On the role of cost-sensitive learning in multi-class brain-computer interfaces.

PubMed

Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

2010-06-01

Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

Camouflage target reconnaissance based on hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Hua, Wenshen; Guo, Tong; Liu, Xun

2015-08-01

Efficient camouflaged target reconnaissance technology makes great influence on modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. Hyperspectral target detection and classification technology are utilized to achieve single class and multi-class camouflaged targets reconnaissance respectively. Constrained energy minimization (CEM), a widely used algorithm in hyperspectral target detection, is employed to achieve one class camouflage target reconnaissance. Then, support vector machine (SVM), a classification method, is proposed to achieve multi-class camouflage target reconnaissance. Experiments have been conducted to demonstrate the efficiency of the proposed method.

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

[Research on the methods for multi-class kernel CSP-based feature extraction].

PubMed

Wang, Jinjia; Zhang, Lingzhi; Hu, Bei

2012-04-01

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

A novel class sensitive hashing technique for large-scale content-based remote sensing image retrieval

NASA Astrophysics Data System (ADS)

Reato, Thomas; Demir, Begüm; Bruzzone, Lorenzo

2017-10-01

This paper presents a novel class sensitive hashing technique in the framework of large-scale content-based remote sensing (RS) image retrieval. The proposed technique aims at representing each image with multi-hash codes, each of which corresponds to a primitive (i.e., land cover class) present in the image. To this end, the proposed method consists of a three-steps algorithm. The first step is devoted to characterize each image by primitive class descriptors. These descriptors are obtained through a supervised approach, which initially extracts the image regions and their descriptors that are then associated with primitives present in the images. This step requires a set of annotated training regions to define primitive classes. A correspondence between the regions of an image and the primitive classes is built based on the probability of each primitive class to be present at each region. All the regions belonging to the specific primitive class with a probability higher than a given threshold are highly representative of that class. Thus, the average value of the descriptors of these regions is used to characterize that primitive. In the second step, the descriptors of primitive classes are transformed into multi-hash codes to represent each image. This is achieved by adapting the kernel-based supervised locality sensitive hashing method to multi-code hashing problems. The first two steps of the proposed technique, unlike the standard hashing methods, allow one to represent each image by a set of primitive class sensitive descriptors and their hash codes. Then, in the last step, the images in the archive that are very similar to a query image are retrieved based on a multi-hash-code-matching scheme. Experimental results obtained on an archive of aerial images confirm the effectiveness of the proposed technique in terms of retrieval accuracy when compared to the standard hashing methods.

Inter-class sparsity based discriminative least square regression.

PubMed

Wen, Jie; Xu, Yong; Li, Zuoyong; Ma, Zhongli; Xu, Yuanrong

2018-06-01

Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring the correlations among samples. The second one is that the used label matrix, i.e., zero-one label matrix is inappropriate for classification. To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), for multi-class classification. Different from other methods, the proposed method pursues that the transformed samples have a common sparsity structure in each class. For this goal, an inter-class sparsity constraint is introduced to the least square regression model such that the margins of samples from the same class can be greatly reduced while those of samples from different classes can be enlarged. In addition, an error term with row-sparsity constraint is introduced to relax the strict zero-one label matrix, which allows the method to be more flexible in learning the discriminative transformation matrix. These factors encourage the method to learn a more compact and discriminative transformation for regression and thus has the potential to perform better than other methods. Extensive experimental results show that the proposed method achieves the best performance in comparison with other methods for multi-class classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multi-residue determination of 210 drugs in pork by ultra-high-performance liquid chromatography-tandem mass spectrometry.

PubMed

Yin, Zhiqiang; Chai, Tingting; Mu, Pengqian; Xu, Nana; Song, Yue; Wang, Xinlu; Jia, Qi; Qiu, Jing

2016-09-09

This paper presents a multi-residue analytical method for 210 drugs in pork using ultra-high-performance liquid chromatography-Q-Trap tandem mass spectrometry (UPLC-MS/MS) within 20min via positive ESI in scheduled multi-reaction monitoring (MRM) mode. The 210 drugs, belonging to 21 different chemical classes, included macrolides, sulfonamides, tetracyclines, β-lactams, β-agonists, aminoglycosides, antiviral drugs, glycosides, phenothiazine, protein anabolic hormones, non-steroidal anti-inflammatory drugs (NSAIDs), quinolones, antifungal drugs, corticosteroids, imidazoles, piperidines, piperazidines, insecticides, amides, alkaloids and others. A rapid and simple preparation method was applied to process the animal tissues, including solvent extraction with an acetonitrile/water mixture (80/20, v/v), defatting and clean-up processes. The recoveries ranged from 52% to 130% with relative standard deviations (RSDs)<20% for spiked concentrations of 10, 50 and 250μg/kg. More than 90% of the analytes achieved low limits of quantification (LOQs)<10μg/kg. The decision limit (CCα), detection capability (CCβ) values were in the range of 2-502μg/kg and 4-505μg/kg, respectively. This method is significant for food safety monitoring and controlling veterinary drug use. Copyright © 2016 Elsevier B.V. All rights reserved.

A Simple Method for Assessment of MDR Bacteria for Over-Expressed Efflux Pumps

PubMed Central

Martins, Marta; McCusker, Matthew P; Viveiros, Miguel; Couto, Isabel; Fanning, Séamus; Pagès, Jean-Marie; Amaral, Leonard

2013-01-01

It is known that bacteria showing a multi-drug resistance phenotype use several mechanisms to overcome the action of antibiotics. As a result, this phenotype can be a result of several mechanisms or a combination of thereof. The main mechanisms of antibiotic resistance are: mutations in target genes (such as DNA gyrase and topoisomerase IV); over-expression of efflux pumps; changes in the cell envelope; down regulation of membrane porins, and modified lipopolysaccharide component of the outer cell membrane (in the case of Gram-negative bacteria). In addition, adaptation to the environment, such as quorum sensing and biofilm formation can also contribute to bacterial persistence. Due to the rapid emergence and spread of bacterial isolates showing resistance to several classes of antibiotics, methods that can rapidly and efficiently identify isolates whose resistance is due to active efflux have been developed. However, there is still a need for faster and more accurate methodologies. Conventional methods that evaluate bacterial efflux pump activity in liquid systems are available. However, these methods usually use common efflux pump substrates, such as ethidium bromide or radioactive antibiotics and therefore, require specialized instrumentation, which is not available in all laboratories. In this review, we will report the results obtained with the Ethidium Bromide-agar Cartwheel method. This is an easy, instrument-free, agar based method that has been modified to afford the simultaneous evaluation of as many as twelve bacterial strains. Due to its simplicity it can be applied to large collections of bacteria to rapidly screen for multi-drug resistant isolates that show an over-expression of their efflux systems. The principle of the method is simple and relies on the ability of the bacteria to expel a fluorescent molecule that is substrate for most efflux pumps, ethidium bromide. In this approach, the higher the concentration of ethidium bromide required to produce fluorescence of the bacterial mass, the greater the efflux capacity of the bacterial cells. We have tested and applied this method to a large number of Gram-positive and Gram-negative bacteria to detect efflux activity among these multi-drug resistant isolates. The presumptive efflux activity detected by the Ethidium Bromide-agar Cartwheel method was subsequently confirmed by the determination of the minimum inhibitory concentration for several antibiotics in the presence and absence of known efflux pump inhibitors. PMID:23589748

Using Bayes factors for multi-factor, biometric authentication

NASA Astrophysics Data System (ADS)

Giffin, A.; Skufca, J. D.; Lao, P. A.

2015-01-01

Multi-factor/multi-modal authentication systems are becoming the de facto industry standard. Traditional methods typically use rates that are point estimates and lack a good measure of uncertainty. Additionally, multiple factors are typically fused together in an ad hoc manner. To be consistent, as well as to establish and make proper use of uncertainties, we use a Bayesian method that will update our estimates and uncertainties as new information presents itself. Our algorithm compares competing classes (such as genuine vs. imposter) using Bayes Factors (BF). The importance of this approach is that we not only accept or reject one model (class), but compare it to others to make a decision. We show using a Receiver Operating Characteristic (ROC) curve that using BF for determining class will always perform at least as well as the traditional combining of factors, such as a voting algorithm. As the uncertainty decreases, the BF result continues to exceed the traditional methods result.

Application of multi-grid methods for solving the Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Demuren, A. O.

1989-01-01

The application of a class of multi-grid methods to the solution of the Navier-Stokes equations for two-dimensional laminar flow problems is discussed. The methods consist of combining the full approximation scheme-full multi-grid technique (FAS-FMG) with point-, line-, or plane-relaxation routines for solving the Navier-Stokes equations in primitive variables. The performance of the multi-grid methods is compared to that of several single-grid methods. The results show that much faster convergence can be procured through the use of the multi-grid approach than through the various suggestions for improving single-grid methods. The importance of the choice of relaxation scheme for the multi-grid method is illustrated.

Application of multi-grid methods for solving the Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Demuren, A. O.

1989-01-01

This paper presents the application of a class of multi-grid methods to the solution of the Navier-Stokes equations for two-dimensional laminar flow problems. The methods consists of combining the full approximation scheme-full multi-grid technique (FAS-FMG) with point-, line- or plane-relaxation routines for solving the Navier-Stokes equations in primitive variables. The performance of the multi-grid methods is compared to those of several single-grid methods. The results show that much faster convergence can be procured through the use of the multi-grid approach than through the various suggestions for improving single-grid methods. The importance of the choice of relaxation scheme for the multi-grid method is illustrated.

Simultaneous determination of multi-residue and multi-class antibiotics in aquaculture shrimps by UPLC-MS/MS.

PubMed

Saxena, Sushil Kumar; Rangasamy, Rajesh; Krishnan, Anoop A; Singh, Dhirendra P; Uke, Sumedh P; Malekadi, Praveen Kumar; Sengar, Anoop S; Mohamed, D Peer; Gupta, Ananda

2018-09-15

An accurate, reliable and fast multi-residue, multi-class method using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was developed and validated for simultaneous determination and quantification of 24 pharmacologically active substances of three different classes (Quinolones including fluoroquinolones, sulphonamides and tetracyclines) in aquaculture shrimps. Sample preparation involves extraction with acetonitrile containing 0.1% formic acid and followed by clean up with n-hexane and 0.1% methanol in water by UPLC-MS/MS within 8 min. The method was validated according to European Commission Decision 2002/657. Acceptable values were obtained for linearity (5-200 μg kg -1 ), specificity, Limit of Quantification (5-10 μg kg -1 ), recovery (between 83 and 100%), repeatability (RSD < 9%), within lab reproducibility (RSD < 15%), reproducibility (RSD ≤ 22%), decision limit (105-116 μg kg -1 ) and detection capability (110-132 μg kg -1 ). The validated method was applied to aquaculture shrimp samples from India. Copyright © 2018 Elsevier Ltd. All rights reserved.

Validation of a method for simultaneous determination of nitroimidazoles, benzimidazoles and chloramphenicols in swine tissues by ultra-high performance liquid chromatography-tandem mass spectrometry.

PubMed

Xia, Xi; Wang, Yuanyuan; Wang, Xia; Li, Yun; Zhong, Feng; Li, Xiaowei; Huang, Yaoling; Ding, Shuangyang; Shen, Jianzhong

2013-05-31

This paper presents a sensitive and confirmatory multi-residue method for the analysis of 23 veterinary drugs and metabolites belonging to three classes (nitroimidazoles, benzimidazoles, and chloramphenicols) in porcine muscle, liver, and kidney. After extracted with ethyl acetate and basic ethyl acetate sequentially, the crude extracts were defatted with hexane and further purified using Oasis MCX solid-phase extraction cartridges. Rapid determination was carried out by ultra-high performance liquid chromatography-electrospray ionization tandem mass spectrometry. Data acquisition was performed under positive and negative mode simultaneously. Recoveries based on matrix-matched calibrations for meat, liver, and kidney ranged from 50.6 to 108.1%. The method quantification limits were in the range of 3-100ng/kg. Copyright © 2012 Elsevier B.V. All rights reserved.

A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.

PubMed

Fan, Ming; Zheng, Bin; Li, Lihua

2015-10-01

Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.

Data fusion algorithm for rapid multi-mode dust concentration measurement system based on MEMS

NASA Astrophysics Data System (ADS)

Liao, Maohao; Lou, Wenzhong; Wang, Jinkui; Zhang, Yan

2018-03-01

As single measurement method cannot fully meet the technical requirements of dust concentration measurement, the multi-mode detection method is put forward, as well as the new requirements for data processing. This paper presents a new dust concentration measurement system which contains MEMS ultrasonic sensor and MEMS capacitance sensor, and presents a new data fusion algorithm for this multi-mode dust concentration measurement system. After analyzing the relation between the data of the composite measurement method, the data fusion algorithm based on Kalman filtering is established, which effectively improve the measurement accuracy, and ultimately forms a rapid data fusion model of dust concentration measurement. Test results show that the data fusion algorithm is able to realize the rapid and exact concentration detection.

The Status of US Multi-campus Colleges and Schools of Pharmacy

PubMed Central

Harrison, Lauren C.; DiPiro, Joseph T.

2010-01-01

Objective To assess the current status of multi-campus colleges and schools of pharmacy within the United States. Methods Data on multi-campus programs, technology, communication, and opinions regarding benefits and challenges were collected from Web sites, e-mail, and phone interviews from all colleges and schools of pharmacy with students in class on more than 1 campus. Results Twenty schools and colleges of pharmacy (18 public and 2 private) had multi-campus programs; 16 ran parallel campuses and 4 ran sequential campuses. Most programs used synchronous delivery of classes. The most frequently reported reasons for establishing the multi-campus program were to have access to a hospital and/or medical campus and clinical resources located away from the main campus and to increase class size. Effectiveness of distance education technology was most often sited as a challenge. Conclusion About 20% of colleges and schools of pharmacy have multi-campus programs most often to facilitate access to clinical resources and to increase class size. These programs expand learning opportunities and face challenges related to technology, resources, and communication. PMID:21088729

Periodic solutions of second-order nonlinear difference equations containing a small parameter. IV - Multi-discrete time method

NASA Technical Reports Server (NTRS)

Mickens, Ronald E.

1987-01-01

It is shown that a discrete multi-time method can be constructed to obtain approximations to the periodic solutions of a special class of second-order nonlinear difference equations containing a small parameter. Three examples illustrating the method are presented.

Design of 3D simulation engine for oilfield safety training

NASA Astrophysics Data System (ADS)

Li, Hua-Ming; Kang, Bao-Sheng

2015-03-01

Aiming at the demand for rapid custom development of 3D simulation system for oilfield safety training, this paper designs and implements a 3D simulation engine based on script-driven method, multi-layer structure, pre-defined entity objects and high-level tools such as scene editor, script editor, program loader. A scripting language been defined to control the system's progress, events and operating results. Training teacher can use this engine to edit 3D virtual scenes, set the properties of entity objects, define the logic script of task, and produce a 3D simulation training system without any skills of programming. Through expanding entity class, this engine can be quickly applied to other virtual training areas.

Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.

PubMed

Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui

2012-11-07

RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.

An assessment of the cultivated cropland class of NLCD 2006 using a multi-source and multi-criteria approach

USGS Publications Warehouse

Danielson, Patrick; Yang, Limin; Jin, Suming; Homer, Collin G.; Napton, Darrell

2016-01-01

We developed a method that analyzes the quality of the cultivated cropland class mapped in the USA National Land Cover Database (NLCD) 2006. The method integrates multiple geospatial datasets and a Multi Index Integrated Change Analysis (MIICA) change detection method that captures spectral changes to identify the spatial distribution and magnitude of potential commission and omission errors for the cultivated cropland class in NLCD 2006. The majority of the commission and omission errors in NLCD 2006 are in areas where cultivated cropland is not the most dominant land cover type. The errors are primarily attributed to the less accurate training dataset derived from the National Agricultural Statistics Service Cropland Data Layer dataset. In contrast, error rates are low in areas where cultivated cropland is the dominant land cover. Agreement between model-identified commission errors and independently interpreted reference data was high (79%). Agreement was low (40%) for omission error comparison. The majority of the commission errors in the NLCD 2006 cultivated crops were confused with low-intensity developed classes, while the majority of omission errors were from herbaceous and shrub classes. Some errors were caused by inaccurate land cover change from misclassification in NLCD 2001 and the subsequent land cover post-classification process.

40 CFR 53.35 - Test procedure for Class II and Class III methods for PM2.5 and PM-2.5

Code of Federal Regulations, 2010 CFR

2010-07-01

... reference method samplers shall be of single-filter design (not multi-filter, sequential sample design... and multiplicative bias (comparative slope and intercept). (1) For each test site, calculate the mean...

40 CFR 53.35 - Test procedure for Class II and Class III methods for PM2.5 and PM-2.5

Code of Federal Regulations, 2011 CFR

2011-07-01

... reference method samplers shall be of single-filter design (not multi-filter, sequential sample design... and multiplicative bias (comparative slope and intercept). (1) For each test site, calculate the mean...

40 CFR 53.35 - Test procedure for Class II and Class III methods for PM2.5 and PM−2.5.

Code of Federal Regulations, 2012 CFR

2012-07-01

... reference method samplers shall be of single-filter design (not multi-filter, sequential sample design... and multiplicative bias (comparative slope and intercept). (1) For each test site, calculate the mean...

Seismic Data Analysis throught Multi-Class Classification.

NASA Astrophysics Data System (ADS)

Anderson, P.; Kappedal, R. D.; Magana-Zook, S. A.

2017-12-01

In this research, we conducted twenty experiments of varying time and frequency bands on 5000seismic signals with the intent of finding a method to classify signals as either an explosion or anearthquake in an automated fashion. We used a multi-class approach by clustering of the data throughvarious techniques. Dimensional reduction was examined through the use of wavelet transforms withthe use of the coiflet mother wavelet and various coefficients to explore possible computational time vsaccuracy dependencies. Three and four classes were generated from the clustering techniques andexamined with the three class approach producing the most accurate and realistic results.

A Design for a Multi-Use Object Editor with Connections

DTIC Science & Technology

1991-10-01

Variables of Class Select 14 3.6.2 Methods of Class Select 14 3.6.3 Application States 14 3.7 Class Clipboard 15 3.8 Class Crestore 15 3.9 Conclusions 15 4...this is not directly related to cutting and past- ing between applications. 3.8 CLASS CRESTORE Class crestore holds a connection until all the

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.

PubMed

Crabtree, Nathaniel M; Moore, Jason H; Bowyer, John F; George, Nysia I

2017-01-01

A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes. These characteristics give CES an advantage over other classification and feature selection algorithms, particularly when the goal is to identify a small number of highly relevant, non-redundant biomarkers. Previously, CESs have been developed only for binary class datasets. In this study, we developed a multi-class CES. The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF). The algorithms were evaluated on three distinct multi-class RNA sequencing datasets. The comparison criteria were run-time, classification accuracy, number of selected features, and stability of selected feature set (as measured by the Tanimoto distance). The performance of each algorithm was data-dependent. CES performed best on the dataset with the smallest sample size, indicating that CES has a unique advantage since the accuracy of most classification methods suffer when sample size is small. The multi-class extension of CES increases the appeal of its application to complex, multi-class datasets in order to identify important biomarkers and features.

Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics.

PubMed

Dutheil, Julien; Gaillard, Sylvain; Bazin, Eric; Glémin, Sylvain; Ranwez, Vincent; Galtier, Nicolas; Belkhir, Khalid

2006-04-04

A large number of bioinformatics applications in the fields of bio-sequence analysis, molecular evolution and population genetics typically share input/output methods, data storage requirements and data analysis algorithms. Such common features may be conveniently bundled into re-usable libraries, which enable the rapid development of new methods and robust applications. We present Bio++, a set of Object Oriented libraries written in C++. Available components include classes for data storage and handling (nucleotide/amino-acid/codon sequences, trees, distance matrices, population genetics datasets), various input/output formats, basic sequence manipulation (concatenation, transcription, translation, etc.), phylogenetic analysis (maximum parsimony, markov models, distance methods, likelihood computation and maximization), population genetics/genomics (diversity statistics, neutrality tests, various multi-locus analyses) and various algorithms for numerical calculus. Implementation of methods aims at being both efficient and user-friendly. A special concern was given to the library design to enable easy extension and new methods development. We defined a general hierarchy of classes that allow the developer to implement its own algorithms while remaining compatible with the rest of the libraries. Bio++ source code is distributed free of charge under the CeCILL general public licence from its website http://kimura.univ-montp2.fr/BioPP.

Avatars Go to Class: A Virtual Environment Soil Science Activity

ERIC Educational Resources Information Center

Mamo, M.; Namuth-Covert, D.; Guru, A.; Nugent, G.; Phillips, L.; Sandall, L.; Kettler, T.; McCallister, D.

2011-01-01

Web 2.0 technology is expanding rapidly from social and gaming uses into the educational applications. Specifically, the multi-user virtual environment (MUVE), such as SecondLife, allows educators to fill the gap of first-hand experience by creating simulated realistic evolving problems/games. In a pilot study, a team of educators at the…

Rapid screening and quantification of residual pesticides and illegal adulterants in red wine by direct analysis in real time mass spectrometry.

