digitally orthorectified with a ground pixel resolution of 20 cm to provide data for intertidal vegetation mapping. Submerged, semi-exposed and exposed eelgrass mead...

Multivariate and Naive Bayes Text Classification Approach to Cost Growth Risk in Department of Defense Acquisition Programs

DTIC Science & Technology

2013-03-01

alerts 0.00011 3.26E-06 alternative 0.000161 0.000426 amp 5.25E-05 0.003127 amplifier 0.001501 0.000277 angular 0.000103 3.26E-06 anticipate 0.000755...0.000217 0.00056 amp 4.07E-05 0.004884 amplifier 0.002158 0.00043 angular 0.000109 4.48E-06 anticipation 0.000136 0.000453 aperture 0.000624...0.000215 instructed 0.00057 4.93E-05 java 0.000258 4.48E-05 refactoring 0.00019 2.69E-05 strike 0.000271 5.83E-05 touches 1.36E-05 9.86E-05

75 FR 36292 - Safety Zone; Bay Swim III, Presque Isle Bay, Erie, PA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-25

... of Presque Isle Bay, Lake Erie, near Erie, Pennsylvania between 9 a.m. to 11 a.m. on June 26, 2010.... The safety zone will encompass specified waters of Presque Isle Bay, Erie, Pennsylvania starting at...-AA00 Safety Zone; Bay Swim III, Presque Isle Bay, Erie, PA AGENCY: Coast Guard, DHS. ACTION: Temporary...

Structure, age and origin of the bay-mouth shoal deposits, Chesapeake Bay, Virginia

USGS Publications Warehouse

Colman, Steven M.; Berquist, C.R.; Hobbs, C. H.

1988-01-01

The mouth of Chesapeake Bay contains a distinctive shoal complex and related deposits that result from the complex interaction of three different processes: (1) progradation of a barrier spit at the southern end of the Delmarva Peninsula, (2) strong, reversing tidal currents that transport and rework sediment brought to the bay mouth from the north, and (3) landward (bayward) net non-tidal circulation and sediment transport. Together, these processes play a major role in changing the configuration of the estuary and filling it with sediment. The deposits at the mouth of the bay hold keys both to the evolution of the bay during the Holocene transgression and to the history of previous generations of the bay. The deposit associated with the shoals at the mouth of the bay, the bay-mouth sand, is a distinct stratigraphic unit composed mostly of uniform, gray, fine sand. The position and internal structure of the unit shows that it is related to near-present sea level, and thus is less than a few thousand years old. The processes affecting the upper surface of the deposit and the patterns of erosion and deposition at this surface are complex, but the geometry and structure of the deposit indicate that it is a coherent unit that is prograding bayward and tending to fill the estuary. The source of the bay-mouth sand is primarily outside the bay in the nearshore zone of the Delmarva Peninsula and on the inner continental shelf. The internal structure of the deposit, its surface morphology, its heavy-mineral composition, bottom-current studies, comparative bathymetry, and sediment budgets all suggest that sand is brought to the bay mouth by southerly longshore drift along the Delmarva Peninsula and then swept into the bay. In addition to building the southward- and bayward-prograding bay-mouth sand, these processes result in sand deposition tens of kilometers into the bay. ?? 1988.

33 CFR 162.125 - Sturgeon Bay and the Sturgeon Bay Ship Canal, Wisc.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Sturgeon Bay and the Sturgeon Bay Ship Canal, Wisc. 162.125 Section 162.125 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) PORTS AND WATERWAYS SAFETY INLAND WATERWAYS NAVIGATION REGULATIONS § 162.125 Sturgeon Bay and the Sturgeon Bay Ship...

Pb’s high sedimentation inside the bay mouth of Jiaozhou Bay

NASA Astrophysics Data System (ADS)

Yang, Dongfang; Miao, Zhenqing; Huang, Xinmin; Wei, Linzhen; Feng, Ming

2017-12-01

Sedimentation is one of the key environmental behaviors of pollutants in the ocean. This paper analyzed the seasonal and temporal variations of Pb’s sedimentation process in Jiaozhou Bay in 1987. Results showed that Pb contents in bottom waters in Jiaozhou Bay in May, July and November 1987 were 1.87-2.60 μg L-1, 15.11-19.68 μg L-1 and 11.08-15.18 μg L-1, and the pollution levels of Pb in May, July and November 1987 were slight, heavy and heavy, respectively. In May 1987, there was low sedimentation process in waters in the outside of the bay mouth, yet were high sedimentation process in waters in the middle and inside of the bay mouth. In July and November 1987, there was low sedimentation process in waters in the outside of the bay mouth, yet were high sedimentation process in waters in the inside of the bay mouth. The seasonal-temporal variation of sedimentation processes of Pb were determined by the variations of sources input and the vertical water’s effect.

A parametric multiclass Bayes error estimator for the multispectral scanner spatial model performance evaluation

NASA Technical Reports Server (NTRS)

Mobasseri, B. G.; Mcgillem, C. D.; Anuta, P. E. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed.

Map showing thickness of young bay mud, southern San Francisco Bay, California

USGS Publications Warehouse

McDonald, Sandra D.; Nichols, Donald R.; Wright, Nancy A.; Atwater, Brian

1978-01-01

Soft water-saturated estuarine deposits less than 10,000 years old underlie the southern part of San Francisco bay and the present and former marshlands that border the bay. Known locally as bay mud or as young bay mud, these deposits, and the estuarine environment that produces them, are of major importance in making decision on land use and development in the San Francisco Bay area. Knowledge of the distribution, thickness, and physical properties of young bay mud is critical to the feasibility, design, and maintenance of structures built on it. Fore this reason, numerous attempts have been made in the past to map or describe these characteristics (Mitchell, 1963; Goldman, 1969; McDonald and Nichols, 1974). The accompanying map of bay-mud thickness significantly revises part of an earlier compilation by Kahle and Goldman (1969) and includes new data from approximately 2400 boreholes, most of which have been drilled during the past 15 years. It also incorporates information on historic margins of San Francisco Bay and its tidal marshes (Nichols and Wright, 1971). Although this map was compelled mostly from data gathered during foundation investigations and construction projects, it is mostly from data gathered during foundation investigations and construction projects, it is not a substitute for such studies. Rather, the map provides regional information for land-use planning, seismic zonation, and design of foundation investigations.

77 FR 14276 - Regulated Navigation Area; Little Bay Bridge Construction, Little Bay, Portsmouth, NH

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-09

...-AA11 Regulated Navigation Area; Little Bay Bridge Construction, Little Bay, Portsmouth, NH AGENCY... under and surrounding the Little Bay and General Sullivan Bridges in order to facilitate construction of the Little Bay Bridge between Newington, NH and Dover, NH. This temporary interim rule is necessary to...

Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women.

PubMed

Nassif, Houssam; Wu, Yirong; Page, David; Burnside, Elizabeth

2012-01-01

Overdiagnosis is a phenomenon in which screening identities cancer which may not go on to cause symptoms or death. Women over 65 who develop breast cancer bear the heaviest burden of overdiagnosis. This work introduces novel machine learning algorithms to improve diagnostic accuracy of breast cancer in aging populations. At the same time, we aim at minimizing unnecessary invasive procedures (thus decreasing false positives) and concomitantly addressing overdiagnosis. We develop a novel algorithm. Logical Differential Prediction Bayes Net (LDP-BN), that calculates the risk of breast disease based on mammography findings. LDP-BN uses Inductive Logic Programming (ILP) to learn relational rules, selects older-specific differentially predictive rules, and incorporates them into a Bayes Net, significantly improving its performance. In addition, LDP-BN offers valuable insight into the classification process, revealing novel older-specific rules that link mass presence to invasive, and calcification presence and lack of detectable mass to DCIS.

HIV-1 drug resistance mutations emerging on darunavir therapy in PI-naive and -experienced patients in the UK.

PubMed

El Bouzidi, Kate; White, Ellen; Mbisa, Jean L; Sabin, Caroline A; Phillips, Andrew N; Mackie, Nicola; Pozniak, Anton L; Tostevin, Anna; Pillay, Deenan; Dunn, David T

2016-12-01

Darunavir is considered to have a high genetic barrier to resistance. Most darunavir-associated drug resistance mutations (DRMs) have been identified through correlation of baseline genotype with virological response in clinical trials. However, there is little information on DRMs that are directly selected by darunavir in clinical settings. We examined darunavir DRMs emerging in clinical practice in the UK. Baseline and post-exposure protease genotypes were compared for individuals in the UK Collaborative HIV Cohort Study who had received darunavir; analyses were stratified for PI history. A selection analysis was used to compare the evolution of subtype B proteases in darunavir recipients and matched PI-naive controls. Of 6918 people who had received darunavir, 386 had resistance tests pre- and post-exposure. Overall, 2.8% (11/386) of these participants developed emergent darunavir DRMs. The prevalence of baseline DRMs was 1.0% (2/198) among PI-naive participants and 13.8% (26/188) among PI-experienced participants. Emergent DRMs developed in 2.0% of the PI-naive group (4 mutations) and 3.7% of the PI-experienced group (12 mutations). Codon 77 was positively selected in the PI-naive darunavir cases, but not in the control group. Our findings suggest that although emergent darunavir resistance is rare, it may be more common among PI-experienced patients than those who are PI-naive. Further investigation is required to explore whether codon 77 is a novel site involved in darunavir susceptibility. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.

Edoxaban vs. warfarin in vitamin K antagonist experienced and naive patients with atrial fibrillation†.

PubMed

O'Donoghue, Michelle L; Ruff, Christian T; Giugliano, Robert P; Murphy, Sabina A; Grip, Laura T; Mercuri, Michele F; Rutman, Howard; Shi, Minggao; Kania, Grzegorz; Cermak, Ondrej; Braunwald, Eugene; Antman, Elliott M

2015-06-14

Edoxaban is an oral, once-daily factor Xa inhibitor that is non-inferior to well-managed warfarin in patients with atrial fibrillation (AF) for the prevention of stroke and systemic embolic events (SEEs). We examined the efficacy and safety of edoxaban vs. warfarin in patients who were vitamin K antagonist (VKA) naive or experienced. ENGAGE AF-TIMI 48 randomized 21 105 patients with AF at moderate-to-high risk of stroke to once-daily edoxaban vs. warfarin. Subjects were followed for a median of 2.8 years. The primary efficacy endpoint was stroke or SEE. As a pre-specified subgroup, we analysed outcomes for those with or without prior VKA experience (>60 consecutive days). Higher-dose edoxaban significantly reduced the risk of stroke or SEE in patients who were VKA naive [hazard ratio (HR) 0.71, 95% confidence interval (CI) 0.56-0.90] and was similar to warfarin in the VKA experienced (HR 1.01, 95% CI 0.82-1.24; P interaction = 0.028). Lower-dose edoxaban was similar to warfarin for stroke or SEE prevention in patients who were VKA naive (HR 0.92, 95% CI 0.73-1.15), but was inferior to warfarin in those who were VKA experienced (HR 1.31, 95% 1.08-1.60; P interaction = 0.019). Both higher-dose and lower-dose edoxaban regimens significantly reduced the risk of major bleeding regardless of prior VKA experience (P interaction = 0.90 and 0.71, respectively). In patients with AF, edoxaban appeared to demonstrate greater efficacy compared with warfarin in patients who were VKA naive than VKA experienced. Edoxaban significantly reduced major bleeding compared with warfarin regardless of prior VKA exposure. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2015. For permissions please email: journals.permissions@oup.com.

Oral immunisation of naive and primed animals with transgenic potato tubers expressing LT-B.

PubMed

Lauterslager, T G; Florack, D E; van der Wal, T J; Molthoff, J W; Langeveld, J P; Bosch, D; Boersma, W J; Hilgers, L A

2001-03-21

The efficacy of edible vaccines produced in potato tubers was examined in mice. Transgenic plants were developed by Agrobacterium tumefaciens-mediated transformation. The antigen selected was the non-toxic B subunit of the Escherichia coli enterotoxin (recLT-B). A synthetic gene coding for recLT-B was made and optimised for expression in potato tubers and accumulation in the endoplasmic reticulum. Introduction of this gene under control of the tuber-specific patatin promoter in potato plants resulted in the production of functional, i.e. Gm1-binding, recLT-B pentamers in tubers. Selected tubers containing about 13 microg of recLT-B per gram fresh weight were used for immunisation. Subcutaneous immunisation with an extract of recLT-B tubers yielded high antibody titres in serum that were similar to those obtained with bacterial recLT-B. The efficacy of oral administration of recLT-B tubers was determined by measuring mucosal and systemic immune responses in naive and primed mice. Animals were primed by subcutaneous injection of an extract of recLT-B tuber plus adjuvant. Naive and primed mice were fed 5 g of tubers ( approximately 65 microg of recLT-B) or were intubated intragastrically with 0.4 ml of tuber extract ( approximately 2 microg of recLT-B). In naive mice, feeding recLT-B tubers or intubation of tuber extract did not induce detectable anti-LT antibody titres. In primed animals, however, oral immunisation resulted in significant anti-LT IgA antibody responses in serum and faeces. Intragastric intubation of tuber extract revealed higher responses than feeding of tubers. These results indicate clearly that functional recLT-B can be produced in potato tubers, that this recombinant protein is immunogenic and that oral administration thereof elicits both systemic and local IgA responses in parentally primed, but not naive, animals.

78 FR 38580 - Special Local Regulation; Tall Ships Celebration Bay City, Bay City, MI

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-27

...-AA08 Special Local Regulation; Tall Ships Celebration Bay City, Bay City, MI AGENCY: Coast Guard, DHS... regulatory act for the celebration specific to Bay City, MI, the Coast Guard recently published a separate... various events throughout the Great Lakes this summer, to include the Tall Ships Celebration Bay City...

Naive and effector B-cell subtypes are increased in chronic rhinosinusitis with polyps

PubMed Central

Miljkovic, Dijana; Psaltis, Alkis; Wormald, Peter-John

2018-01-01

Background: Recent studies demonstrated that B cells and their chemoattractants are elevated in the nasal mucosa of patients with chronic rhinosinusitis (CRS) with nasal polyposis (CRSwNP). However, the presence of naive B cells and of plasmablasts and memory B-cell subsets in the mucosa and periphery of the same patient with CRS is yet to be characterized. Objective: Here we sought to quantify naive, plasmablasts, and memory B cells in mucosal tissue and peripheral blood of patients with CRSwNP, patients with CRS without nasal polyps (CRSsNP), and control patients. Methods: Polyps, mucosa, and peripheral blood samples were prospectively collected from the patients with CRS and from the non-CRS controls. We used flow cytometry to distinguish among naive, plasmablast, and memory B cells in sinus tissue and peripheral blood. Results: A total of 45 patients were recruited for the study. The patients with CRSwNP had significantly increased mucosal B-cell numbers versus the controls (3.39 ± 4.05% versus 0.39 ± 1.05% of live cells; p < 0.01, Kruskal-Wallis test), which included naive B cells (0.61 ± 0.94 versus 0.11 ± 0.24% of live cells; p < 0.03, Kruskal-Wallis test), plasmablasts (0.06 ± 0.26 versus 0.00 ± 0.00% of live cells; p < 0.055, Kruskal-Wallis test), and memory B cells (0.62 ± 1.26 versus 0.05 ± 0.15% of live cells; p < 0.02, Kruskal-Wallis test). Conclusion: Our study identified increased frequencies of different B-cell subtypes in the mucosa of patients with CRSwNP but not in the peripheral blood. We also found that patients with CRSwNP had significantly increased B-cell subtypes compared with the patients with CRSsNP and the controls. These results implied a potential role for mucosal B cells in the ongoing inflammation in patients with CRSwNP. PMID:29336281

Wavelet-based energy features for glaucomatous image classification.

PubMed

Dua, Sumeet; Acharya, U Rajendra; Chowriappa, Pradeep; Sree, S Vinitha

2012-01-01

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

James Bay

Atmospheric Science Data Center

2013-04-17

article title:  Hudson Bay and James Bay, Canada   ... which scatters more light in the backward direction. This example illustrates how multi-angle viewing can distinguish physical structures ... MD. The MISR data were obtained from the NASA Langley Research Center Atmospheric Science Data Center in Hampton, VA. Image ...

The Effect of Naive Ideas on Students' Reasoning about Electricity and Magnetism

ERIC Educational Resources Information Center

Leppavirta, Johanna

2012-01-01

Traditional multiple-choice concept inventories measure students' critical conceptual understanding and are designed to reveal students' naive or alternate ideas. The overall scores, however, give little information about the state of students' knowledge and the consistency of reasoning. This study investigates whether students have consistent…

Chesapeake Bay study

NASA Technical Reports Server (NTRS)

Love, W. J.

1972-01-01

The objectives and scope of the Chesapeake Bay study are discussed. The physical, chemical, biological, political, and social phenomena of concern to the Chesapeake Bay area are included in the study. The construction of a model of the bay which will provide a means of accurately studying the interaction of the ecological factors is described. The application of the study by management organizations for development, enhancement, conservation, preservation, and restoration of the resources is examined.

CD72 ligation regulates defective naive newborn B cell responses.

PubMed

Howard, L M; Reen, D J

1997-02-01

The biological basis for reduced Ig production by naive newborn B cells compared to adult peripheral blood B cells is not fully understood. In a Con A + IL-2 T cell-dependent system using "competent" adult T cells, adult B cells produced large amounts of IgM, IgG, and IgA, while cord B cells were restricted to low levels of only IgM production. Cord B cell activation was also diminished. The contribution of specific B-T cell contact-mediated events to the diminished cord B cell response in this system, using mAbs to CD40, CD28, CD80, and CD72, were investigated, as well as regulation of B cell Ig production by cytokines. alphaCD72 ligation increased cord B cell activation and IgM production, but did not affect adult B cells. Blocking alphaCD40 mAb inhibited cord B cell Ig production completely, but only partly inhibited adult B cell Ig production even at high concentration, suggesting a greater sensitivity of cord B cells to disruption of the CD40-CD40L interaction. Addition of IL-10 did not increase cord B cell Ig production, while adult B cell Ig production was increased. However, combined addition of IL-10 and alphaCD72 significantly increased cord B cell Ig production over that in the presence of either alphaCD72 or IL-10 alone, but had no effect on adult B cells over that of IL-10 alone. These data suggest that the diminished T cell-dependent response of cord B cells is due to reduced or absent CD72 ligation. CD72 ligation plays an important role in the induction of primary responses by naive B cells. CD72 modulation of naive B cell sensitivity to IL-10 stimulation may have implications in the induction of class switch, which is deficient in newborn B cells. Since all T cells express CD5 constitutively, these data also suggest the existence of another ligand for CD72.

Mid-Bay Islands Hydrodynamics and Sedimentation Modeling Study, Chesapeake Bay

DTIC Science & Technology

2006-08-01

largest estuary in the United States, extending more than 150 miles from its seaward end at the Atlantic Ocean to the bayward end at the entrance to...water enters the bay from more than 150 major rivers and streams at approximately 80,000 cu ft/sec. Ocean tides enter the bay through the Atlantic ...Ocean entrance and C&D Canal. The mean range of tides in the bay varies from approximately 1 ft on the western shore to 3 ft at the Atlantic Ocean

Children's Naive Theories of Intelligence Influence Their Metacognitive Judgments

ERIC Educational Resources Information Center

Miele, David B.; Son, Lisa K.; Metcalfe, Janet

2013-01-01

Recent studies have shown that the metacognitive judgments adults infer from their experiences of encoding effort vary in accordance with their naive theories of intelligence. To determine whether this finding extends to elementary schoolchildren, a study was conducted in which 27 third graders (M[subscript age] = 8.27) and 24 fifth graders…

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

Recent thymic emigrants and mature naive T cells exhibit differential DNA methylation at key cytokine loci.

PubMed

Berkley, Amy M; Hendricks, Deborah W; Simmons, Kalynn B; Fink, Pamela J

2013-06-15

Recent thymic emigrants (RTEs) are the youngest T cells in the lymphoid periphery and exhibit phenotypic and functional characteristics distinct from those of their more mature counterparts in the naive peripheral T cell pool. We show in this study that the Il2 and Il4 promoter regions of naive CD4(+) RTEs are characterized by site-specific hypermethylation compared with those of both mature naive (MN) T cells and the thymocyte precursors of RTEs. Thus, RTEs do not merely occupy a midpoint between the thymus and the mature T cell pool, but represent a distinct transitional T cell population. Furthermore, RTEs and MN T cells exhibit distinct CpG DNA methylation patterns both before and after activation. Compared with MN T cells, RTEs express higher levels of several enzymes that modify DNA methylation, and inhibiting methylation during culture allows RTEs to reach MN T cell levels of cytokine production. Collectively, these data suggest that the functional differences that distinguish RTEs from MN T cells are influenced by epigenetic mechanisms and provide clues to a mechanistic basis for postthymic maturation.

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard.

PubMed

Liu, Rui; Chen, Yun; Wu, Jianping; Gao, Lei; Barrett, Damian; Xu, Tingbao; Li, Xiaojuan; Li, Linyi; Huang, Chang; Yu, Jia

2017-04-01

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies. © 2016 Society for Risk Analysis.

Risk forewarning model for rice grain Cd pollution based on Bayes theory.

PubMed

Wu, Bo; Guo, Shuhai; Zhang, Lingyan; Li, Fengmei

2018-03-15

Cadmium (Cd) pollution of rice grain caused by Cd-contaminated soils is a common problem in southwest and central south China. In this study, utilizing the advantages of the Bayes classification statistical method, we established a risk forewarning model for rice grain Cd pollution, and put forward two parameters (the prior probability factor and data variability factor). The sensitivity analysis of the model parameters illustrated that sample size and standard deviation influenced the accuracy and applicable range of the model. The accuracy of the model was improved by the self-renewal of the model through adding the posterior data into the priori data. Furthermore, this method can be used to predict the risk probability of rice grain Cd pollution under similar soil environment, tillage and rice varietal conditions. The Bayes approach thus represents a feasible method for risk forewarning of heavy metals pollution of agricultural products caused by contaminated soils. Copyright © 2017 Elsevier B.V. All rights reserved.

Section 14 Detailed Project Report, Emergency Shoreline Protection, Portersville Bay Mobile County, Alabama

DTIC Science & Technology

1990-05-01

MOBILE COUNTY, ALABAMA MAY 1990 FT~~f f r’ep l c sl m 0 F UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE rw’hen Dat. Entered) REPOT DCUMNTATON...1990 Portersville Bay, Mobile County, Ala. 6. PERFORMING ORG. REPORT NUMBER 7. AUJTHOR(*) 8. CONTRACT OR GRANT NUMBER(*.) Johnny L. Grandison 9. PE...RFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM1 ELEMENT, PROJECT, TASK U.S. Army En(7ineer District, Mobile AREA& ORK UNIT NUMBERS Plan Develop

76 FR 38020 - Safety Zone; Bay Point Fireworks, Bay Point Marina; Marblehead, OH

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-29

...-AA00 Safety Zone; Bay Point Fireworks, Bay Point Marina; Marblehead, OH AGENCY: Coast Guard, DHS... from portions of Lake Erie for the Bay Point Fireworks. This temporary safety zone is necessary to protect spectators and vessels from the hazards associated with fireworks displays. DATES: This regulation...

78 FR 30765 - Safety Zone; Bay Village Independence Day Fireworks, Lake Erie, Bay Village, OH

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-23

...-AA00 Safety Zone; Bay Village Independence Day Fireworks, Lake Erie, Bay Village, OH AGENCY: Coast... zone on Lake Erie, Bay Village, OH. This safety zone is intended to restrict vessels from a portion of Lake Erie during the Bay Village Independence Day Fireworks display. This temporary safety zone is...

77 FR 39420 - Safety Zone; Bay Village Independence Day Fireworks, Lake Erie, Bay Village, OH

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-03

...-AA00 Safety Zone; Bay Village Independence Day Fireworks, Lake Erie, Bay Village, OH AGENCY: Coast... zone on Lake Erie, Bay Village, OH. This safety zone is intended to restrict vessels from a portion of Lake Erie during the Bay Village Independence Day Fireworks display. This temporary safety zone is...

75 FR 54771 - Safety Zone; Thunder on the Bay, Chesapeake Bay, Buckroe Beach Park, Hampton, VA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-09

...-AA00 Safety Zone; Thunder on the Bay, Chesapeake Bay, Buckroe Beach Park, Hampton, VA AGENCY: Coast... safety zone on the navigable waters of Chesapeake Bay in Hampton, VA in support of the Thunder on the Bay.... 0 2. Add Sec. 165.T05-0755 to read as follows: Sec. 165.T05-0755 Safety Zone; Thunder on the Bay...