PubMed

Guo, Tianyang; Fang, Pingping; Jiang, Juanjuan; Zhang, Feng; Yong, Wei; Liu, Jiahui; Dong, Yiyang

2016-11-04

A rapid method to screen and quantify multi-class analytic targets in red wine has been developed by direct analysis in real time (DART) coupled with triple quadruple tandem mass spectrometry (QqQ-MS). A modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) procedure was used for increasing analytical speed and reducing matrix effect, and the multiple reaction monitoring (MRM) in DART-MS/MS ensured accurate analysis. One bottle of wine containing 50 pesticides and 12 adulterants, i.e., preservatives, antioxidant, sweeteners, and azo dyes, could be totally determined less than 12min. This method exhibited proper linearity (R 2 ≥0.99) in the range of 1-1000ng/mL for pesticides and 10-5000ng/mL for adulterants. The limits of detection (LODs) were obtained in a 0.5-50ng/mL range for pesticides and 5-50ng/mL range for adulterants, and the limits of quantification (LOQs) were in a 1-100ng/mL range for pesticides and 10-250ng/mL range for adulterants. Three spiked levels for each analyte in wine were evaluated, and the recoveries were in a scope of 75-120%. The results demonstrated DART-MS/MS was a rapid and simple method, and could be applied to rapid analyze residual pesticides and illegal adulterants in a large quantities of red wine. Copyright © 2016 Elsevier B.V. All rights reserved.

Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

PubMed

Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

2015-01-01

Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

Simultaneous Determination of Multiple Classes of Hydrophilic and Lipophilic Components in Shuang-Huang-Lian Oral Liquid Formulations by UPLC-Triple Quadrupole Linear Ion Trap Mass Spectrometry.

PubMed

Liang, Jun; Sun, Hui-Min; Wang, Tian-Long

2017-11-24

The Shuang-Huang-Lian (SHL) oral liquid is a combined herbal prescription used in the treatment of acute upper respiratory tract infection, acute bronchitis and pneumonia. Multiple constituents are considered to be responsible for the therapeutic effects of SHL. However, the quantitation of the multi-components from multiple classes is still unsatisfactory because of the high complexity of constituents in SHL. In this study, an accurate, rapid, and specific UPLC-MS/MS method was established for simultaneous quantification of 18 compounds from multiple classes in SHL oral liquid formulations. Chromatographic separation was performed on a HSS T3 (1.8 μm, 2.1 mm × 100 mm) column, using a gradient mobile phase system of 0.1% formic acid in acetonitrile and 0.1% formic acid in water at a flow rate of 0.2 mL·min -1 ; the run time was 23 min. The MS was operated in negative electrospray ionization (ESI - ) for analysis of 18 compounds using multiple reaction monitoring (MRM) mode. UPLC-ESI - -MRM-MS/MS method showed good linear relationships ( R ² > 0.999), repeatability (RSD < 3%), precisions (RSD < 3%) and recovery (84.03-101.62%). The validated method was successfully used to determine multiple classes of hydrophilic and lipophilic components in the SHL oral liquids. Finally, principal component analysis (PCA) was used to classify and differentiate SHL oral liquid samples attributed to different manufacturers of China. The proposed UPLC-ESI - -MRM-MS/MS coupled with PCA has been elucidated to be a simple and reliable method for quality evaluation of SHL oral liquids.

A multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan

An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the affected area in the case of a nuclear event. This imagery served as a second source of data to augment results from the time series approach. The classifications from the two approaches were integrated using an a posteriori probability-based fusion approach. This was done by establishing a relationship between the classes, obtained after classification of the two data sources. Despite the coarse spatial resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion-based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. This fusion thus contributed to classification accuracy refinement, with a few additional advantages, such as correction for cloud cover and providing for an approach that is robust against point-in-time seasonal anomalies, due to the inclusion of multi-temporal data. We concluded that this approach is capable of generating land cover maps of acceptable accuracy and rapid turnaround, which in turn can yield reliable estimates of crop acreage of a region. The final algorithm is part of an automated software tool, which can be used by emergency response personnel to generate a nuclear ingestion pathway information product within a few hours of data collection.

40 CFR 53.35 - Test procedure for Class II and Class III methods for PM 2.5 and PM −2.5.

Code of Federal Regulations, 2014 CFR

2014-07-01

... section. All reference method samplers shall be of single-filter design (not multi-filter, sequential sample design). Each candidate method shall be setup and operated in accordance with its associated... precision specified in table C-4 of this subpart. (g) Test for additive and multiplicative bias (comparative...

40 CFR 53.35 - Test procedure for Class II and Class III methods for PM 2.5 and PM −2.5.

Code of Federal Regulations, 2013 CFR

2013-07-01

... section. All reference method samplers shall be of single-filter design (not multi-filter, sequential sample design). Each candidate method shall be setup and operated in accordance with its associated... precision specified in table C-4 of this subpart. (g) Test for additive and multiplicative bias (comparative...

Multifunctional Collaborative Modeling and Analysis Methods in Engineering Science

NASA Technical Reports Server (NTRS)

Ransom, Jonathan B.; Broduer, Steve (Technical Monitor)

2001-01-01

Engineers are challenged to produce better designs in less time and for less cost. Hence, to investigate novel and revolutionary design concepts, accurate, high-fidelity results must be assimilated rapidly into the design, analysis, and simulation process. This assimilation should consider diverse mathematical modeling and multi-discipline interactions necessitated by concepts exploiting advanced materials and structures. Integrated high-fidelity methods with diverse engineering applications provide the enabling technologies to assimilate these high-fidelity, multi-disciplinary results rapidly at an early stage in the design. These integrated methods must be multifunctional, collaborative, and applicable to the general field of engineering science and mechanics. Multifunctional methodologies and analysis procedures are formulated for interfacing diverse subdomain idealizations including multi-fidelity modeling methods and multi-discipline analysis methods. These methods, based on the method of weighted residuals, ensure accurate compatibility of primary and secondary variables across the subdomain interfaces. Methods are developed using diverse mathematical modeling (i.e., finite difference and finite element methods) and multi-fidelity modeling among the subdomains. Several benchmark scalar-field and vector-field problems in engineering science are presented with extensions to multidisciplinary problems. Results for all problems presented are in overall good agreement with the exact analytical solution or the reference numerical solution. Based on the results, the integrated modeling approach using the finite element method for multi-fidelity discretization among the subdomains is identified as most robust. The multiple-method approach is advantageous when interfacing diverse disciplines in which each of the method's strengths are utilized. The multifunctional methodology presented provides an effective mechanism by which domains with diverse idealizations are interfaced. This capability rapidly provides the high-fidelity results needed in the early design phase. Moreover, the capability is applicable to the general field of engineering science and mechanics. Hence, it provides a collaborative capability that accounts for interactions among engineering analysis methods.

78 FR 33098 - Prospective Grant of Co-Exclusive Licenses: Multi-Focal Structured Illumination Microscopy...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-03

...-Exclusive Licenses: Multi-Focal Structured Illumination Microscopy Systems and Methods AGENCY: National... pertains to a system and method for digital confocal microscopy that rapidly processes enhanced images. In particular, the invention is a method for digital confocal microscopy that includes a digital mirror device...

Application of molecularly imprinted polymer based matrix solid phase dispersion for determination of fluoroquinolones, tetracyclines and sulfonamides in meat.

PubMed

Wang, Geng Nan; Zhang, Lei; Song, Yi Ping; Liu, Ju Xiang; Wang, Jian Ping

2017-10-15

In this study, a type of novel mixed-template molecularly imprinted polymer was synthesized that was able to recognize 8 fluoroquinolones, 8 sulfonamides and 4 tetracyclines simultaneously with recoveries higher than 92%. Then the polymer was used to develop a matrix solid phase dispersion method for simultaneous extraction of the 20 drugs in pork followed by determination with ultra performance liquid chromatography. During the experiments, the MMIP amount, washing solvent and elution solvent were optimized respectively. The limits of detection of this method for the 20 drugs in pork were in the range of 0.5-3.0ngg -1 , and the intra-day and inter-day recoveries from the fortified blank samples were in the range of 74.5%-102.7%. Therefore, this method could be used as a rapid, simple, specific and sensitive method for multi-determination of the residues of the three classes of drugs in meat. Copyright © 2017 Elsevier B.V. All rights reserved.

SIMULTANEOUS CONCENTRATION AND REAL-TIME DETECTION OF MULTIPLE CLASSES OF MICROBIAL PATHOGENS FROM DRINKING WATER

EPA Science Inventory

Key CCL viruses will be rapidly detected at low levels in water samples concentrated by a rapid HFUF or a new thin-sheet (TSM) electropositive filter adsorption-elution method and compared with the approved EPA method (1MDS VIRADEL). A unified and rapid virus concentration, n...

EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

NASA Astrophysics Data System (ADS)

Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

2016-08-01

Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

Multi-class SVM model for fMRI-based classification and grading of liver fibrosis

NASA Astrophysics Data System (ADS)

Freiman, M.; Sela, Y.; Edrei, Y.; Pappo, O.; Joskowicz, L.; Abramovitch, R.

2010-03-01

We present a novel non-invasive automatic method for the classification and grading of liver fibrosis from fMRI maps based on hepatic hemodynamic changes. This method automatically creates a model for liver fibrosis grading based on training datasets. Our supervised learning method evaluates hepatic hemodynamics from an anatomical MRI image and three T2*-W fMRI signal intensity time-course scans acquired during the breathing of air, air-carbon dioxide, and carbogen. It constructs a statistical model of liver fibrosis from these fMRI scans using a binary-based one-against-all multi class Support Vector Machine (SVM) classifier. We evaluated the resulting classification model with the leave-one out technique and compared it to both full multi-class SVM and K-Nearest Neighbor (KNN) classifications. Our experimental study analyzed 57 slice sets from 13 mice, and yielded a 98.2% separation accuracy between healthy and low grade fibrotic subjects, and an overall accuracy of 84.2% for fibrosis grading. These results are better than the existing image-based methods which can only discriminate between healthy and high grade fibrosis subjects. With appropriate extensions, our method may be used for non-invasive classification and progression monitoring of liver fibrosis in human patients instead of more invasive approaches, such as biopsy or contrast-enhanced imaging.

Developing rapid methods for analyzing upland riparian functions and values.

PubMed

Hruby, Thomas

2009-06-01

Regulators protecting riparian areas need to understand the integrity, health, beneficial uses, functions, and values of this resource. Up to now most methods providing information about riparian areas are based on analyzing condition or integrity. These methods, however, provide little information about functions and values. Different methods are needed that specifically address this aspect of riparian areas. In addition to information on functions and values, regulators have very specific needs that include: an analysis at the site scale, low cost, usability, and inclusion of policy interpretations. To meet these needs a rapid method has been developed that uses a multi-criteria decision matrix to categorize riparian areas in Washington State, USA. Indicators are used to identify the potential of the site to provide a function, the potential of the landscape to support the function, and the value the function provides to society. To meet legal needs fixed boundaries for assessment units are established based on geomorphology, the distance from "Ordinary High Water Mark" and different categories of land uses. Assessment units are first classified based on ecoregions, geomorphic characteristics, and land uses. This simplifies the data that need to be collected at a site, but it requires developing and calibrating a separate model for each "class." The approach to developing methods is adaptable to other locations as its basic structure is not dependent on local conditions.

Rapid screening and identification of multi-class substances of very high concern in textiles using liquid chromatography-hybrid linear ion trap orbitrap mass spectrometry.

PubMed

Zhang, Li; Luo, Xin; Niu, Zengyuan; Ye, Xiwen; Tang, Zhixu; Yao, Peng

2015-03-20

A new analytical method was established and validated for the analysis of 19 substances of very high concern (SVHCs) in textiles, including phthalic acid esters (PAEs), organotins (OTs), perfluorochemicals (PFCs) and flame retardants (FRs). After ultrasonic extraction in methanol, the textile samples were analyzed by high performance liquid chromatography-hybrid linear ion trap Orbitrap high resolution mass spectrometry (HPLC-LTQ/Orbitrap). The values of LOQ were in the range of 2-200mg/kg. Recoveries at two levels (at the LOQ and at half the limit of regulation) ranged from 68% to 120%, and the repeatability was lower than 13%. This method was successfully applied to the screening of SVHCs in commercial textile samples and is useful for the fast screening of various SVHCs. Copyright © 2015 Elsevier B.V. All rights reserved.

Low Wind Speed Turbine Project Phase II: The Application of Medium-Voltage Electrical Apparatus to the Class of Variable Speed Multi-Megawatt Low Wind Speed Turbines; 15 June 2004--30 April 2005

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

Erdman, W.; Behnke, M.

2005-11-01

Kilowatt ratings of modern wind turbines have progressed rapidly from 50 kW to 1,800 kW over the past 25 years, with 3.0- to 7.5-MW turbines expected in the next 5 years. The premise of this study is simple: The rapid growth of wind turbine power ratings and the corresponding growth in turbine electrical generation systems and associated controls are quickly making low-voltage (LV) electrical design approaches cost-ineffective. This report provides design detail and compares the cost of energy (COE) between commercial LV-class wind power machines and emerging medium-voltage (MV)-class multi-megawatt wind technology. The key finding is that a 2.5% reductionmore » in the COE can be achieved by moving from LV to MV systems. This is a conservative estimate, with a 3% to 3.5% reduction believed to be attainable once purchase orders to support a 250-turbine/year production level are placed. This evaluation considers capital costs as well as installation, maintenance, and training requirements for wind turbine maintenance personnel. Subsystems investigated include the generator, pendant cables, variable-speed converter, and padmount transformer with switchgear. Both current-source and voltage-source converter/inverter MV topologies are compared against their low-voltage, voltage-source counterparts at the 3.0-, 5.0-, and 7.5-MW levels.« less

Development and validation of rapid multiresidue and multi-class analysis for antibiotics and anthelmintics in feed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry.

PubMed

Robert, Christelle; Brasseur, Pierre-Yves; Dubois, Michel; Delahaut, Philippe; Gillard, Nathalie

2016-08-01

A new multi-residue method for the analysis of veterinary drugs, namely amoxicillin, chlortetracycline, colistins A and B, doxycycline, fenbendazole, flubendazole, ivermectin, lincomycin, oxytetracycline, sulfadiazine, tiamulin, tilmicosin and trimethoprim, was developed and validated for feed. After acidic extraction, the samples were centrifuged, purified by SPE and analysed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry. Quantitative validation was done in accordance with the guidelines laid down in European Commission Decision 2002/657/CE. Matrix-matched calibration with internal standards was used to reduce matrix effects. The target level was set at the authorised carryover level (1%) and validation levels were set at 0.5%, 1% and 1.5%. Method performances were evaluated by the following parameters: linearity (0.986 < R(2) < 0.999), precision (repeatability < 12.4% and reproducibility < 14.0%), accuracy (89% < recovery < 107%), sensitivity, decision limit (CCα), detection capability (CCβ), selectivity and expanded measurement uncertainty (k = 2).This method has been used successfully for three years for routine monitoring of antibiotic residues in feeds during which period 20% of samples were found to exceed the 1% authorised carryover limit and were deemed non-compliant.

Use of three-point taper systems in timber cruising

Treesearch

James W. Flewelling; Richard L. Ernst; Lawrence M. Raynes

2000-01-01

Tree volumes and profiles are often estimated as functions of total height and DBH. Alternative estimators include form-class methods, importance sampling, the centroid method, and multi-point profile (taper) estimation systems; all of these require some measurement or estimate of upper stem diameters. The multi-point profile system discussed here allows for upper stem...

Coordination of fractional-order nonlinear multi-agent systems via distributed impulsive control

NASA Astrophysics Data System (ADS)

Ma, Tiedong; Li, Teng; Cui, Bing

2018-01-01

The coordination of fractional-order nonlinear multi-agent systems via distributed impulsive control method is studied in this paper. Based on the theory of impulsive differential equations, algebraic graph theory, Lyapunov stability theory and Mittag-Leffler function, two novel sufficient conditions for achieving the cooperative control of a class of fractional-order nonlinear multi-agent systems are derived. Finally, two numerical simulations are verified to illustrate the effectiveness and feasibility of the proposed method.

Novel Virtual Screening Approach for the Discovery of Human Tyrosinase Inhibitors

PubMed Central

Ai, Ni; Welsh, William J.; Santhanam, Uma; Hu, Hong; Lyga, John

2014-01-01

Tyrosinase is the key enzyme involved in the human pigmentation process, as well as the undesired browning of fruits and vegetables. Compounds inhibiting tyrosinase catalytic activity are an important class of cosmetic and dermatological agents which show high potential as depigmentation agents used for skin lightening. The multi-step protocol employed for the identification of novel tyrosinase inhibitors incorporated the Shape Signatures computational algorithm for rapid screening of chemical libraries. This algorithm converts the size and shape of a molecule, as well its surface charge distribution and other bio-relevant properties, into compact histograms (signatures) that lend themselves to rapid comparison between molecules. Shape Signatures excels at scaffold hopping across different chemical families, which enables identification of new actives whose molecular structure is distinct from other known actives. Using this approach, we identified a novel class of depigmentation agents that demonstrated promise for skin lightening product development. PMID:25426625

Novel virtual screening approach for the discovery of human tyrosinase inhibitors.

PubMed

Ai, Ni; Welsh, William J; Santhanam, Uma; Hu, Hong; Lyga, John

2014-01-01

Tyrosinase is the key enzyme involved in the human pigmentation process, as well as the undesired browning of fruits and vegetables. Compounds inhibiting tyrosinase catalytic activity are an important class of cosmetic and dermatological agents which show high potential as depigmentation agents used for skin lightening. The multi-step protocol employed for the identification of novel tyrosinase inhibitors incorporated the Shape Signatures computational algorithm for rapid screening of chemical libraries. This algorithm converts the size and shape of a molecule, as well its surface charge distribution and other bio-relevant properties, into compact histograms (signatures) that lend themselves to rapid comparison between molecules. Shape Signatures excels at scaffold hopping across different chemical families, which enables identification of new actives whose molecular structure is distinct from other known actives. Using this approach, we identified a novel class of depigmentation agents that demonstrated promise for skin lightening product development.

Heterogeneous Multi-Metric Learning for Multi-Sensor Fusion

DTIC Science & Technology

2011-07-01

distance”. One of the most widely used methods is the k-nearest neighbor ( KNN ) method [4], which labels an input data sample to be the class with majority...despite of its simplicity, it can be an effective candidate and can be easily extended to handle multiple sensors. Distance based method such as KNN relies...Neighbor (LMNN) method [21] which will be briefly reviewed in the sequel. LMNN method tries to learn an optimal metric specifically for KNN classifier. The

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Patterns of Adolescent Sexual Behavior Predicting Young Adult Sexually Transmitted Infections: A Latent Class Analysis Approach

PubMed Central

Vasilenko, Sara A.; Kugler, Kari C.; Butera, Nicole M.; Lanza, Stephanie T.

2014-01-01

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity. PMID:24449152

Patterns of adolescent sexual behavior predicting young adult sexually transmitted infections: a latent class analysis approach.

PubMed

Vasilenko, Sara A; Kugler, Kari C; Butera, Nicole M; Lanza, Stephanie T

2015-04-01

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity.

Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.

PubMed

Li, Jinyan; Fong, Simon; Sung, Yunsick; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2016-01-01

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

A multi-temporal analysis approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.

2012-06-01

Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.

exprso: an R-package for the rapid implementation of machine learning algorithms.

PubMed

Quinn, Thomas; Tylee, Daniel; Glatt, Stephen

2016-01-01

Machine learning plays a major role in many scientific investigations. However, non-expert programmers may struggle to implement the elaborate pipelines necessary to build highly accurate and generalizable models. We introduce exprso , a new R package that is an intuitive machine learning suite designed specifically for non-expert programmers. Built initially for the classification of high-dimensional data, exprso uses an object-oriented framework to encapsulate a number of common analytical methods into a series of interchangeable modules. This includes modules for feature selection, classification, high-throughput parameter grid-searching, elaborate cross-validation schemes (e.g., Monte Carlo and nested cross-validation), ensemble classification, and prediction. In addition, exprso also supports multi-class classification (through the 1-vs-all generalization of binary classifiers) and the prediction of continuous outcomes.

Multi-echo acquisition

PubMed Central

Posse, Stefan

2011-01-01

The rapid development of fMRI was paralleled early on by the adaptation of MR spectroscopic imaging (MRSI) methods to quantify water relaxation changes during brain activation. This review describes the evolution of multi-echo acquisition from high-speed MRSI to multi-echo EPI and beyond. It highlights milestones in the development of multi-echo acquisition methods, such as the discovery of considerable gains in fMRI sensitivity when combining echo images, advances in quantification of the BOLD effect using analytical biophysical modeling and interleaved multi-region shimming. The review conveys the insight gained from combining fMRI and MRSI methods and concludes with recent trends in ultra-fast fMRI, which will significantly increase temporal resolution of multi-echo acquisition. PMID:22056458

Joint Geophysical Inversion With Multi-Objective Global Optimization Methods

NASA Astrophysics Data System (ADS)

Lelievre, P. G.; Bijani, R.; Farquharson, C. G.

2015-12-01

Pareto multi-objective global optimization (PMOGO) methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. We are applying PMOGO methods to three classes of inverse problems. The first class are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The second class of problems are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the third class we consider a fundamentally different type of inversion in which a model comprises wireframe surfaces representing contacts between rock units; the physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. This third class of problem is essentially a geometry inversion, which can be used to recover the unknown geometry of a target body or to investigate the viability of a proposed Earth model. Joint inversion is greatly simplified for the latter two problem classes because no additional mathematical coupling measure is required in the objective function. PMOGO methods can solve numerically complicated problems that could not be solved with standard descent-based local minimization methods. This includes the latter two classes of problems mentioned above. There are significant increases in the computational requirements when PMOGO methods are used but these can be ameliorated using parallelization and problem dimension reduction strategies.

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

NASA Astrophysics Data System (ADS)

Hagensieker, Ron; Roscher, Ribana; Rosentreter, Johannes; Jakimow, Benjamin; Waske, Björn

2017-12-01

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.

Comparison of microleakage of Scotchbond™ Universal Adhesive with methacrylate resin in Class V restorations by two methods: Swept source optical coherence tomography and dye penetration

PubMed Central

Kermanshah, Hamid; Khorsandian, Hossein

2017-01-01

Background: One of the most important factors in restoration failure is microleakage at the restoration interface. Furthermore, a new generation of bonding, Scotchbond Universal (multi-mode adhesive), has been introduced to facilitate the bonding steps. The aim of this study was to compare the microleakage of Class V cavities restored using Scotchbond™ Universal with Scotchbond Multi-Purpose in two procedures. Materials and Methods: Eighteen freshly extracted human molars were used in this study. Thirty-six standardized Class V cavities were prepared on the buccal and lingual surfaces. The teeth were divided into three groups: (1) Group A: Scotchbond Universal with “self-etching” procedure and nanohybrid composite Filtek Z350. (2) Group B: Scotchbond Universal with “total etching” procedure and Filtek Z350. (3) Group C: Scotchbond Multi-Purpose and Filtek Z350. Microleakage at enamel and dentinal margins was evaluated after thermocycling under 5000 cycles by two methods of microleakage assay: swept source optical coherence tomography (OCT) and dye penetration. Wilcoxon's signed-rank test and Kruskal–Wallis test were used to analyze microleakage. Results: In silver nitrate dye penetration method, group A exhibited the minimum microleakage at dentin margins and group C exhibited the minimum microleakage at enamel margins (P < 0.05). Furthermore, in OCT method, group C demonstrated the minimum microleakage at enamel margins (P = 0.047), with no difference in the microleakage rate at dentin margins. Conclusion: Scotchbond Universal with “self-etching” procedure at dentin margin exhibited more acceptable performance compared to the Scotchbond Multi-Purpose with the two methods. PMID:28928782

Using multi-class queuing network to solve performance models of e-business sites.

PubMed

Zheng, Xiao-ying; Chen, De-ren

2004-01-01

Due to e-business's variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

An evil face? Verbal evaluative multi-CS conditioning enhances face-evoked mid-latency magnetoencephalographic responses

PubMed Central

Junghöfer, Markus; Rehbein, Maimu Alissa; Maitzen, Julius; Schindler, Sebastian

2017-01-01

Abstract Humans have a remarkable capacity for rapid affective learning. For instance, using first-order US such as odors or electric shocks, magnetoencephalography (MEG) studies of multi-CS conditioning demonstrate enhanced early (<150 ms) and mid-latency (150–300 ms) visual evoked responses to affectively conditioned faces, together with changes in stimulus evaluation. However, particularly in social contexts, human affective learning is often mediated by language, a class of complex higher-order US. To elucidate mechanisms of this type of learning, we investigate how face processing changes following verbal evaluative multi-CS conditioning. Sixty neutral expression male faces were paired with phrases about aversive crimes (30) or neutral occupations (30). Post conditioning, aversively associated faces evoked stronger magnetic fields in a mid-latency interval between 220 and 320 ms, localized primarily in left visual cortex. Aversively paired faces were also rated as more arousing and more unpleasant, evaluative changes occurring both with and without contingency awareness. However, no early MEG effects were found, implying that verbal evaluative conditioning may require conceptual processing and does not engage rapid, possibly sub-cortical, pathways. Results demonstrate the efficacy of verbal evaluative multi-CS conditioning and indicate both common and distinct neural mechanisms of first- and higher-order multi-CS conditioning, thereby informing theories of associative learning. PMID:28008078

An evil face? Verbal evaluative multi-CS conditioning enhances face-evoked mid-latency magnetoencephalographic responses.

PubMed

Junghöfer, Markus; Rehbein, Maimu Alissa; Maitzen, Julius; Schindler, Sebastian; Kissler, Johanna

2017-04-01

Humans have a remarkable capacity for rapid affective learning. For instance, using first-order US such as odors or electric shocks, magnetoencephalography (MEG) studies of multi-CS conditioning demonstrate enhanced early (<150 ms) and mid-latency (150-300 ms) visual evoked responses to affectively conditioned faces, together with changes in stimulus evaluation. However, particularly in social contexts, human affective learning is often mediated by language, a class of complex higher-order US. To elucidate mechanisms of this type of learning, we investigate how face processing changes following verbal evaluative multi-CS conditioning. Sixty neutral expression male faces were paired with phrases about aversive crimes (30) or neutral occupations (30). Post conditioning, aversively associated faces evoked stronger magnetic fields in a mid-latency interval between 220 and 320 ms, localized primarily in left visual cortex. Aversively paired faces were also rated as more arousing and more unpleasant, evaluative changes occurring both with and without contingency awareness. However, no early MEG effects were found, implying that verbal evaluative conditioning may require conceptual processing and does not engage rapid, possibly sub-cortical, pathways. Results demonstrate the efficacy of verbal evaluative multi-CS conditioning and indicate both common and distinct neural mechanisms of first- and higher-order multi-CS conditioning, thereby informing theories of associative learning. © The Author (2016). Published by Oxford University Press.

Multi-class segmentation of neuronal electron microscopy images using deep learning

NASA Astrophysics Data System (ADS)

Khobragade, Nivedita; Agarwal, Chirag

2018-03-01

Study of connectivity of neural circuits is an essential step towards a better understanding of functioning of the nervous system. With the recent improvement in imaging techniques, high-resolution and high-volume images are being generated requiring automated segmentation techniques. We present a pixel-wise classification method based on Bayesian SegNet architecture. We carried out multi-class segmentation on serial section Transmission Electron Microscopy (ssTEM) images of Drosophila third instar larva ventral nerve cord, labeling the four classes of neuron membranes, neuron intracellular space, mitochondria and glia / extracellular space. Bayesian SegNet was trained using 256 ssTEM images of 256 x 256 pixels and tested on 64 different ssTEM images of the same size, from the same serial stack. Due to high class imbalance, we used a class-balanced version of Bayesian SegNet by re-weighting each class based on their relative frequency. We achieved an overall accuracy of 93% and a mean class accuracy of 88% for pixel-wise segmentation using this encoder-decoder approach. On evaluating the segmentation results using similarity metrics like SSIM and Dice Coefficient, we obtained scores of 0.994 and 0.886 respectively. Additionally, we used the network trained using the 256 ssTEM images of Drosophila third instar larva for multi-class labeling of ISBI 2012 challenge ssTEM dataset.

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

DTIC Science & Technology

2010-10-28

rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of...analyze toxicological mechanisms for two military- unique explosive compounds 2,4,6-trinitrotolune (TNT) and 1,3,5- trinitro-1,3,5-triazacyclohexane...also known as Royal Demolition eXplosive or RDX) [7,8]. These two compounds exhibit distinctive toxicological properties that are accompanied by

Multi-step high-throughput conjugation platform for the development of antibody-drug conjugates.

PubMed

Andris, Sebastian; Wendeler, Michaela; Wang, Xiangyang; Hubbuch, Jürgen

2018-07-20

Antibody-drug conjugates (ADCs) form a rapidly growing class of biopharmaceuticals which attracts a lot of attention throughout the industry due to its high potential for cancer therapy. They combine the specificity of a monoclonal antibody (mAb) and the cell-killing capacity of highly cytotoxic small molecule drugs. Site-specific conjugation approaches involve a multi-step process for covalent linkage of antibody and drug via a linker. Despite the range of parameters that have to be investigated, high-throughput methods are scarcely used so far in ADC development. In this work an automated high-throughput platform for a site-specific multi-step conjugation process on a liquid-handling station is presented by use of a model conjugation system. A high-throughput solid-phase buffer exchange was successfully incorporated for reagent removal by utilization of a batch cation exchange step. To ensure accurate screening of conjugation parameters, an intermediate UV/Vis-based concentration determination was established including feedback to the process. For conjugate characterization, a high-throughput compatible reversed-phase chromatography method with a runtime of 7 min and no sample preparation was developed. Two case studies illustrate the efficient use for mapping the operating space of a conjugation process. Due to the degree of automation and parallelization, the platform is capable of significantly reducing process development efforts and material demands and shorten development timelines for antibody-drug conjugates. Copyright © 2018 Elsevier B.V. All rights reserved.

An Active Learning Approach to Teach Advanced Multi-Predictor Modeling Concepts to Clinicians

ERIC Educational Resources Information Center

Samsa, Gregory P.; Thomas, Laine; Lee, Linda S.; Neal, Edward M.

2012-01-01

Clinicians have characteristics--high scientific maturity, low tolerance for symbol manipulation and programming, limited time outside of class--that limit the effectiveness of traditional methods for teaching multi-predictor modeling. We describe an active-learning based approach that shows particular promise for accommodating these…

77 FR 73498 - Self-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Notice of Filing and...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-10

... Effectiveness of a Proposed Rule Change To Amend Its Rule Related to Multi-Class Broad- Based Index Option... Rule Change The Exchange proposes to amend its rule related to multi-class broad-based index option... is to (i) clarify that the term ``Multi-Class Broad-Based Index Option Spread Order (Multi-Class...

Analytical method for fast screening and confirmation of multi-class veterinary drug residues in fish and shrimp by LC-MS/MS.

PubMed

Kim, Junghyun; Suh, Joon Hyuk; Cho, Hyun-Deok; Kang, Wonjae; Choi, Yong Seok; Han, Sang Beom

2016-01-01

A multi-class, multi-residue analytical method based on LC-MS/MS detection was developed for the screening and confirmation of 28 veterinary drug and metabolite residues in flatfish, shrimp and eel. The chosen veterinary drugs are prohibited or unauthorised compounds in Korea, which were categorised into various chemical classes including nitroimidazoles, benzimidazoles, sulfones, quinolones, macrolides, phenothiazines, pyrethroids and others. To achieve fast and simultaneous extraction of various analytes, a simple and generic liquid extraction procedure using EDTA-ammonium acetate buffer and acetonitrile, without further clean-up steps, was applied to sample preparation. The final extracts were analysed by ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). The method was validated for each compound in each matrix at three different concentrations (5, 10 and 20 ng g(-1)) in accordance with Codex guidelines (CAC/GL 71-2009). For most compounds, the recoveries were in the range of 60-110%, and precision, expressed as the relative standard deviation (RSD), was in the range of 5-15%. The detection capabilities (CCβs) were below or equal to 5 ng g(-1), which indicates that the developed method is sufficient to detect illegal fishery products containing the target compounds above the residue limit (10 ng g(-1)) of the new regulatory system (Positive List System - PLS).

Consistency of clinical biomechanical measures between three different institutions: implications for multi-center biomechanical and epidemiological research.

PubMed

Myer, Gregory D; Wordeman, Samuel C; Sugimoto, Dai; Bates, Nathaniel A; Roewer, Benjamin D; Medina McKeon, Jennifer M; DiCesare, Christopher A; Di Stasi, Stephanie L; Barber Foss, Kim D; Thomas, Staci M; Hewett, Timothy E

2014-05-01

Multi-center collaborations provide a powerful alternative to overcome the inherent limitations to single-center investigations. Specifically, multi-center projects can support large-scale prospective, longitudinal studies that investigate relatively uncommon outcomes, such as anterior cruciate ligament injury. This project was conceived to assess within- and between-center reliability of an affordable, clinical nomogram utilizing two-dimensional video methods to screen for risk of knee injury. The authors hypothesized that the two-dimensional screening methods would provide good-to-excellent reliability within and between institutions for assessment of frontal and sagittal plane biomechanics. Nineteen female, high school athletes participated. Two-dimensional video kinematics of the lower extremity during a drop vertical jump task were collected on all 19 study participants at each of the three facilities. Within-center and between-center reliability were assessed with intra- and inter-class correlation coefficients. Within-center reliability of the clinical nomogram variables was consistently excellent, but between-center reliability was fair-to-good. Within-center intra-class correlation coefficient for all nomogram variables combined was 0.98, while combined between-center inter-class correlation coefficient was 0.63. Injury risk screening protocols were reliable within and repeatable between centers. These results demonstrate the feasibility of multi-site biomechanical studies and establish a framework for further dissemination of injury risk screening algorithms. Specifically, multi-center studies may allow for further validation and optimization of two-dimensional video screening tools. 2b.

LORENE: Spectral methods differential equations solver

NASA Astrophysics Data System (ADS)

Gourgoulhon, Eric; Grandclément, Philippe; Marck, Jean-Alain; Novak, Jérôme; Taniguchi, Keisuke

2016-08-01

LORENE (Langage Objet pour la RElativité NumériquE) solves various problems arising in numerical relativity, and more generally in computational astrophysics. It is a set of C++ classes and provides tools to solve partial differential equations by means of multi-domain spectral methods. LORENE classes implement basic structures such as arrays and matrices, but also abstract mathematical objects, such as tensors, and astrophysical objects, such as stars and black holes.

Analysis of Rapid Multi-Focal Zone ARFI Imaging

PubMed Central

Rosenzweig, Stephen; Palmeri, Mark; Nightingale, Kathryn

2015-01-01

Acoustic radiation force impulse (ARFI) imaging has shown promise for visualizing structure and pathology within multiple organs; however, because the contrast depends on the push beam excitation width, image quality suffers outside of the region of excitation. Multi-focal zone ARFI imaging has previously been used to extend the region of excitation (ROE), but the increased acquisition duration and acoustic exposure have limited its utility. Supersonic shear wave imaging has previously demonstrated that through technological improvements in ultrasound scanners and power supplies, it is possible to rapidly push at multiple locations prior to tracking displacements, facilitating extended depth of field shear wave sources. Similarly, ARFI imaging can utilize these same radiation force excitations to achieve tight pushing beams with a large depth of field. Finite element method simulations and experimental data are presented demonstrating that single- and rapid multi-focal zone ARFI have comparable image quality (less than 20% loss in contrast), but the multi-focal zone approach has an extended axial region of excitation. Additionally, as compared to single push sequences, the rapid multi-focal zone acquisitions improve the contrast to noise ratio by up to 40% in an example 4 mm diameter lesion. PMID:25643078

Eco-friendly LC-MS/MS method for analysis of multi-class micropollutants in tap, fountain, and well water from northern Portugal.

PubMed

Barbosa, Marta O; Ribeiro, Ana R; Pereira, Manuel F R; Silva, Adrián M T

2016-11-01

Organic micropollutants present in drinking water (DW) may cause adverse effects for public health, and so reliable analytical methods are required to detect these pollutants at trace levels in DW. This work describes the first green analytical methodology for multi-class determination of 21 pollutants in DW: seven pesticides, an industrial compound, 12 pharmaceuticals, and a metabolite (some included in Directive 2013/39/EU or Decision 2015/495/EU). A solid-phase extraction procedure followed by ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (offline SPE-UHPLC-MS/MS) method was optimized using eco-friendly solvents, achieving detection limits below 0.20 ng L -1 . The validated analytical method was successfully applied to DW samples from different sources (tap, fountain, and well waters) from different locations in the north of Portugal, as well as before and after bench-scale UV and ozonation experiments in spiked tap water samples. Thirteen compounds were detected, many of them not regulated yet, in the following order of frequency: diclofenac > norfluoxetine > atrazine > simazine > warfarin > metoprolol > alachlor > chlorfenvinphos > trimethoprim > clarithromycin ≈ carbamazepine ≈ PFOS > citalopram. Hazard quotients were also estimated for the quantified substances and suggested no adverse effects to humans. Graphical Abstract Occurrence and removal of multi-class micropollutants in drinking water, analyzed by an eco-friendly LC-MS/MS method.

Use of multi-frequency, multi-polarization, multi-angle airborne radars for class discrimination in a southern temperature forest

NASA Technical Reports Server (NTRS)

Mehta, N. C.

1984-01-01

The utility of radar scatterometers for discrimination and characterization of natural vegetation was investigated. Backscatter measurements were acquired with airborne multi-frequency, multi-polarization, multi-angle radar scatterometers over a test site in a southern temperate forest. Separability between ground cover classes was studied using a two-class separability measure. Very good separability is achieved between most classes. Longer wavelength is useful in separating trees from non-tree classes, while shorter wavelength and cross polarization are helpful for discrimination among tree classes. Using the maximum likelihood classifier, 50% overall classification accuracy is achieved using a single, short-wavelength scatterometer channel. Addition of multiple incidence angles and another radar band improves classification accuracy by 20% and 50%, respectively, over the single channel accuracy. Incorporation of a third radar band seems redundant for vegetation classification. Vertical transmit polarization is critically important for all classes.

A novel framework for change detection in bi-temporal polarimetric SAR images

NASA Astrophysics Data System (ADS)

Pirrone, Davide; Bovolo, Francesca; Bruzzone, Lorenzo

2016-10-01

Last years have seen relevant increase of polarimetric Synthetic Aperture Radar (SAR) data availability, thanks to satellite sensors like Sentinel-1 or ALOS-2 PALSAR-2. The augmented information lying in the additional polarimetric channels represents a possibility for better discriminate different classes of changes in change detection (CD) applications. This work aims at proposing a framework for CD in multi-temporal multi-polarization SAR data. The framework includes both a tool for an effective visual representation of the change information and a method for extracting the multiple-change information. Both components are designed to effectively handle the multi-dimensionality of polarimetric data. In the novel representation, multi-temporal intensity SAR data are employed to compute a polarimetric log-ratio. The multitemporal information of the polarimetric log-ratio image is represented in a multi-dimensional features space, where changes are highlighted in terms of magnitude and direction. This representation is employed to design a novel unsupervised multi-class CD approach. This approach considers a sequential two-step analysis of the magnitude and the direction information for separating non-changed and changed samples. The proposed approach has been validated on a pair of Sentinel-1 data acquired before and after the flood in Tamil-Nadu in 2015. Preliminary results demonstrate that the representation tool is effective and that the use of polarimetric SAR data is promising in multi-class change detection applications.

Pedagogical Practices: The Case of Multi-Class Teaching in Fiji Primary School

ERIC Educational Resources Information Center

Lingam, Govinda I.

2007-01-01

Multi-class teaching is a common phenomenon in small schools not only in Fiji, but also in many countries. The aim of the present study was to determine the teaching styles adopted by teachers in the context of multi-class teaching. A qualitative case study research design was adopted. This included a school with multi-class teaching as the norm.…

A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

NASA Astrophysics Data System (ADS)

Wu, Wei; Zhao, Dewei; Zhang, Huan

2015-12-01

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

Dual-responsive and Multi-functional Plasmonic Hydrogel Valves and Biomimetic Architectures Formed with Hydrogel and Gold Nanocolloids

PubMed Central

Song, Ji Eun; Cho, Eun Chul

2016-01-01

We present a straightforward approach with high moldability for producing dual-responsive and multi-functional plasmonic hydrogel valves and biomimetic architectures that reversibly change volumes and colors in response to temperature and ion variations. Heating of a mixture of hybrid colloids (gold nanoparticles assembled on a hydrogel colloid) and hydrogel colloids rapidly induces (within 30 min) the formation of hydrogel architectures resembling mold shapes (cylinder, fish, butterfly). The biomimetic fish and butterfly display reversible changes in volumes and colors with variations of temperature and ionic conditions in aqueous solutions. The cylindrical plasmonic valves installed in flow tubes rapidly control water flow rate in on-off manner by responding to these stimuli. They also report these changes in terms of their colors. Therefore, the approach presented here might be helpful in developing new class of biomimetic and flow control systems where liquid conditions should be visually notified (e.g., glucose or ion concentration changes). PMID:27703195

Biodegradable and adjustable sol-gel glass based hybrid scaffolds from multi-armed oligomeric building blocks.

PubMed

Kascholke, Christian; Hendrikx, Stephan; Flath, Tobias; Kuzmenka, Dzmitry; Dörfler, Hans-Martin; Schumann, Dirk; Gressenbuch, Mathias; Schulze, F Peter; Schulz-Siegmund, Michaela; Hacker, Michael C

2017-11-01

Biodegradability is a crucial characteristic to improve the clinical potential of sol-gel-derived glass materials. To this end, a set of degradable organic/inorganic class II hybrids from a tetraethoxysilane(TEOS)-derived silica sol and oligovalent cross-linker oligomers containing oligo(d,l-lactide) domains was developed and characterized. A series of 18 oligomers (Mn: 1100-3200Da) with different degrees of ethoxylation and varying length of oligoester units was established and chemical composition was determined. Applicability of an established indirect rapid prototyping method enabled fabrication of a total of 85 different hybrid scaffold formulations from 3-isocyanatopropyltriethoxysilane-functionalized macromers. In vitro degradation was analyzed over 12months and a continuous linear weight loss (0.2-0.5wt%/d) combined with only moderate material swelling was detected which was controlled by oligo(lactide) content and matrix hydrophilicity. Compressive strength (2-30MPa) and compressive modulus (44-716MPa) were determined and total content, oligo(ethylene oxide) content, oligo(lactide) content and molecular weight of the oligomeric cross-linkers as well as material porosity were identified as the main factors determining hybrid mechanics. Cytocompatibility was assessed by cell culture experiments with human adipose tissue-derived stem cells (hASC). Cell migration into the entire scaffold pore network was indicated and continuous proliferation over 14days was found. ALP activity linearly increased over 2weeks indicating osteogenic differentiation. The presented glass-based hybrid concept with precisely adjustable material properties holds promise for regenerative purposes. Adaption of degradation kinetics toward physiological relevance is still an unmet challenge of (bio-)glass engineering. We therefore present a glass-derived hybrid material with adjustable degradation. A flexible design concept based on degradable multi-armed oligomers was combined with an established indirect rapid prototyping method to produce a systematic set of porous sol-gel-derived class II hybrid scaffolds. Mechanical properties in the range of cancellous bone were narrowly controlled by hybrid composition. The oligoester introduction resulted in significantly increased compressive moduli. Cytocompatible hybrids degraded in physiologically relevant time frames and a promising linear and controllable weight loss profile was found. To our knowledge, our degradation study represents the most extensive long-term investigation of sol-gel-derived class II hybrids. Due to the broad adjustability of material properties, our concept offers potential for engineering of biodegradable hybrid materials for versatile applications. Copyright © 2017. Published by Elsevier Ltd.