77 FR 44140 - Drawbridge Operation Regulation; Sturgeon Bay Ship Canal, Sturgeon Bay, WI

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-27

... Maple-Oregon Bridges so vehicular traffic congestion would not develop on downtown Sturgeon Bay streets... movement of vehicular traffic in Sturgeon Bay. The Sturgeon Bay Ship Canal is approximately 8.6 miles long... significant increase in vehicular and vessel traffic during the peak tourist and navigation season between...

75 FR 8297 - Tongass National Forest, Thorne Bay Ranger District, Thorne Bay, AK

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-24

..., Thorne Bay, AK AGENCY: Forest Service, USDA. ACTION: Cancellation of Notice of intent to prepare an... Roberts, Zone Planner, Thorne Bay Ranger District, Tongass National Forest, P.O. Box 19001, Thorne Bay, AK 99919, telephone: 907-828-3250. SUPPLEMENTARY INFORMATION: The 47,007-acre Kosciusko Project Area is...

A Landsat-Based Assessment of Mobile Bay Land Use and Land Cover Change from 1974 to 2008

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Ellis, Jean; Smoot, James; Swann, Roberta; Graham, William

2009-01-01

encompass Mobile Bay. In doing so, each date of Landsat data was classified using an end-user defined modified Anderson level 1 classification scheme. LULC classifications were refined using a decision rule approach in conjunction with available C-CAP products. Individual dates of LULC classifications were validated by image interpretation of stratified random locations on raw Landsat color composite imagery in combination with higher resolution remote sensing and in-situ reference data. The results indicate that during the 34-year study period, urban areas increased from 96,688 to 150,227 acres, representing a 55.37% increase, or 1.63% per annum. Most of the identified urban expansion results from conversion of rural forest and agriculture to urban cover types. Final LULC mapping and metadata products were produced for the entire study area as well as watersheds of concern within the study area. Final project products, including LULC trend information, were incorporated into the Mobile Bay NEP State of the Bay report. Products and metadata were transferred to NOAA NCDDC to allow free online accessibility and use by GOMA partners and by the public.

Soils and Vegetation of the Khaipudyr Bay Coast of the Barents Sea

NASA Astrophysics Data System (ADS)

Shamrikova, E. V.; Deneva, S. V.; Panyukov, A. N.; Kubik, O. S.

2018-04-01

Soils and vegetation of the coastal zone of the Khaipudyr Bay of the Barents Sea have been examined and compared with analogous objects in the Karelian coastal zone of the White Sea. The environmental conditions of these two areas are somewhat different: the climate of the Khaipudyr Bay coast is more severe, and the seawater salinity is higher (32-33‰ in the Khaipudyr Bay and 25-26‰ in the White Sea). The soil cover patterns of both regions are highly variable. Salt-affected marsh soils (Tidalic Fluvisols) are widespread. The complicated mesotopography includes high geomorphic positions that are not affected by tidal water. Under these conditions, zonal factors of pedogenesis predominate and lead to the development of Cryic Folic Histosols and Histic Reductaquic Cryosols. On low marshes, the concentrations of soluble Ca2+, K+ + Na+, Cl-, and SO2- 4 ions in the soils of the Khaipudyr Bay coast are two to four times higher than those in the analogous soils of Karelian coast. Cluster analysis of a number of soil characteristics allows separation of three soils groups: soils of low marshes, soils of middle-high marshes, and soils of higher positions developing under the impact of zonal factors together with the aerial transfer and deposition of seawater drops. The corresponding plant communities are represented by coastal sedge cenoses, forb-grassy halophytic cenoses, and zonal cenoses of hypoarctic tundra. It is argued that the grouping of marsh soils in the new substantivegenetic classification system of Russian soils requires further elaboration.

DEXAMETHASONE IMPLANT FOR DIABETIC MACULAR EDEMA IN NAIVE COMPARED WITH REFRACTORY EYES: The International Retina Group Real-Life 24-Month Multicenter Study. The IRGREL-DEX Study.

PubMed

Iglicki, Matias; Busch, Catharina; Zur, Dinah; Okada, Mali; Mariussi, Miriana; Chhablani, Jay Kumar; Cebeci, Zafer; Fraser-Bell, Samantha; Chaikitmongkol, Voraporn; Couturier, Aude; Giancipoli, Ermete; Lupidi, Marco; Rodríguez-Valdés, Patricio J; Rehak, Matus; Fung, Adrian Tien-Chin; Goldstein, Michaella; Loewenstein, Anat

2018-04-24

To investigate efficacy and safety of repeated dexamethasone (DEX) implants over 24 months, in diabetic macular edema (DME) eyes that were treatment naive compared with eyes refractory to anti-vascular endothelial growth factor treatment, in a real-life environment. This multicenter international retrospective study assessed best-corrected visual acuity and central subfield thickness (CST) of naive and refractory eyes to anti-vascular endothelial growth factor injections treated with dexamethasone implants. Safety data (intraocular pressure rise and cataract surgery) were recorded. A total of 130 eyes from 125 patients were included. Baseline best-corrected visual acuity and CST were similar for naive (n = 71) and refractory eyes (n = 59). Both groups improved significantly in vision after 24 months (P < 0.001). However, naive eyes gained statistically significantly more vision than refractory eyes (+11.3 ± 10.0 vs. 7.3 ± 2.7 letters, P = 0.01) and were more likely to gain ≥10 letters (OR 3.31, 95% CI 1.19-9.24, P = 0.02). At 6, 12, and 24 months, CST was significantly decreased compared with baseline in both naive and refractory eyes; however, CST was higher in refractory eyes than in naive eyes (CST 279 ± 61 vs. 313 ± 125 μm, P = 0.10). Over a follow-up of 24 months, vision improved in diabetic macular edema eyes after treatment with dexamethasone implants, both in eyes that were treatment naive and eyes refractory to anti-vascular endothelial growth factor treatment; however, improvement was greater in naive eyes.

Physical processes in a coupled bay-estuary coastal system: Whitsand Bay and Plymouth Sound

NASA Astrophysics Data System (ADS)

Uncles, R. J.; Stephens, J. A.; Harris, C.

2015-09-01

Whitsand Bay and Plymouth Sound are located in the southwest of England. The Bay and Sound are separated by the ∼2-3 km-wide Rame Peninsula and connected by ∼10-20 m-deep English Channel waters. Results are presented from measurements of waves and currents, drogue tracking, surveys of salinity, temperature and turbidity during stratified and unstratified conditions, and bed sediment surveys. 2D and 3D hydrodynamic models are used to explore the generation of tidally- and wind-driven residual currents, flow separation and the formation of the Rame eddy, and the coupling between the Bay and the Sound. Tidal currents flow around the Rame Peninsula from the Sound to the Bay between approximately 3 h before to 2 h after low water and form a transport path between them that conveys lower salinity, higher turbidity waters from the Sound to the Bay. These waters are then transported into the Bay as part of the Bay-mouth limb of the Rame eddy and subsequently conveyed to the near-shore, east-going limb and re-circulated back towards Rame Head. The Simpson-Hunter stratification parameter indicates that much of the Sound and Bay are likely to stratify thermally during summer months. Temperature stratification in both is pronounced during summer and is largely determined by coastal, deeper-water stratification offshore. Small tidal stresses in the Bay are unable to move bed sediment of the observed sizes. However, the Bay and Sound are subjected to large waves that are capable of driving a substantial bed-load sediment transport. Measurements show relatively low levels of turbidity, but these respond rapidly to, and have a strong correlation with, wave height.

Circulation in a bay influenced by flooding of a river discharging outside the bay

NASA Astrophysics Data System (ADS)

Kakehi, Shigeho; Takagi, Takamasa; Okabe, Katsuaki; Takayanagi, Kazufumi

2017-03-01

To investigate the influence of a river discharging outside a bay on circulation in the bay, we carried out current and salinity measurements from mooring systems and hydrographic observations in Matsushima Bay, Japan, and off the Naruse River, which discharges outside the bay. Previously, enhancement of horizontal circulation in the bay induced by increased freshwater input from the Naruse River was reported to have degraded the seedling yield of wild Pacific oysters in the bay, but the freshwater inflow from the river was not directly measured. Our hydrographic observations in Katsugigaura Strait, approximately 3 km southwest of the Naruse River mouth, detected freshwater derived from the river. The mooring data revealed that freshwater discharged by the river flowed into Matsushima Bay via the strait and that the freshwater transport increased when the river was in flood. The inflow through straits other than Katsugigaura was estimated by a box model analysis to be 26-145 m3 s-1 under normal river discharge conditions, and it decreased to 6 m3 s-1 during flood conditions. During flood events, the salt and water budgets in the bay were maintained by the horizontal circulation: inflow occurred mainly via Katsugigaura Strait, and outflow was mainly via other straits.

Nicotinic Acid Adenine Dinucleotide Phosphate Plays a Critical Role in Naive and Effector Murine T Cells but Not Natural Regulatory T Cells*

PubMed Central

Ali, Ramadan A.; Camick, Christina; Wiles, Katherine; Walseth, Timothy F.; Slama, James T.; Bhattacharya, Sumit; Giovannucci, David R.; Wall, Katherine A.

2016-01-01

Nicotinic acid adenine dinucleotide phosphate (NAADP), the most potent Ca2+ mobilizing second messenger discovered to date, has been implicated in Ca2+ signaling in some lymphomas and T cell clones. In contrast, the role of NAADP in Ca2+ signaling or the identity of the Ca2+ stores targeted by NAADP in conventional naive T cells is less clear. In the current study, we demonstrate the importance of NAADP in the generation of Ca2+ signals in murine naive T cells. Combining live-cell imaging methods and a pharmacological approach using the NAADP antagonist Ned-19, we addressed the involvement of NAADP in the generation of Ca2+ signals evoked by TCR stimulation and the role of this signal in downstream physiological end points such as proliferation, cytokine production, and other responses to stimulation. We demonstrated that acidic compartments in addition to the endoplasmic reticulum were the Ca2+ stores that were sensitive to NAADP in naive T cells. NAADP was shown to evoke functionally relevant Ca2+ signals in both naive CD4 and naive CD8 T cells. Furthermore, we examined the role of this signal in the activation, proliferation, and secretion of effector cytokines by Th1, Th2, Th17, and CD8 effector T cells. Overall, NAADP exhibited a similar profile in mediating Ca2+ release in effector T cells as in their counterpart naive T cells and seemed to be equally important for the function of these different subsets of effector T cells. This profile was not observed for natural T regulatory cells. PMID:26728458

A Corpus-Based Approach for Automatic Thai Unknown Word Recognition Using Boosting Techniques

NASA Astrophysics Data System (ADS)

Techo, Jakkrit; Nattee, Cholwich; Theeramunkong, Thanaruk

While classification techniques can be applied for automatic unknown word recognition in a language without word boundary, it faces with the problem of unbalanced datasets where the number of positive unknown word candidates is dominantly smaller than that of negative candidates. To solve this problem, this paper presents a corpus-based approach that introduces a so-called group-based ranking evaluation technique into ensemble learning in order to generate a sequence of classification models that later collaborate to select the most probable unknown word from multiple candidates. Given a classification model, the group-based ranking evaluation (GRE) is applied to construct a training dataset for learning the succeeding model, by weighing each of its candidates according to their ranks and correctness when the candidates of an unknown word are considered as one group. A number of experiments have been conducted on a large Thai medical text to evaluate performance of the proposed group-based ranking evaluation approach, namely V-GRE, compared to the conventional naïve Bayes classifier and our vanilla version without ensemble learning. As the result, the proposed method achieves an accuracy of 90.93±0.50% when the first rank is selected while it gains 97.26±0.26% when the top-ten candidates are considered, that is 8.45% and 6.79% improvement over the conventional record-based naïve Bayes classifier and the vanilla version. Another result on applying only best features show 93.93±0.22% and up to 98.85±0.15% accuracy for top-1 and top-10, respectively. They are 3.97% and 9.78% improvement over naive Bayes and the vanilla version. Finally, an error analysis is given.

Tidal and residual circulation in a semi-arid bay: Coquimbo Bay, Chile

NASA Astrophysics Data System (ADS)

Valle-Levinson, Arnoldo; Moraga, Julio; Olivares, Jorge; Blanco, José Luis

2000-11-01

Velocity profiles and time-series data were combined with conductivity-temperature-depth (CTD) casts to describe the general circulation at tidal and subtidal scales in a bay of semi-arid climate, Coquimbo Bay (˜30°S), Chile. This was the first study that used a towed acoustic Doppler current profiler (ADCP) in coastal Chilean waters and is one of the very few in semi-arid bays. The ADCP was towed for two semi-diurnal tidal cycles in early austral autumn, between March 23 and 24, 1997 along a triangular trajectory that covered most of the bay. Additional data consisted of moored current meters and CTD casts. The observations indicated the presence of a surface layer, above the pycnocline, that showed predominantly diurnal variability forced by the breeze regime and by tides. The tidal circulation in the surface layer featured amplitudes of 10 cm/s within an anticyclonic gyre that occupied most of the bay. The subtidal circulation in the surface was characterized by a pair of counter-rotating gyres. The northernmost three-fourths of the bay showed an anticyclonic gyre, and the observations over the southern fourth implied a cyclonic gyre. The subtidal anticyclonic gyre had a counterpart rotating in opposite direction within a lower layer, underneath the pycnocline. The lower layer showed semidiurnal variability in addition to diurnal variability and was insulated by the pycnocline from heat and momentum fluxes through the air-water interface. Circulations that resemble estuarine and anti-estuarine patterns were found associated with the subtidal gyres. A horizontal divergence related to a 10 cm/s near-surface outflow around Point Tortuga, to the south of the bay entrance, allowed the development of upward motion off the Point, as evidenced by the tilt of the isopycnals at the entrance to the bay.

Integrative genetic risk prediction using non-parametric empirical Bayes classification.

PubMed

Zhao, Sihai Dave

2017-06-01

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa. © 2016, The International Biometric Society.

Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

NASA Astrophysics Data System (ADS)

Wang, Leilei; Cheng, Jinyong

2018-03-01

Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.

Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

PubMed

Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

2015-01-01

Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

Homeostasis of naive and memory CD4+ T cells: IL-2 and IL-7 differentially regulate the balance between proliferation and Fas-mediated apoptosis.

PubMed

Jaleco, Sara; Swainson, Louise; Dardalhon, Valérie; Burjanadze, Maryam; Kinet, Sandrina; Taylor, Naomi

2003-07-01

Cytokines play a crucial role in the maintenance of polyclonal naive and memory T cell populations. It has previously been shown that ex vivo, the IL-7 cytokine induces the proliferation of naive recent thymic emigrants (RTE) isolated from umbilical cord blood but not mature adult-derived naive and memory human CD4(+) T cells. We find that the combination of IL-2 and IL-7 strongly promotes the proliferation of RTE, whereas adult CD4(+) T cells remain relatively unresponsive. Immunological activity is controlled by a balance between proliferation and apoptotic cell death. However, the relative contributions of IL-2 and IL-7 in regulating these processes in the absence of MHC/peptide signals are not known. Following exposure to either IL-2 or IL-7 alone, RTE, as well as mature naive and memory CD4(+) T cells, are rendered only minimally sensitive to Fas-mediated cell death. However, in the presence of the two cytokines, Fas engagement results in a high level of caspase-dependent apoptosis in both RTE as well as naive adult CD4(+) T cells. In contrast, equivalently treated memory CD4(+) T cells are significantly less sensitive to Fas-induced cell death. The increased susceptibility of RTE and naive CD4(+) T cells to Fas-induced apoptosis correlates with a significantly higher IL-2/IL-7-induced Fas expression on these T cell subsets than on memory CD4(+) T cells. Thus, IL-2 and IL-7 regulate homeostasis by modulating the equilibrium between proliferation and apoptotic cell death in RTE and mature naive and memory T cell subsets.

Bayes classification of terrain cover using normalized polarimetric data

NASA Technical Reports Server (NTRS)

Yueh, H. A.; Swartz, A. A.; Kong, J. A.; Shin, R. T.; Novak, L. M.

1988-01-01

The normalized polarimetric classifier (NPC) which uses only the relative magnitudes and phases of the polarimetric data is proposed for discrimination of terrain elements. The probability density functions (PDFs) of polarimetric data are assumed to have a complex Gaussian distribution, and the marginal PDF of the normalized polarimetric data is derived by adopting the Euclidean norm as the normalization function. The general form of the distance measure for the NPC is also obtained. It is demonstrated that for polarimetric data with an arbitrary PDF, the distance measure of NPC will be independent of the normalization function selected even when the classifier is mistrained. A complex Gaussian distribution is assumed for the polarimetric data consisting of grass and tree regions. The probability of error for the NPC is compared with those of several other single-feature classifiers. The classification error of NPCs is shown to be independent of the normalization function.

Chesapeake Bay TMDL

EPA Pesticide Factsheets

In 2010 EPA established the Chesapeake Bay TMDL, a comprehensive pollution diet with accountability measures to restore clean water in the bay and local waters. It set limits for nutrients and sediment to meet water quality standards across the watershed

77 FR 38488 - Safety Zone; Alexandria Bay Chamber of Commerce, St. Lawrence River, Alexandria Bay, NY

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-28

... 1625-AA00 Safety Zone; Alexandria Bay Chamber of Commerce, St. Lawrence River, Alexandria Bay, NY... temporary safety zone on the St. Lawrence River, Alexandria Bay, New York. This safety zone is intended to restrict vessels from a portion of the St. Lawrence River during the Alexandria Bay Chamber of Commerce...

Impairment of DNA Methylation Maintenance Is the Main Cause of Global Demethylation in Naive Embryonic Stem Cells.

PubMed

von Meyenn, Ferdinand; Iurlaro, Mario; Habibi, Ehsan; Liu, Ning Qing; Salehzadeh-Yazdi, Ali; Santos, Fátima; Petrini, Edoardo; Milagre, Inês; Yu, Miao; Xie, Zhenqing; Kroeze, Leonie I; Nesterova, Tatyana B; Jansen, Joop H; Xie, Hehuang; He, Chuan; Reik, Wolf; Stunnenberg, Hendrik G

2016-06-16

Global demethylation is part of a conserved program of epigenetic reprogramming to naive pluripotency. The transition from primed hypermethylated embryonic stem cells (ESCs) to naive hypomethylated ones (serum-to-2i) is a valuable model system for epigenetic reprogramming. We present a mathematical model, which accurately predicts global DNA demethylation kinetics. Experimentally, we show that the main drivers of global demethylation are neither active mechanisms (Aicda, Tdg, and Tet1-3) nor the reduction of de novo methylation. UHRF1 protein, the essential targeting factor for DNMT1, is reduced upon transition to 2i, and so is recruitment of the maintenance methylation machinery to replication foci. Concurrently, there is global loss of H3K9me2, which is needed for chromatin binding of UHRF1. These mechanisms synergistically enforce global DNA hypomethylation in a replication-coupled fashion. Our observations establish the molecular mechanism for global demethylation in naive ESCs, which has key parallels with those operating in primordial germ cells and early embryos. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Impaired P600 in neuroleptic naive patients with first-episode schizophrenia.

PubMed

Papageorgiou, C; Kontaxakis, V P; Havaki-Kontaxaki, B J; Stamouli, S; Vasios, C; Asvestas, P; Matsopoulos, G K; Kontopantelis, E; Rabavilas, A; Uzunoglu, N; Christodoulou, G N

2001-09-17

Deficits of working memory (WM) are recognized as an important pathological feature in schizophrenia. Since the P600 component of event related potentials has been hypothesized that represents aspects of second-pass parsing processes of information processing, and is related to WM, the present study focuses on P600 elicited during a WM test in drug-naive first-episode schizophrenics (FES) compared to healthy controls. We examined 16 drug-naive first-episode schizophrenic patients and 23 healthy controls matched for age and sex. Compared with controls schizophrenic patients showed reduced P600 amplitude on left temporoparietal region and increased P600 amplitude on left occipital region. With regard to the latency, the patients exhibited significantly prolongation on right temporoparietal region. The obtained pattern of differences classified correctly 89.20% of patients. Memory performance of patients was also significantly impaired relative to controls. Our results suggest that second-pass parsing process of information processing, as indexed by P600, elicited during a WM test, is impaired in FES. Moreover, these findings lend support to the view that the auditory WM in schizophrenia involves or affects a circuitry including temporoparietal and occipital brain areas.

33 CFR 334.762 - Naval Support Activity Panama City; North Bay and West Bay; restricted areas.

Code of Federal Regulations, 2011 CFR

2011-07-01

... City; North Bay and West Bay; restricted areas. 334.762 Section 334.762 Navigation and Navigable Waters... REGULATIONS § 334.762 Naval Support Activity Panama City; North Bay and West Bay; restricted areas. (a) The..., Naval Support Activity, Panama City Florida, and such agencies as he/she may designate. [73 FR 52927...

33 CFR 334.762 - Naval Support Activity Panama City; North Bay and West Bay; restricted areas.

Code of Federal Regulations, 2010 CFR

2010-07-01

... City; North Bay and West Bay; restricted areas. 334.762 Section 334.762 Navigation and Navigable Waters... REGULATIONS § 334.762 Naval Support Activity Panama City; North Bay and West Bay; restricted areas. (a) The..., Naval Support Activity, Panama City Florida, and such agencies as he/she may designate. [73 FR 52927...

Nicotinic Acid Adenine Dinucleotide Phosphate Plays a Critical Role in Naive and Effector Murine T Cells but Not Natural Regulatory T Cells.

PubMed

Ali, Ramadan A; Camick, Christina; Wiles, Katherine; Walseth, Timothy F; Slama, James T; Bhattacharya, Sumit; Giovannucci, David R; Wall, Katherine A

2016-02-26

Nicotinic acid adenine dinucleotide phosphate (NAADP), the most potent Ca(2+) mobilizing second messenger discovered to date, has been implicated in Ca(2+) signaling in some lymphomas and T cell clones. In contrast, the role of NAADP in Ca(2+) signaling or the identity of the Ca(2+) stores targeted by NAADP in conventional naive T cells is less clear. In the current study, we demonstrate the importance of NAADP in the generation of Ca(2+) signals in murine naive T cells. Combining live-cell imaging methods and a pharmacological approach using the NAADP antagonist Ned-19, we addressed the involvement of NAADP in the generation of Ca(2+) signals evoked by TCR stimulation and the role of this signal in downstream physiological end points such as proliferation, cytokine production, and other responses to stimulation. We demonstrated that acidic compartments in addition to the endoplasmic reticulum were the Ca(2+) stores that were sensitive to NAADP in naive T cells. NAADP was shown to evoke functionally relevant Ca(2+) signals in both naive CD4 and naive CD8 T cells. Furthermore, we examined the role of this signal in the activation, proliferation, and secretion of effector cytokines by Th1, Th2, Th17, and CD8 effector T cells. Overall, NAADP exhibited a similar profile in mediating Ca(2+) release in effector T cells as in their counterpart naive T cells and seemed to be equally important for the function of these different subsets of effector T cells. This profile was not observed for natural T regulatory cells. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Feature selection for the classification of traced neurons.

PubMed

López-Cabrera, José D; Lorenzo-Ginori, Juan V

2018-06-01

The great availability of computational tools to calculate the properties of traced neurons leads to the existence of many descriptors which allow the automated classification of neurons from these reconstructions. This situation determines the necessity to eliminate irrelevant features as well as making a selection of the most appropriate among them, in order to improve the quality of the classification obtained. The dataset used contains a total of 318 traced neurons, classified by human experts in 192 GABAergic interneurons and 126 pyramidal cells. The features were extracted by means of the L-measure software, which is one of the most used computational tools in neuroinformatics to quantify traced neurons. We review some current feature selection techniques as filter, wrapper, embedded and ensemble methods. The stability of the feature selection methods was measured. For the ensemble methods, several aggregation methods based on different metrics were applied to combine the subsets obtained during the feature selection process. The subsets obtained applying feature selection methods were evaluated using supervised classifiers, among which Random Forest, C4.5, SVM, Naïve Bayes, Knn, Decision Table and the Logistic classifier were used as classification algorithms. Feature selection methods of types filter, embedded, wrappers and ensembles were compared and the subsets returned were tested in classification tasks for different classification algorithms. L-measure features EucDistanceSD, PathDistanceSD, Branch_pathlengthAve, Branch_pathlengthSD and EucDistanceAve were present in more than 60% of the selected subsets which provides evidence about their importance in the classification of this neurons. Copyright © 2018 Elsevier B.V. All rights reserved.