Estimation of soil erosion risk within an important agricultural sub-watershed in Bursa, Turkey, in relation to rapid urbanization.

PubMed

Ozsoy, Gokhan; Aksoy, Ertugrul

2015-07-01

This paper integrates the Revised Universal Soil Loss Equation (RUSLE) with a GIS model to investigate the spatial distribution of annual soil loss and identify areas of soil erosion risk in the Uluabat sub-watershed, an important agricultural site in Bursa Province, Turkey. The total soil loss from water erosion was 473,274 Mg year(-1). Accordingly, 60.3% of the surveyed area was classified into a very low erosion risk class while 25.7% was found to be in high and severe erosion risk classes. Soil loss had a close relationship with land use and topography. The most severe erosion risk typically occurs on ridges and steep slopes where agriculture, degraded forest, and shrubs are the main land uses and cover types. Another goal of this study was to use GIS to reveal the multi-year urbanization status caused by rapid urbanization that constitutes another soil erosion risk in this area. Urbanization has increased by 57.7% and the most areal change was determined in class I lands at a rate of 80% over 25 years. Urbanization was identified as one of the causes of excessive soil loss in the study area.

Establishing and performing targeted multi-residue analysis for lipid mediators and fatty acids in small clinical plasma samples.

USDA-ARS?s Scientific Manuscript database

LC-MS/MS and GC-MS based targeted metabolomics is typically conducted by analyzing and quantifying a cascade of metabolites with methods specifically developed for the metabolite class. Here we describe an approach for the development of multi-residue analytical profiles, calibration standards, and ...

A mass spectrometry primer for mass spectrometry imaging

PubMed Central

Rubakhin, Stanislav S.; Sweedler, Jonathan V.

2011-01-01

Mass spectrometry imaging (MSI), a rapidly growing subfield of chemical imaging, employs mass spectrometry (MS) technologies to create single- and multi-dimensional localization maps for a variety of atoms and molecules. Complimentary to other imaging approaches, MSI provides high chemical specificity and broad analyte coverage. This powerful analytical toolset is capable of measuring the distribution of many classes of inorganics, metabolites, proteins and pharmaceuticals in chemically and structurally complex biological specimens in vivo, in vitro, and in situ. The MSI approaches highlighted in this Methods in Molecular Biology volume provide flexibility of detection, characterization, and identification of multiple known and unknown analytes. The goal of this chapter is to introduce investigators who may be unfamiliar with MS to the basic principles of the mass spectrometric approaches as used in MSI. In addition to guidelines for choosing the most suitable MSI method for specific investigations, cross-references are provided to the chapters in this volume that describe the appropriate experimental protocols. PMID:20680583

A validation study of a rapid field-based rating system for discriminating among flow permanence classes of headwater streams in South Carolina

EPA Science Inventory

Rapid field-based protocols for classifying flow permanence of headwater streams are needed to inform timely regulatory decisions. Such an existing method was developed for and has been used in North Carolina since 1997. The method uses ordinal scoring of 26 geomorphology, hydr...

Trajectory classes of cannabis use and heavy drinking among rural African American adolescents: multi-level predictors of class membership.

PubMed

Barton, Allen W; Brody, Gene H; Zapolski, Tamika C B; Goings, Trenette C; Kogan, Steven M; Windle, Michael; Yu, Tianyi

2018-02-17

To inform research on the etiology and prevention of substance use among rural African American youth by (a) identifying developmental trajectory classes of cannabis use and heavy drinking across adolescence and young adulthood and (b) examining associations between trajectory class membership and multi-level assessments of risk factors. A prospective study spanning 9 years with assessments of cannabis use and heavy drinking, the catecholamines epinephrine and norepinephrine, perceived stress and psychosocial risk factors. Rural communities in the southeastern United States. African American youth (n = 518). Participants were assessed for cannabis use and heavy drinking at seven assessments beginning at 16 years of age and continuing to 25 years of age. At age 19, participants provided overnight urine voids that were assayed for catecholamines, a biological marker of life stress resulting from sympathetic nervous system activation. At ages 16 and 19, participants provided information on malleable psychosocial risk factors. Latent class growth models revealed three distinct trajectory classes for cannabis use and for heavy drinking. Higher levels of circulating stress hormones and perceived stress were associated with classes reporting greater substance use over time (all Ps < 0.05). A composite of selected risk factors discriminated class membership (all Ps < 0.05). Trajectory classes characterized by rapid usage increases in early adulthood exhibited the greatest increase in deviant peer affiliations between ages 16 and 19 years. Rural African American youth's cannabis use and heavy drinking across adolescence and young adulthood demonstrate distinct developmental courses; a small number of risk factors and measures of biological and perceived stress differentiate class membership prognostically. Variability over time in these measures, specifically an increase in deviant peer affiliation, may help to account for steep increases in young adulthood. © 2018 Society for the Study of Addiction.

Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures.

PubMed

Cao, Peng; Liu, Xiaoli; Yang, Jinzhu; Zhao, Dazhe; Huang, Min; Zhang, Jian; Zaiane, Osmar

2017-12-01

Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becoming more and more critical and emphasized at the earliest stages. However, the high dimensionality and imbalanced data issues are two major challenges in the study of computer aided AD diagnosis. The greatest limitations of existing dimensionality reduction and over-sampling methods are that they assume a linear relationship between the MRI features (predictor) and the disease status (response). To better capture the complicated but more flexible relationship, we propose a multi-kernel based dimensionality reduction and over-sampling approaches. We combined Marginal Fisher Analysis with ℓ 2,1 -norm based multi-kernel learning (MKMFA) to achieve the sparsity of region-of-interest (ROI), which leads to simultaneously selecting a subset of the relevant brain regions and learning a dimensionality transformation. Meanwhile, a multi-kernel over-sampling (MKOS) was developed to generate synthetic instances in the optimal kernel space induced by MKMFA, so as to compensate for the class imbalanced distribution. We comprehensively evaluate the proposed models for the diagnostic classification (binary class and multi-class classification) including all subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The experimental results not only demonstrate the proposed method has superior performance over multiple comparable methods, but also identifies relevant imaging biomarkers that are consistent with prior medical knowledge. Copyright © 2017 Elsevier Ltd. All rights reserved.

An integrated strategy using UPLC-QTOF-MSE and UPLC-QTOF-MRM (enhanced target) for pharmacokinetics study of wine processed Schisandra Chinensis fructus in rats.

PubMed

Liu, Kuangyi; Song, Yonggui; Liu, Yali; Peng, Mi; Li, Hanyun; Li, Xueliang; Feng, Bingwei; Xu, Pengfei; Su, Dan

2017-05-30

Currently the pharmacokinetic (PK) research of herbal medicines is still limited and facing critical technical challenges on quantitative analysis of multi-components from biological matrices which often accompanied by lacking of authentic standards and low concentration. This present work contributes to the development of an integrated strategy for extensive pharmacokinetics assessments, and a selective and sensitive method independent of authentic standards for multi-components analysis based on the use of ultra-performance liquid chromatography/quadrupole-time-of-flight/MS E (UPLC-TOF-MS E ) and UPLC-TOF-MRM (rnhanced target). Initially, phytochemicals were identified by UPLC-TOF-MS E analysis, subsequently the identified components were matched with authentic standards and pre-classified, and UPLC-QTOF-MRM method optimized and developed. To guarantee reliable results, three rules are necessary: (1) detection with a mass error of less than 5ppm; (2) same class chemical compositions with structural high similarity between analytes with and without authentic reference substance; (3) a matching retention time between TOF-MRM mode and TOF-MS E within 0.2min. The developed and validated method was applied for the simultaneous determination of 12 lignans in rat plasma after administered with wine processed Schisandra Chinensis fructus (WPSCF) extract. Such an approach was found capable of providing extensive pharmacokinetic profiles of multi-components absorbed into blood after oral administrated with WPSCF extract. The results also indicated that significant difference in pharmacokinetics parameters of dibenzocyclooctadiene lignans was observed between schizandrin and gomisin compounds. For lignans, the absorption via gastrointestinal tract were all rapid and maintained relatively long retention time, especially for schisantherin A and schisantherin B with higher plasma exposure. Copyright © 2017 Elsevier B.V. All rights reserved.

The numerical solution of linear multi-term fractional differential equations: systems of equations

NASA Astrophysics Data System (ADS)

Edwards, John T.; Ford, Neville J.; Simpson, A. Charles

2002-11-01

In this paper, we show how the numerical approximation of the solution of a linear multi-term fractional differential equation can be calculated by reduction of the problem to a system of ordinary and fractional differential equations each of order at most unity. We begin by showing how our method applies to a simple class of problems and we give a convergence result. We solve the Bagley Torvik equation as an example. We show how the method can be applied to a general linear multi-term equation and give two further examples.

Addressing multi-label imbalance problem of surgical tool detection using CNN.

PubMed

Sahu, Manish; Mukhopadhyay, Anirban; Szengel, Angelika; Zachow, Stefan

2017-06-01

A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during convolutional neural network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance runtime prediction. Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. The analysis on tool imbalance, backed by the empirical results, indicates the need and superiority of the proposed framework over state-of-the-art techniques.

Freeze-thaw approach: A practical sample preparation strategy for residue analysis of multi-class veterinary drugs in chicken muscle.

PubMed

Zhang, Meiyu; Li, Erfen; Su, Yijuan; Song, Xuqin; Xie, Jingmeng; Zhang, Yingxia; He, Limin

2018-06-01

Seven drugs from different classes, namely, fluoroquinolones (enrofloxacin, ciprofloxacin, sarafloxacin), sulfonamides (sulfadimidine, sulfamonomethoxine), and macrolides (tilmicosin, tylosin), were used as test compounds in chickens by oral administration, a simple extraction step after cryogenic freezing might allow the effective extraction of multi-class veterinary drug residues from minced chicken muscles by mix vortexing. On basis of the optimized freeze-thaw approach, a convenient, selective, and reproducible liquid chromatography with tandem mass spectrometry method was developed. At three spiking levels in blank chicken and medicated chicken muscles, average recoveries of the analytes were in the range of 71-106 and 63-119%, respectively. All the relative standard deviations were <20%. The limits of quantification of analytes were 0.2-5.0 ng/g. Regardless of the chicken levels, there were no significant differences (P > 0.05) in the average contents of almost any of the analytes in medicated chickens between this method and specific methods in the literature for the determination of specific analytes. Finally, the developed method was successfully extended to the monitoring of residues of 55 common veterinary drugs in food animal muscles. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

Evaluation of metabolites extraction strategies for identifying different brain regions and their relationship with alcohol preference and gender difference using NMR metabolomics.

PubMed

Wang, Jie; Zeng, Hao-Long; Du, Hongying; Liu, Zeyuan; Cheng, Ji; Liu, Taotao; Hu, Ting; Kamal, Ghulam Mustafa; Li, Xihai; Liu, Huili; Xu, Fuqiang

2018-03-01

Metabolomics generate a profile of small molecules from cellular/tissue metabolism, which could directly reflect the mechanisms of complex networks of biochemical reactions. Traditional metabolomics methods, such as OPLS-DA, PLS-DA are mainly used for binary class discrimination. Multiple groups are always involved in the biological system, especially for brain research. Multiple brain regions are involved in the neuronal study of brain metabolic dysfunctions such as alcoholism, Alzheimer's disease, etc. In the current study, 10 different brain regions were utilized for comparative studies between alcohol preferring and non-preferring rats, male and female rats respectively. As many classes are involved (ten different regions and four types of animals), traditional metabolomics methods are no longer efficient for showing differentiation. Here, a novel strategy based on the decision tree algorithm was employed for successfully constructing different classification models to screen out the major characteristics of ten brain regions at the same time. Subsequently, this method was also utilized to select the major effective brain regions related to alcohol preference and gender difference. Compared with the traditional multivariate statistical methods, the decision tree could construct acceptable and understandable classification models for multi-class data analysis. Therefore, the current technology could also be applied to other general metabolomics studies involving multi class data. Copyright © 2017 Elsevier B.V. All rights reserved.

A Mixtures-of-Trees Framework for Multi-Label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods. PMID:25927011

Generation of multi annual land use and crop rotation data for regional agro-ecosystem modeling

NASA Astrophysics Data System (ADS)

Waldhoff, G.; Lussem, U.; Sulis, M.; Bareth, G.

2017-12-01

For agro-ecosystem modeling on a regional scale with systems like the Community Land Model (CLM), detailed crop type and crop rotation information on the parcel-level is of key importance. Only with this, accurate assessments of the fluxes associated with the succession of crops and their management are possible. However, sophisticated agro-ecosystem modeling for large regions is only feasible at grid resolutions, which are much coarser than the spatial resolution of modern land use maps (usually ca. 30 m). As a result, much of the original information content of the maps has to be dismissed during resampling. Here we present our mapping approach for the Rur catchment (located in the west of Germany), which was developed to address these demands and issues. We integrated remote sensing and geographic information system (GIS) methods to classify multi temporal images of (e.g.) Landsat, RapidEye and Sentinel-2 to generate annual crop maps for the years 2008-2017 at 15 m spatial resolution (accuracy always ca. 90 %). A key aspect of our method is the consideration of crop phenology for the data selection and the analysis. In a GIS, the annul crop maps were integrated to a crop sequence dataset from which the major crop rotations were derived (based on the 10-years). To retain the multi annual crop succession and crop area information at coarser grid resolutions, cell-based land use fractions, including other land use classes were calculated for each year and for various target cell sizes (1-32 arc seconds). The resulting datasets contain the contribution (in percent) of every land use class to each cell. Our results show that parcels with the major crop types can be differentiated with a high accuracy and on an annual basis. The analysis of the crop sequence data revealed a very large number of different crop rotations, but only relatively few crop rotations cover larger areas. This strong diversity emphasizes the importance of information on crop rotations to reduce uncertainties in agro-ecosystem modeling. Through the combination of the multi annual land use fractions, the resulting datasets additionally inform about land use changes and trends within the coarser grid cells. We see this as a major advantage, because we are able to maintain much more precise land use information when a coarser cell size is used.

Using electroretinograms and multi-model inference to identify spectral classes of photoreceptors and relative opsin expression levels

PubMed Central

2017-01-01

Understanding how individual photoreceptor cells factor in the spectral sensitivity of a visual system is essential to explain how they contribute to the visual ecology of the animal in question. Existing methods that model the absorption of visual pigments use templates which correspond closely to data from thin cross-sections of photoreceptor cells. However, few modeling approaches use a single framework to incorporate physical parameters of real photoreceptors, which can be fused, and can form vertical tiers. Akaike’s information criterion (AICc) was used here to select absorptance models of multiple classes of photoreceptor cells that maximize information, given visual system spectral sensitivity data obtained using extracellular electroretinograms and structural parameters obtained by histological methods. This framework was first used to select among alternative hypotheses of photoreceptor number. It identified spectral classes from a range of dark-adapted visual systems which have between one and four spectral photoreceptor classes. These were the velvet worm, Principapillatus hitoyensis, the branchiopod water flea, Daphnia magna, normal humans, and humans with enhanced S-cone syndrome, a condition in which S-cone frequency is increased due to mutations in a transcription factor that controls photoreceptor expression. Data from the Asian swallowtail, Papilio xuthus, which has at least five main spectral photoreceptor classes in its compound eyes, were included to illustrate potential effects of model over-simplification on multi-model inference. The multi-model framework was then used with parameters of spectral photoreceptor classes and the structural photoreceptor array kept constant. The goal was to map relative opsin expression to visual pigment concentration. It identified relative opsin expression differences for two populations of the bluefin killifish, Lucania goodei. The modeling approach presented here will be useful in selecting the most likely alternative hypotheses of opsin-based spectral photoreceptor classes, using relative opsin expression and extracellular electroretinography. PMID:28740757

Using electroretinograms and multi-model inference to identify spectral classes of photoreceptors and relative opsin expression levels.

PubMed

Lessios, Nicolas

2017-01-01

Understanding how individual photoreceptor cells factor in the spectral sensitivity of a visual system is essential to explain how they contribute to the visual ecology of the animal in question. Existing methods that model the absorption of visual pigments use templates which correspond closely to data from thin cross-sections of photoreceptor cells. However, few modeling approaches use a single framework to incorporate physical parameters of real photoreceptors, which can be fused, and can form vertical tiers. Akaike's information criterion (AIC c ) was used here to select absorptance models of multiple classes of photoreceptor cells that maximize information, given visual system spectral sensitivity data obtained using extracellular electroretinograms and structural parameters obtained by histological methods. This framework was first used to select among alternative hypotheses of photoreceptor number. It identified spectral classes from a range of dark-adapted visual systems which have between one and four spectral photoreceptor classes. These were the velvet worm, Principapillatus hitoyensis , the branchiopod water flea, Daphnia magna , normal humans, and humans with enhanced S-cone syndrome, a condition in which S-cone frequency is increased due to mutations in a transcription factor that controls photoreceptor expression. Data from the Asian swallowtail, Papilio xuthus , which has at least five main spectral photoreceptor classes in its compound eyes, were included to illustrate potential effects of model over-simplification on multi-model inference. The multi-model framework was then used with parameters of spectral photoreceptor classes and the structural photoreceptor array kept constant. The goal was to map relative opsin expression to visual pigment concentration. It identified relative opsin expression differences for two populations of the bluefin killifish, Lucania goodei . The modeling approach presented here will be useful in selecting the most likely alternative hypotheses of opsin-based spectral photoreceptor classes, using relative opsin expression and extracellular electroretinography.

Multi-class multi-residue analysis of veterinary drugs in meat using enhanced matrix removal lipid cleanup and liquid chromatography-tandem mass spectrometry.

PubMed

Zhao, Limian; Lucas, Derick; Long, David; Richter, Bruce; Stevens, Joan

2018-05-11

This study presents the development and validation of a quantitation method for the analysis of multi-class, multi-residue veterinary drugs using lipid removal cleanup cartridges, enhanced matrix removal lipid (EMR-Lipid), for different meat matrices by liquid chromatography tandem mass spectrometry detection. Meat samples were extracted using a two-step solid-liquid extraction followed by pass-through sample cleanup. The method was optimized based on the buffer and solvent composition, solvent additive additions, and EMR-Lipid cartridge cleanup. The developed method was then validated in five meat matrices, porcine muscle, bovine muscle, bovine liver, bovine kidney and chicken liver to evaluate the method performance characteristics, such as absolute recoveries and precision at three spiking levels, calibration curve linearity, limit of quantitation (LOQ) and matrix effect. The results showed that >90% of veterinary drug analytes achieved satisfactory recovery results of 60-120%. Over 97% analytes achieved excellent reproducibility results (relative standard deviation (RSD) < 20%), and the LOQs were 1-5 μg/kg in the evaluated meat matrices. The matrix co-extractive removal efficiency by weight provided by EMR-lipid cartridge cleanup was 42-58% in samples. The post column infusion study showed that the matrix ion suppression was reduced for samples with the EMR-Lipid cartridge cleanup. The reduced matrix ion suppression effect was also confirmed with <15% frequency of compounds with significant quantitative ion suppression (>30%) for all tested veterinary drugs in all of meat matrices. The results showed that the two-step solid-liquid extraction provides efficient extraction for the entire spectrum of veterinary drugs, including the difficult classes such as tetracyclines, beta-lactams etc. EMR-Lipid cartridges after extraction provided efficient sample cleanup with easy streamlined protocol and minimal impacts on analytes recovery, improving method reliability and consistency. Copyright © 2018 Elsevier B.V. All rights reserved.

Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification.

PubMed

Mirza, Bilal; Lin, Zhiping

2016-08-01

In this paper, a meta-cognitive online sequential extreme learning machine (MOS-ELM) is proposed for class imbalance and concept drift learning. In MOS-ELM, meta-cognition is used to self-regulate the learning by selecting suitable learning strategies for class imbalance and concept drift problems. MOS-ELM is the first sequential learning method to alleviate the imbalance problem for both binary class and multi-class data streams with concept drift. In MOS-ELM, a new adaptive window approach is proposed for concept drift learning. A single output update equation is also proposed which unifies various application specific OS-ELM methods. The performance of MOS-ELM is evaluated under different conditions and compared with methods each specific to some of the conditions. On most of the datasets in comparison, MOS-ELM outperforms the competing methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

Chemiluminescence microarrays in analytical chemistry: a critical review.

PubMed

Seidel, Michael; Niessner, Reinhard

2014-09-01

Multi-analyte immunoassays on microarrays and on multiplex DNA microarrays have been described for quantitative analysis of small organic molecules (e.g., antibiotics, drugs of abuse, small molecule toxins), proteins (e.g., antibodies or protein toxins), and microorganisms, viruses, and eukaryotic cells. In analytical chemistry, multi-analyte detection by use of analytical microarrays has become an innovative research topic because of the possibility of generating several sets of quantitative data for different analyte classes in a short time. Chemiluminescence (CL) microarrays are powerful tools for rapid multiplex analysis of complex matrices. A wide range of applications for CL microarrays is described in the literature dealing with analytical microarrays. The motivation for this review is to summarize the current state of CL-based analytical microarrays. Combining analysis of different compound classes on CL microarrays reduces analysis time, cost of reagents, and use of laboratory space. Applications are discussed, with examples from food safety, water safety, environmental monitoring, diagnostics, forensics, toxicology, and biosecurity. The potential and limitations of research on multiplex analysis by use of CL microarrays are discussed in this review.

Effect of Multi Modal Representations on the Critical Thinking Skills of the Fifth Grade Students

ERIC Educational Resources Information Center

Öz, Muhittin; Memis, Esra Kabatas

2018-01-01

The purpose of this study was to explore the effects of multi modal representations within writing to learn activities on students' critical thinking. Mixed method was used. The participants included 32 students 5th grade from elementary school. The groups were randomly selected as a control group and the other class was selected as the…

Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms

ERIC Educational Resources Information Center

Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei

2013-01-01

This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…

Output Feedback Stabilization for a Class of Multi-Variable Bilinear Stochastic Systems with Stochastic Coupling Attenuation

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

Zhang, Qichun; Zhou, Jinglin; Wang, Hong

In this paper, stochastic coupling attenuation is investigated for a class of multi-variable bilinear stochastic systems and a novel output feedback m-block backstepping controller with linear estimator is designed, where gradient descent optimization is used to tune the design parameters of the controller. It has been shown that the trajectories of the closed-loop stochastic systems are bounded in probability sense and the stochastic coupling of the system outputs can be effectively attenuated by the proposed control algorithm. Moreover, the stability of the stochastic systems is analyzed and the effectiveness of the proposed method has been demonstrated using a simulated example.

Multi-class method for determination of veterinary drug residues and other contaminants in infant formula by ultra performance liquid chromatography-tandem mass spectrometry.

PubMed

Zhan, Jia; Zhong, Ying-ying; Yu, Xue-jun; Peng, Jin-feng; Chen, Shubing; Yin, Ju-yi; Zhang, Jia-Jie; Zhu, Yan

2013-06-01

A rapid, simple and generic analytical method which was able to simultaneously determine 220 undesirable chemical residues in infant formula had been developed. The method comprised of extraction with acetonitrile, clean-up by low temperature and water precipitation, and analysis by ultra performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry (UPLC-ESI-MS-MS) using multiple reaction monitoring (MRM) mode. Most fat materials in acetonitrile extract were eliminated by low temperature clean-up. The water precipitation, providing a necessary and supplementary cleanup, could avoid losses of hydrophobic analytes (avermectins, ionophores). Average recoveries for spiked infant formula were in the range from 57% to 147% with associated RSD values between 1% and 28%. For over 80% of the analytes, the recoveries were between 70% and 120% with RSD values in the range of 1-15%. The limits of quantification (LOQs) were from 0.01 to 5 μg/kg, which were usually sufficient to verify the compliance of products with legal tolerances. Application of this method in routine monitoring programs would imply a drastic reduction of both effort and time. Copyright © 2012 Elsevier Ltd. All rights reserved.

Bioinspired Cell-Derived Nanovesicles versus Exosomes as Drug Delivery Systems: a Cost-Effective Alternative.

PubMed

Goh, Wei Jiang; Zou, Shui; Ong, Wei Yi; Torta, Federico; Alexandra, Alvarez Fernandez; Schiffelers, Raymond M; Storm, Gert; Wang, Jiong-Wei; Czarny, Bertrand; Pastorin, Giorgia

2017-10-30

Cell Derived Nanovesicles (CDNs) have been developed from the rapidly expanding field of exosomes, representing a class of bioinspired Drug Delivery Systems (DDS). However, translation to clinical applications is limited by the low yield and multi-step approach in isolating naturally secreted exosomes. Here, we show the first demonstration of a simple and rapid production method of CDNs using spin cups via a cell shearing approach, which offers clear advantages in terms of yield and cost-effectiveness over both traditional exosomes isolation, and also existing CDNs fabrication techniques. The CDNs obtained were of a higher protein yield and showed similarities in terms of physical characterization, protein and lipid analysis to both exosomes and CDNs previously reported in the literature. In addition, we investigated the mechanisms of cellular uptake of CDNs in vitro and their biodistribution in an in vivo mouse tumour model. Colocalization of the CDNs at the tumour site in a cancer mouse model was demonstrated, highlighting the potential for CDNs as anti-cancer strategy. Taken together, the results suggest that CDNs could provide a cost-effective alternative to exosomes as an ideal drug nanocarrier.

[Fast discrimination of edible vegetable oil based on Raman spectroscopy].

PubMed

Zhou, Xiu-Jun; Dai, Lian-Kui; Li, Sheng

2012-07-01

A novel method to fast discriminate edible vegetable oils by Raman spectroscopy is presented. The training set is composed of different edible vegetable oils with known classes. Based on their original Raman spectra, baseline correction and normalization were applied to obtain standard spectra. Two characteristic peaks describing the unsaturated degree of vegetable oil were selected as feature vectors; then the centers of all classes were calculated. For an edible vegetable oil with unknown class, the same pretreatment and feature extraction methods were used. The Euclidian distances between the feature vector of the unknown sample and the center of each class were calculated, and the class of the unknown sample was finally determined by the minimum distance. For 43 edible vegetable oil samples from seven different classes, experimental results show that the clustering effect of each class was more obvious and the class distance was much larger with the new feature extraction method compared with PCA. The above classification model can be applied to discriminate unknown edible vegetable oils rapidly and accurately.

Direct immersion single drop micro-extraction method for multi-class pesticides analysis in mango using GC-MS.

PubMed

Pano-Farias, Norma S; Ceballos-Magaña, Silvia G; Muñiz-Valencia, Roberto; Jurado, Jose M; Alcázar, Ángela; Aguayo-Villarreal, Ismael A

2017-12-15

Due the negative effects of pesticides on environment and human health, more efficient and environmentally friendly methods are needed. In this sense, a simple, fast, free from memory effects and economical direct-immersion single drop micro-extraction (SDME) method and GC-MS for multi-class pesticides determination in mango samples was developed. Sample pre-treatment using ultrasound-assisted solvent extraction and factors affecting the SDME procedure (extractant solvent, drop volume, stirring rate, ionic strength, time, pH and temperature) were optimized using factorial experimental design. This method presented high sensitive (LOD: 0.14-169.20μgkg -1 ), acceptable precision (RSD: 0.7-19.1%), satisfactory recovery (69-119%) and high enrichment factors (20-722). Several obtained LOQs are below the MRLs established by the European Commission; therefore, the method could be applied for pesticides determination in routing analysis and custom laboratories. Moreover, this method has shown to be suitable for determination of some of the studied pesticides in lime, melon, papaya, banana, tomato, and lettuce. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Analysis of commercial specifications and grades of wild and cultivated Gentianae Macrophyllae Radix based on multi-indicative constituents].

PubMed

Yang, Yan-Mei; Lin, Li; Lu, You-Yuan; Ma, Xiao-Hui; Jin, Ling; Zhu, Tian-Tian

2016-03-01

The study is aimed to analyze the commercial specifications and grades of wild and cultivated Gentianae Macrophllae Radix based on multi-indicative constituents. The seven kinds of main chemical components containing in Gentianae Macrophyllae Radix were determined by UPLC, and then the quality levels of chemical component of Gentianae Macrophyllae Radix were clustered and classified by modern statistical methods (canonical correspondence analysis, Fisher discriminant analysis and so on). The quality indices were selected and their correlations were analyzed. Lastly, comprehensively quantitative grade division for quality under different commodity-specifications and different grades of same commodity-specifications of wild and planting were divided. The results provide a basis for a reasonable division of specification and grade of the commodity of Gentianae Macrophyllae Radix. The range of quality evaluation of main index components (gentiopicrin, loganin acid and swertiamarin) was proposed, and the Herbal Quality Index (HQI) was introduced. The rank discriminant function was established based on the quality by Fisher discriminant analysis. According to the analysis, the quality of wild and cultivated Luobojiao, one of the commercial specification of Gentianae Macrophyllae Radix was the best, Mahuajiao, the other commercial specification, was average , Xiaoqinjiao was inferior. Among grades, the quality of first-class cultivated Luobojiao was the worst, of second class secondary, and the third class the best; The quality of the first-class of wild Luobojiao was secondary, and the second-class the best; The quality of the second-class of Mahuajiao was secondary, and the first-class was the best; the quality of first-class Xiaoqinjiao was secondary, and the second-class was the better one between the two grades, but not obvious significantly. The method provides a new idea and method for evaluation of comprehensively quantitative on the quality of Gentianae Macrophyllae Radix. Copyright© by the Chinese Pharmaceutical Association.

An improved multi-exposure approach for high quality holographic femtosecond laser patterning

NASA Astrophysics Data System (ADS)

Zhang, Chenchu; Hu, Yanlei; Li, Jiawen; Lao, Zhaoxin; Ni, Jincheng; Chu, Jiaru; Huang, Wenhao; Wu, Dong

2014-12-01

High efficiency two photon polymerization through single exposure via spatial light modulator (SLM) has been used to decrease the fabrication time and rapidly realize various micro/nanostructures, but the surface quality remains a big problem due to the speckle noise of optical intensity distribution at the defocused plane. Here, a multi-exposure approach which used tens of computer generate holograms successively loaded on SLM is presented to significantly improve the optical uniformity without losing efficiency. By applying multi-exposure, we found that the uniformity at the defocused plane was increased from ˜0.02 to ˜0.6 according to our simulation. The fabricated two series of letters "HELLO" and "USTC" under single-and multi-exposure in our experiment also verified that the surface quality was greatly improved. Moreover, by this method, several kinds of beam splitters with high quality, e.g., 2 × 2, 5 × 5 Daman, and complex nonseperate 5 × 5, gratings were fabricated with both of high quality and short time (<1 min, 95% time-saving). This multi-exposure SLM-two-photon polymerization method showed the promising prospect in rapidly fabricating and integrating various binary optical devices and their systems.

Surface plasmon resonance imaging for label-free detection of foodborne pathogens and toxins

USDA-ARS?s Scientific Manuscript database

More rapid and efficient detection methods for foodborne pathogenic bacteria and toxins are needed to address the long assay time and limitations in multiplex capacity. Surface plasmon resonance imaging (SPRi) is an emerging optical technique, which allows for rapid and label-free screening of multi...

Ingenious Snake: An Adaptive Multi-Class Contours Extraction

NASA Astrophysics Data System (ADS)

Li, Baolin; Zhou, Shoujun

2018-04-01

Active contour model (ACM) plays an important role in computer vision and medical image application. The traditional ACMs were used to extract single-class of object contours. While, simultaneous extraction of multi-class of interesting contours (i.e., various contours with closed- or open-ended) have not been solved so far. Therefore, a novel ACM model named “Ingenious Snake” is proposed to adaptively extract these interesting contours. In the first place, the ridge-points are extracted based on the local phase measurement of gradient vector flow field; the consequential ridgelines initialization are automated with high speed. Secondly, the contours’ deformation and evolvement are implemented with the ingenious snake. In the experiments, the result from initialization, deformation and evolvement are compared with the existing methods. The quantitative evaluation of the structure extraction is satisfying with respect of effectiveness and accuracy.

Simultaneous liquid chromatography-tandem mass spectrometry analysis of brominated flame retardants (tetrabromobisphenol A and hexabromocyclododecane diastereoisomers) in French breast milk.

PubMed

Inthavong, Chanthadary; Hommet, Frédéric; Bordet, François; Rigourd, Virginie; Guérin, Thierry; Dragacci, Sylviane

2017-11-01

TBBPA and HBCDs are the two classes of flame retardants that are still allowed for use by the European Commission. In May 2013, HBCDs were listed as Persistent Organic Pollutants under the Stockholm Convention, and they were banned with an exemption on EPS/XPS for cavity wall insulation. This study describes the development and optimisation of a rapid LC-ESI-MS/MS method using isotopic dilution quantification including a simplified extraction step using a mixture of solvents and sulphuric acid hydrolysis followed by the one-shot analysis of TBBPA and each of the α-, β- and γ-HBCD diastereoisomers. The limits of detection and quantification (LOD and LOQ) were 0.5 and 2.5 ng g -1 (lipid weight, lw) for TBBPA and HBCD diastereoisomers, respectively. The method was applied to analyse 106 samples of individual mature breast milk. TBBPA was quantified in 42% of these samples within a range of

An efficient predictor-corrector-based dynamic mesh method for multi-block structured grid with extremely large deformation and its applications

NASA Astrophysics Data System (ADS)

Guo, Tongqing; Chen, Hao; Lu, Zhiliang

2018-05-01

Aiming at extremely large deformation, a novel predictor-corrector-based dynamic mesh method for multi-block structured grid is proposed. In this work, the dynamic mesh generation is completed in three steps. At first, some typical dynamic positions are selected and high-quality multi-block grids with the same topology are generated at those positions. Then, Lagrange interpolation method is adopted to predict the dynamic mesh at any dynamic position. Finally, a rapid elastic deforming technique is used to correct the small deviation between the interpolated geometric configuration and the actual instantaneous one. Compared with the traditional methods, the results demonstrate that the present method shows stronger deformation ability and higher dynamic mesh quality.

Direct Parametric Image Reconstruction in Reduced Parameter Space for Rapid Multi-Tracer PET Imaging.

PubMed

Cheng, Xiaoyin; Li, Zhoulei; Liu, Zhen; Navab, Nassir; Huang, Sung-Cheng; Keller, Ulrich; Ziegler, Sibylle; Shi, Kuangyu

2015-02-12

The separation of multiple PET tracers within an overlapping scan based on intrinsic differences of tracer pharmacokinetics is challenging, due to limited signal-to-noise ratio (SNR) of PET measurements and high complexity of fitting models. In this study, we developed a direct parametric image reconstruction (DPIR) method for estimating kinetic parameters and recovering single tracer information from rapid multi-tracer PET measurements. This is achieved by integrating a multi-tracer model in a reduced parameter space (RPS) into dynamic image reconstruction. This new RPS model is reformulated from an existing multi-tracer model and contains fewer parameters for kinetic fitting. Ordered-subsets expectation-maximization (OSEM) was employed to approximate log-likelihood function with respect to kinetic parameters. To incorporate the multi-tracer model, an iterative weighted nonlinear least square (WNLS) method was employed. The proposed multi-tracer DPIR (MTDPIR) algorithm was evaluated on dual-tracer PET simulations ([18F]FDG and [11C]MET) as well as on preclinical PET measurements ([18F]FLT and [18F]FDG). The performance of the proposed algorithm was compared to the indirect parameter estimation method with the original dual-tracer model. The respective contributions of the RPS technique and the DPIR method to the performance of the new algorithm were analyzed in detail. For the preclinical evaluation, the tracer separation results were compared with single [18F]FDG scans of the same subjects measured 2 days before the dual-tracer scan. The results of the simulation and preclinical studies demonstrate that the proposed MT-DPIR method can improve the separation of multiple tracers for PET image quantification and kinetic parameter estimations.

A validation study of a rapid field-based rating system for discriminating among flow permanence classes of headwater streams in South Carolina

Treesearch

William Wenerick; Ken M. Fritz; Mitchell S. Kostich

2016-01-01

Classifying streams according to permanence is important in determining regulatory jurisdiction and in implementing pollution control programs. Administrators of these programs need rapid methods for making timely and defensible decisions.

Project Based Learning in Multi-Grade Class

ERIC Educational Resources Information Center

Ciftci, Sabahattin; Baykan, Ayse Aysun

2013-01-01

The purpose of this study is to evaluate project based learning in multi-grade classes. This study, based on a student-centered learning approach, aims to analyze students' and parents' interpretations. The study was done in a primary village school belonging to the Centre of Batman, already adapting multi-grade classes in their education system,…

Research of Face Recognition with Fisher Linear Discriminant

NASA Astrophysics Data System (ADS)

Rahim, R.; Afriliansyah, T.; Winata, H.; Nofriansyah, D.; Ratnadewi; Aryza, S.

2018-01-01

Face identification systems are developing rapidly, and these developments drive the advancement of biometric-based identification systems that have high accuracy. However, to develop a good face recognition system and to have high accuracy is something that’s hard to find. Human faces have diverse expressions and attribute changes such as eyeglasses, mustache, beard and others. Fisher Linear Discriminant (FLD) is a class-specific method that distinguishes facial image images into classes and also creates distance between classes and intra classes so as to produce better classification.

Development of a rapid screening test for veterinary sedatives and the beta-blocker carazolol in porcine kidney by ELISA.

PubMed

Cooper, John; Delahaut, Phillippe; Fodey, Terence L; Elliott, Christopher T

2004-02-01

Sedatives and tranquillisers are frequently used to reduce stress during the transportation of food producing animals. The most widely used classes of sedatives include the butyrophenone azaperone, the phenothiazines acepromazine, propionylpromazine, chlorpromazine and the [small beta]-blocker, carazolol. For regulatory control purposes, tolerances for azaperone and carazolol have been set by the European Union as 100 and 25 [micro sign]g kg(-1), respectively. Furthermore, the use of the phenothiazines is prohibited and therefore has a zero tolerance. A method for the detection of residues of five tranquillisers and one [small beta]-blocker using a single ELISA plate has been developed. Kidney samples (2.5 g) were extracted with dichloromethane and applied to a competitive enzyme immunoassay using three polyclonal antibodies raised in rabbits against azaperol, propionylpromazine and carazolol conjugates. In sample matrix, the azaperol antibody cross-reacted 28.0% with azaperone and the propionylpromazine antibody cross-reacted 24.9% with acepromazine and 11.7% with chlorpromazine. In the ELISA, the detection capabilities of the six sedatives, azaperol, azaperone, carazolol, acepromazine, chlorpromazine, and propionylpromazine are 5, 15, 5, 5, 20 and 5 [micro sign]g kg(-1), respectively. The proposed method is a sensitive and rapid multi-residue technique that offers a cost effective alternative to current published procedures, without any concession on the ability to detect sedative misuse.

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

PubMed

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

SYNTHESIS AND APPLICATION OF A NEW CLASS OF STABILIZED NANOSCALE IRON PARTICLES FOR RAPID DESTRUCTION OF CHLORINATED HYDROCARBONS IN SOIL AND GROUNDWATER

EPA Science Inventory

In accord with the research objectives, this project achieved the following major results:

1. Developed a new method for synthesizing a new class of starch- or carboxymethyl cellulose stabilized ZVI nanoparticles of controllable size and transportability. ...

2. Real-Time PCR Typing of Escherichia coli Based on Multiple Single Nucleotide Polymorphisms--a Convenient and Rapid Method.

   PubMed

   Lager, Malin; Mernelius, Sara; Löfgren, Sture; Söderman, Jan

   2016-01-01

   Healthcare-associated infections caused by Escherichia coli and antibiotic resistance due to extended-spectrum beta-lactamase (ESBL) production constitute a threat against patient safety. To identify, track, and control outbreaks and to detect emerging virulent clones, typing tools of sufficient discriminatory power that generate reproducible and unambiguous data are needed. A probe based real-time PCR method targeting multiple single nucleotide polymorphisms (SNP) was developed. The method was based on the multi locus sequence typing scheme of Institute Pasteur and by adaptation of previously described typing assays. An 8 SNP-panel that reached a Simpson's diversity index of 0.95 was established, based on analysis of sporadic E. coli cases (ESBL n = 27 and non-ESBL n = 53). This multi-SNP assay was used to identify the sequence type 131 (ST131) complex according to the Achtman's multi locus sequence typing scheme. However, it did not fully discriminate within the complex but provided a diagnostic signature that outperformed a previously described detection assay. Pulsed-field gel electrophoresis typing of isolates from a presumed outbreak (n = 22) identified two outbreaks (ST127 and ST131) and three different non-outbreak-related isolates. Multi-SNP typing generated congruent data except for one non-outbreak-related ST131 isolate. We consider multi-SNP real-time PCR typing an accessible primary generic E. coli typing tool for rapid and uniform type identification.

3. Development of a matrix-assisted laser desorption ionization mass spectrometric method for rapid process-monitoring of phthalocyanine compounds.

   PubMed

   Chen, Yi-Ting; Wang, Fu-Shing; Li, Zhendong; Li, Liang; Ling, Yong-Chien

   2012-07-29

   Phthalocyanines (PCs), an important class of chemicals widely used in many industrial sectors, are macrocyclic compounds possessing a heteroaromatic π-electron system with optical properties influenced by chemical structures and impurities or by-products introduced during the synthesis process. Analytical tools allowing for rapid monitoring of the synthesis processes are of significance for the development of new PCs with improved performance in many application areas. In this work, we report a matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (TOFMS) method for rapid and convenient monitoring of PC synthesis reactions. For this class of compounds, intact molecular ions could be detected by MALDI using retinoic acid as matrix. It was shown that relative quantification results of two PC compounds could be generated by MALDI MS. This method was applied to monitor the bromination reactions of nickel- and copper-containing PCs. It was demonstrated that, compared to the traditional UV-visible method, the MALDI MS method offers the advantage of higher sensitivity while providing chemical species and relative quantification information on the reactants and products, which are crucial to process monitoring. Copyright © 2012 Elsevier B.V. All rights reserved.

4. Teachers' Lived Experiences about Teaching-Learning Process in Multi-Grade Classes

   ERIC Educational Resources Information Center

   Mortazavizadeh, Seyyed Heshmatollah; Nili, Mohammad Reza; Isfahani, Ahmad Reza Nasr; Hassani, Mohammad

   2017-01-01

   This study seeks to recognize teachers' lived experiences about teaching-learning process in multi-grade classes. The approach of the study is qualitative under the rubric of phenomenological studies. The statistical population consisted of the teachers of multi-grade classes in a non-prosperous province and a prosperous one. 14 teachers were…

78 FR 73211 - Self-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Notice of Filing of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-05

... Change Relating to Multi-Class Spread Orders November 29, 2013. Pursuant to Section 19(b)(1) of the... Substance of the Proposed Rule Change CBOE proposes to amend its rule related to Multi-Class Broad-Based Index Option Spread Orders (referred to herein as ``Multi-Class Spread Orders''). The text of the...

High-throughput screening for multi-class veterinary drug residues in animal muscle using liquid chromatography/tandem mass spectrometry with on-line solid-phase extraction.