Interleukin-7 induces HIV replication in primary naive T cells through a nuclear factor of activated T cell (NFAT)-dependent pathway

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

Managlia, Elizabeth Z.; Landay, Alan; Al-Harthi, Lena

2006-07-05

Interleukin (IL)-7 plays several roles critical to T cell maturation, survival, and homeostasis. Because of these functions, IL-7 is under investigation as an immune-modulator for therapeutic use in lymphopenic clinical conditions, including HIV. We reported that naive T cells, typically not permissive to HIV, can be productively infected when pre-treated with IL-7. We evaluated the mechanism by which IL-7-mediates this effect. IL-7 potently up-regulated the transcriptional factor NFAT, but had no effect on NF{kappa}B. Blocking NFAT activity using a number of reagents, such as Cyclosporin A, FK-506, or the NFAT-specific inhibitor known as VIVIT peptide, all markedly reduced IL-7-mediated inductionmore » of HIV replication in naive T cells. Additional neutralization of cytokines present in IL-7-treated cultures and/or those that have NFAT-binding sequences within their promotors indicated that IL-10, IL-4, and most significantly IFN{gamma}, all contribute to IL-7-induction of HIV productive replication in naive T cells. These data clarify the mechanism by which IL-7 can overcome the block to HIV productive infection in naive T cells, despite their quiescent cell status. These findings are relevant to the treatment of HIV disease and understanding HIV pathogenesis in the naive CD4+ T cell compartment, especially in light of the vigorous pursuit of IL-7 as an in vivo immune modulator.« less

33 CFR 110.224 - San Francisco Bay, San Pablo Bay, Carquinez Strait, Suisun Bay, Sacramento River, San Joaquin...

Code of Federal Regulations, 2014 CFR

2014-07-01

... Bay, Sacramento River, San Joaquin River, and connecting waters, CA. (a) General regulations. (1..., Carquinez Strait, Suisun Bay, Sacramento River, San Joaquin River, and connecting waters, CA. 110.224... notified to move by the Captain of the Port. (4) No vessel may anchor within a tunnel, cable, or pipeline...

33 CFR 110.224 - San Francisco Bay, San Pablo Bay, Carquinez Strait, Suisun Bay, Sacramento River, San Joaquin...

Code of Federal Regulations, 2013 CFR

2013-07-01

... Bay, Sacramento River, San Joaquin River, and connecting waters, CA. (a) General regulations. (1..., Carquinez Strait, Suisun Bay, Sacramento River, San Joaquin River, and connecting waters, CA. 110.224... notified to move by the Captain of the Port. (4) No vessel may anchor within a tunnel, cable, or pipeline...

33 CFR 110.224 - San Francisco Bay, San Pablo Bay, Carquinez Strait, Suisun Bay, Sacramento River, San Joaquin...

Code of Federal Regulations, 2012 CFR

2012-07-01

... Bay, Sacramento River, San Joaquin River, and connecting waters, CA. (a) General regulations. (1..., Carquinez Strait, Suisun Bay, Sacramento River, San Joaquin River, and connecting waters, CA. 110.224... notified to move by the Captain of the Port. (4) No vessel may anchor within a tunnel, cable, or pipeline...

Classification of wetlands vegetation using small scale color infrared imagery

NASA Technical Reports Server (NTRS)

Williamson, F. S. L.

1975-01-01

A classification system for Chesapeake Bay wetlands was derived from the correlation of film density classes and actual vegetation classes. The data processing programs used were developed by the Laboratory for the Applications of Remote Sensing. These programs were tested for their value in classifying natural vegetation, using digitized data from small scale aerial photography. Existing imagery and the vegetation map of Farm Creek Marsh were used to determine the optimal number of classes, and to aid in determining if the computer maps were a believable product.

Bay-Ocean Coupling and the Proximal Fate of Water-Borne Material Exported from San Francisco Bay

NASA Astrophysics Data System (ADS)

Largier, J. L.

2012-12-01

The coupling between San Francisco Bay and the ocean is poorly known: how ocean waters intrude into bay and how bay waters flow out into the ocean. Here we address the outflow from the Bay with a view to describing the proximal fate of water-borne material in the coastal ocean, specifically finer particles and dissolved material. Flow trajectories longer than that in the tidal jet are thus the focus of this study - time scales of hours to days. We present data collected in both winter/runoff and spring/upwelling seasons that reflect the importance of tides and also the importance of wind, which introduces either northward or southward along-coast flow. Southward flow is offshore and typically this Bay effluent is removed from nearshore waters. In contrast, northward flow tends to remain attached to shore, and there is persistent presence of Bay waters in the surface layer up to Point Reyes, only occasionally separated from the coast by local upwelling within Drakes Bay. Perhaps most dramatic is outflow during winter storms, when wind is southerly and pushes water on shore in Drakes Bay as well as inducing an intense flow around Point Reyes, which turns cyclonically to reconnect with the shore in the vicinity of Bodega Bay before forming a wind-accelerated coast-attached current that looks very much like a coastal buoyancy current. This is the time when large volumes of low-salinity and high-load waters are exported from the Bay.

Classification Algorithms for Big Data Analysis, a Map Reduce Approach

NASA Astrophysics Data System (ADS)

Ayma, V. A.; Ferreira, R. S.; Happ, P.; Oliveira, D.; Feitosa, R.; Costa, G.; Plaza, A.; Gamba, P.

2015-03-01

Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA's machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

Clinical and Mucosal Immune Correlates of HIV-1 Semen Levels in Antiretroviral-Naive Men

PubMed Central

Marsh, Angie K.; Huibner, Sanja; Shahabi, Kamnoosh; Liu, Cindy; Contente, Tania; Nagelkerke, Nico J. D.; Kovacs, Colin; Benko, Erika; Price, Lance; MacDonald, Kelly S.; Kaul, Rupert

2017-01-01

Abstract Background. This study was done to characterize parameters associated with semen human immunodeficiency virus (HIV)-1 ribonucleic acid (RNA) viral load (VL) variability in HIV-infected, therapy-naive men. Methods. Paired blood and semen samples were collected from 30 HIV-infected, therapy-naive men who have sex with men, and 13 participants were observed longitudinally for up to 1 year. Human immunodeficiency virus RNA, bacterial load by 16S RNA, herpesvirus (Epstein-Barr virus and cytomegalovirus [CMV]) shedding, and semen cytokines/chemokines were quantified, and semen T-cell subsets were assessed by multiparameter flow cytometry. Results. Semen HIV RNA was detected at 93% of visits, with >50% of men shedding high levels of virus (defined as >5000 copies/mL). In the baseline cross-sectional analysis, an increased semen HIV VL correlated with local CMV reactivation, the semen bacterial load, and semen inflammatory cytokines, particularly interleukin (IL)-8. T cells in semen were more activated than blood, and there was an increased frequency of Th17 cells and γδ-T-cells. Subsequent prospective analysis demonstrated striking interindividual variability in HIV and CMV shedding patterns, and only semen IL-8 levels and the blood VL were independently associated with semen HIV levels. Conclusions. Several clinical and immune parameters were associated with increased HIV semen levels in antiretroviral therapy-naive men, with induction of local proinflammatory cytokines potentially acting as a common pathway. PMID:28534034

A model-based test for treatment effects with probabilistic classifications.

PubMed

Cavagnaro, Daniel R; Davis-Stober, Clintin P

2018-05-21

Within modern psychology, computational and statistical models play an important role in describing a wide variety of human behavior. Model selection analyses are typically used to classify individuals according to the model(s) that best describe their behavior. These classifications are inherently probabilistic, which presents challenges for performing group-level analyses, such as quantifying the effect of an experimental manipulation. We answer this challenge by presenting a method for quantifying treatment effects in terms of distributional changes in model-based (i.e., probabilistic) classifications across treatment conditions. The method uses hierarchical Bayesian mixture modeling to incorporate classification uncertainty at the individual level into the test for a treatment effect at the group level. We illustrate the method with several worked examples, including a reanalysis of the data from Kellen, Mata, and Davis-Stober (2017), and analyze its performance more generally through simulation studies. Our simulations show that the method is both more powerful and less prone to type-1 errors than Fisher's exact test when classifications are uncertain. In the special case where classifications are deterministic, we find a near-perfect power-law relationship between the Bayes factor, derived from our method, and the p value obtained from Fisher's exact test. We provide code in an online supplement that allows researchers to apply the method to their own data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Brentuximab vedotin (SGN-35) in patients with transplant-naive relapsed/refractory Hodgkin lymphoma.

PubMed

Sasse, Stephanie; Rothe, Achim; Goergen, Helen; Eichenauer, Dennis A; Lohri, Andreas; Kreher, Stephan; Jäger, Ulrich; Bangard, Christopher; Kuhnert, Georg; Böll, Boris; von Tresckow, Bastian; Engert, Andreas

2013-10-01

Only limited data are available on the role of brentuximab vedotin (SGN-35) in transplant-naive relapsed or refractory patients with Hodgkin lymphoma (HL). We thus retrospectively analyzed 14 patients with primary refractory or relapsed HL who were treated with brentuximab vedotin as single agent in a named patient program, who had not received prior high-dose chemotherapy (HDCT) and autologous stem cell transplant (ASCT) due to refractory disease (n = 9), comorbidity (n = 4) and unknown reasons (n = 1). Brentuximab vedotin resulted in an overall response rate of 71% (10/14) with five complete responses (CRs). Five of those patients with refractory disease and four patients with relevant comorbidity responded. Consolidating ASCT (n = 4) or allogeneic SCT (n = 1) was performed in five patients. Median progression-free survival (PFS) was 9 months and the median overall survival (OS) was not reached. These data indicate the therapeutic efficacy of brentuximab vedotin in chemotherapy-refractory transplant-naive patients with HL.

77 FR 50921 - Safety Zone: Bay Bridge Load Transfer Safety Zone, San Francisco Bay, San Francisco, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-23

...-AA00 Safety Zone: Bay Bridge Load Transfer Safety Zone, San Francisco Bay, San Francisco, CA AGENCY... the Bay Bridge Load Transfer Safety Zone from August 1, 2012 through October 31, 2012. This safety... Bay Bridge from the temporary suspension arrangement to the permanent suspension arrangement, the...

33 CFR 167.101 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 2 2011-07-01 2011-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas. 167.101 Section 167.101 Navigation and Navigable... the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas. (a) A precautionary...

33 CFR 167.101 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 2 2013-07-01 2013-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas. 167.101 Section 167.101 Navigation and Navigable... the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas. (a) A precautionary...

33 CFR 167.101 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 2 2014-07-01 2014-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas. 167.101 Section 167.101 Navigation and Navigable... the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas. (a) A precautionary...

33 CFR 167.101 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 2 2012-07-01 2012-07-01 false In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas. 167.101 Section 167.101 Navigation and Navigable... the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: Precautionary areas. (a) A precautionary...

Benthic habitat classification in Lignumvitae Key Basin, Florida Bay, using the U.S. Geological Survey Along-Track Reef Imaging System (ATRIS)

USGS Publications Warehouse

Reich, C.D.; Zawada, D.G.; Thompson, P.R.; Reynolds, C.E.; Spear, A.H.; Umberger, D.K.; Poore, R.Z.

2011-01-01

The Comprehensive Everglades Restoration Plan (CERP) funded in partnership between the U.S. Army Corps of Engineers, South Florida Water Management District, and other Federal, local and Tribal members has in its mandate a guideline to protect and restore freshwater flows to coastal environments to pre-1940s conditions (CERP, 1999). Historic salinity data are sparse for Florida Bay, so it is difficult for water managers to decide what the correct quantity, quality, timing, and distribution of freshwater are to maintain a healthy and productive estuarine ecosystem. Proxy records of seasurface temperature (SST) and salinity have proven useful in south Florida. Trace-element chemistry on foraminifera and molluscan shells preserved in shallow-water sediments has provided some information on historical salinity and temperature variability in coastal settings, but little information is available for areas within the main part of Florida Bay (Brewster-Wingard and others, 1996). Geochemistry of coral skeletons can be used to develop subannually resolved proxy records for SST and salinity. Previous studies suggest corals, specifically Solenastrea bournoni, present in the lower section of Florida Bay near Lignumvitae Key, may be suitable for developing records of SST and salinity for the past century, but the distribution and species composition of the bay coral community have not been well documented (Hudson and others, 1989; Swart and others, 1999). Oddly, S. bournoni thrives in the study area because it can grow on a sandy substratum and can tolerate highly turbid water. Solenastrea bournoni coral heads in this area should be ideally located to provide a record (~100-150 years) of past temperature and salinity variations in Florida Bay. The goal of this study was to utilize the U.S. Geological Survey's (USGS) Along-Track Reef Imaging System (ATRIS) capability to further our understanding of the abundance, distribution, and size of corals in the Lignumvitae Key Basin. The

Sound Classification in Hearing Aids Inspired by Auditory Scene Analysis

NASA Astrophysics Data System (ADS)

Büchler, Michael; Allegro, Silvia; Launer, Stefan; Dillier, Norbert

2005-12-01

A sound classification system for the automatic recognition of the acoustic environment in a hearing aid is discussed. The system distinguishes the four sound classes "clean speech," "speech in noise," "noise," and "music." A number of features that are inspired by auditory scene analysis are extracted from the sound signal. These features describe amplitude modulations, spectral profile, harmonicity, amplitude onsets, and rhythm. They are evaluated together with different pattern classifiers. Simple classifiers, such as rule-based and minimum-distance classifiers, are compared with more complex approaches, such as Bayes classifier, neural network, and hidden Markov model. Sounds from a large database are employed for both training and testing of the system. The achieved recognition rates are very high except for the class "speech in noise." Problems arise in the classification of compressed pop music, strongly reverberated speech, and tonal or fluctuating noises.

PCANet: A Simple Deep Learning Baseline for Image Classification?

PubMed

Chan, Tsung-Han; Jia, Kui; Gao, Shenghua; Lu, Jiwen; Zeng, Zinan; Ma, Yi

2015-12-01

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Genetic characterization and antiretroviral resistance mutations among treatment-naive HIV-infected individuals in Jiaxing, China.

PubMed

Guo, Jinlei; Yan, Yong; Zhang, Jiafeng; Ji, Jimei; Ge, Zhijian; Ge, Rui; Zhang, Xiaofei; Wang, Henghui; Chen, Zhongwen; Luo, Jianyong

2017-03-14

The aim of this study was to characterize HIV-1 genotypes and antiretroviral resistance mutations among treatment-naive HIV-infected individuals in Jiaxing, China. The HIV-1 partial polymerase (pol) genes in 93 of the 99 plasma samples were successfully amplified and analyzed. Phylogenetic analysis revealed the existence of five HIV-1 genotypes, of which the most prevalent genotype was CRF01_AE (38.7%), followed by CRF07_BC (34.4%), CRF08_BC (16.1%), subtype B/B' (5.4%), and CRF55_01B (2.1%). Besides, three types of unique recombination forms (URFs) were also observed, including C/F2/A1, CRF01_AE/B, and CRF08_BC/CRF07_BC. Among 93 amplicons, 46.2% had drug resistance-associated mutations, including 23.7% for protease inhibitors (PIs) mutations, 1.1% for nucleoside reverse transcriptase inhibitors (NRTIs) mutations, and 20.4% for non-nucleoside reverse transcriptase inhibitors (NNRTIs) mutations. Six (6.5%) out of 93 treatment-naive subjects were identified to be resistant to one or more NNRTIs, while resistance to NRTIs or PIs was not observed. Our study showed the genetic diversity of HIV-1 strains circulating in Jiaxing and a relative high proportion of antiretroviral resistance mutations among treatment-naive patients, indicating a serious challenge for HIV prevention and treatment program.

A Combined Omics Approach to Generate the Surface Atlas of Human Naive CD4+ T Cells during Early T-Cell Receptor Activation*

PubMed Central

Graessel, Anke; Hauck, Stefanie M.; von Toerne, Christine; Kloppmann, Edda; Goldberg, Tatyana; Koppensteiner, Herwig; Schindler, Michael; Knapp, Bettina; Krause, Linda; Dietz, Katharina; Schmidt-Weber, Carsten B.; Suttner, Kathrin

2015-01-01

Naive CD4+ T cells are the common precursors of multiple effector and memory T-cell subsets and possess a high plasticity in terms of differentiation potential. This stem-cell-like character is important for cell therapies aiming at regeneration of specific immunity. Cell surface proteins are crucial for recognition and response to signals mediated by other cells or environmental changes. Knowledge of cell surface proteins of human naive CD4+ T cells and their changes during the early phase of T-cell activation is urgently needed for a guided differentiation of naive T cells and may support the selection of pluripotent cells for cell therapy. Periodate oxidation and aniline-catalyzed oxime ligation technology was applied with subsequent quantitative liquid chromatography-tandem MS to generate a data set describing the surface proteome of primary human naive CD4+ T cells and to monitor dynamic changes during the early phase of activation. This led to the identification of 173 N-glycosylated surface proteins. To independently confirm the proteomic data set and to analyze the cell surface by an alternative technique a systematic phenotypic expression analysis of surface antigens via flow cytometry was performed. This screening expanded the previous data set, resulting in 229 surface proteins, which were expressed on naive unstimulated and activated CD4+ T cells. Furthermore, we generated a surface expression atlas based on transcriptome data, experimental annotation, and predicted subcellular localization, and correlated the proteomics result with this transcriptional data set. This extensive surface atlas provides an overall naive CD4+ T cell surface resource and will enable future studies aiming at a deeper understanding of mechanisms of T-cell biology allowing the identification of novel immune targets usable for the development of therapeutic treatments. PMID:25991687

Discharge, water-quality characteristics, and nutrient loads from McKay Bay, Delaney Creek, and East Bay, Tampa, Florida, 1991-1993

USGS Publications Warehouse

Stoker, Y.E.; Levesque, V.A.; Fritz, E.M.

1996-01-01

Nutrient enrichment in Tampa Bay has caused a decline in water quality in the estuary. Efforts to reduce the nutrient loading to Tampa Bay have resulted in improvement in water quality from 1981 to 1991. However, Tampa Bay still is onsidered enriched with nutrients. Water quality in East Bay (located at the northeastern part of Hillsborough Bay, which is an embayment in Tampa Bay) is not improving at the same rate as the rest of the bay. East Bay is the center of shipping activity in Tampa Bay and the seventh largest port in the United States. One of the primary cargoes is phosphate ore and related products such as fertilizer. The potential for nutrient loading to East Bay from shipping activities is high and has not previously been measured. Nitrogen and phosphorus loads from East Bay to Hillsborough Bay were measured during selected time periods during June 1992 through May 1993; these data were used to estimate seasonal and annual loads. These loads were evaluated to determine whether the loss of fertilizer products from shipping activities resulted in increased nutrient loading to Hillsborough Bay. Discharge was measured, and water-quality samples were collected at the head of East Bay (exiting McKay Bay), and at the mouth of East Bay. Discharge and nitrogen and phosphorus concentrations for the period June 1992 through May 1993 were used to compute loads. Discharges from McKay Bay, Delaney Creek, and East Bay are highly variable because of the effect of tide. Flow patterns during discharge measurements generally were unidirectional in McKay Bay and Delaney Creek, but more complex, bidirectional patterns were observed at the mouth of East Bay. Tidally affected discharge data were digitally filtered with the Godin filter to remove the effects of tide so that residual, or net, discharge could be determined. Daily mean discharge from McKay Bay ranged from -1,900 to 2,420 cubic feet per second; from Delaney Creek, -3.8 to 162 cubic feet per second; and from East

46 CFR 7.20 - Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and...

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 1 2013-10-01 2013-10-01 false Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and easterly entrance to Long Island Sound, NY. 7.20 Section 7.20... Atlantic Coast § 7.20 Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island...

46 CFR 7.20 - Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and...

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 1 2011-10-01 2011-10-01 false Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and easterly entrance to Long Island Sound, NY. 7.20 Section 7.20... Atlantic Coast § 7.20 Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island...

46 CFR 7.20 - Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and...

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 1 2014-10-01 2014-10-01 false Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and easterly entrance to Long Island Sound, NY. 7.20 Section 7.20... Atlantic Coast § 7.20 Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island...

46 CFR 7.20 - Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and...

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 1 2012-10-01 2012-10-01 false Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and easterly entrance to Long Island Sound, NY. 7.20 Section 7.20... Atlantic Coast § 7.20 Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island...

46 CFR 7.20 - Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and...

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 1 2010-10-01 2010-10-01 false Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island Sound and easterly entrance to Long Island Sound, NY. 7.20 Section 7.20... Atlantic Coast § 7.20 Nantucket Sound, Vineyard Sound, Buzzards Bay, Narragansett Bay, MA, Block Island...

Evaluating the Impact of Land Use Change on Submerged Aquatic Vegetation Stressors in Mobile Bay

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Estes, Maurice G., Jr.; Quattrochi, Dale; Thom, Ronald; Woodruff, Dana; Judd, Chaeli; Ellis, Jean; Watson, Brian; Rodriquez, Hugo; Johnson, Hoyt

2009-01-01

Alabama coastal systems have been subjected to increasing pressure from a variety of activities including urban and rural development, shoreline modifications, industrial activities, and dredging of shipping and navigation channels. The impacts on coastal ecosystems are often observed through the use of indicator species. One such indicator species for aquatic ecosystem health is submerged aquatic vegetation (SAV). Watershed and hydrodynamic modeling has been performed to evaluate the impact of land use change in Mobile and Baldwin counties on SAV stressors and controlling factors (temperature, salinity, and sediment) in Mobile Bay. Watershed modeling using the Loading Simulation Package in C++ (LSPC) was performed for all watersheds contiguous to Mobile Bay for land use scenarios in 1948, 1992, 2001, and 2030. Landsat-derived National Land Cover Data (NLCD) were used in the 1992 and 2001 simulations after having been reclassified to a common classification scheme. The Prescott Spatial Growth Model was used to project the 2030 land use scenario based on current trends. The LSPC model simulations provided output on changes in flow, temperature, and sediment for 22 discharge points into the Bay. Theses results were inputted in the Environmental Fluid Dynamics Computer Code (EFDC) hydrodynamic model to generate data on changes in temperature, salinity, and sediment on a grid with four vertical profiles throughout Mobile Bay. The changes in the aquatic ecosystem were used to perform an ecological analysis to evaluate the impact on SAV habitat suitability. This is the key product benefiting the Mobile Bay coastal environmental managers that integrates the influences of temperature, salinity, and sediment due to land use driven flow changes with the restoration potential of SAVs.

CASCO BAY PLAN

EPA Science Inventory

Casco Bay lies at the heart of Maine's most populated area. The health of its waters, wetlands, and wildlife depend in large part on the activities of the quarter-million residents who live in its watershed. Less than 30 years ago, portions of Casco Bay were off-limits to recr...

78 FR 62293 - Safety Zone, Oyster Festival 30th Anniversary Fireworks Display, Oyster Bay; Oyster Bay, NY

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-15

... Safety Zone, Oyster Festival 30th Anniversary Fireworks Display, Oyster Bay; Oyster Bay, NY AGENCY: Coast... zone on the navigable waters of Oyster Bay near Oyster Bay, NY for the Oyster Festival 30th Anniversary... Oyster Festival 30th Anniversary Fireworks Display is scheduled for October 19, 2013 and is one of...

Chesapeake Bay: Introduction to an Ecosystem.

ERIC Educational Resources Information Center

Environmental Protection Agency, Washington, DC.

The Chesapeake Bay is the largest estuary in the contiguous United States. The Bay and its tidal tributaries make up the Chesapeake Bay ecosystem. This document, which focuses of various aspects of this ecosystem, is divided into four major parts. The first part traces the geologic history of the Bay, describes the overall physical structure of…

75 FR 16005 - Drawbridge Operation Regulation; Port of Coos Bay Railroad Bridge, Coos Bay, North Bend, OR

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-31

...-AA09 Drawbridge Operation Regulation; Port of Coos Bay Railroad Bridge, Coos Bay, North Bend, OR AGENCY... regulation for the Coos Bay Railroad Bridge, Coos Bay, mile 9.0, at North Bend, Oregon to delete the... necessary to make the sound signals used at the bridge consistent with other bridges in the area and to...

Sentimental analysis of Amazon reviews using naïve bayes on laptop products with MongoDB and R

NASA Astrophysics Data System (ADS)

Kamal Hassan, Mohan; Prasanth Shakthi, Sana; Sasikala, R.

2017-11-01

Start In Today’s era the e-commerce is developing rapidly these years, buying products on-line has become more and more fashionable owing to its variety of options, low cost value (high discounts) and quick supply systems, so abundant folks intend to do online shopping. In the meantime the standard and delivery of merchandise is uneven, fake branded products are delivered. We use product users review comments about product and review about retailers from Amazon as data set and classify review text by subjectivity/objectivity and negative/positive attitude of buyer. Such reviews are helpful to some extent, promising both the shoppers and products makers. This paper presents an empirical study of efficacy of classifying product review by tagging the keyword. In the present study, we tend to analyse the fundamentals of determining, positive and negative approach towards the product. Thus we hereby propose completely different approaches by removing the unstructured data and then classifying comments employing Naive Bayes algorithm.