PubMed

Tang, Hubert Po-On; Ho, Clare; Lai, Shirley Sau-Ling

2006-01-01

A rapid qualitative method using on-line column-switching liquid chromatography/tandem mass spectrometry (LC/MS/MS) was developed and validated for screening 13 target veterinary drugs: four macrolides - erythromycin A, josamycin (leucomycin A3), kitasamycin (leucomycin A5), and tylosin A; six (fluoro)quinolones - ciprofloxacin, danofloxacin, enrofloxacin, flumequine, oxolinic acid, and sarafloxacin; and lincomycin, virginiamycin M1, and trimethoprim in different animal muscles. Clindamycin, norfloxacin, nalidixic acid, oleandomycin, ormetoprim, and roxithromycin were used as the internal standards. After simple deproteination and analyte extraction of muscle samples using acetonitrile, the supernatant was subjected to on-line cleanup and direct analysis by LC/MS/MS. On-line cleanup with an extraction cartridge packed with hydrophilic-hydrophobic polymer sorbent followed by fast LC using a short C18 column resulted in a total analysis cycle of 6 min for 19 drugs. This screening method considerably reduced the time and the cost for the quantitative and confirmatory analyses. The application of a control point approach was also introduced and explained. Copyright (c) 2006 John Wiley & Sons, Ltd.

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.

PubMed

Borchani, Hanen; Bielza, Concha; Toro, Carlos; Larrañaga, Pedro

2013-03-01

Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries. Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input instance in the data set has to be assigned simultaneously to multiple output class variables that are not necessarily binary. In this paper, we introduce a new method, named MB-MBC, for learning MBCs from data by determining the Markov blanket around each class variable using the HITON algorithm. Our method is applied to both reverse transcriptase and protease data sets obtained from the Stanford HIV-1 database. Regarding the prediction of antiretroviral combination therapies, the experimental study shows promising results in terms of classification accuracy compared with state-of-the-art MBC learning algorithms. For reverse transcriptase inhibitors, we get 71% and 11% in mean and global accuracy, respectively; while for protease inhibitors, we get more than 84% and 31% in mean and global accuracy, respectively. In addition, the analysis of MBC graphical structures lets us gain insight into both known and novel interactions between reverse transcriptase and protease inhibitors and their respective resistance mutations. MB-MBC algorithm is a valuable tool to analyze the HIV-1 reverse transcriptase and protease inhibitors prediction problem and to discover interactions within and between these two classes of inhibitors. Copyright © 2012 Elsevier B.V. All rights reserved.

StruLocPred: structure-based protein subcellular localisation prediction using multi-class support vector machine.

PubMed

Zhou, Wengang; Dickerson, Julie A

2012-01-01

Knowledge of protein subcellular locations can help decipher a protein's biological function. This work proposes new features: sequence-based: Hybrid Amino Acid Pair (HAAP) and two structure-based: Secondary Structural Element Composition (SSEC) and solvent accessibility state frequency. A multi-class Support Vector Machine is developed to predict the locations. Testing on two established data sets yields better prediction accuracies than the best available systems. Comparisons with existing methods show comparable results to ESLPred2. When StruLocPred is applied to the entire Arabidopsis proteome, over 77% of proteins with known locations match the prediction results. An implementation of this system is at http://wgzhou.ece. iastate.edu/StruLocPred/.

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

Larsen, E.W.

A class of Projected Discrete-Ordinates (PDO) methods is described for obtaining iterative solutions of discrete-ordinates problems with convergence rates comparable to those observed using Diffusion Synthetic Acceleration (DSA). The spatially discretized PDO solutions are generally not equal to the DSA solutions, but unlike DSA, which requires great care in the use of spatial discretizations to preserve stability, the PDO solutions remain stable and rapidly convergent with essentially arbitrary spatial discretizations. Numerical results are presented which illustrate the rapid convergence and the accuracy of solutions obtained using PDO methods with commonplace differencing methods.

Conceptual design for an AIUC multi-purpose spectrograph camera using DMD technology

NASA Astrophysics Data System (ADS)

Rukdee, S.; Bauer, F.; Drass, H.; Vanzi, L.; Jordan, A.; Barrientos, F.

2017-02-01

Current and upcoming massive astronomical surveys are expected to discover a torrent of objects, which need groundbased follow-up observations to characterize their nature. For transient objects in particular, rapid early and efficient spectroscopic identification is needed. In particular, a small-field Integral Field Unit (IFU) would mitigate traditional slit losses and acquisition time. To this end, we present the design of a Digital Micromirror Device (DMD) multi-purpose spectrograph camera capable of running in several modes: traditional longslit, small-field patrol IFU, multi-object and full-field IFU mode via Hadamard spectra reconstruction. AIUC Optical multi-purpose CAMera (AIUCOCAM) is a low-resolution spectrograph camera of R 1,600 covering the spectral range of 0.45-0.85 μm. We employ a VPH grating as a disperser, which is removable to allow an imaging mode. This spectrograph is envisioned for use on a 1-2 m class telescope in Chile to take advantage of good site conditions. We present design decisions and challenges for a costeffective robotized spectrograph. The resulting instrument is remarkably versatile, capable of addressing a wide range of scientific topics.

Development and Validation of Rapid Assessment Indices of Condition for Coastal for Coastal Wetlands in Southern New England, USA

EPA Science Inventory

Vegetation, soils, on-site disturbances, and watershed land use and land cover were assessed at 81 coastal wetland sites using the New England Rapid Asssessment Method (NERAM). Condition indices (CIs) were derived from various combinations of the multi-dimensional data using pri...

Model selection and assessment for multi­-species occupancy models

USGS Publications Warehouse

Broms, Kristin M.; Hooten, Mevin B.; Fitzpatrick, Ryan M.

2016-01-01

While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction.

A posteriori error estimation for multi-stage Runge–Kutta IMEX schemes

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

Chaudhry, Jehanzeb H.; Collins, J. B.; Shadid, John N.

Implicit–Explicit (IMEX) schemes are widely used for time integration methods for approximating solutions to a large class of problems. In this work, we develop accurate a posteriori error estimates of a quantity-of-interest for approximations obtained from multi-stage IMEX schemes. This is done by first defining a finite element method that is nodally equivalent to an IMEX scheme, then using typical methods for adjoint-based error estimation. Furthermore, the use of a nodally equivalent finite element method allows a decomposition of the error into multiple components, each describing the effect of a different portion of the method on the total error inmore » a quantity-of-interest.« less

A posteriori error estimation for multi-stage Runge–Kutta IMEX schemes

DOE PAGES

Chaudhry, Jehanzeb H.; Collins, J. B.; Shadid, John N.

2017-02-05

Implicit–Explicit (IMEX) schemes are widely used for time integration methods for approximating solutions to a large class of problems. In this work, we develop accurate a posteriori error estimates of a quantity-of-interest for approximations obtained from multi-stage IMEX schemes. This is done by first defining a finite element method that is nodally equivalent to an IMEX scheme, then using typical methods for adjoint-based error estimation. Furthermore, the use of a nodally equivalent finite element method allows a decomposition of the error into multiple components, each describing the effect of a different portion of the method on the total error inmore » a quantity-of-interest.« less

Grouped gene selection and multi-classification of acute leukemia via new regularized multinomial regression.

PubMed

Li, Juntao; Wang, Yanyan; Jiang, Tao; Xiao, Huimin; Song, Xuekun

2018-05-09

Diagnosing acute leukemia is the necessary prerequisite to treating it. Multi-classification on the gene expression data of acute leukemia is help for diagnosing it which contains B-cell acute lymphoblastic leukemia (BALL), T-cell acute lymphoblastic leukemia (TALL) and acute myeloid leukemia (AML). However, selecting cancer-causing genes is a challenging problem in performing multi-classification. In this paper, weighted gene co-expression networks are employed to divide the genes into groups. Based on the dividing groups, a new regularized multinomial regression with overlapping group lasso penalty (MROGL) has been presented to simultaneously perform multi-classification and select gene groups. By implementing this method on three-class acute leukemia data, the grouped genes which work synergistically are identified, and the overlapped genes shared by different groups are also highlighted. Moreover, MROGL outperforms other five methods on multi-classification accuracy. Copyright © 2017. Published by Elsevier B.V.

Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

NASA Technical Reports Server (NTRS)

Rai, Man Mohan

2006-01-01

Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

Distributed multi-sensor particle filter for bearings-only tracking

NASA Astrophysics Data System (ADS)

Zhang, Jungen; Ji, Hongbing

2012-02-01

In this article, the classical bearings-only tracking (BOT) problem for a single target is addressed, which belongs to the general class of non-linear filtering problems. Due to the fact that the radial distance observability of the target is poor, the algorithm-based sequential Monte-Carlo (particle filtering, PF) methods generally show instability and filter divergence. A new stable distributed multi-sensor PF method is proposed for BOT. The sensors process their measurements at their sites using a hierarchical PF approach, which transforms the BOT problem from Cartesian coordinate to the logarithmic polar coordinate and separates the observable components from the unobservable components of the target. In the fusion centre, the target state can be estimated by utilising the multi-sensor optimal information fusion rule. Furthermore, the computation of a theoretical Cramer-Rao lower bound is given for the multi-sensor BOT problem. Simulation results illustrate that the proposed tracking method can provide better performances than the traditional PF method.

Is scaffold hopping a reliable indicator for the ability of computational methods to identify structurally diverse active compounds?

NASA Astrophysics Data System (ADS)

Dimova, Dilyana; Bajorath, Jürgen

2017-07-01

Computational scaffold hopping aims to identify core structure replacements in active compounds. To evaluate scaffold hopping potential from a principal point of view, regardless of the computational methods that are applied, a global analysis of conventional scaffolds in analog series from compound activity classes was carried out. The majority of analog series was found to contain multiple scaffolds, thus enabling the detection of intra-series scaffold hops among closely related compounds. More than 1000 activity classes were found to contain increasing proportions of multi-scaffold analog series. Thus, using such activity classes for scaffold hopping analysis is likely to overestimate the scaffold hopping (core structure replacement) potential of computational methods, due to an abundance of artificial scaffold hops that are possible within analog series.

Object recognition through a multi-mode fiber

NASA Astrophysics Data System (ADS)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Rapid assessment of the antigenic integrity of tetrameric HLA complexes by human monoclonal HLA antibodies.

PubMed

Eijsink, Chantal; Kester, Michel G D; Franke, Marry E I; Franken, Kees L M C; Heemskerk, Mirjam H M; Claas, Frans H J; Mulder, Arend

2006-08-31

The ability of tetrameric major histocompatibility complex (MHC) class I-peptide complexes (tetramers) to detect antigen-specific T lymphocyte responses has yielded significant information about the generation of in vivo immunity in numerous antigenic systems. Here we present a novel method for rapid validation of tetrameric HLA molecules based on the presence of allodeterminants. Human monoclonal antibodies (mAbs) recognizing polymorphic determinants on HLA class I were immobilized on polystyrene microparticles and used to probe the structural integrity of tetrameric HLA class I molecules by flow cytometry. A total of 22 tetramers, based on HLA-A1, A2, A3, A24, B7 and B8 were reactive with their counterpart mAbs, thus confirming their antigenic integrity. A positive outcome of this mAb test ensures that tetrameric HLA class I can be used with greater confidence in subsequent functional assays.

Benchmark of Machine Learning Methods for Classification of a SENTINEL-2 Image

NASA Astrophysics Data System (ADS)

Pirotti, F.; Sunar, F.; Piragnolo, M.

2016-06-01

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performance.

An improved multi-exposure approach for high quality holographic femtosecond laser patterning

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

Zhang, Chenchu; Hu, Yanlei, E-mail: huyl@ustc.edu.cn, E-mail: jwl@ustc.edu.cn; Li, Jiawen, E-mail: huyl@ustc.edu.cn, E-mail: jwl@ustc.edu.cn

High efficiency two photon polymerization through single exposure via spatial light modulator (SLM) has been used to decrease the fabrication time and rapidly realize various micro/nanostructures, but the surface quality remains a big problem due to the speckle noise of optical intensity distribution at the defocused plane. Here, a multi-exposure approach which used tens of computer generate holograms successively loaded on SLM is presented to significantly improve the optical uniformity without losing efficiency. By applying multi-exposure, we found that the uniformity at the defocused plane was increased from ∼0.02 to ∼0.6 according to our simulation. The fabricated two series ofmore » letters “HELLO” and “USTC” under single-and multi-exposure in our experiment also verified that the surface quality was greatly improved. Moreover, by this method, several kinds of beam splitters with high quality, e.g., 2 × 2, 5 × 5 Daman, and complex nonseperate 5 × 5, gratings were fabricated with both of high quality and short time (<1 min, 95% time-saving). This multi-exposure SLM-two-photon polymerization method showed the promising prospect in rapidly fabricating and integrating various binary optical devices and their systems.« less

Mapping raised bogs with an iterative one-class classification approach

NASA Astrophysics Data System (ADS)

Mack, Benjamin; Roscher, Ribana; Stenzel, Stefanie; Feilhauer, Hannes; Schmidtlein, Sebastian; Waske, Björn

2016-10-01

Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative OCC approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative OCC outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier.

Learning by Doing--Teaching Systematic Review Methods in 8 Weeks

ERIC Educational Resources Information Center

Li, Tianjing; Saldanha, Ian J.; Vedula, S. Swaroop; Yu, Tsung; Rosman, Lori; Twose, Claire; Goodman, Steven N.; Dickersin, Kay

2014-01-01

Objective: The objective of this paper is to describe the course "Systematic Reviews and Meta-analysis" at the Johns Hopkins Bloomberg School of Public Health. Methods: A distinct feature of our course is a group project in which students, assigned to multi-disciplinary groups, conduct a systematic review. In-class sessions comprise…

Optimization of sample preparation by central composite design for multi-class determination of veterinary drugs in bovine muscle, kidney and liver by ultra-high-performance liquid chromatographic-tandem mass spectrometry.

PubMed

Rizzetti, Tiele M; de Souza, Maiara P; Prestes, Osmar D; Adaime, Martha B; Zanella, Renato

2018-04-25

In this study a simple and fast multi-class method for the determination of veterinary drugs in bovine liver, kidney and muscle was developed. The method employed acetonitrile for extraction followed by clean-up with EMR-Lipid® sorbent and trichloracetic acid. Tests indicated that the use of TCA was most effective when added in the final step of the clean-up procedure instead of during extraction. Different sorbents were tested and optimized using central composite design and the analytes determined by ultra-high-performance liquid chromatographic-tandem mass spectrometry (UHPLC-MS/MS). The method was validated according the European Commission Decision 2002/657 presenting satisfactory results for 69 veterinary drugs in bovine liver and 68 compounds in bovine muscle and kidney. The method was applied in real samples and in proficiency tests and proved to be adequate for routine analysis. Residues of abamectin, doramectin, eprinomectin and ivermectin were found in samples of bovine muscle and only ivermectin in bovine liver. Copyright © 2017 Elsevier Ltd. All rights reserved.

Unstructured Finite Elements and Dynamic Meshing for Explicit Phase Tracking in Multiphase Problems

NASA Astrophysics Data System (ADS)

Chandra, Anirban; Yang, Fan; Zhang, Yu; Shams, Ehsan; Sahni, Onkar; Oberai, Assad; Shephard, Mark

2017-11-01

Multi-phase processes involving phase change at interfaces, such as evaporation of a liquid or combustion of a solid, represent an interesting class of problems with varied applications. Large density ratio across phases, discontinuous fields at the interface and rapidly evolving geometries are some of the inherent challenges which influence the numerical modeling of multi-phase phase change problems. In this work, a mathematically consistent and robust computational approach to address these issues is presented. We use stabilized finite element methods on mixed topology unstructured grids for solving the compressible Navier-Stokes equations. Appropriate jump conditions derived from conservations laws across the interface are handled by using discontinuous interpolations, while the continuity of temperature and tangential velocity is enforced using a penalty parameter. The arbitrary Lagrangian-Eulerian (ALE) technique is utilized to explicitly track the interface motion. Mesh at the interface is constrained to move with the interface while elsewhere it is moved using the linear elasticity analogy. Repositioning is applied to the layered mesh that maintains its structure and normal resolution. In addition, mesh modification is used to preserve the quality of the volumetric mesh. This work is supported by the U.S. Army Grants W911NF1410301 and W911NF16C0117.

Systematic cloning of human minisatellites from ordered array charomid libraries.

PubMed

Armour, J A; Povey, S; Jeremiah, S; Jeffreys, A J

1990-11-01

We present a rapid and efficient method for the isolation of minisatellite loci from human DNA. The method combines cloning a size-selected fraction of human MboI DNA fragments in a charomid vector with hybridization screening of the library in ordered array. Size-selection of large MboI fragments enriches for the longer, more variable minisatellites and reduces the size of the library required. The library was screened with a series of multi-locus probes known to detect a large number of hypervariable loci in human DNA. The gridded library allowed both the rapid processing of positive clones and the comparative evaluation of the different multi-locus probes used, in terms of both the relative success in detecting hypervariable loci and the degree of overlap between the sets of loci detected. We report 23 new human minisatellite loci isolated by this method, which map to 14 autosomes and the sex chromosomes.

A Method of Neighbor Classes Based SVM Classification for Optical Printed Chinese Character Recognition

PubMed Central

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR. PMID:23536777

Digitally switchable multi-focal lens using freeform optics.

PubMed

Wang, Xuan; Qin, Yi; Hua, Hong; Lee, Yun-Han; Wu, Shin-Tson

2018-04-16

Optical technologies offering electrically tunable optical power have found a broad range of applications, from head-mounted displays for virtual and augmented reality applications to microscopy. In this paper, we present a novel design and prototype of a digitally switchable multi-focal lens (MFL) that offers the capability of rapidly switching the optical power of the system among multiple foci. It consists of a freeform singlet and a customized programmable optical shutter array (POSA). Time-multiplexed multiple foci can be obtained by electrically controlling the POSA to switch the light path through different segments of the freeform singlet rapidly. While this method can be applied to a broad range of imaging and display systems, we experimentally demonstrate a proof-of-concept prototype for a multi-foci imaging system.

Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding.

PubMed

Tenzer, S; Peters, B; Bulik, S; Schoor, O; Lemmel, C; Schatz, M M; Kloetzel, P-M; Rammensee, H-G; Schild, H; Holzhütter, H-G

2005-05-01

Epitopes presented by major histocompatibility complex (MHC) class I molecules are selected by a multi-step process. Here we present the first computational prediction of this process based on in vitro experiments characterizing proteasomal cleavage, transport by the transporter associated with antigen processing (TAP) and MHC class I binding. Our novel prediction method for proteasomal cleavages outperforms existing methods when tested on in vitro cleavage data. The analysis of our predictions for a new dataset consisting of 390 endogenously processed MHC class I ligands from cells with known proteasome composition shows that the immunological advantage of switching from constitutive to immunoproteasomes is mainly to suppress the creation of peptides in the cytosol that TAP cannot transport. Furthermore, we show that proteasomes are unlikely to generate MHC class I ligands with a C-terminal lysine residue, suggesting processing of these ligands by a different protease that may be tripeptidyl-peptidase II (TPPII).

Stochastic weighted particle methods for population balance equations with coagulation, fragmentation and spatial inhomogeneity

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

Lee, Kok Foong; Patterson, Robert I.A.; Wagner, Wolfgang

2015-12-15

Graphical abstract: -- Highlights: •Problems concerning multi-compartment population balance equations are studied. •A class of fragmentation weight transfer functions is presented. •Three stochastic weighted algorithms are compared against the direct simulation algorithm. •The numerical errors of the stochastic solutions are assessed as a function of fragmentation rate. •The algorithms are applied to a multi-dimensional granulation model. -- Abstract: This paper introduces stochastic weighted particle algorithms for the solution of multi-compartment population balance equations. In particular, it presents a class of fragmentation weight transfer functions which are constructed such that the number of computational particles stays constant during fragmentation events. Themore » weight transfer functions are constructed based on systems of weighted computational particles and each of it leads to a stochastic particle algorithm for the numerical treatment of population balance equations. Besides fragmentation, the algorithms also consider physical processes such as coagulation and the exchange of mass with the surroundings. The numerical properties of the algorithms are compared to the direct simulation algorithm and an existing method for the fragmentation of weighted particles. It is found that the new algorithms show better numerical performance over the two existing methods especially for systems with significant amount of large particles and high fragmentation rates.« less

Multi-residue method for the analysis of 85 current-use and legacy pesticides in bed and suspended sediments

USGS Publications Warehouse

Smalling, K.L.; Kuivila, K.M.

2008-01-01

A multi-residue method was developed for the simultaneous determination of 85 current-use and legacy organochlorine pesticides in a single sediment sample. After microwave-assisted extraction, clean-up of samples was optimized using gel permeation chromatography and either stacked carbon and alumina solid-phase extraction cartridges or a deactivated Florisil column. Analytes were determined by gas chromatography with ion-trap mass spectrometry and electron capture detection. Method detection limits ranged from 0.6 to 8.9 ??g/kg dry weight. Bed and suspended sediments from a variety of locations were analyzed to validate the method and 29 pesticides, including at least 1 from every class, were detected.

A novel integrated approach for path following and directional stability control of road vehicles after a tire blow-out

NASA Astrophysics Data System (ADS)

Wang, Fei; Chen, Hong; Guo, Konghui; Cao, Dongpu

2017-09-01

The path following and directional stability are two crucial problems when a road vehicle experiences a tire blow-out or sudden tire failure. Considering the requirement of rapid road vehicle motion control during a tire blow-out, this article proposes a novel linearized decoupling control procedure with three design steps for a class of second order multi-input-multi-output non-affine system. The evaluating indicators for controller performance are presented and a performance related control parameter distribution map is obtained based on the stochastic algorithm which is an innovation for non-blind parameter adjustment in engineering implementation. The analysis on the robustness of the proposed integrated controller is also performed. The simulation studies for a range of driving conditions are conducted, to demonstrate the effectiveness of the proposed controller.

Airborne Multi-Spectral Minefield Survey

DTIC Science & Technology

2005-05-01

Swedish Defence Research Agency), GEOSPACE (Austria), GTD ( Ingenieria de Sistemas y Software Industrial, Spain), IMEC (Ineruniversity MicroElectronic...RTO-MP-SET-092 18 - 1 UNCLASSIFIED/UNLIMITED UNCLASSIFIED/UNLIMITED Airborne Multi-Spectral Minefield Survey Dirk-Jan de Lange, Eric den...actions is the severe lack of baseline information. To respond to this in a rapid way, cost-efficient data acquisition methods are a key issue. de

Multi-Source Fusion for Explosive Hazard Detection in Forward Looking Sensors

DTIC Science & Technology

2016-12-01

include; (1) Investigating (a) thermal, (b) synthetic aperture acoustics ( SAA ) and (c) voxel space Radar for buried and side threat attacks. (2...detection. (3) With respect to SAA , we developed new approaches in the time and frequency domains for analyzing signature of concealed targets (called...Fraz). We also developed a method to extract a multi-spectral signature from SAA and deep learning was used on limited training and class imbalance

Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems

DTIC Science & Technology

2013-05-28

those of the support vector machine and relevance vector machine, and the model runs more quickly than the other algorithms . When one class occurs...incremental support vector machine algorithm for online learning when fewer than 50 data points are available. (a) Papers published in peer-reviewed journals...learning environments, where data processing occurs one observation at a time and the classification algorithm improves over time with new

Immune Centroids Over-Sampling Method for Multi-Class Classification

DTIC Science & Technology

2015-05-22

recognize to specific antigens . The response of a receptor to an antigen can activate its hosting B-cell. Activated B-cell then proliferates and...modifying N.K. Jerne’s theory. The theory states that in a pre-existing group of lympho- cytes ( specifically B cells), a specific antigen only...the clusters of each small class, which have high data density, called global immune centroids over-sampling (denoted as Global-IC). Specifically

Multi-functional acetyl-CoA carboxylase from Brassica napus is encoded by a multi-gene family: indication for plastidic localization of at least one isoform.

PubMed

Schulte, W; Töpfer, R; Stracke, R; Schell, J; Martini, N

1997-04-01

Three genes coding for different multifunctional acetyl-CoA carboxylase (ACCase; EC 6.4.1.2) isoenzymes from Brassica napus were isolated and divided into two major classes according to structural features in their 5' regions: class I comprises two genes with an additional coding exon of approximately 300 bp at the 5' end, and class II is represented by one gene carrying an intron of 586 bp in its 5' untranslated region. Fusion of the peptide sequence encoded by the additional first exon of a class I ACCase gene to the jellyfish Aequorea victoria green fluorescent protein (GFP) and transient expression in tobacco protoplasts targeted GFP to the chloroplasts. In contrast to the deduced primary structure of the biotin carboxylase domain encoded by the class I gene, the corresponding amino acid sequence of the class II ACCase shows higher identity with that of the Arabidopsis ACCase, both lacking a transit peptide. The Arabidopsis ACCase has been proposed to be a cytosolic isoenzyme. These observations indicate that the two classes of ACCase genes encode plastidic and cytosolic isoforms of multi-functional, eukaryotic type, respectively, and that B. napus contains at least one multi-functional ACCase besides the multi-subunit, prokaryotic type located in plastids. Southern blot analysis of genomic DNA from B. napus, Brassica rapa, and Brassica oleracea, the ancestors of amphidiploid rapeseed, using a fragment of a multi-functional ACCase gene as a probe revealed that ACCase is encoded by a multi-gene family of at least five members.

Algorithm Development and Application of High Order Numerical Methods for Shocked and Rapid Changing Solutions

DTIC Science & Technology

2007-12-06

high order well-balanced schemes to a class of hyperbolic systems with source terms, Boletin de la Sociedad Espanola de Matematica Aplicada, v34 (2006...schemes to a class of hyperbolic systems with source terms, Boletin de la Sociedad Espanola de Matematica Aplicada, v34 (2006), pp.69-80. 39. Y. Xu and C.-W

Rapid multi-modality preregistration based on SIFT descriptor.

PubMed

Chen, Jian; Tian, Jie

2006-01-01

This paper describes the scale invariant feature transform (SIFT) method for rapid preregistration of medical image. This technique originates from Lowe's method wherein preregistration is achieved by matching the corresponding keypoints between two images. The computational complexity has been reduced when we applied SIFT preregistration method before refined registration due to its O(n) exponential calculations. The features of SIFT are highly distinctive and invariant to image scaling and rotation, and partially invariant to change in illumination and contrast, it is robust and repeatable for cursorily matching two images. We also altered the descriptor so our method can deal with multimodality preregistration.

An intelligent service matching method for mechanical equipment condition monitoring using the fibre Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Zhang, Fan; Zhou, Zude; Liu, Quan; Xu, Wenjun

2017-02-01

Due to the advantages of being able to function under harsh environmental conditions and serving as a distributed condition information source in a networked monitoring system, the fibre Bragg grating (FBG) sensor network has attracted considerable attention for equipment online condition monitoring. To provide an overall conditional view of the mechanical equipment operation, a networked service-oriented condition monitoring framework based on FBG sensing is proposed, together with an intelligent matching method for supporting monitoring service management. In the novel framework, three classes of progressive service matching approaches, including service-chain knowledge database service matching, multi-objective constrained service matching and workflow-driven human-interactive service matching, are developed and integrated with an enhanced particle swarm optimisation (PSO) algorithm as well as a workflow-driven mechanism. Moreover, the manufacturing domain ontology, FBG sensor network structure and monitoring object are considered to facilitate the automatic matching of condition monitoring services to overcome the limitations of traditional service processing methods. The experimental results demonstrate that FBG monitoring services can be selected intelligently, and the developed condition monitoring system can be re-built rapidly as new equipment joins the framework. The effectiveness of the service matching method is also verified by implementing a prototype system together with its performance analysis.

JDFTx: Software for joint density-functional theory

DOE PAGES

Sundararaman, Ravishankar; Letchworth-Weaver, Kendra; Schwarz, Kathleen A.; ...

2017-11-14

Density-functional theory (DFT) has revolutionized computational prediction of atomic-scale properties from first principles in physics, chemistry and materials science. Continuing development of new methods is necessary for accurate predictions of new classes of materials and properties, and for connecting to nano- and mesoscale properties using coarse-grained theories. JDFTx is a fully-featured open-source electronic DFT software designed specifically to facilitate rapid development of new theories, models and algorithms. Using an algebraic formulation as an abstraction layer, compact C++11 code automatically performs well on diverse hardware including GPUs (Graphics Processing Units). This code hosts the development of joint density-functional theory (JDFT) thatmore » combines electronic DFT with classical DFT and continuum models of liquids for first-principles calculations of solvated and electrochemical systems. In addition, the modular nature of the code makes it easy to extend and interface with, facilitating the development of multi-scale toolkits that connect to ab initio calculations, e.g. photo-excited carrier dynamics combining electron and phonon calculations with electromagnetic simulations.« less

JDFTx: Software for joint density-functional theory

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

Sundararaman, Ravishankar; Letchworth-Weaver, Kendra; Schwarz, Kathleen A.

Density-functional theory (DFT) has revolutionized computational prediction of atomic-scale properties from first principles in physics, chemistry and materials science. Continuing development of new methods is necessary for accurate predictions of new classes of materials and properties, and for connecting to nano- and mesoscale properties using coarse-grained theories. JDFTx is a fully-featured open-source electronic DFT software designed specifically to facilitate rapid development of new theories, models and algorithms. Using an algebraic formulation as an abstraction layer, compact C++11 code automatically performs well on diverse hardware including GPUs (Graphics Processing Units). This code hosts the development of joint density-functional theory (JDFT) thatmore » combines electronic DFT with classical DFT and continuum models of liquids for first-principles calculations of solvated and electrochemical systems. In addition, the modular nature of the code makes it easy to extend and interface with, facilitating the development of multi-scale toolkits that connect to ab initio calculations, e.g. photo-excited carrier dynamics combining electron and phonon calculations with electromagnetic simulations.« less

Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature.

PubMed

Wang, Xinglong; Rak, Rafal; Restificar, Angelo; Nobata, Chikashi; Rupp, C J; Batista-Navarro, Riza Theresa B; Nawaz, Raheel; Ananiadou, Sophia

2011-10-03

The selection of relevant articles for curation, and linking those articles to experimental techniques confirming the findings became one of the primary subjects of the recent BioCreative III contest. The contest's Protein-Protein Interaction (PPI) task consisted of two sub-tasks: Article Classification Task (ACT) and Interaction Method Task (IMT). ACT aimed to automatically select relevant documents for PPI curation, whereas the goal of IMT was to recognise the methods used in experiments for identifying the interactions in full-text articles. We proposed and compared several classification-based methods for both tasks, employing rich contextual features as well as features extracted from external knowledge sources. For IMT, a new method that classifies pair-wise relations between every text phrase and candidate interaction method obtained promising results with an F1 score of 64.49%, as tested on the task's development dataset. We also explored ways to combine this new approach and more conventional, multi-label document classification methods. For ACT, our classifiers exploited automatically detected named entities and other linguistic information. The evaluation results on the BioCreative III PPI test datasets showed that our systems were very competitive: one of our IMT methods yielded the best performance among all participants, as measured by F1 score, Matthew's Correlation Coefficient and AUC iP/R; whereas for ACT, our best classifier was ranked second as measured by AUC iP/R, and also competitive according to other metrics. Our novel approach that converts the multi-class, multi-label classification problem to a binary classification problem showed much promise in IMT. Nevertheless, on the test dataset the best performance was achieved by taking the union of the output of this method and that of a multi-class, multi-label document classifier, which indicates that the two types of systems complement each other in terms of recall. For ACT, our system exploited a rich set of features and also obtained encouraging results. We examined the features with respect to their contributions to the classification results, and concluded that contextual words surrounding named entities, as well as the MeSH headings associated with the documents were among the main contributors to the performance.

Quantitative Medical Image Analysis for Clinical Development of Therapeutics

NASA Astrophysics Data System (ADS)

Analoui, Mostafa

There has been significant progress in development of therapeutics for prevention and management of several disease areas in recent years, leading to increased average life expectancy, as well as of quality of life, globally. However, due to complexity of addressing a number of medical needs and financial burden of development of new class of therapeutics, there is a need for better tools for decision making and validation of efficacy and safety of new compounds. Numerous biological markers (biomarkers) have been proposed either as adjunct to current clinical endpoints or as surrogates. Imaging biomarkers are among rapidly increasing biomarkers, being examined to expedite effective and rational drug development. Clinical imaging often involves a complex set of multi-modality data sets that require rapid and objective analysis, independent of reviewer's bias and training. In this chapter, an overview of imaging biomarkers for drug development is offered, along with challenges that necessitate quantitative and objective image analysis. Examples of automated and semi-automated analysis approaches are provided, along with technical review of such methods. These examples include the use of 3D MRI for osteoarthritis, ultrasound vascular imaging, and dynamic contrast enhanced MRI for oncology. Additionally, a brief overview of regulatory requirements is discussed. In conclusion, this chapter highlights key challenges and future directions in this area.

A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

PubMed

Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

2017-07-01

As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

Transductive multi-view zero-shot learning.

PubMed

Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Gong, Shaogang

2015-11-01

Most existing zero-shot learning approaches exploit transfer learning via an intermediate semantic representation shared between an annotated auxiliary dataset and a target dataset with different classes and no annotation. A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and applied without adaptation to the target dataset. In this paper we identify two inherent limitations with these approaches. First, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. The second limitation is the prototype sparsity problem which refers to the fact that for each target class, only a single prototype is available for zero-shot learning given a semantic representation. To overcome this problem, a novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space. It effectively exploits the complementary information offered by different semantic representations and takes advantage of the manifold structures of multiple representation spaces in a coherent manner. We demonstrate through extensive experiments that the proposed approach (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) significantly outperforms existing methods for both zero-shot and N-shot recognition on three image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.

Russian Snap Military Exercise in March of 2015; What Implications did this Exercise Have

DTIC Science & Technology

2017-06-09

Russia can mobilize rapidly the nation for war, shift substantial forces in its interior to meet any threat, and that Russia is willing to use military...Further, it demonstrates to any observer that Russia can mobilize rapidly the nation for war, shift substantial forces in its interior to meet any...the steps of qualitative research method in a class on Advanced Research Methods, September 12, 2016. 65 Robert K. Yin, Case Study Research: Design

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

PubMed Central

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

One step DNA assembly for combinatorial metabolic engineering.

PubMed

Coussement, Pieter; Maertens, Jo; Beauprez, Joeri; Van Bellegem, Wouter; De Mey, Marjan

2014-05-01

The rapid and efficient assembly of multi-step metabolic pathways for generating microbial strains with desirable phenotypes is a critical procedure for metabolic engineering, and remains a significant challenge in synthetic biology. Although several DNA assembly methods have been developed and applied for metabolic pathway engineering, many of them are limited by their suitability for combinatorial pathway assembly. The introduction of transcriptional (promoters), translational (ribosome binding site (RBS)) and enzyme (mutant genes) variability to modulate pathway expression levels is essential for generating balanced metabolic pathways and maximizing the productivity of a strain. We report a novel, highly reliable and rapid single strand assembly (SSA) method for pathway engineering. The method was successfully optimized and applied to create constructs containing promoter, RBS and/or mutant enzyme libraries. To demonstrate its efficiency and reliability, the method was applied to fine-tune multi-gene pathways. Two promoter libraries were simultaneously introduced in front of two target genes, enabling orthogonal expression as demonstrated by principal component analysis. This shows that SSA will increase our ability to tune multi-gene pathways at all control levels for the biotechnological production of complex metabolites, achievable through the combinatorial modulation of transcription, translation and enzyme activity. Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

A class of multi-period semi-variance portfolio for petroleum exploration and development

NASA Astrophysics Data System (ADS)

Guo, Qiulin; Li, Jianzhong; Zou, Caineng; Guo, Yujuan; Yan, Wei

2012-10-01

Variance is substituted by semi-variance in Markowitz's portfolio selection model. For dynamic valuation on exploration and development projects, one period portfolio selection is extended to multi-period. In this article, a class of multi-period semi-variance exploration and development portfolio model is formulated originally. Besides, a hybrid genetic algorithm, which makes use of the position displacement strategy of the particle swarm optimiser as a mutation operation, is applied to solve the multi-period semi-variance model. For this class of portfolio model, numerical results show that the mode is effective and feasible.

A Multi-Class, Interdisciplinary Project Using Elementary Statistics

ERIC Educational Resources Information Center

Reese, Margaret

2012-01-01

This article describes a multi-class project that employs statistical computing and writing in a statistics class. Three courses, General Ecology, Meteorology, and Introductory Statistics, cooperated on a project for the EPA's Student Design Competition. The continuing investigation has also spawned several undergraduate research projects in…

A Pseudo-Temporal Multi-Grid Relaxation Scheme for Solving the Parabolized Navier-Stokes Equations

NASA Technical Reports Server (NTRS)

White, J. A.; Morrison, J. H.

1999-01-01

A multi-grid, flux-difference-split, finite-volume code, VULCAN, is presented for solving the elliptic and parabolized form of the equations governing three-dimensional, turbulent, calorically perfect and non-equilibrium chemically reacting flows. The space marching algorithms developed to improve convergence rate and or reduce computational cost are emphasized. The algorithms presented are extensions to the class of implicit pseudo-time iterative, upwind space-marching schemes. A full approximate storage, full multi-grid scheme is also described which is used to accelerate the convergence of a Gauss-Seidel relaxation method. The multi-grid algorithm is shown to significantly improve convergence on high aspect ratio grids.

Classification and Compression of Multi-Resolution Vectors: A Tree Structured Vector Quantizer Approach

DTIC Science & Technology

2002-01-01

their expression profile and for classification of cells into tumerous and non- tumerous classes. Then we will present a parallel tree method for... cancerous cells. We will use the same dataset and use tree structured classifiers with multi-resolution analysis for classifying cancerous from non- cancerous ...cells. We have the expressions of 4096 genes from 98 different cell types. Of these 98, 72 are cancerous while 26 are non- cancerous . We are interested

Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening

PubMed Central

Pan, Rui; Wang, Hansheng; Li, Runze

2016-01-01

This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition. PMID:28127109

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

PubMed

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Thin-plate spline analysis of treatment effects of rapid maxillary expansion and face mask therapy in early Class III malocclusions.

PubMed

Baccetti, T; Franchi, L; McNamara, J A

1999-06-01

An effective morphometric method (thin-plate spline analysis) was applied to evaluate shape changes in the craniofacial configuration of a sample of 23 children with Class III malocclusions in the early mixed dentition treated with rapid maxillary expansion and face mask therapy, and compared with a sample of 17 children with untreated Class III malocclusions. Significant treatment-induced changes involved both the maxilla and the mandible. Major deformations consisted of forward displacement of the maxillary complex from the pterygoid region and of anterior morphogenetic rotation of the mandible, due to a significant upward and forward direction of growth of the mandibular condyle. Significant differences in size changes due to reduced increments in mandibular dimensions were associated with significant shape changes in the treated group.

Airplane detection in remote sensing images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

Multi-class, multi-residue analysis of pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and novel flame retardants in fish using fast, low-pressure gas chromatography-tandem mass spectrometry.

PubMed

Sapozhnikova, Yelena; Lehotay, Steven J

2013-01-03

A multi-class, multi-residue method for the analysis of 13 novel flame retardants, 18 representative pesticides, 14 polychlorinated biphenyl (PCB) congeners, 16 polycyclic aromatic hydrocarbons (PAHs), and 7 polybrominated diphenyl ether (PBDE) congeners in catfish muscle was developed and evaluated using fast low pressure gas chromatography triple quadrupole tandem mass spectrometry (LP-GC/MS-MS). The method was based on a QuEChERS (quick, easy, cheap, effective, rugged, safe) extraction with acetonitrile and dispersive solid-phase extraction (d-SPE) clean-up with zirconium-based sorbent prior to LP-GC/MS-MS analysis. The developed method was evaluated at 4 spiking levels and further validated by analysis of NIST Standard Reference Materials (SRMs) 1974B and 1947. Sample preparation for a batch of 10 homogenized samples took about 1h/analyst, and LP-GC/MS-MS analysis provided fast separation of multiple analytes within 9min achieving high throughput. With the use of isotopically labeled internal standards, recoveries of all but one analyte were between 70 and 120% with relative standard deviations less than 20% (n=5). The measured values for both SRMs agreed with certified/reference values (72-119% accuracy) for the majority of analytes. The detection limits were 0.1-0.5ng g(-1) for PCBs, 0.5-10ng g(-1) for PBDEs, 0.5-5ng g(-1) for select pesticides and PAHs and 1-10ng g(-1) for flame retardants. The developed method was successfully applied for analysis of catfish samples from the market. Published by Elsevier B.V.

Vesselness propagation: a fast interactive vessel segmentation method

NASA Astrophysics Data System (ADS)

Cai, Wenli; Dachille, Frank; Harris, Gordon J.; Yoshida, Hiroyuki

2006-03-01

With the rapid development of multi-detector computed tomography (MDCT), resulting in increasing temporal and spatial resolution of data sets, clinical use of computed tomographic angiography (CTA) is rapidly increasing. Analysis of vascular structures is much needed in CTA images; however, the basis of the analysis, vessel segmentation, can still be a challenging problem. In this paper, we present a fast interactive method for CTA vessel segmentation, called vesselness propagation. This method is a two-step procedure, with a pre-processing step and an interactive step. During the pre-processing step, a vesselness volume is computed by application of a CTA transfer function followed by a multi-scale Hessian filtering. At the interactive stage, the propagation is controlled interactively in terms of the priority of the vesselness. This method was used successfully in many CTA applications such as the carotid artery, coronary artery, and peripheral arteries. It takes less than one minute for a user to segment the entire vascular structure. Thus, the proposed method provides an effective way of obtaining an overview of vascular structures.

Analytical studies on the instabilities of heterogeneous intelligent traffic flow

NASA Astrophysics Data System (ADS)

Ngoduy, D.

2013-10-01

It has been widely reported in literature that a small perturbation in traffic flow such as a sudden deceleration of a vehicle could lead to the formation of traffic jams without a clear bottleneck. These traffic jams are usually related to instabilities in traffic flow. The applications of intelligent traffic systems are a potential solution to reduce the amplitude or to eliminate the formation of such traffic instabilities. A lot of research has been conducted to theoretically study the effect of intelligent vehicles, for example adaptive cruise control vehicles, using either computer simulation or analytical method. However, most current analytical research has only applied to single class traffic flow. To this end, the main topic of this paper is to perform a linear stability analysis to find the stability threshold of heterogeneous traffic flow using microscopic models, particularly the effect of intelligent vehicles on heterogeneous (or multi-class) traffic flow instabilities. The analytical results will show how intelligent vehicle percentages affect the stability of multi-class traffic flow.

Rapid analysis of adulterations in Chinese lotus root powder (LRP) by near-infrared (NIR) spectroscopy coupled with chemometric class modeling techniques.

PubMed

Xu, Lu; Shi, Peng-Tao; Ye, Zi-Hong; Yan, Si-Min; Yu, Xiao-Ping

2013-12-01

This paper develops a rapid analysis method for adulteration identification of a popular traditional Chinese food, lotus root powder (LRP), by near-infrared spectroscopy and chemometrics. 85 pure LRP samples were collected from 7 main lotus producing areas of China to include most if not all of the significant variations likely to be encountered in unknown authentic materials. To evaluate the model specificity, 80 adulterated LRP samples prepared by blending pure LRP with different levels of four cheaper and commonly used starches were measured and predicted. For multivariate quality models, two class modeling methods, the traditional soft independent modeling of class analogy (SIMCA) and a recently proposed partial least squares class model (PLSCM) were used. Different data preprocessing techniques, including smoothing, taking derivative and standard normal variate (SNV) transformation were used to improve the classification performance. The results indicate that smoothing, taking second-order derivatives and SNV can improve the class models by enhancing signal-to-noise ratio, reducing baseline and background shifts. The most accurate and stable models were obtained with SNV spectra for both SIMCA (sensitivity 0.909 and specificity 0.938) and PLSCM (sensitivity 0.909 and specificity 0.925). Moreover, both SIMCA and PLSCM could detect LRP samples mixed with 5% (w/w) or more other cheaper starches, including cassava, sweet potato, potato and maize starches. Although it is difficult to perform an exhaustive collection of all pure LRP samples and possible adulterations, NIR spectrometry combined with class modeling techniques provides a reliable and effective method to detect most of the current LRP adulterations in Chinese market. Copyright © 2013 Elsevier Ltd. All rights reserved.