Bay BC's: A Multidisciplinary Approach To Teaching about the Chesapeake Bay.

ERIC Educational Resources Information Center

Slattery, Britt Eckhardt

The Chesapeake Bay is the largest estuary in North America, providing food and habitat for an abundance of fish and wildlife. This booklet provides lesson plans for lower elementary students introducing the Chesapeake, its inhabitants, and pollution problems, and suggesting ways that individuals can contribute to the Bay's restoration. Background…

75 FR 17329 - Safety Zone; Big Bay Fourth of July Fireworks, San Diego Bay, San Diego, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-06

...-AA00 Safety Zone; Big Bay Fourth of July Fireworks, San Diego Bay, San Diego, CA AGENCY: Coast Guard... safety zone on the navigable waters of the San Diego Bay in support of the Big Bay July Fourth Show to Benefit the San Diego Armed Services YMCA. This temporary safety zone is necessary to provide for the...

Exploring the Autonomous Economic World of Children: A Mixed Methods Study of Kids' Naive Economic Theories Incorporating Ethnographic and Behavioral Economics Methodologies

ERIC Educational Resources Information Center

Jennings, Amanda Brooke

2017-01-01

Children construct meaning from their economic experiences in the form of naive theories and use these theories to explain the relationships between their actions and the outcomes. Inevitably, due to their lack of economic literacy, these theories will be incomplete. Through curriculum design that acknowledges and addresses these naive theories,…

In Vitro Measles Virus Infection of Human Lymphocyte Subsets Demonstrates High Susceptibility and Permissiveness of both Naive and Memory B Cells

PubMed Central

Laksono, Brigitta M.; Grosserichter-Wagener, Christina; de Vries, Rory D.; Langeveld, Simone A. G.; Brem, Maarten D.; van Dongen, Jacques J. M.; Koopmans, Marion P. G.

2018-01-01

ABSTRACT Measles is characterized by a transient immune suppression, leading to an increased risk of opportunistic infections. Measles virus (MV) infection of immune cells is mediated by the cellular receptor CD150, expressed by subsets of lymphocytes, dendritic cells, macrophages, and thymocytes. Previous studies showed that human and nonhuman primate memory T cells express higher levels of CD150 than naive cells and are more susceptible to MV infection. However, limited information is available about the CD150 expression and relative susceptibility to MV infection of B-cell subsets. In this study, we assessed the susceptibility and permissiveness of naive and memory T- and B-cell subsets from human peripheral blood or tonsils to in vitro MV infection. Our study demonstrates that naive and memory B cells express CD150, but at lower frequencies than memory T cells. Nevertheless, both naive and memory B cells proved to be highly permissive to MV infection. Furthermore, we assessed the susceptibility and permissiveness of various functionally distinct T and B cells, such as helper T (TH) cell subsets and IgG- and IgA-positive memory B cells, in peripheral blood and tonsils. We demonstrated that TH1TH17 cells and plasma and germinal center B cells were the subsets most susceptible and permissive to MV infection. Our study suggests that both naive and memory B cells, along with several other antigen-experienced lymphocytes, are important target cells of MV infection. Depletion of these cells potentially contributes to the pathogenesis of measles immune suppression. IMPORTANCE Measles is associated with immune suppression and is often complicated by bacterial pneumonia, otitis media, or gastroenteritis. Measles virus infects antigen-presenting cells and T and B cells, and depletion of these cells may contribute to lymphopenia and immune suppression. Measles has been associated with follicular exhaustion in lymphoid tissues in humans and nonhuman primates, emphasizing

Diurnal variation of oxygen and carbonate system parameters in Tampa Bay and Florida Bay

USGS Publications Warehouse

Yates, K.K.; Dufore, C.; Smiley, N.; Jackson, C.; Halley, R.B.

2007-01-01

Oxygen and carbonate system parameters were measured, in situ, over diurnal cycles in Tampa Bay and Florida Bay, Florida. All system parameters showed distinct diurnal trends in Tampa Bay with an average range of diurnal variation of 39.1 μmol kg− 1 for total alkalinity, 165.1 μmol kg− 1 for total CO2, 0.22 for pH, 0.093 mmol L− 1 for dissolved oxygen, and 218.1 μatm for pCO2. Average range of diurnal variation for system parameters in Tampa Bay was 73% to 93% of the seasonal range of variability for dissolved oxygen and pH. All system parameters measured in Florida Bay showed distinct variation over diurnal time-scales. However, clear diurnal trends were less evident. The average range of diurnal variability in Florida Bay was 62.8 μmol kg− 1 for total alkalinity, 130.4 μmol kg− 1 for total CO2, 0.13 for pH, 0.053 mmol L− 1 for dissolved oxygen, and 139.8 μatm for pCO2. The average range of diurnal variation was 14% to 102% of the seasonal ranges for these parameters. Diurnal variability in system parameters was most influenced by primary productivity and respiration of benthic communities in Tampa Bay, and by precipitation and dissolution of calcium carbonate in Florida Bay. Our data indicate that use of seasonal data sets without careful consideration of diurnal variability may impart significant error in calculations of annual carbon and oxygen budgets. These observations reinforce the need for higher temporal resolution measurements of oxygen and carbon system parameters in coastal ecosystems.

NASA Ames DEVELOP Interns Collaborate with the South Bay Salt Pond Restoration Project to Monitor and Study Restoration Efforts using NASA's Satellites

NASA Technical Reports Server (NTRS)

Newcomer, Michelle E.; Kuss, Amber Jean; Nguyen, Andrew; Schmidt, Cynthia L.

2012-01-01

In the past, natural tidal marshes in the south bay were segmented by levees and converted into ponds for use in salt production. In an effort to provide habitat for migratory birds and other native plants and animals, as well as to rebuild natural capital, the South Bay Salt Pond Restoration Project (SBSPRP) is focused on restoring a portion of the over 15,000 acres of wetlands in California's South San Francisco Bay. The process of restoration begins when a levee is breached; the bay water and sediment flow into the ponds and eventually restore natural tidal marshes. Since the spring of 2010 the NASA Ames Research Center (ARC) DEVELOP student internship program has collaborated with the South Bay Salt Pond Restoration Project (SBSPRP) to study the effects of these restoration efforts and to provide valuable information to assist in habitat management and ecological forecasting. All of the studies were based on remote sensing techniques -- NASA's area of expertise in the field of Earth Science, and used various analytical techniques such as predictive modeling, flora and fauna classification, and spectral detection, to name a few. Each study was conducted by a team of aspiring scientists as a part of the DEVELOP program at Ames.

Discharge between San Antonio Bay and Aransas Bay, southern Gulf Coast, Texas, May-September 1999

USGS Publications Warehouse

East, Jeffery W.

2001-01-01

Along the Gulf Coast of Texas, many estuaries and bays are important habitat and nurseries for aquatic life. San Antonio Bay and Aransas Bay, located about 50 and 30 miles northeast, respectively, of Corpus Christi, are two important estuarine nurseries on the southern Gulf Coast of Texas (fig. 1). According to the Texas Parks and Wildlife Department, “Almost 80 percent of the seagrasses [along the Texas Gulf Coast] are located in the Laguna Madre, an estuary that begins just south of Corpus Christi Bay and runs southward 140 miles to South Padre Island. Most of the remaining seagrasses, about 45,000 acres, are located in the heavily traveled San Antonio, Aransas and Corpus Christi Bay areas” (Shook, 2000).Population growth has led to greater demands on water supplies in Texas. The Texas Water Development Board, the Texas Parks and Wildlife Department, and the Texas Natural Resource Conservation Commission have the cooperative task of determining inflows required to maintain the ecological health of the State’s streams, rivers, bays, and estuaries. To determine these inflow requirements, the three agencies collect data and conduct studies on the need for instream flows and freshwater/ saline water inflows to Texas estuaries.To assist in the determination of freshwater inflow requirements, the U.S. Geological Survey (USGS), in cooperation with the Texas Water Development Board, conducted a hydrographic survey of discharge (flow) between San Antonio Bay and Aransas Bay during the period May–September 1999. Automated instrumentation and acoustic technology were used to maximize the amount and quality of data that were collected, while minimizing personnel requirements. This report documents the discharge measured at two sites between the bays during May–September 1999 and describes the influences of meteorologic (wind and tidal) and hydrologic (freshwater inflow) conditions on discharge between the two bays. The movement of water between the bays is

Multilayer perceptron, fuzzy sets, and classification

NASA Technical Reports Server (NTRS)

Pal, Sankar K.; Mitra, Sushmita

1992-01-01

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Achieving ventricular rate control using metoprolol in β-blocker-naive patients vs patients on chronic β-blocker therapy.

PubMed

Kuang, Patricia; Mah, Nathan D; Barton, Cassie A; Miura, Andrea J; Tanas, Laura R; Ran, Ran

2016-03-01

The objective of the study is to evaluate the difference in ventricular rate control using an intravenous (IV) metoprolol regimen commonly used in clinical practice in patients receiving chronic β-blocker therapy compared to patients considered β-blocker naive admitted to the emergency department (ED) for atrial fibrillation (AF) with rapid ventricular rate. A single-center retrospective cohort study of adult ED patients who were admitted with a rapid ventricular rate of 120 beats per minute (bpm) or greater and treated with IV metoprolol was performed. Rate control was defined as either a decrease in ventricular rate to less than 100 bpm or a 20% decrease in heart rate to less than 120 bpm after metoprolol administration. Patient demographics, differences in length of stay, and adverse events were recorded. A total of 398 patients were included in the study, with 79.4% (n=316) receiving chronic β-blocker therapy. Patients considered to be β-blocker naive were more likely to achieve successful rate control with IV metoprolol compared to patients on chronic β-blocker therapy (56.1% vs 42.4%; P=.03). β-Blocker-naive status was associated with a shorter length of stay in comparison to patients receiving chronic β-blocker therapy (1.79 vs 2.64 days; P<.01). Intravenous metoprolol for the treatment of atrial fibrillation with rapid ventricular rate was associated with a higher treatment response in patients considered β-blocker naive compared to patients receiving chronic β-blocker therapy. Copyright © 2015 Elsevier Inc. All rights reserved.

Classification accuracies of physical activities using smartphone motion sensors.

PubMed

Wu, Wanmin; Dasgupta, Sanjoy; Ramirez, Ernesto E; Peterson, Carlyn; Norman, Gregory J

2012-10-05

Over the past few years, the world has witnessed an unprecedented growth in smartphone use. With sensors such as accelerometers and gyroscopes on board, smartphones have the potential to enhance our understanding of health behavior, in particular physical activity or the lack thereof. However, reliable and valid activity measurement using only a smartphone in situ has not been realized. To examine the validity of the iPod Touch (Apple, Inc.) and particularly to understand the value of using gyroscopes for classifying types of physical activity, with the goal of creating a measurement and feedback system that easily integrates into individuals' daily living. We collected accelerometer and gyroscope data for 16 participants on 13 activities with an iPod Touch, a device that has essentially the same sensors and computing platform as an iPhone. The 13 activities were sitting, walking, jogging, and going upstairs and downstairs at different paces. We extracted time and frequency features, including mean and variance of acceleration and gyroscope on each axis, vector magnitude of acceleration, and fast Fourier transform magnitude for each axis of acceleration. Different classifiers were compared using the Waikato Environment for Knowledge Analysis (WEKA) toolkit, including C4.5 (J48) decision tree, multilayer perception, naive Bayes, logistic, k-nearest neighbor (kNN), and meta-algorithms such as boosting and bagging. The 10-fold cross-validation protocol was used. Overall, the kNN classifier achieved the best accuracies: 52.3%-79.4% for up and down stair walking, 91.7% for jogging, 90.1%-94.1% for walking on a level ground, and 100% for sitting. A 2-second sliding window size with a 1-second overlap worked the best. Adding gyroscope measurements proved to be more beneficial than relying solely on accelerometer readings for all activities (with improvement ranging from 3.1% to 13.4%). Common categories of physical activity and sedentary behavior (walking, jogging, and

Anterior Cingulate Volumetric Alterations in Treatment-Naive Adults with ADHD: A Pilot Study

ERIC Educational Resources Information Center

Makris, Nikos; Seidman, Larry J.; Valera, Eve M.; Biederman, Joseph; Monuteaux, Michael C.; Kennedy, David N.; Caviness, Verne S., Jr.; Bush, George; Crum, Katherine; Brown, Ariel B.; Faraone, Stephen V.

2010-01-01

Objective: We sought to examine preliminary results of brain alterations in anterior cingulate cortex (ACC) in treatment-naive adults with ADHD. The ACC is a central brain node for the integration of cognitive control and allocation of attention, affect and drive. Thus its anatomical alteration may give rise to impulsivity, hyperactivity and…

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

NASA Astrophysics Data System (ADS)

Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

75 FR 11837 - Chesapeake Bay Watershed Initiative

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-12

... DEPARTMENT OF AGRICULTURE Commodity Credit Corporation Chesapeake Bay Watershed Initiative AGENCY...: Notice of availability of program funds for the Chesapeake Bay Watershed Initiative. SUMMARY: The... through the Chesapeake Bay Watershed Initiative for agricultural producers in the Chesapeake Bay watershed...

Urban Greening Bay Area

EPA Pesticide Factsheets

Information about the San Francisco Bay Water Quality Project (SFBWQP) Urban Greening Bay Area, a large-scale effort to re-envision urban landscapes to include green infrastructure (GI) making communities more livable and reducing stormwater runoff.

Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data

NASA Technical Reports Server (NTRS)

Rignot, E.; Chellappa, R.

1993-01-01

We present a maximum a posteriori (MAP) classifier for classifying multifrequency, multilook, single polarization SAR intensity data into regions or ensembles of pixels of homogeneous and similar radar backscatter characteristics. A model for the prior joint distribution of the multifrequency SAR intensity data is combined with a Markov random field for representing the interactions between region labels to obtain an expression for the posterior distribution of the region labels given the multifrequency SAR observations. The maximization of the posterior distribution yields Bayes's optimum region labeling or classification of the SAR data or its MAP estimate. The performance of the MAP classifier is evaluated by using computer-simulated multilook SAR intensity data as a function of the parameters in the classification process. Multilook SAR intensity data are shown to yield higher classification accuracies than one-look SAR complex amplitude data. The MAP classifier is extended to the case in which the radar backscatter from the remotely sensed surface varies within the SAR image because of incidence angle effects. The results obtained illustrate the practicality of the method for combining SAR intensity observations acquired at two different frequencies and for improving classification accuracy of SAR data.

Concentration of PSP (Paralytic Shellfish Poisoning) Toxin On Shellfish From Inner Ambon Bay and Kao Bay North Halmahera

NASA Astrophysics Data System (ADS)

Pello, F. S.; Haumahu, S.; Huliselan, N. V.; Tuapattinaja, M. A.

2017-10-01

The Inner Ambon Bay and Kao Bay have potential on fisheries resources which one of them is molluscs. Molluscs especially for class bivalve have economical values and are consumed by coastal community. The research had been done to analyze saxitoxin (STX) concentration on bivalves from Kao Bay and Inner Ambon Bay. The Saxitoxin Elisa Test Kit Protocol was used to determine saxitoxin concentration. The measurement showed that the highest concentration of saxitoxin (392.42 µg STXeq/100g shellfish meat) was Gafrarium tumidum from Ambon Bay, whereas concentration of saxitoxin (321.83 µg STXeq/100g shellfish meat) was Mactra mera from Kao Bay

Spatial modeling and classification of corneal shape.

PubMed

Marsolo, Keith; Twa, Michael; Bullimore, Mark A; Parthasarathy, Srinivasan

2007-03-01

One of the most promising applications of data mining is in biomedical data used in patient diagnosis. Any method of data analysis intended to support the clinical decision-making process should meet several criteria: it should capture clinically relevant features, be computationally feasible, and provide easily interpretable results. In an initial study, we examined the feasibility of using Zernike polynomials to represent biomedical instrument data in conjunction with a decision tree classifier to distinguish between the diseased and non-diseased eyes. Here, we provide a comprehensive follow-up to that work, examining a second representation, pseudo-Zernike polynomials, to determine whether they provide any increase in classification accuracy. We compare the fidelity of both methods using residual root-mean-square (rms) error and evaluate accuracy using several classifiers: neural networks, C4.5 decision trees, Voting Feature Intervals, and Naïve Bayes. We also examine the effect of several meta-learning strategies: boosting, bagging, and Random Forests (RFs). We present results comparing accuracy as it relates to dataset and transformation resolution over a larger, more challenging, multi-class dataset. They show that classification accuracy is similar for both data transformations, but differs by classifier. We find that the Zernike polynomials provide better feature representation than the pseudo-Zernikes and that the decision trees yield the best balance of classification accuracy and interpretability.

77 FR 30443 - Safety Zone; Alexandria Bay Chamber of Commerce, St. Lawrence River, Alexandria Bay, NY

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-23

...-AA00 Safety Zone; Alexandria Bay Chamber of Commerce, St. Lawrence River, Alexandria Bay, NY AGENCY... Commerce fireworks display. The safety zone established by this proposed rule is necessary to protect... spectators and vessels during the Alexandria Bay Chamber of Commerce fireworks display. Regulatory Analyses...

Description of gravity cores from San Pablo Bay and Carquinez Strait, San Francisco Bay, California

USGS Publications Warehouse

Woodrow, Donald L.; John L. Chin,; Wong, Florence L.; Fregoso, Theresa A.; Jaffe, Bruce E.

2017-06-27

Seventy-two gravity cores were collected by the U.S. Geological Survey in 1990, 1991, and 2000 from San Pablo Bay and Carquinez Strait, California. The gravity cores collected within San Pablo Bay contain bioturbated laminated silts and sandy clays, whole and broken bivalve shells (mostly mussels), fossil tube structures, and fine-grained plant or wood fragments. Gravity cores from the channel wall of Carquinez Strait east of San Pablo Bay consist of sand and clay layers, whole and broken bivalve shells (less than in San Pablo Bay), trace fossil tubes, and minute fragments of plant material.

Chesapeake Bay TMDL Document

EPA Pesticide Factsheets

This page provides the Total Maximum Daily Load (TMDL) report for the Chesapeake Bay. It includes the executive summary, main report, and appendices. The Chesapeake Bay TMDL was established by U.S. EPA Region 3 on December 29, 2010

KLF4 Nuclear Export Requires ERK Activation and Initiates Exit from Naive Pluripotency.

PubMed

Dhaliwal, Navroop K; Miri, Kamelia; Davidson, Scott; Tamim El Jarkass, Hala; Mitchell, Jennifer A

2018-04-10

Cooperative action of a transcription factor complex containing OCT4, SOX2, NANOG, and KLF4 maintains the naive pluripotent state; however, less is known about the mechanisms that disrupt this complex, initiating exit from pluripotency. We show that, as embryonic stem cells (ESCs) exit pluripotency, KLF4 protein is exported from the nucleus causing rapid decline in Nanog and Klf4 transcription; as a result, KLF4 is the first pluripotency transcription factor removed from transcription-associated complexes during differentiation. KLF4 nuclear export requires ERK activation, and phosphorylation of KLF4 by ERK initiates interaction of KLF4 with nuclear export factor XPO1, leading to KLF4 export. Mutation of the ERK phosphorylation site in KLF4 (S132) blocks KLF4 nuclear export, the decline in Nanog, Klf4, and Sox2 mRNA, and differentiation. These findings demonstrate that relocalization of KLF4 to the cytoplasm is a critical first step in exit from the naive pluripotent state and initiation of ESC differentiation. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Prevalence of Dyslipidemia Among Antiretroviral-Naive HIV-Infected Individuals in China

PubMed Central

Shen, Yinzhong; Wang, Jiangrong; Wang, Zhenyan; Qi, Tangkai; Song, Wei; Tang, Yang; Liu, Li; Zhang, Renfang; Lu, Hongzhou

2015-01-01

Abstract Little is known about the epidemiological features of dyslipidemia among antiretroviral-naive HIV-infected individuals in China. We used a cross-sectional study design to estimate the prevalence of dyslipidemia in this population, and to identify risk factors associated with the presence of dyslipidemia. One thousand five hundred and eighteen antiretroviral-naive HIV-infected individuals and 347 HIV-negative subjects in China were enrolled during 2009 to 2010. Demographics and medical histories were recorded. After an overnight fast, serum samples were collected to measure lipid levels. Factors associated with the presence of dyslipidemia were analyzed by logistic regression. Mean total cholesterol (TC), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL) levels were lower in HIV-positive than HIV-negative subjects, but mean triglyceride (TG) was higher in HIV-positive subjects. The overall prevalence of dyslipidemia in HIV-positive and HIV-negative groups did not differ (75.6% vs. 73.7%, P = 0.580). However, the prevalence of high TC (8.4% vs. 28.2%, P < 0.001) and high LDL (8.5% vs. 62.6%, P < 0.001) was lower in HIV-positive than HIV-negative subjects, and the prevalence of high TG (33.9% vs. 17.0%, P < 0.001) and low HDL (59.6% vs. 11.2%, P < 0.001) was higher in HIV-positive than HIV-negative subjects. Logistic analysis showed that HIV positivity was significantly associated with both an increased risk of high TG and low HDL and a decreased risk of high TC and high LDL. The mean levels of TC, of LDL and of HDL showed an increasing trend with increasing CD4 count in HIV-positive subjects. Multivariable logistic regression found that lower CD4 count was significantly associated with both an increased risk of high TG and low HDL and a decreased risk of high TC in HIV-positive subjects. Among antiretroviral-naive HIV-infected Chinese adults, there was a high prevalence of dyslipidemia characterized by

USGS Tampa Bay Pilot Study

USGS Publications Warehouse

Yates, K.K.; Cronin, T. M.; Crane, M.; Hansen, M.; Nayeghandi, A.; Swarzenski, P.; Edgar, T.; Brooks, G.R.; Suthard, B.; Hine, A.; Locker, S.; Willard, D.A.; Hastings, D.; Flower, B.; Hollander, D.; Larson, R.A.; Smith, K.

2007-01-01

Providing a web-based digital information management system of information for scientists and the public, including a system that supports the work of those officials who must make decisions that affect the state of the bay. The Tampa Bay Study is in its sixth year and will continue through September 2007. This paper presents a non-inclusive summary of key findings associated with the six primary project components listed above. Component 4 (above) is described in detail in the following chapter 13. More information on the Tampa Bay Study is available from our on-line digital information system for the Tampa Bay Study at http://gulfsci.usgs.gov.

Facility Layout Problems Using Bays: A Survey

NASA Astrophysics Data System (ADS)

Davoudpour, Hamid; Jaafari, Amir Ardestani; Farahani, Leila Najafabadi

2010-06-01

Layout design is one of the most important activities done by industrial Engineers. Most of these problems have NP hard Complexity. In a basic layout design, each cell is represented by a rectilinear, but not necessarily convex polygon. The set of fully packed adjacent polygons is known as a block layout (Asef-Vaziri and Laporte 2007). Block layout is divided by slicing tree and bay layout. In bay layout, departments are located in vertical columns or horizontal rows, bays. Bay layout is used in real worlds especially in concepts such as semiconductor and aisles. There are several reviews in facility layout; however none of them focus on bay layout. The literature analysis given here is not limited to specific considerations about bay layout design. We present a state of art review for bay layout considering some issues such as the used objectives, the techniques of solving and the integration methods in bay.

76 FR 37641 - Safety Zone; Independence Day Fireworks Celebration for the City of Half Moon Bay, Half Moon Bay, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-28

...-AA00 Safety Zone; Independence Day Fireworks Celebration for the City of Half Moon Bay, Half Moon Bay... temporary safety zone in the navigable waters of Half Moon Bay, off of Pillar Point Harbor beach, Half Moon Bay, CA in support of the Independence Day Fireworks Celebration for the City of Half Moon Bay...

Automated annotation of functional imaging experiments via multi-label classification

PubMed Central

Turner, Matthew D.; Chakrabarti, Chayan; Jones, Thomas B.; Xu, Jiawei F.; Fox, Peter T.; Luger, George F.; Laird, Angela R.; Turner, Jessica A.

2013-01-01

Identifying the experimental methods in human neuroimaging papers is important for grouping meaningfully similar experiments for meta-analyses. Currently, this can only be done by human readers. We present the performance of common machine learning (text mining) methods applied to the problem of automatically classifying or labeling this literature. Labeling terms are from the Cognitive Paradigm Ontology (CogPO), the text corpora are abstracts of published functional neuroimaging papers, and the methods use the performance of a human expert as training data. We aim to replicate the expert's annotation of multiple labels per abstract identifying the experimental stimuli, cognitive paradigms, response types, and other relevant dimensions of the experiments. We use several standard machine learning methods: naive Bayes (NB), k-nearest neighbor, and support vector machines (specifically SMO or sequential minimal optimization). Exact match performance ranged from only 15% in the worst cases to 78% in the best cases. NB methods combined with binary relevance transformations performed strongly and were robust to overfitting. This collection of results demonstrates what can be achieved with off-the-shelf software components and little to no pre-processing of raw text. PMID:24409112

Chesapeake Bay Watershed - Protecting the Chesapeake Bay and its rivers through science, restoration, and partnership

USGS Publications Warehouse

,

2012-01-01

The Chesapeake Bay, the Nation's largest estuary, has been degraded due to the impact of human-population increase, which has doubled since 1950, resulting in degraded water quality, loss of habitat, and declines in populations of biological communities. Since the mid-1980s, the Chesapeake Bay Program (CBP), a multi-agency partnership which includes the Department of Interior (DOI), has worked to restore the Bay ecosystem. The U.S. Geological Survey (USGS) has the critical role of providing unbiased scientific information that is utilized to document and understand ecosystem change to help assess the effectiveness of restoration strategies in the Bay and its watershed. The USGS revised its Chesapeake Bay science plan for 2006-2011 to address the collective needs of the CBP, DOI, and USGS with a mission to provide integrated science for improved understanding and management of the Bay ecosystem. The USGS science themes for this mission are: Causes and consequences of land-use change; Impact of climate change and associated hazards; Factors affecting water quality and quantity; Ability of habitat to support fish and bird populations; and Synthesis and forecasting to improve ecosystem assessment, conservation, and restoration.

In Vitro Measles Virus Infection of Human Lymphocyte Subsets Demonstrates High Susceptibility and Permissiveness of both Naive and Memory B Cells.

PubMed

Laksono, Brigitta M; Grosserichter-Wagener, Christina; de Vries, Rory D; Langeveld, Simone A G; Brem, Maarten D; van Dongen, Jacques J M; Katsikis, Peter D; Koopmans, Marion P G; van Zelm, Menno C; de Swart, Rik L

2018-04-15

Measles is characterized by a transient immune suppression, leading to an increased risk of opportunistic infections. Measles virus (MV) infection of immune cells is mediated by the cellular receptor CD150, expressed by subsets of lymphocytes, dendritic cells, macrophages, and thymocytes. Previous studies showed that human and nonhuman primate memory T cells express higher levels of CD150 than naive cells and are more susceptible to MV infection. However, limited information is available about the CD150 expression and relative susceptibility to MV infection of B-cell subsets. In this study, we assessed the susceptibility and permissiveness of naive and memory T- and B-cell subsets from human peripheral blood or tonsils to in vitro MV infection. Our study demonstrates that naive and memory B cells express CD150, but at lower frequencies than memory T cells. Nevertheless, both naive and memory B cells proved to be highly permissive to MV infection. Furthermore, we assessed the susceptibility and permissiveness of various functionally distinct T and B cells, such as helper T (T H ) cell subsets and IgG- and IgA-positive memory B cells, in peripheral blood and tonsils. We demonstrated that T H 1T H 17 cells and plasma and germinal center B cells were the subsets most susceptible and permissive to MV infection. Our study suggests that both naive and memory B cells, along with several other antigen-experienced lymphocytes, are important target cells of MV infection. Depletion of these cells potentially contributes to the pathogenesis of measles immune suppression. IMPORTANCE Measles is associated with immune suppression and is often complicated by bacterial pneumonia, otitis media, or gastroenteritis. Measles virus infects antigen-presenting cells and T and B cells, and depletion of these cells may contribute to lymphopenia and immune suppression. Measles has been associated with follicular exhaustion in lymphoid tissues in humans and nonhuman primates, emphasizing the

33 CFR 165.1187 - Security Zones; Golden Gate Bridge and the San Francisco-Oakland Bay Bridge, San Francisco Bay...

Code of Federal Regulations, 2011 CFR

2011-07-01

... Bridge and the San Francisco-Oakland Bay Bridge, San Francisco Bay, California. 165.1187 Section 165.1187... Limited Access Areas Eleventh Coast Guard District § 165.1187 Security Zones; Golden Gate Bridge and the San Francisco-Oakland Bay Bridge, San Francisco Bay, California. (a) Location. All waters extending...

33 CFR 165.1187 - Security Zones; Golden Gate Bridge and the San Francisco-Oakland Bay Bridge, San Francisco Bay...

Code of Federal Regulations, 2010 CFR

2010-07-01

... Bridge and the San Francisco-Oakland Bay Bridge, San Francisco Bay, California. 165.1187 Section 165.1187... Limited Access Areas Eleventh Coast Guard District § 165.1187 Security Zones; Golden Gate Bridge and the San Francisco-Oakland Bay Bridge, San Francisco Bay, California. (a) Location. All waters extending...

Nutrients and Narragansett Bay

EPA Science Inventory

Narragansett Bay has been heavily fertilized by anthropogenic nutrients for almost 120 years. This presentation discusses the first introductions of human sources of nutrients to the Bay, via sewage and urban runoff, in the late 1890s through to the recent reductions in sewage ef...

Bay breeze climatology at two sites along the Chesapeake bay from 1986-2010: Implications for surface ozone.

PubMed

Stauffer, Ryan M; Thompson, Anne M

Hourly surface meteorological measurements were coupled with surface ozone (O 3 ) mixing ratio measurements at Hampton, Virginia and Baltimore, Maryland, two sites along the Chesapeake Bay in the Mid-Atlantic United States, to examine the behavior of surface O 3 during bay breeze events and quantify the impact of the bay breeze on local O 3 pollution. Analyses were performed for the months of May through September for the years 1986 to 2010. The years were split into three groups to account for increasingly stringent environmental regulations that reduced regional emissions of nitrogen oxides (NO x ): 1986-1994, 1995-2002, and 2003-2010. Each day in the 25-year record was marked either as a bay breeze day, a non-bay breeze day, or a rainy/cloudy day based on the meteorological data. Mean eight hour (8-h) averaged surface O 3 values during bay breeze events were 3 to 5 parts per billion by volume (ppbv) higher at Hampton and Baltimore than on non-bay breeze days in all year periods. Anomalies from mean surface O 3 were highest in the afternoon at both sites during bay breeze days in the 2003-2010 study period. In conjunction with an overall lowering of baseline O 3 after the 1995-2002 period, the percentage of total exceedances of the Environmental Protection Agency (EPA) 75 ppbv 8-h O 3 standard that occurred on bay breeze days increased at Hampton for 2003-2010, while remaining steady at Baltimore. These results suggest that bay breeze circulations are becoming more important to causing exceedance events at particular sites in the region, and support the hypothesis of Martins et al. (2012) that highly localized meteorology increasingly drives air quality events at Hampton.

Maraviroc: perspectives for use in antiretroviral-naive HIV-1-infected patients.

PubMed

Vandekerckhove, Linos; Verhofstede, Chris; Vogelaers, Dirk

2009-06-01

Maraviroc (Pfizer's UK-427857, Selzentry or Celsentri outside the USA) is the first agent in the new class of oral HIV-1 entry inhibitors to acquire approval by the US Food and Drug Administration and the European Medicine Agency. Considering the mechanism of action, it is expected that this drug will be effective only in a subpopulation of HIV-1-infected people, namely those harbouring the R5 virus. The favourable toxicity profile of the drug has been demonstrated in Phase III clinical trials in treatment-naive (MERIT) and treatment-experienced (MOTIVATE) patients. In the latter population, maraviroc showed a superior antiviral efficacy and immunological activity compared with optimized backbone therapy + placebo. However, in MERIT, a prospective double-blind, randomized trial in treatment-naive patients, maraviroc + zidovudine/lamivudine failed to prove non-inferiority to efavirenz + zidovudine/lamivudine as standard of care regimen in the 48 week intention-to-treat analysis. Using an assay with higher sensitivity for minority CXCR4-using (X4) HIV variants (the enhanced Trofile assay-Monogram), non-inferiority was reached for the maraviroc- versus efavirenz-based combination. These data indicate the important impact of the sensitivity of tropism testing on treatment outcome of maraviroc-containing regimens. This paper discusses both the prospective and retrospective analyses of the MERIT data and highlights the impact of these results on daily practice in HIV care.

BAY REGION ATMOSPHERIC CHEMISTRY EXPERIMENT (BRACE)

EPA Science Inventory

The Tampa Bay Estuary Program (TBEP) was formed in 1991 to assist the community in developing a comprehensive plan to restore and protect Tampa Bay. The ecological indicator of the health of the Bay is the coverage of seagrasses, historically in decline, which are important to...

78 FR 14185 - Safety Zone; MODU KULLUK; Kiliuda Bay, Kodiak Island, AK to Captains Bay, Unalaska Island, AK

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-05

... Mobile Offshore Drilling Unit (MODU) KULLUK currently located in Kiliuda Bay, Kodiak Island, Alaska with... large ocean-going drill vessel, while it is under tow from Kiliuda Bay, Kodiak Island to Captains Bay...

Robust through-the-wall radar image classification using a target-model alignment procedure.

PubMed

Smith, Graeme E; Mobasseri, Bijan G

2012-02-01

A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) ≤ 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates ≥ 97%. © 2011 IEEE

Consistent latent position estimation and vertex classification for random dot product graphs.

PubMed

Sussman, Daniel L; Tang, Minh; Priebe, Carey E

2014-01-01

In this work, we show that using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the $(k)$-nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from Wikipedia.

Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers.

PubMed

Prinyakupt, Jaroonrut; Pluempitiwiriyawej, Charnchai

2015-06-30

Blood smear microscopic images are routinely investigated by haematologists to diagnose most blood diseases. However, the task is quite tedious and time consuming. An automatic detection and classification of white blood cells within such images can accelerate the process tremendously. In this paper we propose a system to locate white blood cells within microscopic blood smear images, segment them into nucleus and cytoplasm regions, extract suitable features and finally, classify them into five types: basophil, eosinophil, neutrophil, lymphocyte and monocyte. Two sets of blood smear images were used in this study's experiments. Dataset 1, collected from Rangsit University, were normal peripheral blood slides under light microscope with 100× magnification; 555 images with 601 white blood cells were captured by a Nikon DS-Fi2 high-definition color camera and saved in JPG format of size 960 × 1,280 pixels at 15 pixels per 1 μm resolution. In dataset 2, 477 cropped white blood cell images were downloaded from CellaVision.com. They are in JPG format of size 360 × 363 pixels. The resolution is estimated to be 10 pixels per 1 μm. The proposed system comprises a pre-processing step, nucleus segmentation, cell segmentation, feature extraction, feature selection and classification. The main concept of the segmentation algorithm employed uses white blood cell's morphological properties and the calibrated size of a real cell relative to image resolution. The segmentation process combined thresholding, morphological operation and ellipse curve fitting. Consequently, several features were extracted from the segmented nucleus and cytoplasm regions. Prominent features were then chosen by a greedy search algorithm called sequential forward selection. Finally, with a set of selected prominent features, both linear and naïve Bayes classifiers were applied for performance comparison. This system was tested on normal peripheral blood smear slide images from two datasets. Two sets

San Francisco and Bay Area, CA, USA

NASA Image and Video Library

1991-06-14

STS040-152-100 (5-14 June 1991) --- Although clouds obscure part of the city of San Francisco and the mouth of San Francisco Bay, development and physiographic features in the immediate vicinity of the bay are well displayed. The photograph clearly shows the eastern part of the city, including the Embarcadero, the Bay Bridge, which was damaged in the 1989 earthquake, and Candlestick Park, San Mateo, and Dumbarton Bridges, cross the southern portion of the bay. Vari-colored salt ponds also rim the southern Bay near Moffett Field. Highway 280 runs along the San Andreas fault south of the city. On the eastern margin of the bay are Berkeley the Sacramento River and the Haywood and Calaveras faults.

77 FR 21890 - Drawbridge Operation Regulation; Sturgeon Bay Ship Canal, Sturgeon Bay, WI

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-12

... Street and Maple-Oregon Bridges so vehicular traffic congestion would not develop on downtown Sturgeon... the efficient movement of vehicular traffic in Sturgeon Bay. The Sturgeon Bay Ship Canal is... experiences a significant increase in vehicular and vessel traffic during the peak tourist and navigation...

Classifying smoking urges via machine learning.

PubMed

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions

Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: retrospective cohort study

PubMed Central

Agniel, Denis; Beam, Andrew; Yorkgitis, Brian; Bicket, Mark; Homer, Mark; Fox, Kathe P; Knecht, Daniel B; McMahill-Walraven, Cheryl N; Palmer, Nathan; Kohane, Isaac

2018-01-01

Abstract Objective To quantify the effects of varying opioid prescribing patterns after surgery on dependence, overdose, or abuse in an opioid naive population. Design Retrospective cohort study. Setting Surgical claims from a linked medical and pharmacy administrative database of 37 651 619 commercially insured patients between 2008 and 2016. Participants 1 015 116 opioid naive patients undergoing surgery. Main outcome measures Use of oral opioids after discharge as defined by refills and total dosage and duration of use. The primary outcome was a composite of misuse identified by a diagnostic code for opioid dependence, abuse, or overdose. Results 568 612 (56.0%) patients received postoperative opioids, and a code for abuse was identified for 5906 patients (0.6%, 183 per 100 000 person years). Total duration of opioid use was the strongest predictor of misuse, with each refill and additional week of opioid use associated with an adjusted increase in the rate of misuse of 44.0% (95% confidence interval 40.8% to 47.2%, P<0.001), and 19.9% increase in hazard (18.5% to 21.4%, P<0.001), respectively. Conclusions Each refill and week of opioid prescription is associated with a large increase in opioid misuse among opioid naive patients. The data from this study suggest that duration of the prescription rather than dosage is more strongly associated with ultimate misuse in the early postsurgical period. The analysis quantifies the association of prescribing choices on opioid misuse and identifies levers for possible impact. PMID:29343479

GPU MrBayes V3.1: MrBayes on Graphics Processing Units for Protein Sequence Data.

PubMed

Pang, Shuai; Stones, Rebecca J; Ren, Ming-Ming; Liu, Xiao-Guang; Wang, Gang; Xia, Hong-ju; Wu, Hao-Yang; Liu, Yang; Xie, Qiang

2015-09-01

We present a modified GPU (graphics processing unit) version of MrBayes, called ta(MC)(3) (GPU MrBayes V3.1), for Bayesian phylogenetic inference on protein data sets. Our main contributions are 1) utilizing 64-bit variables, thereby enabling ta(MC)(3) to process larger data sets than MrBayes; and 2) to use Kahan summation to improve accuracy, convergence rates, and consequently runtime. Versus the current fastest software, we achieve a speedup of up to around 2.5 (and up to around 90 vs. serial MrBayes), and more on multi-GPU hardware. GPU MrBayes V3.1 is available from http://sourceforge.net/projects/mrbayes-gpu/. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Virological Failure and HIV-1 Drug Resistance Mutations among Naive and Antiretroviral Pre-Treated Patients Entering the ESTHER Program of Calmette Hospital in Cambodia

PubMed Central

Limsreng, Setha; Him, Sovanvatey; Nouhin, Janin; Hak, Chanroeurn; Srun, Chanvatey; Viretto, Gerald; Ouk, Vara; Delfraissy, Jean Francois; Ségéral, Olivier

2014-01-01

Introduction In resource limited settings, patients entering an antiretroviral therapy (ART) program comprise ART naive and ART pre-treated patients who may show differential virological outcomes. Methods This retrospective study, conducted in 2010–2012 in the HIV clinic of Calmette Hospital located in Phnom Penh (Cambodia) assessed virological failure (VF) rates and patterns of drug resistance of naive and pre-treated patients. Naive and ART pre-treated patients were included when a Viral Load (VL) was performed during the first year of ART for naive subjects or at the first consultation for pre-treated individuals. Patients showing Virological failure (VF) (>1,000 copies/ml) underwent HIV DR genotyping testing. Interpretation of drug resistance mutations was done according to 2013 version 23 ANRS algorithms. Results On a total of 209 patients, 164 (78.4%) were naive and 45 (21.5%) were ART pre-treated. Their median initial CD4 counts were 74 cells/mm3 (IQR: 30–194) and 279 cells/mm3 (IQR: 103–455) (p<0.001), respectively. Twenty seven patients (12.9%) exhibited VF (95% CI: 8.6–18.2%), including 10 naive (10/164, 6.0%) and 17 pre-treated (17/45, 37.8%) patients (p<0.001). Among these viremic patients, twenty-two (81.4%) were sequenced in reverse transcriptase and protease coding regions. Overall, 19 (86.3%) harbored ≥1 drug resistance mutations (DRMs) whereas 3 (all belonging to pre-treated patients) harbored wild-types viruses. The most frequent DRMs were M184V (86.3%), K103N (45.5%) and thymidine analog mutations (TAMs) (40.9%). Two (13.3%) pre-treated patients harbored viruses that showed a multi-nucleos(t)ide resistance including Q151M, K65R, E33A/D, E44A/D mutations. Conclusion In Cambodia, VF rates were low for naive patients but the emergence of DRMs to NNRTI and 3TC occurred relatively quickly in this subgroup. In pre-treated patients, VF rates were much higher and TAMs were relatively common. HIV genotypic assays before ART initiation and for

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

Ideal discrimination of discrete clinical endpoints using multilocus genotypes.

PubMed

Hahn, Lance W; Moore, Jason H

2004-01-01

Multifactor Dimensionality Reduction (MDR) is a method for the classification and prediction of discrete clinical endpoints using attributes constructed from multilocus genotype data. Empirical studies with both real and simulated data suggest that MDR has good power for detecting gene-gene interactions in the absence of independent main effects. The purpose of this study is to develop an objective, theory-driven approach to evaluate the strengths and limitations of MDR. To accomplish this goal, we borrow concepts from ideal observer analysis used in visual perception to evaluate the theoretical limits of classifying and predicting discrete clinical endpoints using multilocus genotype data. We conclude that MDR ideally discriminates between low risk and high risk subjects using attributes constructed from multilocus genotype data. We also how that the classification approach used once a multilocus attribute is constructed is similar to that of a naive Bayes classifier. This study provides a theoretical foundation for the continued development, evaluation, and application of the MDR as a data mining tool in the domain of statistical genetics and genetic epidemiology.

Implementation and performance evaluation of acoustic denoising algorithms for UAV

NASA Astrophysics Data System (ADS)

Chowdhury, Ahmed Sony Kamal

Unmanned Aerial Vehicles (UAVs) have become popular alternative for wildlife monitoring and border surveillance applications. Elimination of the UAV's background noise and classifying the target audio signal effectively are still a major challenge. The main goal of this thesis is to remove UAV's background noise by means of acoustic denoising techniques. Existing denoising algorithms, such as Adaptive Least Mean Square (LMS), Wavelet Denoising, Time-Frequency Block Thresholding, and Wiener Filter, were implemented and their performance evaluated. The denoising algorithms were evaluated for average Signal to Noise Ratio (SNR), Segmental SNR (SSNR), Log Likelihood Ratio (LLR), and Log Spectral Distance (LSD) metrics. To evaluate the effectiveness of the denoising algorithms on classification of target audio, we implemented Support Vector Machine (SVM) and Naive Bayes classification algorithms. Simulation results demonstrate that LMS and Discrete Wavelet Transform (DWT) denoising algorithm offered superior performance than other algorithms. Finally, we implemented the LMS and DWT algorithms on a DSP board for hardware evaluation. Experimental results showed that LMS algorithm's performance is robust compared to DWT for various noise types to classify target audio signals.

Applying Data Mining Techniques to Improve Breast Cancer Diagnosis.

PubMed

Diz, Joana; Marreiros, Goreti; Freitas, Alberto

2016-09-01

In the field of breast cancer research, and more than ever, new computer aided diagnosis based systems have been developed aiming to reduce diagnostic tests false-positives. Within this work, we present a data mining based approach which might support oncologists in the process of breast cancer classification and diagnosis. The present study aims to compare two breast cancer datasets and find the best methods in predicting benign/malignant lesions, breast density classification, and even for finding identification (mass / microcalcification distinction). To carry out these tasks, two matrices of texture features extraction were implemented using Matlab, and classified using data mining algorithms, on WEKA. Results revealed good percentages of accuracy for each class: 89.3 to 64.7 % - benign/malignant; 75.8 to 78.3 % - dense/fatty tissue; 71.0 to 83.1 % - finding identification. Among the different tests classifiers, Naive Bayes was the best to identify masses texture, and Random Forests was the first or second best classifier for the majority of tested groups.

Induction of cross-priming of naive CD8+ T lymphocytes by recombinant bacillus Calmette-Guerin that secretes heat shock protein 70-major membrane protein-II fusion protein.

PubMed

Mukai, Tetsu; Maeda, Yumi; Tamura, Toshiki; Matsuoka, Masanori; Tsukamoto, Yumiko; Makino, Masahiko

2009-11-15

Because Mycobacterium bovis bacillus Calmette-Guérin (BCG) unconvincingly activates human naive CD8(+) T cells, a rBCG (BCG-70M) that secretes a fusion protein comprising BCG-derived heat shock protein (HSP)70 and Mycobacterium leprae-derived major membrane protein (MMP)-II, one of the immunodominant Ags of M. leprae, was newly constructed to potentiate the ability of activating naive CD8(+) T cells through dendritic cells (DC). BCG-70M secreted HSP70-MMP-II fusion protein in vitro, which stimulated DC to produce IL-12p70 through TLR2. BCG-70M-infected DC activated not only memory and naive CD8(+) T cells, but also CD4(+) T cells of both types to produce IFN-gamma. The activation of these naive T cells by BCG-70M was dependent on the MHC and CD86 molecules on BCG-70M-infected DC, and was significantly inhibited by pretreatment of DC with chloroquine. Both brefeldin A and lactacystin significantly inhibited the activation of naive CD8(+) T cells by BCG-70M through DC. Thus, the CD8(+) T cell activation may be induced by cross-presentation of Ags through a TAP- and proteosome-dependent cytosolic pathway. When naive CD8(+) T cells were stimulated by BCG-70M-infected DC in the presence of naive CD4(+) T cells, CD62L(low)CD8(+) T cells and perforin-producing CD8(+) T cells were efficiently produced. MMP-II-reactive CD4(+) and CD8(+) memory T cells were efficiently produced in C57BL/6 mice by infection with BCG-70M. These results indicate that BCG-70M activated DC, CD4(+) T cells, and CD8(+) T cells, and the combination of HSP70 and MMP-II may be useful for inducing better T cell activation.

Draft Detailed Project Report and Draft Environmental Assessment. Neah Bay Navigation Improvements, Neah Bay, Washington

DTIC Science & Technology

1993-11-01

navigation improvements for Neah Bay, Clallam Bay, and Port Angeles was begun under the Puget Sound and Adjacent Waters, General Investigations authority. The...Regonnaissance Report and Plan of Study. Puget Sound and Adjacent Waters. Washington. Northern Olympic Peninsula Shallow-Draft Naviaation Study, August 1983...operators from having to make long trips from the fishing grounds near Neah Bay to ports farther east in the Strait of Juan de Fuca or in Puget Sound . 9 9

33 CFR 117.871 - Coos Bay.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 1 2014-07-01 2014-07-01 false Coos Bay. 117.871 Section 117.871 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY BRIDGES DRAWBRIDGE OPERATION REGULATIONS Specific Requirements Oregon § 117.871 Coos Bay. The draw of the Port of Coos Bay railroad bridge...

33 CFR 117.871 - Coos Bay.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 1 2013-07-01 2013-07-01 false Coos Bay. 117.871 Section 117.871 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY BRIDGES DRAWBRIDGE OPERATION REGULATIONS Specific Requirements Oregon § 117.871 Coos Bay. The draw of the Port of Coos Bay railroad bridge...

33 CFR 117.871 - Coos Bay.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 1 2012-07-01 2012-07-01 false Coos Bay. 117.871 Section 117.871 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY BRIDGES DRAWBRIDGE OPERATION REGULATIONS Specific Requirements Oregon § 117.871 Coos Bay. The draw of the Port of Coos Bay railroad bridge...

33 CFR 117.871 - Coos Bay.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false Coos Bay. 117.871 Section 117.871 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY BRIDGES DRAWBRIDGE OPERATION REGULATIONS Specific Requirements Oregon § 117.871 Coos Bay. The draw of the Port of Coos Bay railroad bridge...

33 CFR 117.871 - Coos Bay.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Coos Bay. 117.871 Section 117.871 Navigation and Navigable Waters COAST GUARD, DEPARTMENT OF HOMELAND SECURITY BRIDGES DRAWBRIDGE OPERATION REGULATIONS Specific Requirements Oregon § 117.871 Coos Bay. The draw of the Port of Coos Bay railroad bridge...

Cognitive Performance Under Electroconvulsive Therapy (ECT) in ECT-Naive Treatment-Resistant Patients With Major Depressive Disorder.

PubMed

Ziegelmayer, Christoph; Hajak, Göran; Bauer, Anne; Held, Marion; Rupprecht, Rainer; Trapp, Wolfgang

2017-06-01

Although electroconvulsive therapy (ECT) is considered a safe and highly effective treatment option for major depressive disorder, there are still some reservations with regard to possible adverse cognitive adverse effects. This is the case despite a large body of evidence showing that these deficits are transient and that there even seems to be a long-term improvement of cognitive functioning level. However, most data concerning cognitive adverse effects stem from studies using mixed samples of treatment-resistant and non-treatment-resistant as well as ECT-naive and non-ECT-naive subjects. Furthermore, neurocognitive measures might partly be sensitive to practice effects and improvements in depressive symptom level. We examined neurocognitive performance in a sample of 20 treatment-resistant and ECT-naive subjects using repeatable neurocognitive tests, whereas changes in depressive symptom level were controlled. Cognitive functioning level was assessed before (baseline), 1 week, and 6 months (follow-up 1 and 2) after (12 to) 15 sessions of unilateral ECT treatment. No adverse cognitive effects were observed in any of the cognitive domains examined. Instead, a significant improvement in verbal working memory performance was found from baseline to follow-up 2. When changes in depressive symptom levels were controlled statistically, this improvement was no longer seen. Although findings that ECT does not lead to longer lasting cognitive deficits caused by ECT were confirmed, our study adds evidence that previous results of a beneficial effect of ECT on cognition might be questioned.

Responses of upland herpetofauna to the restoration of Carolina Bays and thinning of forested Bay Margins.

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

Ledvina, Joseph A.

2008-05-01

Research on the effects of wetland restoration on reptiles and amphibians is becoming more common, but almost all of these studies have observed the colonization of recently disturbed habitats that were completely dry at the time of restoration. In a similar manner, investigations herpetofaunal responses to forest management have focused on clearcuts, and less intensive stand manipulations are not as well studied. To evaluate community and population responses of reptiles and amphibians to hydrology restoration and canopy removal in the interior of previously degraded Carolina bays, I monitored herpetofauna in the uplands adjacent to six historically degraded Carolina bays atmore » the Savannah River Site (SRS) in South Carolina for four years after restoration. To evaluate the effects of forest thinning on upland herpetofauna, forests were thinned in the margins of three of these bays. I used repeated measures ANOVA to compare species richness and diversity and the abundance of selected species and guilds between these bays and with those at three reference bays that were not historically drained and three control bays that remained degraded. I also used Non-metric Multidimensional Scaling (NMDS) to look for community-level patterns based treatments.« less

A Bayes linear Bayes method for estimation of correlated event rates.

PubMed

Quigley, John; Wilson, Kevin J; Walls, Lesley; Bedford, Tim

2013-12-01

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well-known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates. © 2013 Society for Risk Analysis.

33 CFR 165.1195 - Regulated Navigation Area; Humboldt Bay Bar Channel and Humboldt Bay Entrance Channel, Humboldt...

Code of Federal Regulations, 2010 CFR

2010-07-01

... Navigation Area (RNA) includes all navigable waters of the Humboldt Bay Bar Channel and the Humboldt Bay Entrance Channel, Humboldt Bay, California. (b) Definitions. As used in this section— COTP means the Captain of the Port as defined in Title 33, Code of Federal Regulations, Section 1.01-30 and 3.55-20...

33 CFR 165.1195 - Regulated Navigation Area; Humboldt Bay Bar Channel and Humboldt Bay Entrance Channel, Humboldt...

Code of Federal Regulations, 2011 CFR

2011-07-01

... Navigation Area (RNA) includes all navigable waters of the Humboldt Bay Bar Channel and the Humboldt Bay Entrance Channel, Humboldt Bay, California. (b) Definitions. As used in this section— COTP means the Captain of the Port as defined in Title 33, Code of Federal Regulations, Section 1.01-30 and 3.55-20...

33 CFR 165.1195 - Regulated Navigation Area; Humboldt Bay Bar Channel and Humboldt Bay Entrance Channel, Humboldt...

Code of Federal Regulations, 2012 CFR

2012-07-01

... Navigation Area (RNA) includes all navigable waters of the Humboldt Bay Bar Channel and the Humboldt Bay Entrance Channel, Humboldt Bay, California. (b) Definitions. As used in this section— COTP means the Captain of the Port as defined in Title 33, Code of Federal Regulations, Section 1.01-30 and 3.55-20...

33 CFR 165.1195 - Regulated Navigation Area; Humboldt Bay Bar Channel and Humboldt Bay Entrance Channel, Humboldt...

Code of Federal Regulations, 2013 CFR

2013-07-01

... Navigation Area (RNA) includes all navigable waters of the Humboldt Bay Bar Channel and the Humboldt Bay Entrance Channel, Humboldt Bay, California. (b) Definitions. As used in this section— COTP means the Captain of the Port as defined in Title 33, Code of Federal Regulations, Section 1.01-30 and 3.55-20...

33 CFR 165.1195 - Regulated Navigation Area; Humboldt Bay Bar Channel and Humboldt Bay Entrance Channel, Humboldt...

Code of Federal Regulations, 2014 CFR

2014-07-01

... Navigation Area (RNA) includes all navigable waters of the Humboldt Bay Bar Channel and the Humboldt Bay Entrance Channel, Humboldt Bay, California. (b) Definitions. As used in this section— COTP means the Captain of the Port as defined in Title 33, Code of Federal Regulations, Section 1.01-30 and 3.55-20...

Naive-like Conversion Overcomes the Limited Differentiation Capacity of Induced Pluripotent Stem Cells*

PubMed Central

Honda, Arata; Hatori, Masanori; Hirose, Michiko; Honda, Chizumi; Izu, Haruna; Inoue, Kimiko; Hirasawa, Ryutaro; Matoba, Shogo; Togayachi, Sumie; Miyoshi, Hiroyuki; Ogura, Atsuo

2013-01-01

Although induced pluripotent stem (iPS) cells are indistinguishable from ES cells in their expression of pluripotent markers, their differentiation into targeted cells is often limited. Here, we examined whether the limited capacity of iPS cells to differentiate into neural lineage cells could be mitigated by improving their base-line level of pluripotency, i.e. by converting them into the so-called “naive” state. In this study, we used rabbit iPS and ES cells because of the easy availability of both cell types and their typical primed state characters. Repeated passages of the iPS cells permitted their differentiation into early neural cell types (neural stem cells, neurons, and glial astrocytes) with efficiencies similar to ES cells. However, unlike ES cells, their ability to differentiate later into neural cells (oligodendrocytes) was severely compromised. In contrast, after these iPS cells had been converted to a naive-like state, they readily differentiated into mature oligodendrocytes developing characteristic ramified branches, which could not be attained even with ES cells. These results suggest that the naive-like conversion of iPS cells might endow them with a higher differentiation capacity. PMID:23880763

Personality matters: individual variation in reactions of naive bird predators to aposematic prey.

PubMed

Exnerová, Alice; Svádová, Katerina Hotová; Fucíková, Eva; Drent, Pieter; Stys, Pavel

2010-03-07

Variation in reactions to aposematic prey is common among conspecific individuals of bird predators. It may result from different individual experience but it also exists among naive birds. This variation may possibly be explained by the effect of personality--a complex of correlated, heritable behavioural traits consistent across contexts. In the great tit (Parus major), two extreme personality types have been defined. 'Fast' explorers are bold, aggressive and routine-forming; 'slow' explorers are shy, non-aggressive and innovative. Influence of personality type on unlearned reaction to aposematic prey, rate of avoidance learning and memory were tested in naive, hand-reared great tits from two opposite lines selected for exploration (slow against fast). The birds were subjected to a sequence of trials in which they were offered aposematic adult firebugs (Pyrrhocoris apterus). Slow birds showed a greater degree of unlearned wariness and learned to avoid the firebugs faster than fast birds. Although birds of both personality types remembered their experience, slow birds were more cautious in the memory test. We conclude that not only different species but also populations of predators that differ in proportions of personality types may have different impacts on survival of aposematic insects under natural conditions.

Content-based image retrieval for interstitial lung diseases using classification confidence

NASA Astrophysics Data System (ADS)

Dash, Jatindra Kumar; Mukhopadhyay, Sudipta; Prabhakar, Nidhi; Garg, Mandeep; Khandelwal, Niranjan

2013-02-01

Content Based Image Retrieval (CBIR) system could exploit the wealth of High-Resolution Computed Tomography (HRCT) data stored in the archive by finding similar images to assist radiologists for self learning and differential diagnosis of Interstitial Lung Diseases (ILDs). HRCT findings of ILDs are classified into several categories (e.g. consolidation, emphysema, ground glass, nodular etc.) based on their texture like appearances. Therefore, analysis of ILDs is considered as a texture analysis problem. Many approaches have been proposed for CBIR of lung images using texture as primitive visual content. This paper presents a new approach to CBIR for ILDs. The proposed approach makes use of a trained neural network (NN) to find the output class label of query image. The degree of confidence of the NN classifier is analyzed using Naive Bayes classifier that dynamically takes a decision on the size of the search space to be used for retrieval. The proposed approach is compared with three simple distance based and one classifier based texture retrieval approaches. Experimental results show that the proposed technique achieved highest average percentage precision of 92.60% with lowest standard deviation of 20.82%.

Toward extending terrestrial laser scanning applications in forestry: a case study of broad- and needle-leaf tree classification

NASA Astrophysics Data System (ADS)

Lin, Yi; Jiang, Miao

2017-01-01

Tree species information is essential for forest research and management purposes, which in turn require approaches for accurate and precise classification of tree species. One such remote sensing technology, terrestrial laser scanning (TLS), has proved to be capable of characterizing detailed tree structures, such as tree stem geometry. Can TLS further differentiate between broad- and needle-leaves? If the answer is positive, TLS data can be used for classification of taxonomic tree groups by directly examining their differences in leaf morphology. An analysis was proposed to assess TLS-represented broad- and needle-leaf structures, followed by a Bayes classifier to perform the classification. Tests indicated that the proposed method can basically implement the task, with an overall accuracy of 77.78%. This study indicates a way of implementing the classification of the two major broad- and needle-leaf taxonomies measured by TLS in accordance to their literal definitions, and manifests the potential of extending TLS applications in forestry.

Florida Bay: A history of recent ecological changes

USGS Publications Warehouse

Fourqurean, J.W.; Robblee, M.B.

1999-01-01

Florida Bay is a unique subtropical estuary at the southern tip of the Florida peninsula. Recent ecological changes (seagrass die-off, algal blooms, increased turbidity) to the Florida Bay ecosystem have focused the attention of the public, commercial interests, scientists, and resource managers on the factors influencing the structure and function of Florida Bay. Restoring Florida Bay to some historic condition is the goal of resource managers, but what is not clear is what an anthropogenically-unaltered Florida Bay would look like. While there is general consensus that human activities have contributed to the changes occurring in the Florida Bay ecosystem, a high degree of natural system variability has made elucidation of the links between human activity and Florida Bay dynamics difficult. Paleoecological analyses, examination of long-term datasets, and directed measurements of aspects of the ecology of Florida Bay all contribute to our understanding of the behavior of the bay, and allow quantification of the magnitude of the recent ecological changes with respect to historical variability of the system.

Variation in the vitreoretinal configuration of Stage 4 retinopathy of prematurity in photocoagulated and treatment naive eyes undergoing vitrectomy

PubMed Central

Gadkari, Salil Sharad; Deshpande, Madan

2017-01-01

Purpose: We sought to document the difference in the vitreoretinal configuration of Stage 4 retinopathy of prematurity (ROP) in photocoagulated and treatment naive eyes undergoing vitrectomy and to correlate it with surgical complexity. Methods: Consecutive eyes posted for vitrectomy with Stage 4 ROP were documented preoperatively using a RetCam for the presence of peripheral traction (PT), presence of central traction just outside the arcades, and presence of traction extending to the lens. A note was made of the following intraoperative events: lensectomy, intraoperative bleeding, and iatrogenic breaks. Wilcoxon rank-sum test was used for analysis. Results: From a total of 46 eyes, 16 and 30 eyes were from the treated and treatment naive group, respectively. More eyes in the treated group had central (P < 0.0001) and lenticular traction (P = 0.022). More eyes in the untreated group had PT (P < 0.0001). A significant number of eyes without photocoagulation needed lensectomy (P = 0.042), and no difference in intraoperative bleeding (P = 0.94) was demonstrable. Iatrogenic retinotomy occurred in three eyes, all naive. Notably, age at surgery was more in the untreated group (P = 0.00008). Conclusion: Vasoproliferative activity in all retinopathies occurs at the junction of the ischemic and nonischemic retina. In the natural course of ROP, this takes place peripherally, at the ridge. In photocoagulated eyes, this junction is displaced posteriorly due to peripheral ablation. Treated eyes manifested with posterior proliferative changes and were more amenable to lens-sparing vitrectomy. Naive eyes were older when they underwent surgery to relieve PT with greater chances of lensectomy and iatrogenic breaks. PMID:28905829

Eastern Hudson Bay, Canada

NASA Image and Video Library

2017-12-08

Each winter sea ice forms over the salty waters of frigid northeastern Canada's Hudson Bay. As sunlight lengthens and weather warms, ice begins to break up and melt, with retreat typically starting in May and melt-out completed sometime in July. Since the 1970s, the timing of sea ice breakup in Hudson Bay has changed, with melting beginning earlier in the spring. The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Aqua satellite captured this a true-color image of Hudson Bay on March 26, 2013. Although snow still covers the surrounding land, ice has already begun to retreat from much of the eastern shore of the Bay visible in the image. A ring of bright white ice remains solidly frozen around the Belcher Islands in the southeast section of the Bay, but this ring is surrounded by blue waters and large chunks of ice. In the southwest, a similar situation is seen at Akimiski Island. The shelf of bright white ice gives way to open water in the south, as well as fractured, blue-tinted ice. The blue tint generally indicates water logging as ice begins to soften. The Aqua satellite captured a similar image of early ice melt on Hudson Bay on April 6, 2012. That image can be viewed at: modis.gsfc.nasa.gov/gallery/individual.php?db_date=2012-0... NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Chesapeake Bay Program Grant Guidance

EPA Pesticide Factsheets

Grant Guidance and appendices for the Chesapeake Bay Program that describes how the U.S. Environmental Protection Agency’s (EPA) Region 3’s Chesapeake Bay Program Office (CBPO) administers grant and cooperative agreement funds.

Subsurface structure of the East Bay Plain ground-water basin: San Francisco Bay to the Hayward fault, Alameda County, California

USGS Publications Warehouse

Catchings, R.D.; Borchers, J.W.; Goldman, M.R.; Gandhok, G.; Ponce, D.A.; Steedman, C.E.

2006-01-01

The area of California between the San Francisco Bay, San Pablo Bay, Santa Clara Valley, and the Diablo Ranges (East Bay Hills), commonly referred to as the 'East Bay', contains the East Bay Plain and Niles Cone ground-water basins. The area has a population of 1.46 million (2003 US Census), largely distributed among several cities, including Alameda, Berkeley, Fremont, Hayward, Newark, Oakland, San Leandro, San Lorenzo, and Union City. Major known tectonic structures in the East Bay area include the Hayward Fault and the Diablo Range to the east and a relatively deep sedimentary basin known as the San Leandro Basin beneath the eastern part of the bay. Known active faults, such as the Hayward, Calaveras, and San Andreas pose significant earthquake hazards to the region, and these and related faults also affect ground-water flow in the San Francisco Bay area. Because most of the valley comprising the San Francisco Bay area is covered by Holocene alluvium or water at the surface, our knowledge of the existence and locations of such faults, their potential hazards, and their effects on ground-water flow within the alluvial basins is incomplete. To better understand the subsurface stratigraphy and structures and their effects on ground-water and earthquake hazards, the U.S. Geological Survey (USGS), in cooperation with the East Bay Municipal Utility District (EBMUD), acquired a series of high-resolution seismic reflection and refraction profiles across the East Bay Plain near San Leandro in June 2002. In this report, we present results of the seismic imaging investigations, with emphasis on ground water.

Impact of the Data Collection on Adverse Events of Anti-HIV Drugs cohort study on abacavir prescription among treatment-naive, HIV-infected patients in Canada.

PubMed

Antoniou, Tony; Gillis, Jennifer; Loutfy, Mona R; Cooper, Curtis; Hogg, Robert S; Klein, Marina B; Machouf, Nima; Montaner, Julio S G; Rourke, Sean B; Tsoukas, Chris; Raboud, Janet M

2014-01-01

To evaluate the trends in abacavir (ABC) prescription among antiretroviral (ARV) medication-naive individuals following the presentation of the Data Collection on Adverse Events of Anti-HIV Drugs (DAD) cohort study. We conducted a retrospective cohort study of ARV medication-naive individuals in the Canadian Observational Cohort (CANOC). Between January 1, 2000, and February 28, 2010, a total of 7280 ARV medication-naive patients were included in CANOC. We observed a significant change in the proportion of new ABC prescriptions immediately following the release of DAD (-11%; 95% confidence interval [CI]: -20% to -2.4%) and in the months following the presentation of these data (-0.66% per month; 95% CI: -1.2% to -0.073%). A post-DAD presentation decrease in the odds of being prescribed ABC versus tenofovir (TDF) was observed (adjusted odds ratio, 0.72 per year, 95% CI: 0.54-0.97). Presentation of the DAD was associated with a significant decrease in ABC use among ARV medication-naive, HIV-positive patients initiating therapy.

Plasma homovanillic acid levels and therapeutic outcome in schizophrenics: comparisons of neuroleptic-naive first-episode patients and patients with disease exacerbation due to neuroleptic discontinuance.

PubMed

Akiyama, K; Tsuchida, K; Kanzaki, A; Ujike, H; Hamamura, T; Kondo, K; Mutoh, S; Miyanagi, K; Kuroda, S; Otsuki, S

1995-11-15

Plasma homovanillic acid (pHVA) levels were measured and the Brief Psychiatric Rating Scale (BPRS) scores were evaluated in 26 schizophrenic patients who had either never been medicated (neuroleptic-naive, first-episode subjects) or whose condition had become exacerbated following neuroleptic discontinuance (exacerbated subjects). All the subjects received medication with a fixed dose of a neuroleptic (haloperidol or fluphenazine, both 9 mg/day) for the first week and variable doses for the subsequent 4 weeks. In the neuroleptic-naive subjects, pHVA levels increased significantly 1 week after starting the protocol; this increase correlated significantly with clinical improvement of the BPRS positive symptom scores at week 5. In the neuroleptic-naive subjects, pHVA levels had declined to the baseline level by week 5. In the exacerbated subjects, there were no significant correlations between pHVA level changes at week 1 and later improvements of the BPRS positive symptom scores. These results suggest that the rise in pHVA levels occurring within 1 week after starting a fixed neuroleptic dose may predict a favorable clinical response in neuroleptic-naive schizophrenic patients.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

Comparative analysis of drug resistance mutations in the human immunodeficiency virus reverse transcriptase gene in patients who are non-responsive, responsive and naive to antiretroviral therapy.

PubMed

Misbah, Mohammad; Roy, Gaurav; Shahid, Mudassar; Nag, Nalin; Kumar, Suresh; Husain, Mohammad

2016-05-01

Drug resistance mutations in the Pol gene of human immunodeficiency virus 1 (HIV-1) are one of the critical factors associated with antiretroviral therapy (ART) failure in HIV-1 patients. The issue of resistance to reverse transcriptase inhibitors (RTIs) in HIV infection has not been adequately addressed in the Indian subcontinent. We compared HIV-1 reverse transcriptase (RT) gene sequences to identify mutations present in HIV-1 patients who were ART non-responders, ART responders and drug naive. Genotypic drug resistance testing was performed by sequencing a 655-bp region of the RT gene from 102 HIV-1 patients, consisting of 30 ART-non-responding, 35 ART-responding and 37 drug-naive patients. The Stanford HIV Resistance Database (HIVDBv 6.2), IAS-USA mutation list, ANRS_09/2012 algorithm, and Rega v8.02 algorithm were used to interpret the pattern of drug resistance. The majority of the sequences (96 %) belonged to subtype C, and a few of them (3.9 %) to subtype A1. The frequency of drug resistance mutations observed in ART-non-responding, ART-responding and drug-naive patients was 40.1 %, 10.7 % and 20.58 %, respectively. It was observed that in non-responders, multiple mutations were present in the same patient, while in responders, a single mutation was found. Some of the drug-naive patients had more than one mutation. Thymidine analogue mutations (TAMs), however, were found in non-responders and naive patients but not in responders. Although drug resistance mutations were widely distributed among ART non-responders, the presence of resistance mutations in the viruses of drug-naive patients poses a big concern in the absence of a genotyping resistance test.

A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.

PubMed

Li, Feifei; Piao, Minghao; Piao, Yongjun; Li, Meijing; Ryu, Keun Ho

2014-10-01

Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.

Acute cognitive impact of antiseizure drugs in naive rodents and corneal-kindled mice.

PubMed

Barker-Haliski, Melissa L; Vanegas, Fabiola; Mau, Matthew J; Underwood, Tristan K; White, H Steve

2016-09-01

Some antiseizure drugs (ASDs) are associated with cognitive liability in patients with epilepsy, thus ASDs without this risk would be preferred. Little comparative pharmacology exists with ASDs in preclinical models of cognition. Few pharmacologic studies exist on the acute effects in rodents with chronic seizures. Predicting risk for cognitive impact with preclinical models may supply valuable ASD differentiation data. ASDs (phenytoin [PHT]; carbamazepine [CBZ]; valproic acid [VPA]; lamotrigine [LTG]; phenobarbital [PB]; tiagabine [TGB]; retigabine [RTG]; topiramate [TPM]; and levetiracetam [LEV]) were administered equivalent to maximal electroshock median effective dose ([ED50]; mice, rats), or median dose necessary to elicit minimal motor impairment (median toxic dose [TD50]; rats). Cognition models with naive adult rodents were novel object/place recognition (NOPR) task with CF-1 mice, and Morris water maze (MWM) with Sprague-Dawley rats. Selected ASDs were also administered to rats prior to testing in an open field. The effect of chronic seizures and ASD administration on cognitive performance in NOPR was also determined with corneal-kindled mice. Mice that did not achieve kindling criterion (partially kindled) were included to examine the effect of electrical stimulation on cognitive performance. Sham-kindled and age-matched mice were also tested. No ASD (ED50) affected latency to locate the MWM platform; TD50 of PB, RTG, TPM, and VPA reduced this latency. In naive mice, CBZ and VPA (ED50) reduced time with the novel object. Of interest, no ASD (ED50) affected performance of fully kindled mice in NOPR, whereas CBZ and LEV improved cognitive performance of partially kindled mice. Standardized approaches to the preclinical evaluation of an ASD's potential cognitive impact are needed to inform drug development. This study demonstrated acute, dose- and model-dependent effects of therapeutically relevant doses of ASDs on cognitive performance of naive mice and

Marine benthic habitat mapping of Muir Inlet, Glacier Bay National Park and Preserve, Alaska, with an evaluation of the Coastal and Marine Ecological Classification Standard III

USGS Publications Warehouse

Trusel, Luke D.; Cochrane, Guy R.; Etherington, Lisa L.; Powell, Ross D.; Mayer, Larry A.

2010-01-01

Seafloor geology and potential benthic habitats were mapped in Muir Inlet, Glacier Bay National Park and Preserve, Alaska, using multibeam sonar, ground-truth information, and geological interpretations. Muir Inlet is a recently deglaciated fjord that is under the influence of glacial and paraglacial marine processes. High glacially derived sediment and meltwater fluxes, slope instabilities, and variable bathymetry result in a highly dynamic estuarine environment and benthic ecosystem. We characterize the fjord seafloor and potential benthic habitats using the Coastal and Marine Ecological Classification Standard (CMECS) recently developed by the National Oceanic and Atmospheric Administration (NOAA) and NatureServe. Substrates within Muir Inlet are dominated by mud, derived from the high glacial debris flux. Water-column characteristics are derived from a combination of conductivity temperature depth (CTD) measurements and circulation-model results. We also present modern glaciomarine sediment accumulation data from quantitative differential bathymetry. These data show Muir Inlet is divided into two contrasting environments: a dynamic upper fjord and a relatively static lower fjord. The accompanying maps represent the first publicly available high-resolution bathymetric surveys of Muir Inlet. The results of these analyses serve as a test of the CMECS and as a baseline for continued mapping and correlations among seafloor substrate, benthic habitats, and glaciomarine processes.

Is Children's Naive Knowledge Consistent?: A Comparison of the Concepts of Sound and Heat

ERIC Educational Resources Information Center

Lautrey, Jacques; Mazens, Karine

2004-01-01

The aim of this study was to shed some light on the organization of naive knowledge, and on the process of conceptual change in everyday physics, more specifically regarding the concepts of sound and heat. Eighty-three 8-year-old children were interviewed individually in order to see if they attributed the properties of objects (such as…

Bay Mills' Bold Approach

ERIC Educational Resources Information Center

Freedman, Eric

2011-01-01

It's a long, long way from Bay Mills Community College, near the shores of frigid Lake Superior, to Detroit. But distance, time and demographics aside, the school and the city are united by Bay Mills' status as the nation's only tribally controlled college that authorizes quasi-public schools, known officially as public school academies. And it's…

The reproducibility of adenosine monophosphate bronchial challenges in mild, steroid-naive asthmatics

PubMed Central

Singh, Dave; Fairwood, Jennifer; Murdoch, Robert; Weeks, Amanda; Russell, Paul; Roy, Kay; Langley, Steve; Woodcock, Ashley

2008-01-01

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Repeated adenosine monophosphate (AMP) challenges are used to assess drug effects in asthma clinical trials, but may be prone to tachyphylaxis when repeated at short intervals. Possible tachyphylaxis at 12- and 24-h intervals has not been studied. WHAT THIS STUDY ADDS Clinically relevant tachyphylaxis after repeated AMP challenges does not occur when repeated at 12- and 24-h intervals. AMP challenges at these intervals can be used to assess drug effects in clinical trials. AIMS Repeated adenosine monophosphate (AMP) challenges are used to assess drug efficacy in clinical trials of mild, steroid-naive asthmatics. Refractoriness has been reported after repeated challenges over short intervals. This study evaluated possible tachyphylaxis after repeated AMP challenges at 12 and 24 h in mild, steroid-naive asthmatics. METHODS This was an open, three-way crossover study. Twenty-six steroid-naive asthmatic subjects were randomized to the following AMP challenge regimens separated by 7–14 days: (A) challenge at 08.00 h, repeated 24 h later; (B) challenge at 08.00 h, repeated 12 and 24 h later; (C) challenge at 20.00 h, repeated 12 h later. Comparisons within day were assessed using 90% confidence intervals (CIs). Non-inferiority approach taken with 1 doubling concentration (DC) as a clinically relevant difference. RESULTS Regimen A: Significant increase in AMP reactivity at 24 h. Mean DC difference was 0.6 (90% CI 0.24, 0.96). Regimen B: No evidence of difference between AMP reactivity at 08.00 h and a repeated challenge 12 h later. Repeated challenge at 24 h caused a significant increase in provocation concentration (PC)20 compared with 12 h (mean DC difference 0.48, 90% CI 0.02, 0.95) and 0 h (mean DC difference 0.82, 90% CI 0.49, 1.14 – the upper CI exceeds the criteria of 1 DC). Challenge regimen C: No difference between challenges; mean DC difference of 0.28 (90% CI −0.2, 0.76). CONCLUSION The small decline in AMP

Experience of dolutegravir in HIV-infected treatment-naive patients from a tertiary care University Hospital in Ireland

PubMed Central

Waqas, Sarmad; O’Connor, Mairead; Levey, Ciara; Mallon, Paddy; Sheehan, Gerard; Patel, Anjali; Avramovic, Gordana; Lambert, John S

2016-01-01

Objective: Dolutegravir, an HIV integrase inhibitor, is a relatively new treatment option. To assess the tolerability, side effects, and time to viral decline to non-detectable in patients newly started on dolutegravir. Methods: Retrospective health care record of 61 consecutive HIV treatment-naive patients started on dolutegravir was reviewed and analysed on SPSS. Results: The mean initial viral load was 160826.05 copies/mL (range, 79–1,126,617 copies/mL). HIV viral load became non-detectable in 63.9% of patients on dolutegravir within 3 months. In all, 60.7% of patients reported no side effects on dolutegravir; 98.4% of the patients claimed full compliance to their antiretrovirals. Conclusion: Dolutegravir was found to be efficacious and well tolerated in HIV-infected treatment-naive patients. PMID:27826447

Holocene sedimentation in Richardson Bay, California

USGS Publications Warehouse

Connor, Cathy L.

1983-01-01

Examination of foraminifers, diatoms, ostracodes, clay mineralogy, and sediment-size variation from 9 borehole sites along the salt-marsh margins of Richardson Bay reveals a record of gradual infilling of fine-grained estuarine sediments. Over the past 10,000 years this area was transformed from a V-shaped Pleistocene stream valley to a flat-floored arm of the San Francisco Bay estuary. A radiocarbon date obtained from a basal peat overlying nonmarine alluvial sand near the town of Mill Valley indicates that stable salt-marsh vegetation was present in the northwestern arm of Richardson Bay 4600?165 years ago and agrees within error limits with a Holocene sea-level curve developed by Atwater, Hedel, and Helley in 1977 for southern San Francisco Bay. The average sedimentation rate over the last 4600 years is estimated to be 0.2 cm/yr for the inner part of the bay. Comparison of early maps with updated versions as well as studies of marsh plant zonations in disturbed and nondisturbed areas shows that almost half of the marsh in Richardson Bay has been leveed or filled since 1899.

Deriving Chesapeake Bay Water Quality Standards

USGS Publications Warehouse

Tango, Peter J.; Batiuk, Richard A.

2013-01-01

Achieving and maintaining the water quality conditions necessary to protect the aquatic living resources of the Chesapeake Bay and its tidal tributaries has required a foundation of quantifiable water quality criteria. Quantitative criteria serve as a critical basis for assessing the attainment of designated uses and measuring progress toward meeting water quality goals of the Chesapeake Bay Program partnership. In 1987, the Chesapeake Bay Program partnership committed to defining the water quality conditions necessary to protect aquatic living resources. Under section 303(c) of the Clean Water Act, States and authorized tribes have the primary responsibility for adopting water quality standards into law or regulation. The Chesapeake Bay Program partnership worked with U.S. Environmental Protection Agency to develop and publish a guidance framework of ambient water quality criteria with designated uses and assessment procedures for dissolved oxygen, water clarity, and chlorophyll a for Chesapeake Bay and its tidal tributaries in 2003. This article reviews the derivation of the water quality criteria, criteria assessment protocols, designated use boundaries, and their refinements published in six addendum documents since 2003 and successfully adopted into each jurisdiction's water quality standards used in developing the Chesapeake Bay Total Maximum Daily Load.

Strain differences in the neural, behavioral, and molecular correlates of sweet and salty taste in naive, ethanol- and sucrose-exposed P and NP rats

PubMed Central

Coleman, Jamison; Williams, Ashley; Phan, Tam-Hao T.; Mummalaneni, Shobha; Melone, Pamela; Ren, ZuoJun; Zhou, Huiping; Mahavadi, Sunila; Murthy, Karnam S.; Katsumata, Tadayoshi; DeSimone, John A.

2011-01-01

Strain differences between naive, sucrose- and ethanol-exposed alcohol-preferring (P) and alcohol-nonpreferring (NP) rats were investigated in their consumption of ethanol, sucrose, and NaCl; chorda tympani (CT) nerve responses to sweet and salty stimuli; and gene expression in the anterior tongue of T1R3 and TRPV1/TRPV1t. Preference for 5% ethanol and 10% sucrose, CT responses to sweet stimuli, and T1R3 expression were greater in naive P rats than NP rats. The enhancement of the CT response to 0.5 M sucrose in the presence of varying ethanol concentrations (0.5–40%) in naive P rats was higher and shifted to lower ethanol concentrations than NP rats. Chronic ingestion of 5% sucrose or 5% ethanol decreased T1R3 mRNA in NP and P rats. Naive P rats also demonstrated bigger CT responses to NaCl+benzamil and greater TRPV1/TRPV1t expression. TRPV1t agonists produced biphasic effects on NaCl+benzamil CT responses, enhancing the response at low concentrations and inhibiting it at high concentrations. The concentration of a TRPV1/TRPV1t agonist (Maillard reacted peptides conjugated with galacturonic acid) that produced a maximum enhancement in the NaCl+benzamil CT response induced a decrease in NaCl intake and preference in P rats. In naive P rats and NP rats exposed to 5% ethanol in a no-choice paradigm, the biphasic TRPV1t agonist vs. NaCl+benzamil CT response profiles were higher and shifted to lower agonist concentrations than in naive NP rats. TRPV1/TRPV1t mRNA expression increased in NP rats but not in P rats exposed to 5% ethanol in a no-choice paradigm. We conclude that P and NP rats differ in T1R3 and TRPV1/TRPV1t expression and neural and behavioral responses to sweet and salty stimuli and to chronic sucrose and ethanol exposure. PMID:21849614

Strain differences in the neural, behavioral, and molecular correlates of sweet and salty taste in naive, ethanol- and sucrose-exposed P and NP rats.

PubMed

Coleman, Jamison; Williams, Ashley; Phan, Tam-Hao T; Mummalaneni, Shobha; Melone, Pamela; Ren, Zuojun; Zhou, Huiping; Mahavadi, Sunila; Murthy, Karnam S; Katsumata, Tadayoshi; DeSimone, John A; Lyall, Vijay

2011-11-01

Strain differences between naive, sucrose- and ethanol-exposed alcohol-preferring (P) and alcohol-nonpreferring (NP) rats were investigated in their consumption of ethanol, sucrose, and NaCl; chorda tympani (CT) nerve responses to sweet and salty stimuli; and gene expression in the anterior tongue of T1R3 and TRPV1/TRPV1t. Preference for 5% ethanol and 10% sucrose, CT responses to sweet stimuli, and T1R3 expression were greater in naive P rats than NP rats. The enhancement of the CT response to 0.5 M sucrose in the presence of varying ethanol concentrations (0.5-40%) in naive P rats was higher and shifted to lower ethanol concentrations than NP rats. Chronic ingestion of 5% sucrose or 5% ethanol decreased T1R3 mRNA in NP and P rats. Naive P rats also demonstrated bigger CT responses to NaCl+benzamil and greater TRPV1/TRPV1t expression. TRPV1t agonists produced biphasic effects on NaCl+benzamil CT responses, enhancing the response at low concentrations and inhibiting it at high concentrations. The concentration of a TRPV1/TRPV1t agonist (Maillard reacted peptides conjugated with galacturonic acid) that produced a maximum enhancement in the NaCl+benzamil CT response induced a decrease in NaCl intake and preference in P rats. In naive P rats and NP rats exposed to 5% ethanol in a no-choice paradigm, the biphasic TRPV1t agonist vs. NaCl+benzamil CT response profiles were higher and shifted to lower agonist concentrations than in naive NP rats. TRPV1/TRPV1t mRNA expression increased in NP rats but not in P rats exposed to 5% ethanol in a no-choice paradigm. We conclude that P and NP rats differ in T1R3 and TRPV1/TRPV1t expression and neural and behavioral responses to sweet and salty stimuli and to chronic sucrose and ethanol exposure.

Chesapeake Bay plume dynamics from LANDSAT

NASA Technical Reports Server (NTRS)

Munday, J. C., Jr.; Fedosh, M. S.

1981-01-01

LANDSAT images with enhancement and density slicing show that the Chesapeake Bay plume usually frequents the Virginia coast south of the Bay mouth. Southwestern (compared to northern) winds spread the plume easterly over a large area. Ebb tide images (compared to flood tide images) show a more dispersed plume. Flooding waters produce high turbidity levels over the shallow northern portion of the Bay mouth.

ASTER Images San Francisco Bay Area

NASA Image and Video Library

2000-04-26

This image of the San Francisco Bay region was acquired on March 3, 2000 by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra satellite. With its 14 spectral bands from the visible to the thermal infrared wavelength region, and its high spatial resolution of 15 to 90 meters about 50 to 300 feet ), ASTER will image Earth for the next 6 years to map and monitor the changing surface of our planet. Image: This image covers an area 60 kilometers (37 miles) wide and 75 kilometers (47 miles) long in three bands of the reflected visible and infrared wavelength region. The combination of bands portrays vegetation in red, and urban areas in gray. Sediment in the Suisun Bay, San Pablo Bay, San Francisco Bay, and the Pacific Ocean shows up as lighter shades of blue. Along the west coast of the San Francisco Peninsula, strong surf can be seen as a white fringe along the shoreline. A powerful rip tide is visible extending westward from Daly City into the Pacific Ocean. In the lower right corner, the wetlands of the South San Francisco Bay National Wildlife Refuge appear as large dark blue and brown polygons. The high spatial resolution of ASTER allows fine detail to be observed in the scene. The main bridges of the area (San Mateo, San Francisco-Oakland Bay, Golden Gate, Richmond-San Rafael, Benicia-Martinez, and Carquinez) are easily picked out, connecting the different communities in the Bay area. Shadows of the towers along the Bay Bridge can be seen over the adjacent bay water. With enlargement the entire road network can be easily mapped; individual buildings are visible, including the shadows of the high-rises in downtown San Francisco. Inset: This enlargement of the San Francisco Airport highlights the high spatial resolution of ASTER. With further enlargement and careful examination, airplanes can be seen at the terminals. http://photojournal.jpl.nasa.gov/catalog/PIA02606

78 FR 59902 - Regulated Navigation Area; Special Buzzards Bay Vessel Regulation, Buzzards Bay, MA

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-30

... DEPARTMENT OF HOMELAND SECURITY Coast Guard 33 CFR Part 165 [Docket No. USCG-2011-0322] RIN 1625-AA11 Regulated Navigation Area; Special Buzzards Bay Vessel Regulation, Buzzards Bay, MA AGENCY: Coast Guard, DHS. ACTION: Notice of extension of comment period. SUMMARY: The Coast Guard is extending the...

19 CFR 7.11 - Guantanamo Bay Naval Station.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 19 Customs Duties 1 2010-04-01 2010-04-01 false Guantanamo Bay Naval Station. 7.11 Section 7.11... TREASURY CUSTOMS RELATIONS WITH INSULAR POSSESSIONS AND GUANTANAMO BAY NAVAL STATION § 7.11 Guantanamo Bay Naval Station. Articles of foreign origin may enter the area (both land and water) of the Guantanamo Bay...

19 CFR 7.11 - Guantanamo Bay Naval Station.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 19 Customs Duties 1 2011-04-01 2011-04-01 false Guantanamo Bay Naval Station. 7.11 Section 7.11... TREASURY CUSTOMS RELATIONS WITH INSULAR POSSESSIONS AND GUANTANAMO BAY NAVAL STATION § 7.11 Guantanamo Bay Naval Station. Articles of foreign origin may enter the area (both land and water) of the Guantanamo Bay...

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

PubMed

He, Qiwei; Veldkamp, Bernard P; Glas, Cees A W; de Vries, Theo

2017-03-01

Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.

Bayesian network modelling of upper gastrointestinal bleeding

NASA Astrophysics Data System (ADS)

Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri

2013-09-01

Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.

Estimation of Freshwater Flow to Joe Bay, South Florida.

NASA Astrophysics Data System (ADS)

Zucker, M. A.; Hittle, C. D.

2002-05-01

During the last century, drainage canals were constructed as part of the Central and Southern Flood Control (C&SF) project. Flood control was achieved but degradation to the Everglades ecosystem was evident. Problems related to Florida Bay include sea grass die off, algae blooms, and extreme salinity conditions. Modifications to the C&SF project are proposed as part of the Comprehensive Everglades Restoration Plan (CERP). One objective of CERP is to improve the timing and distribution of freshwater flow within the Everglades ecosystem and to Florida Bay. Several CERP projects propose changes to the existing canal network that borders Everglades National Park (ENP) in southern Miami-Dade County. An examination of flows to Joe Bay, a small embayment on the northeastern shores of Florida Bay, has provided baseline information on current spatial and temporal water deliveries prior to CERP modifications. Understanding the existing complex water delivery system and the effects the system has on Everglades hydrology will provide a necessary benchmark against which to measure restoration success. The study was initiated by the U.S. Geological Survey (USGS) in May 1999 to estimate creek flows to Joe Bay and determine the relative amounts derived from Taylor Slough and overflow from the C-111 Canal. It is important to understand the source of freshwater to Joe Bay before it enters Florida Bay. Taylor Slough transports freshwater to northeastern Florida Bay from the northwest while overflow from the C-111 Canal provides freshwater to northeastern Florida Bay from the northeast. Joe Bay, receives part of the freshwater from each of these sources via sheet flow and small estuarine creeks, and subsequently discharges southward to northeastern Florida Bay via Trout Creek. Trout Creek contributes approximately 50 percent of the total freshwater flow to northeastern Florida Bay (Hittle 2001). Eight non-gaged creeks entering Joe Bay were selected for acoustic Doppler current

Cell of origin associated classification of B-cell malignancies by gene signatures of the normal B-cell hierarchy.

PubMed

Johnsen, Hans Erik; Bergkvist, Kim Steve; Schmitz, Alexander; Kjeldsen, Malene Krag; Hansen, Steen Møller; Gaihede, Michael; Nørgaard, Martin Agge; Bæch, John; Grønholdt, Marie-Louise; Jensen, Frank Svendsen; Johansen, Preben; Bødker, Julie Støve; Bøgsted, Martin; Dybkær, Karen

2014-06-01

Recent findings have suggested biological classification of B-cell malignancies as exemplified by the "activated B-cell-like" (ABC), the "germinal-center B-cell-like" (GCB) and primary mediastinal B-cell lymphoma (PMBL) subtypes of diffuse large B-cell lymphoma and "recurrent translocation and cyclin D" (TC) classification of multiple myeloma. Biological classification of B-cell derived cancers may be refined by a direct and systematic strategy where identification and characterization of normal B-cell differentiation subsets are used to define the cancer cell of origin phenotype. Here we propose a strategy combining multiparametric flow cytometry, global gene expression profiling and biostatistical modeling to generate B-cell subset specific gene signatures from sorted normal human immature, naive, germinal centrocytes and centroblasts, post-germinal memory B-cells, plasmablasts and plasma cells from available lymphoid tissues including lymph nodes, tonsils, thymus, peripheral blood and bone marrow. This strategy will provide an accurate image of the stage of differentiation, which prospectively can be used to classify any B-cell malignancy and eventually purify tumor cells. This report briefly describes the current models of the normal B-cell subset differentiation in multiple tissues and the pathogenesis of malignancies originating from the normal germinal B-cell hierarchy.

33 CFR 167.100 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: General.

Code of Federal Regulations, 2013 CFR

2013-07-01

... SEPARATION SCHEMES Description of Traffic Separation Schemes and Precautionary Areas § 167.100 In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: General. The traffic separation scheme in the...

33 CFR 167.100 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: General.

Code of Federal Regulations, 2014 CFR

2014-07-01

... SEPARATION SCHEMES Description of Traffic Separation Schemes and Precautionary Areas § 167.100 In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: General. The traffic separation scheme in the...

33 CFR 167.100 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: General.

Code of Federal Regulations, 2011 CFR

2011-07-01

... SEPARATION SCHEMES Description of Traffic Separation Schemes and Precautionary Areas § 167.100 In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: General. The traffic separation scheme in the...

33 CFR 167.100 - In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: General.

Code of Federal Regulations, 2012 CFR

2012-07-01

... SEPARATION SCHEMES Description of Traffic Separation Schemes and Precautionary Areas § 167.100 In the approaches to Narragansett Bay, RI, and Buzzards Bay, MA: General. The traffic separation scheme in the...

Recent results of Daya Bay reactor neutrino experiment

NASA Astrophysics Data System (ADS)

Leitner, R.; Daya Bay Collaboration

2017-04-01

The Daya Bay reactor neutrino experiment has been designed to precisely measure the least known neutrino mixing angle θ13. In March 2012, Daya Bay collaboration announced [Daya Bay Collaboration (F. P. An et al.), Observation of electron-antineutrino disappearance at Daya Bay, Phys. Rev. Lett. 108 (2012) 171803] the observation of non-zero value of sin2 ⁡ 2θ13. Because of large statistics of detected antineutrinos and excellent performance of the experiment, Daya Bay continuously improves the precision of world best measurement of sin2 ⁡ 2θ13. In addition it provides results on neutrino mass splitting Δ mee2 competitive with measurements of other experiments, results on precise measurement of reactor fluxes and on limits of the existence of hypothetical fourth neutrino. In this paper, we report the results available by the time of the 6th Capri workshop: the measurement of oscillation parameters sin2 ⁡ (2θ13) = 0.084 ± 0.005 and | Δmee2 | = (2.42 ± 0.11) ×10-3eV2 [Daya Bay Collaboration (F. P. An et al.), New Measurement of Antineutrino Oscillation with the Full Detector Configuration at Daya Bay, Phys. Rev. Lett. 115 (2015) no. 11, 111802], searches for sterile neutrinos [Daya Bay Collaboration (F. P. An et al.) Search for a Light Sterile Neutrino at Daya Bay, Phys. Rev. Lett. 113 (2014) 141802] and precise measurement of reactor neutrino flux [Daya Bay Collaboration (F. P. An et al.), Measurement of the Reactor Anti-neutrino Flux and Spectrum at Daya Bay, Phys. Rev. Lett. 116 (2016) no. 6, 061801]. These are based on 621 days of measurement, most of the data has been taken in full detector configuration. More precise results [Daya Bay Collaboration (F. P. An et al.), Measurement of electron antineutrino oscillation based on 1230 days of operation of the Daya Bay experiment, arxiv:arXiv:1610.04802] with 1230 days of operation have been presented few weeks later at the Neutrino 2016 conference.

Global Symmetries of Naive and Staggered Fermions in Arbitrary Dimensions

NASA Astrophysics Data System (ADS)

Kieburg, Mario; Würfel, Tim R.

2018-03-01

It is well-known that staggered fermions do not necessarily satisfy the same global symmetries as the continuum theory. We analyze the mechanism behind this phenomenon for arbitrary dimension and gauge group representation. For this purpose we vary the number of lattice sites between even and odd parity in each single direction. Since the global symmetries are manifest in the lowest eigenvalues of the Dirac operator, the spectral statistics and also the symmetry breaking pattern will be affected. We analyze these effects and compare our predictions with Monte-Carlo simulations of naive Dirac operators in the strong coupling limit. This proceeding is a summary of our work [1].

Predicting flight delay based on multiple linear regression

NASA Astrophysics Data System (ADS)

Ding, Yi

2017-08-01

Delay of flight has been regarded as one of the toughest difficulties in aviation control. How to establish an effective model to handle the delay prediction problem is a significant work. To solve the problem that the flight delay is difficult to predict, this study proposes a method to model the arriving flights and a multiple linear regression algorithm to predict delay, comparing with Naive-Bayes and C4.5 approach. Experiments based on a realistic dataset of domestic airports show that the accuracy of the proposed model approximates 80%, which is further improved than the Naive-Bayes and C4.5 approach approaches. The result testing shows that this method is convenient for calculation, and also can predict the flight delays effectively. It can provide decision basis for airport authorities.

78 FR 39610 - Safety Zone; Big Bay Boom, San Diego Bay; San Diego, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-02

..., Protection of Children from Environmental Health Risks and Safety Risks. This rule is not an economically significant rule and does not create an environmental risk to health or risk to safety that may...-AA00 Safety Zone; Big Bay Boom, San Diego Bay; San Diego, CA AGENCY: Coast Guard, DHS. ACTION...

Classical Swine Fever Outbreak after Modified Live LOM Strain Vaccination in Naive Pigs, South Korea

PubMed Central

Je, Sang H.; Kwon, Taeyong; Yoo, Sung J.; Lee, Dong-Uk; Lee, SeungYoon; Richt, Juergen A.

2018-01-01

We report classical swine fever outbreaks occurring in naive pig herds on Jeju Island, South Korea, after the introduction of the LOM vaccine strain. Two isolates from sick pigs had >99% identity with the vaccine stain. LOM strain does not appear safe; its use in the vaccine should be reconsidered. PMID:29553332

The bay area wildfire forum

Treesearch

Todd E. Bruce

1995-01-01

Fire agencies throughout the San Francisco Bay Area formed a grassroots organization to influence a firesafe environment. The Bay Area Wildfire Forum (BAWF) was organized in 1992 to coordinate wildland fire training while promoting and encouraging further activities regarding wildland firefighting and fire prevention.

Zeb1-Hdac2-eNOS circuitry identifies early cardiovascular precursors in naive mouse embryonic stem cells.

PubMed

Cencioni, Chiara; Spallotta, Francesco; Savoia, Matteo; Kuenne, Carsten; Guenther, Stefan; Re, Agnese; Wingert, Susanne; Rehage, Maike; Sürün, Duran; Siragusa, Mauro; Smith, Jacob G; Schnütgen, Frank; von Melchner, Harald; Rieger, Michael A; Martelli, Fabio; Riccio, Antonella; Fleming, Ingrid; Braun, Thomas; Zeiher, Andreas M; Farsetti, Antonella; Gaetano, Carlo

2018-03-29

Nitric oxide (NO) synthesis is a late event during differentiation of mouse embryonic stem cells (mESC) and occurs after release from serum and leukemia inhibitory factor (LIF). Here we show that after release from pluripotency, a subpopulation of mESC, kept in the naive state by 2i/LIF, expresses endothelial nitric oxide synthase (eNOS) and endogenously synthesizes NO. This eNOS/NO-positive subpopulation (ESNO+) expresses mesendodermal markers and is more efficient in the generation of cardiovascular precursors than eNOS/NO-negative cells. Mechanistically, production of endogenous NO triggers rapid Hdac2 S-nitrosylation, which reduces association of Hdac2 with the transcriptional repression factor Zeb1, allowing mesendodermal gene expression. In conclusion, our results suggest that the interaction between Zeb1, Hdac2, and eNOS is required for early mesendodermal differentiation of naive mESC.

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID

Short Term Sediment Exchange Between Marshes and Bays Using Beryllium-7 as a Tracer, Fourleague Bay, Louisiana.

NASA Astrophysics Data System (ADS)

Restreppo, G. A.; Bentley, S. J.; Xu, K.; Wang, J.

2016-12-01

Modern delta models focus on the availability and exchange of coarse sediment as one of the major factors of deltaic growth or decay. Fine-grained sediment exchange within a river's delta is relatively poorly understood, as is the impact that this exchange has on land building and land loss. To better understand the dynamics of fine grain sediment exchange between river mouth, adjacent bays, and marshland, sediment cores from Fourleague Bay, LA, were collected and analyzed for 7Be, a naturally occurring radioisotope that serves as a marker for recently deposited sediment. Time-series push cores were collected every two months at ten sites, five located across a longitudinal transect in the middle bay and five located along adjacent marshes, from May 2015 to May 2016. All sites fall within 11 to 28 km of the Atchafalaya Delta, along a gradient extending towards the open ocean. Cores were extruded in 2 cm intervals, dried, ground, and analyzed via gamma spectrometry for the presence of 7Be. Inventories of 7Be were then calculated and used to determine bimonthly sedimentation rates over the course twelve months. Sediment deposition on the bay floor and marsh surface were then compared to Atchafalaya River discharge, wind speed and direction, and wave action. Preliminary results indicate patterns of initial fluvial sediment transfer from river to bay floor, then bay floor to marsh surface, with decreasing fluvial influence towards the open ocean. Sediment transport from bay to marsh appears to be coupled with meteorological forcing that induces bay-floor sediment resuspension and the flooding of marsh surfaces. This indirect mechanism of fluvial sediment supply to wetland surfaces may extend the region of influence for sediment delivery from man-made river-sediment diversions.

Naive helper T cells from BCG-vaccinated volunteers produce IFN-gamma and IL-5 to mycobacterial antigen-pulsed dendritic cells.

PubMed

Kowalewicz-Kulbat, Magdalena; Kaźmierczak, Dominik; Donevski, Stefan; Biet, Franck; Pestel, Joël; Rudnicka, Wiesława

2008-01-01

Mycobacterium bovis bacillus Calmette-Guérin (BCG) is a live vaccine that has been used in routine vaccination against tuberculosis for nearly 80 years. However, its efficacy is controversial. The failure of BCG vaccination may be at least partially explained by the induction of poor or inappropriate host responses. Dendritic cells (DCs) are likely to play a key role in the induction of immune response to mycobacteria by polarizing the reactivity of T lymphocytes toward a Th1 profile, contributing to the generation of protective cellular immunity against mycobacteria. In this study we aimed to investigate the production of Th1 and Th2 cytokines by naive CD4+ T cells to mycobacterial antigen-pulsed DCs in the group of young, healthy BCG vaccinated volunteers. The response of naive helper T cells was compared with the response of total blood lymphocytes. Our present results clearly showed that circulating naive CD45RA+CD4+ lymphocytes from BCG-vaccinated subjects can become effector helper cells producing IFN-gamma and IL-5 under the stimulation by autologous dendritic cells presenting mycobacterial protein antigen-PPD or infected with live M. bovis BCG bacilli.

Characteristic appearances of fundus autofluorescence in treatment-naive and active polypoidal choroidal vasculopathy: a retrospective study of 170 patients.

PubMed

Zhao, Xinyu; Xia, Song; Chen, Youxin

2018-06-01

To investigate the characteristic appearances of fundus autofluorescence (FAF) in patients with treatment-naive and active polypoidal choroidal vasculopathy (PCV). Cases with the diagnosis of treatment-naive and active PCV from November 2012 to May 2017 at Peking Union Medical College Hospital were retrospectively reviewed. All patients underwent comprehensive ophthalmologic examination. Autofluorescence (AF) findings were described at the retinal sites of the corresponding lesions identified and diagnosed using indocyanine green angiography and spectral-domain optical coherence tomography. One hundred seventy patients with 192 affected eyes were included. The logMAR BCVA of the patients were 0.53 ± 0.28. The six AF patterns of 243 polypoidal lesions were confluent hypo-AF with hyper-AF ring (49.8%), confluent hypo-AF (22.6%), hyper-AF with hypo-AF ring (3.7%), granular hypo-AF (7.0%), blocked hypo-AF due to hemorrhage (8.6%), and polyps without apparent AF changes (8.2%). For 146 branching vascular networks (BVNs), 97.3% were granular hypo-AF, and others were blocked hypo-AF due to hemorrhage. In eyes with treatment-naive and active PCV, the polypoidal lesions and BVNs induce characteristic FAF changes. FAF images provide reliable adjunct reference for the diagnosis of PCV.

Bayes and the Law.

PubMed

Fenton, Norman; Neil, Martin; Berger, Daniel

2016-06-01

Although the last forty years has seen considerable growth in the use of statistics in legal proceedings, it is primarily classical statistical methods rather than Bayesian methods that have been used. Yet the Bayesian approach avoids many of the problems of classical statistics and is also well suited to a broader range of problems. This paper reviews the potential and actual use of Bayes in the law and explains the main reasons for its lack of impact on legal practice. These include misconceptions by the legal community about Bayes' theorem, over-reliance on the use of the likelihood ratio and the lack of adoption of modern computational methods. We argue that Bayesian Networks (BNs), which automatically produce the necessary Bayesian calculations, provide an opportunity to address most concerns about using Bayes in the law.

Learning on the Big Bay

ERIC Educational Resources Information Center

Philp, Michael J.

1978-01-01

Anne Arundel Community College uses the Chesapeake Bay for a flexible ocean engineering technology program which includes mechanical, electrical, and environmental options for transfer and/or vocational students, and adult education programs covering such subjects as sailing, Bay history, boat building, scuba-diving, and marine biology. (RT)

BOOK REVIEW OF "CHESAPEAKE BAY BLUES: SCIENCE, POLITICS, AND THE STRUGGLE TO SAVE THE BAY"

EPA Science Inventory

This is a book review of "Chesapeake Bay Blues: Science, Politics, and the Struggle to Save the Bay". This book is very well written and provides an easily understandable description of the political challenges faced by those proposing new or more stringent environmental regulat...

Remote sensing of submerged aquatic vegetation in lower Chesapeake Bay - A comparison of Landsat MSS to TM imagery

NASA Technical Reports Server (NTRS)

Ackleson, S. G.; Klemas, V.

1987-01-01

Landsat MSS and TM imagery, obtained simultaneously over Guinea Marsh, VA, as analyzed and compares for its ability to detect submerged aquatic vegetation (SAV). An unsupervised clustering algorithm was applied to each image, where the input classification parameters are defined as functions of apparent sensor noise. Class confidence and accuracy were computed for all water areas by comparing the classified images, pixel-by-pixel, to rasterized SAV distributions derived from color aerial photography. To illustrate the effect of water depth on classification error, areas of depth greater than 1.9 m were masked, and class confidence and accuracy recalculated. A single-scattering radiative-transfer model is used to illustrate how percent canopy cover and water depth affect the volume reflectance from a water column containing SAV. For a submerged canopy that is morphologically and optically similar to Zostera marina inhabiting Lower Chesapeake Bay, dense canopies may be isolated by masking optically deep water. For less dense canopies, the effect of increasing water depth is to increase the apparent percent crown cover, which may result in classification error.

Latest results from Daya Bay

NASA Astrophysics Data System (ADS)

Vorobel, Vit; Daya Bay Collaboration

2017-07-01

The Daya Bay Reactor Neutrino Experiment was designed to measure θ 13, the smallest mixing angle in the three-neutrino mixing framework, with unprecedented precision. The experiment consists of eight functionally identical detectors placed underground at different baselines from three pairs of nuclear reactors in South China. Since Dec. 2011, the experiment has been running stably for more than 4 years, and has collected the largest reactor anti-neutrino sample to date. Daya Bay is able to greatly improve the precision on θ 13 and to make an independent measurement of the effective mass splitting in the electron antineutrino disappearance channel. Daya Bay can also perform a number of other precise measurements, such as a high-statistics determination of the absolute reactor antineutrino flux and spectrum, as well as a search for sterile neutrino mixing, among others. The most recent results from Daya Bay are discussed in this paper, as well as the current status and future prospects of the experiment.

Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

PubMed Central

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance. PMID:24695550

Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.

Status and Assessment of Chesapeake Bay Wildlife Contamination

USGS Publications Warehouse

Heinz, G.H.; Wiemeyer, Stanley N.; Clark, D.R.; Albers, P.H.; Henry, P.; Batiuk, R.A.

1992-01-01

As an integral component of its priority setting process, the Chesapeake Bay Program`s Toxics Subcommittee has sought the expertise of Chesapeake Bay researchers and managers in developing a series of Chesapeake Bay toxics status and assessment papers. In the report, evidence for historical and current contaminant effects on key bird species, mammals, reptiles and amphibians which inhabit the Chesapeake Bay basin is examined. For each group of wildlife species, a general overview of effects caused by specific toxic substances is followed by detailed accounts of contaminant effects on selected species. Sponsored by Environmental Protection Agency, Annapolis, MD. Chesapeake Bay Program.

A new local-global approach for classification.

PubMed

Peres, R T; Pedreira, C E

2010-09-01

In this paper, we propose a new local-global pattern classification scheme that combines supervised and unsupervised approaches, taking advantage of both, local and global environments. We understand as global methods the ones concerned with the aim of constructing a model for the whole problem space using the totality of the available observations. Local methods focus into sub regions of the space, possibly using an appropriately selected subset of the sample. In the proposed method, the sample is first divided in local cells by using a Vector Quantization unsupervised algorithm, the LBG (Linde-Buzo-Gray). In a second stage, the generated assemblage of much easier problems is locally solved with a scheme inspired by Bayes' rule. Four classification methods were implemented for comparison purposes with the proposed scheme: Learning Vector Quantization (LVQ); Feedforward Neural Networks; Support Vector Machine (SVM) and k-Nearest Neighbors. These four methods and the proposed scheme were implemented in eleven datasets, two controlled experiments, plus nine public available datasets from the UCI repository. The proposed method has shown a quite competitive performance when compared to these classical and largely used classifiers. Our method is simple concerning understanding and implementation and is based on very intuitive concepts. Copyright 2010 Elsevier Ltd. All rights reserved.

The World's Students in Bay Area Universities.

ERIC Educational Resources Information Center

Duggan, Susan J.; Wollitzer, Peter A.

Results of a survey of international student exchange programs in the Bay Area of California are presented. Information is provided on the following: the top 10 countries of origin for Bay Area foreign students; 10 Bay Area institutions with the highest number of foreign students; 10 area institutions with the highest percent of full-time…

Phenotypic and Genotypic Shifts in Hepatitis B Virus in Treatment-Naive Patients, Taiwan, 2008-2012.

PubMed

Yeh, Chau-Ting; Liang, Kung-Hao; Chang, Ming-Ling; Hsu, Chao-Wei; Chen, Yi-Cheng; Lin, Chih-Lang; Lin, Wey-Ran; Lai, Ming-Wei

2017-05-01

We examined the characteristic changes of hepatitis B virus (HBV) in antiviral drug treatment-naive patients referred for pretreatment evaluation in Taiwan during 2008-2012. Over time, we observed substantial decreases in the prevalence of HBV e antigen (HBeAg) and increasing prevalence of the precore G1899A mutation and HBV-DNA levels in HBeAg-positive patients.

Insulin sensitivity and beta-cell function in protease inhibitor-treated and -naive human immunodeficiency virus-infected children.

PubMed

Bitnun, Ari; Sochett, Etienne; Dick, Paul T; To, Teresa; Jefferies, Craig; Babyn, Paul; Forbes, Jack; Read, Stanley; King, Susan M

2005-01-01

Previous pediatric studies have failed to demonstrate a clear association between protease inhibitor (PI) therapy and abnormal glucose homeostasis in HIV-infected children. To define more precisely the impact of PI therapy on glucose homeostasis in this population, we performed the insulin-modified frequent-sampling iv glucose tolerance test on 33 PI-treated and 15 PI-naive HIV-infected children. Other investigations included fasting serum lipids; glucose, insulin, and C-peptide; single-slice abdominal computed tomography; and, in a subset of PI-treated children, an oral glucose tolerance test. There were no differences between the two groups with respect to fasting serum insulin or C-peptide, homeostatic model assessment insulin resistance, or quantitative insulin sensitivity check index. The mean insulin sensitivity index of PI-treated and PI-naive children was 6.93 +/- 6.37 and 10.58 +/- 12.93 x 10(-4)min(-1) [microU/ml](-1), respectively (P = 0.17). The mean disposition index for the two groups was 1840 +/- 1575 and 3708 +/- 3005 x 10(-4)min(-1) (P = 0.013), respectively. After adjusting for potential confounding variables using multiple regression analysis, the insulin sensitivity index and disposition index of PI-treated children were significantly lower than that of PI-naive children (P = 0.01 for both). In PI-treated but not PI-naive children, insulin sensitivity correlated inversely with visceral adipose tissue area (r = -0.43, P = 0.01) and visceral to sc adipose tissue ratio (r = -0.49, P = 0.004). Mildly impaired glucose tolerance was noted in four of 21 PI-treated subjects tested. Our results demonstrate not only that PI therapy reduces insulin sensitivity in HIV-infected children but also that it impairs the beta-cell response to this reduction in insulin sensitivity and, in a subset of children, leads to the development of impaired glucose tolerance. The presence of insulin resistance, dyslipidemia, and the significant correlation of reduced insulin

Differential effects of ibogaine on local cerebral glucose utilization in drug-naive and morphine-dependent rats.

PubMed

Levant, Beth; Pazdernik, Thomas L

2004-04-02

Ibogaine, a hallucinogenic indole alkaloid, has been proposed as a treatment for addiction to opioids and other drugs of abuse. The mechanism for its putative anti-addictive effects is unknown. In this study, the effects of ibogaine on local cerebral glucose utilization (LCGU) were determined in freely moving, drug-naive, or morphine-dependent adult, male, Sprague-Dawley rats using the [(14)C]2-deoxyglucose (2-DG) method. Morphine-dependent rats were treated with increasing doses of morphine (5-25 mg/kg, s.c., b.i.d.) and then maintained at 25 mg/kg (b.i.d.) for 4-7 days. For the 2-DG procedure, rats were injected with saline or ibogaine (40 mg/kg, i.p.). 2-DG was administered 1 h after administration of ibogaine. The rate of LCGU was determined by quantitative autoradiography in 46 brain regions. In drug-naive animals, ibogaine produced significant increases in LCGU in the parietal, cingulate, and occipital cortices and cerebellum compared to controls consistent with its activity as a hallucinogen and a tremorogen. Morphine-dependent rats had only minor alterations in LCGU at the time assessed in this experiment. However, in morphine-dependent animals, ibogaine produced a global decrease in LCGU that was greatest in brain regions such as the lateral and medial preoptic areas, nucleus of the diagonal band, nucleus accumbens shell, inferior colliculus, locus coeruleus, and flocculus compared to morphine-dependent animals treated with saline. These findings indicate that ibogaine produces distinctly different effects on LCGU in drug-naive and morphine-dependent rats. This suggests that different mechanisms may underlie ibogaine's hallucinogenic and anti-addictive effects.

Role of remote sensing in Bay measurements

NASA Technical Reports Server (NTRS)

Mugler, J. P., Jr.; Godfrey, J. P.; Hickman, G. D.; Hovis, W. G.; Pearson, A. O.; Weaver, K. N.

1978-01-01

Remote measurements of a number of surface or near surface parameters for baseline definition and specialized studies, remote measurements of episodic events, and remote measurements of the Bay lithosphere are considered in terms of characterizing and understanding the ecology of the Chesapeake Bay. Geologic processes and features best suited for information enhancement by remote sensing methods are identified. These include: (1) rates of sedimentation in the Bay; (2) rates of erosion of Bay shorelines; (3) spatial distribution and geometry of aquifers; (4) mapping of Karst terrain (sinkholes); and (5) mapping of fracture patterns. Recommendations for studying problem areas identified are given.

Gulf of Mexico integrated science - Tampa Bay study, the impact of groundwater and contaminants on Tampa Bay

USGS Publications Warehouse

Swarzenski, Peter W.

2005-01-01

Despite the recreational and economic value of coastal bays and estuaries, these ecosystems are often among our most 'troubled' natural environments. Urbanization, agriculture, mining, and shipping are just a few activities that can have a profound and lasting impact on the coastal zone. In order to maintain a healthy coastal ecosystem, it is crucial to develop reasonable management practices around expert scientific information. We still have much to learn about the quantity and quality of groundwater being discharged into Tampa Bay, Florida. We also need to improve our knowledge of a wide range of contaminants entering the bay and must be able to determine where they accumulate in seafloor sediments. Such buried contaminants can potentially be harmful to biota if they are released to the water column. U.S. Geological Survey (USGS) scientists and research partners from the University of South Florida (USF), the University of Florida (UF), and the Florida Marine Research Institute (FMRI) are mapping sources of groundwater, measuring groundwater flow into Tampa Bay, and assessing the impact of contaminants and sediments on bay water quality and ecosystem health.

Spatial estimation from remotely sensed data via empirical Bayes models

NASA Technical Reports Server (NTRS)

Hill, J. R.; Hinkley, D. V.; Kostal, H.; Morris, C. N.

1984-01-01

Multichannel satellite image data, available as LANDSAT imagery, are recorded as a multivariate time series (four channels, multiple passovers) in two spatial dimensions. The application of parametric empirical Bayes theory to classification of, and estimating the probability of, each crop type at each of a large number of pixels is considered. This theory involves both the probability distribution of imagery data, conditional on crop types, and the prior spatial distribution of crop types. For the latter Markov models indexed by estimable parameters are used. A broad outline of the general theory reveals several questions for further research. Some detailed results are given for the special case of two crop types when only a line transect is analyzed. Finally, the estimation of an underlying continuous process on the lattice is discussed which would be applicable to such quantities as crop yield.

Clew Bay, Ireland

NASA Image and Video Library

2016-07-26

Clew Bay is in County Mayo, Republic of Ireland. It contains Ireland's best example of sunken glacial drumlins. Clew Bay is associated with Grace O'Malley, the Pirate Queen during Elizabethan times; and Dorinish, a private island purchased by John Lennon. The drumlins are low hills formed from glacial sediment deposited at the end of the last Ice Age. The image was acquired May 31, 2016, covers an area of 22.5 by 26.2 km, and is located at 53.9 degrees north, 9.6 degrees west. http://photojournal.jpl.nasa.gov/catalog/PIA18670

East Bay Municipal Utility District Settlement

EPA Pesticide Factsheets

The East Bay Municipal Utility District and its seven member communities own and operate a separate sanitary sewer system, which serves approximately 650,000 customers on the eastern side of San Francisco Bay in California.

10. VIEW TO NORTHEAST FROM WITHIN SOUTHWEST BAY, LOOKING THROUGH ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

10. VIEW TO NORTHEAST FROM WITHIN SOUTHWEST BAY, LOOKING THROUGH CENTER BAY AND INTO NORTHEAST BAY. NOTE TRAVELING BRIDGE CRANE OVERHEAD AND SWINGING BOOM CRANES ATTACHED TO COLUMNS ON RIGHT AND LEFT. NOTE ALSO THE DIFFERENCE IN TRUSSES SUPPORTING CRANEWAY TRACKS FOR SIDE BAYS AND CENTER BAY. TRUSSES SUPPORTING CRANEWAY TRACKS IN SIDE BAYS (CENTER FOREGROUND AND FAR BACKGROUND) ARE HOWE TRUSSES WITH WOOD DIAGONALS AND STEEL VERTICALS. TRUSSES SUPPORTING CRANEWAY TRACKS IN CENTER BAY (MID BACKGROUND) ARE PRATT TRUSSES WITH WOOD VERTICALS AND DIAGONALS. - Rosie the Riveter National Historical Park, Auxiliary Plate Shop, 912 Harbour Way, Richmond, Contra Costa County, CA

78 FR 55216 - Safety Zone: Suisun Bay Electromagnetic Scan and Ordnance Recovery, Suisun Bay, Concord, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-10

...-AA00 Safety Zone: Suisun Bay Electromagnetic Scan and Ordnance Recovery, Suisun Bay, Concord, CA AGENCY... (MOTCO) electromagnetic scan and ordnance recovery operations. These safety zones are established to... 10, 2013 that they intend to conduct an intrusive electromagnetic scan and ordnance recovery...

Prediction of cold and heat patterns using anthropometric measures based on machine learning.

PubMed

Lee, Bum Ju; Lee, Jae Chul; Nam, Jiho; Kim, Jong Yeol

2018-01-01

To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns. Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures. In women, the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men, the best indicator was BMI. In experiments using a combination of measures, the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression, and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression. Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.

Linear and Order Statistics Combiners for Pattern Classification

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep; Lau, Sonie (Technical Monitor)

2001-01-01

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

Lessons from monitoring water quality in San Francisco Bay

USGS Publications Warehouse

Cloern, J.E.; Schraga, T.S.; Lopez, C.B.; Labiosa, R.

2003-01-01

Bay Area residents feel a sense of responsibility to protect San Francisco Bay and keep it healthy. Some even dream about the recovery of fish stocks so they can sustain commercial fishing once again inside the Bay. How is our Bay doing? Is it highly polluted or pretty clean? How does its health compare with other estuaries in the United States? Are things getting better or worse? Does costly wastewater treatment have benefits? What are the biggest threats to the Bay and how can we reduce or eliminate those threats? How will the Bay change in the future? These questions can only be answered with investments in study and monitoring, and they are the driving force behind the Regional Monitoring Program (RMP). We describe here some selected results from water quality surveillance conducted by the U.S. Geological Survey (USGS) as one component of the RMP. We present results as lessons about how the Bay works as a complex dynamic system, and we show how these lessons are relevant to the broad RMP objectives supporting Bay protection and management.

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

The Neoglacial landscape and human history of Glacier Bay, Glacier Bay National Park and Preserve, southeast Alaska, USA

USGS Publications Warehouse

Connor, C.; Streveler, G.; Post, A.; Monteith, D.; Howell, W.

2009-01-01

The Neoglacial landscape of the Huna Tlingit homeland in Glacier Bay is recreated through new interpretations of the lower Bay's fjordal geomorphology, late Quaternary geology and its ethnographic landscape. Geological interpretation is enhanced by 38 radiocarbon dates compiled from published and unpublished sources, as well as 15 newly dated samples. Neoglacial changes in ice positions, outwash and lake extents are reconstructed for c. 5500?????"200 cal. yr ago, and portrayed as a set of three landscapes at 1600?????"1000, 500?????"300 and 300?????"200 cal. yr ago. This history reveals episodic ice advance towards the Bay mouth, transforming it from a fjordal seascape into a terrestrial environment dominated by glacier outwash sediments and ice-marginal lake features. This extensive outwash plain was building in lower Glacier Bay by at least 1600 cal. yr ago, and had filled the lower bay by 500 cal. yr ago. The geologic landscape evokes the human-described landscape found in the ethnographic literature. Neoglacial climate and landscape dynamism created difficult but endurable environmental conditions for the Huna Tlingit people living there. Choosing to cope with environmental hardship was perhaps preferable to the more severely deteriorating conditions outside of the Bay as well as conflicts with competing groups. The central portion of the outwash plain persisted until it was overridden by ice moving into Icy Strait between AD 1724?????"1794. This final ice advance was very abrupt after a prolonged still-stand, evicting the Huna Tlingit from their Glacier Bay homeland. ?? 2009 SAGE Publications.