Metal Oxide Gas Sensor Drift Compensation Using a Two-Dimensional Classifier Ensemble

PubMed Central

Liu, Hang; Chu, Renzhi; Tang, Zhenan

2015-01-01

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector machines. We compare the performance of the strategy with those of competing methods in an experiment based on a public dataset that was compiled over a period of three years. The experimental results demonstrate that the two-dimensional ensemble outperforms the other methods considered. Furthermore, we propose a pre-aging process inspired by that applied to the sensors to improve the stability of the classifier ensemble. The experimental results demonstrate that the weight of each multi-class classifier model in the ensemble remains fairly static before and after the addition of new classifier models to the ensemble, when a pre-aging procedure is applied. PMID:25942640

Rapid prediction of ochratoxin A-producing strains of Penicillium on dry-cured meat by MOS-based electronic nose.

PubMed

Lippolis, Vincenzo; Ferrara, Massimo; Cervellieri, Salvatore; Damascelli, Anna; Epifani, Filomena; Pascale, Michelangelo; Perrone, Giancarlo

2016-02-02

The availability of rapid diagnostic methods for monitoring ochratoxigenic species during the seasoning processes for dry-cured meats is crucial and constitutes a key stage in order to prevent the risk of ochratoxin A (OTA) contamination. A rapid, easy-to-perform and non-invasive method using an electronic nose (e-nose) based on metal oxide semiconductors (MOS) was developed to discriminate dry-cured meat samples in two classes based on the fungal contamination: class P (samples contaminated by OTA-producing Penicillium strains) and class NP (samples contaminated by OTA non-producing Penicillium strains). Two OTA-producing strains of Penicillium nordicum and two OTA non-producing strains of Penicillium nalgiovense and Penicillium salamii, were tested. The feasibility of this approach was initially evaluated by e-nose analysis of 480 samples of both Yeast extract sucrose (YES) and meat-based agar media inoculated with the tested Penicillium strains and incubated up to 14 days. The high recognition percentages (higher than 82%) obtained by Discriminant Function Analysis (DFA), either in calibration and cross-validation (leave-more-out approach), for both YES and meat-based samples demonstrated the validity of the used approach. The e-nose method was subsequently developed and validated for the analysis of dry-cured meat samples. A total of 240 e-nose analyses were carried out using inoculated sausages, seasoned by a laboratory-scale process and sampled at 5, 7, 10 and 14 days. DFA provided calibration models that permitted discrimination of dry-cured meat samples after only 5 days of seasoning with mean recognition percentages in calibration and cross-validation of 98 and 88%, respectively. A further validation of the developed e-nose method was performed using 60 dry-cured meat samples produced by an industrial-scale seasoning process showing a total recognition percentage of 73%. The pattern of volatile compounds of dry-cured meat samples was identified and characterized by a developed HS-SPME/GC-MS method. Seven volatile compounds (2-methyl-1-butanol, octane, 1R-α-pinene, d-limonene, undecane, tetradecanal, 9-(Z)-octadecenoic acid methyl ester) allowed discrimination between dry-cured meat samples of classes P and NP. These results demonstrate that MOS-based electronic nose can be a useful tool for a rapid screening in preventing OTA contamination in the cured meat supply chain. Copyright © 2015 Elsevier B.V. All rights reserved.

A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

PubMed

Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

2017-09-01

Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

Deep learning architectures for multi-label classification of intelligent health risk prediction.

PubMed

Maxwell, Andrew; Li, Runzhi; Yang, Bei; Weng, Heng; Ou, Aihua; Hong, Huixiao; Zhou, Zhaoxian; Gong, Ping; Zhang, Chaoyang

2017-12-28

Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging problem that warrants further studies.

Multi-angle backscatter classification and sub-bottom profiling for improved seafloor characterization

NASA Astrophysics Data System (ADS)

Alevizos, Evangelos; Snellen, Mirjam; Simons, Dick; Siemes, Kerstin; Greinert, Jens

2018-06-01

This study applies three classification methods exploiting the angular dependence of acoustic seafloor backscatter along with high resolution sub-bottom profiling for seafloor sediment characterization in the Eckernförde Bay, Baltic Sea Germany. This area is well suited for acoustic backscatter studies due to its shallowness, its smooth bathymetry and the presence of a wide range of sediment types. Backscatter data were acquired using a Seabeam1180 (180 kHz) multibeam echosounder and sub-bottom profiler data were recorded using a SES-2000 parametric sonar transmitting 6 and 12 kHz. The high density of seafloor soundings allowed extracting backscatter layers for five beam angles over a large part of the surveyed area. A Bayesian probability method was employed for sediment classification based on the backscatter variability at a single incidence angle, whereas Maximum Likelihood Classification (MLC) and Principal Components Analysis (PCA) were applied to the multi-angle layers. The Bayesian approach was used for identifying the optimum number of acoustic classes because cluster validation is carried out prior to class assignment and class outputs are ordinal categorical values. The method is based on the principle that backscatter values from a single incidence angle express a normal distribution for a particular sediment type. The resulting Bayesian classes were well correlated to median grain sizes and the percentage of coarse material. The MLC method uses angular response information from five layers of training areas extracted from the Bayesian classification map. The subsequent PCA analysis is based on the transformation of these five layers into two principal components that comprise most of the data variability. These principal components were clustered in five classes after running an external cluster validation test. In general both methods MLC and PCA, separated the various sediment types effectively, showing good agreement (kappa >0.7) with the Bayesian approach which also correlates well with ground truth data (r2 > 0.7). In addition, sub-bottom data were used in conjunction with the Bayesian classification results to characterize acoustic classes with respect to their geological and stratigraphic interpretation. The joined interpretation of seafloor and sub-seafloor data sets proved to be an efficient approach for a better understanding of seafloor backscatter patchiness and to discriminate acoustically similar classes in different geological/bathymetric settings.

Rapid detection of multiple class pharmaceuticals in both municipal wastewater and sludge with ultra high performance liquid chromatography tandem mass spectrometry.

PubMed

Yuan, Xiangjuan; Qiang, Zhimin; Ben, Weiwei; Zhu, Bing; Liu, Junxin

2014-09-01

This work described the development, optimization and validation of an analytical method for rapid detection of multiple-class pharmaceuticals in both municipal wastewater and sludge samples based on ultrasonic solvent extraction, solid-phase extraction, and ultra high performance liquid chromatography-tandem mass spectrometry quantification. The results indicated that the developed method could effectively extract all the target pharmaceuticals (25) in a single process and analyze them within 24min. The recoveries of the target pharmaceuticals were in the range of 69%-131% for wastewater and 54%-130% for sludge at different spiked concentration levels. The method quantification limits in wastewater and sludge ranged from 0.02 to 0.73ng/L and from 0.02 to 1.00μg/kg, respectively. Subsequently, this method was validated and applied for residual pharmaceutical analysis in a wastewater treatment plant located in Beijing, China. All the target pharmaceuticals were detected in the influent samples with concentrations varying from 0.09ng/L (tiamulin) to 15.24μg/L (caffeine); meanwhile, up to 23 pharmaceuticals were detected in sludge samples with concentrations varying from 60ng/kg (sulfamethizole) to 8.55mg/kg (ofloxacin). The developed method demonstrated its selectivity, sensitivity, and reliability for detecting multiple-class pharmaceuticals in complex matrices such as municipal wastewater and sludge. Copyright © 2014. Published by Elsevier B.V.

Caspian Rapid Assessment Method: a localized procedure for assessment of wetlands at southern fringe of the Caspian Sea.

PubMed

Khorami Pour, Sanaz; Monavari, Seyed Masoud; Riazi, Borhan; Khorasani, Nematollah

2015-07-01

Although Iran is of founders of the Ramsar Convention, there is no comprehensive information available in the country on the status of wetlands in the past or at present. There is also no specific guideline for assessing the status of wetlands in the basin of the Caspian Sea as an ecosystem with unique ecological features. The main aim of this study was to develop a new procedure called "Caspian Rapid Assessment Method" (CRAM) for assessment of wetlands at southern fringe of the Caspian Sea. To this end, 16 rapid assessment methods analyzed by US EPA in 2003 were reviewed to provide an inventory of rapid assessment indices. Excluding less important indices, the inventory was short-listed based on Delphi panelists' consensus. The CRAM was developed with 6 main criteria and 12 sub-criteria. The modified method was used to assess three important wetlands of Anzali, Boojagh and Miyankaleh at the southern border of the Caspian Sea. According to the obtained results, the highest score of 60 was assigned to the Anzali Wetland. Obtaining the scores of 56 and 47, Miyankaleh and Boojagh wetlands were ranked in the next priorities, respectively. At final stage, the accuracy of CRAM prioritization values was confirmed using the Friedman test. All of the wetlands were classified into category II, which indicates destroyed wetlands with rehabilitation potentials. In recent years, serious threats have deteriorated the wetlands from class III (normal condition) to the class II.

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

PubMed Central

Forsberg, Erica M; Huan, Tao; Rinehart, Duane; Benton, H Paul; Warth, Benedikt; Hilmers, Brian; Siuzdak, Gary

2018-01-01

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LCLC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ~30 min. PMID:29494574

Spectrum Recombination.

ERIC Educational Resources Information Center

Greenslade, Thomas B., Jr.

1984-01-01

Describes several methods of executing lecture demonstrations involving the recombination of the spectrum. Groups the techniques into two general classes: bringing selected portions of the spectrum together using lenses or mirrors and blurring the colors by rapid movement or foreshortening. (JM)

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

PubMed

Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

2018-02-15

A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

The Use of Biotin to Demonstrate Immunohistochemistry, Western Blotting, and Dot Blots in University Practical Classes

ERIC Educational Resources Information Center

Millar, Thomas James; Knighton, Ronald; Chuck, Jo-Anne

2012-01-01

Immunological detection of proteins is an essential method to demonstrate to undergraduate biology students, however, is often difficult in resource and time poor student laboratory sessions. This method describes a failsafe method to rapidly and economically demonstrate this technique using biotinylated proteins or biotin itself as targets for…

The status of US multi-campus colleges and schools of pharmacy.

PubMed

Harrison, Lauren C; Congdon, Heather Brennan; DiPiro, Joseph T

2010-09-10

To assess the current status of multi-campus colleges and schools of pharmacy within the United States. Data on multi-campus programs, technology, communication, and opinions regarding benefits and challenges were collected from Web sites, e-mail, and phone interviews from all colleges and schools of pharmacy with students in class on more than 1 campus. Twenty schools and colleges of pharmacy (18 public and 2 private) had multi-campus programs; 16 ran parallel campuses and 4 ran sequential campuses. Most programs used synchronous delivery of classes. The most frequently reported reasons for establishing the multi-campus program were to have access to a hospital and/or medical campus and clinical resources located away from the main campus and to increase class size. Effectiveness of distance education technology was most often sited as a challenge. About 20% of colleges and schools of pharmacy have multi-campus programs most often to facilitate access to clinical resources and to increase class size. These programs expand learning opportunities and face challenges related to technology, resources, and communication.

Development and validation of a rapid multi-class method for the confirmation of fourteen prohibited medicinal additives in pig and poultry compound feed by liquid chromatography-tandem mass spectrometry.

PubMed

Cronly, Mark; Behan, P; Foley, B; Malone, E; Earley, S; Gallagher, M; Shearan, P; Regan, L

2010-12-01

A confirmatory method has been developed to allow for the analysis of fourteen prohibited medicinal additives in pig and poultry compound feed. These compounds are prohibited for use as feed additives although some are still authorised for use in medicated feed. Feed samples are extracted by acetonitrile with addition of sodium sulfate. The extracts undergo a hexane wash to aid with sample purification. The extracts are then evaporated to dryness and reconstituted in initial mobile phase. The samples undergo an ultracentrifugation step prior to injection onto the LC-MS/MS system and are analysed in a run time of 26 min. The LC-MS/MS system is run in MRM mode with both positive and negative electrospray ionisation. The method was validated over three days and is capable of quantitatively analysing for metronidazole, dimetridazole, ronidazole, ipronidazole, chloramphenicol, sulfamethazine, dinitolimide, ethopabate, carbadox and clopidol. The method is also capable of qualitatively analysing for sulfadiazine, tylosin, virginiamycin and avilamycin. A level of 100 microg kg(-1) was used for validation purposes and the method is capable of analysing to this level for all the compounds. Validation criteria of trueness, precision, repeatability and reproducibility along with measurement uncertainty are calculated for all analytes. Copyright (c) 2010 Elsevier B.V. All rights reserved.

UFLC-ESI-MS/MS analysis of multiple mycotoxins in medicinal and edible Areca catechu.

PubMed

Liu, Hongmei; Luo, Jiaoyang; Kong, Weijun; Liu, Qiutao; Hu, Yichen; Yang, Meihua

2016-05-01

A robust, sensitive and reliable ultra fast liquid chromatography combined with electrospray ionization tandem mass spectrometry (UFLC-ESI-MS/MS) was optimized and validated for simultaneous identification and quantification of eleven mycotoxins in medicinal and edible Areca catechu, based on one-step extraction without any further clean-up. Separation and quantification were performed in both positive and negative modes under multiple reaction monitoring (MRM) in a single run with zearalanone (ZAN) as internal standard. The chromatographic conditions and MS/MS parameters were carefully optimized. Matrix-matched calibration was recommended to reduce matrix effects and improve accuracy, showing good linearity within wide concentration ranges. Limits of quantification (LOQ) were lower than 50 μg kg(-1), while limits of detection (LOD) were in the range of 0.1-20 μg kg(-1). The accuracy of the developed method was validated for recoveries, ranging from 85% to 115% with relative standard deviation (RSD) ≤14.87% at low level, from 75% to 119% with RSD ≤ 14.43% at medium level and from 61% to 120% with RSD ≤ 13.18% at high level, respectively. Finally, the developed multi-mycotoxin method was applied for screening of these mycotoxins in 24 commercial samples. Only aflatoxin B2 and zearalenone were found in 2 samples. This is the first report on the application of UFLC-ESI(+/-)-MS/MS for multi-class mycotoxins in A. catechu. The developed method with many advantages of simple pretreatment, rapid determination and high sensitivity is a proposed candidate for large-scale detection and quantification of multiple mycotoxins in other complex matrixes. Copyright © 2016 Elsevier Ltd. All rights reserved.

SYMPATHETIC SOLAR FILAMENT ERUPTIONS

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

Wang, Rui; Liu, Ying D.; Zimovets, Ivan

2016-08-10

The 2015 March 15 coronal mass ejection as one of the two that together drove the largest geomagnetic storm of solar cycle 24 so far was associated with sympathetic filament eruptions. We investigate the relations between the different filaments involved in the eruption. A surge-like small-scale filament motion is confirmed as the trigger that initiated the erupting filament with multi-wavelength observations and using a forced magnetic field extrapolation method. When the erupting filament moved to an open magnetic field region, it experienced an obvious acceleration process and was accompanied by a C-class flare and the rise of another larger filamentmore » that eventually failed to erupt. We measure the decay index of the background magnetic field, which presents a critical height of 118 Mm. Combining with a potential field source surface extrapolation method, we analyze the distributions of the large-scale magnetic field, which indicates that the open magnetic field region may provide a favorable condition for F2 rapid acceleration and have some relation with the largest solar storm. The comparison between the successful and failed filament eruptions suggests that the confining magnetic field plays an important role in the preconditions for an eruption.« less

The multi-reference retaining the excitation degree perturbation theory: A size-consistent, unitary invariant, and rapidly convergent wavefunction based ab initio approach

NASA Astrophysics Data System (ADS)

Fink, Reinhold F.

2009-02-01

The retaining the excitation degree (RE) partitioning [R.F. Fink, Chem. Phys. Lett. 428 (2006) 461(20 September)] is reformulated and applied to multi-reference cases with complete active space (CAS) reference wave functions. The generalised van Vleck perturbation theory is employed to set up the perturbation equations. It is demonstrated that this leads to a consistent and well defined theory which fulfils all important criteria of a generally applicable ab initio method: The theory is proven numerically and analytically to be size-consistent and invariant with respect to unitary orbital transformations within the inactive, active and virtual orbital spaces. In contrast to most previously proposed multi-reference perturbation theories the necessary condition for a proper perturbation theory to fulfil the zeroth order perturbation equation is exactly satisfied with the RE partitioning itself without additional projectors on configurational spaces. The theory is applied to several excited states of the benchmark systems CH2 , SiH2 , and NH2 , as well as to the lowest states of the carbon, nitrogen and oxygen atoms. In all cases comparisons are made with full configuration interaction results. The multi-reference (MR)-RE method is shown to provide very rapidly converging perturbation series. Energy differences between states of similar configurations converge even faster.

[Molecular characterization of resistance mechanisms: methicillin resistance Staphylococcus aureus, extended spectrum β-lactamases and carbapenemases].

PubMed

Oteo, Jesús; Belén Aracil, María

2015-07-01

Multi-drug resistance in bacterial pathogens increases morbidity and mortality in infected patients and it is a threat to public health concern by their high capacity to spread. For both reasons, the rapid detection of multi-drug resistant bacteria is critical. Standard microbiological procedures require 48-72 h to provide the antimicrobial susceptibility results, thus there is emerging interest in the development of rapid detection techniques. In recent years, the use of selective and differential culture-based methods has widely spread. However, the capacity for detecting antibiotic resistance genes and their low turnaround times has made molecular methods a reference for diagnosis of multidrug resistance. This review focusses on the molecular methods for detecting some mechanisms of antibiotic resistance with a high clinical and epidemiological impact: a) Enzymatic resistance to broad spectrum β-lactam antibiotics in Enterobacteriaceae, mainly extended spectrum β-lactamases (ESBL) and carbapenemases; and b) methicillin resistance in Staphylococcus aureus. Copyright © 2015 Elsevier España, S.L.U. All rights reserved.

Linear Distributed GaN MMIC Power Amplifier with Improved Power-added Efficiency

DTIC Science & Technology

2017-03-01

Laboratories, 3011 Malibu Canyon Road, Malibu, CA 90265 Abstract: We report on a multi-octave (100 MHz ‒ 8 GHz), linear nonuniform distributed...amplifier (NDPA) in a MMIC architecture using scaled 120-nm short-gate- length GaN HEMTs. The linear NDPAs were built with six sections in a nonuniform ...MHz ‒ 8 GHz) GaN MMIC nonuniform distributed amplifier (NDPA) with built-in linearization and a gm3 cancellation method in class A and class C

The use of multi representative learning materials: definitive, macroscopic, microscopic, symbolic, and practice in analyzing students’ concept understanding

NASA Astrophysics Data System (ADS)

Susilaningsih, E.; Wulandari, C.; Supartono; Kasmui; Alighiri, D.

2018-03-01

This research aims to compose learning material which contains definitive macroscopic, microscopic and symbolic to analyze students’ conceptual understanding in acid-base learning materials. This research was conducted in eleven grade, natural science class, senior high school 1 (SMAN 1) Karangtengah, Demak province, Indonesia as the low level of students’ conceptual understanding and the high level of students’ misconception. The data collecting technique is by test to assess the cognitive aspect, questionnaire to assess students’ responses to multi representative learning materials (definitive, macroscopic, microscopic, symbolic), and observation to assess students’ macroscopic aspects. Three validators validate the multi-representative learning materials (definitive, macroscopic, microscopic, symbolic). The results of the research show that the multi-representative learning materials (definitive, macroscopic, microscopes, symbolic) being used is valid in the average score 62 of 75. The data is analyzed using the descriptive qualitative method. The results of the research show that 72.934 % students understand, 7.977 % less understand, 8.831 % do not understand, and 10.256 % misconception. In comparison, the second experiment class shows 54.970 % students understand, 5.263% less understand, 11.988 % do not understand, 27.777 % misconception. In conclusion, the application of multi representative learning materials (definitive, macroscopic, microscopic, symbolic) can be used to analyze the students’ understanding of acid-base materials.

The effectiveness of multi modal representation text books to improve student's scientific literacy of senior high school students

NASA Astrophysics Data System (ADS)

Zakiya, Hanifah; Sinaga, Parlindungan; Hamidah, Ida

2017-05-01

The results of field studies showed the ability of science literacy of students was still low. One root of the problem lies in the books used in learning is not oriented toward science literacy component. This study focused on the effectiveness of the use of textbook-oriented provisioning capability science literacy by using multi modal representation. The text books development method used Design Representational Approach Learning to Write (DRALW). Textbook design which was applied to the topic of "Kinetic Theory of Gases" is implemented in XI grade students of high school learning. Effectiveness is determined by consideration of the effect and the normalized percentage gain value, while the hypothesis was tested using Independent T-test. The results showed that the textbooks which were developed using multi-mode representation science can improve the literacy skills of students. Based on the size of the effect size textbooks developed with representation multi modal was found effective in improving students' science literacy skills. The improvement was occurred in all the competence and knowledge of scientific literacy. The hypothesis testing showed that there was a significant difference on the ability of science literacy between class that uses textbooks with multi modal representation and the class that uses the regular textbook used in schools.

Delineating slowly and rapidly evolving fractions of the Drosophila genome.

PubMed

Keith, Jonathan M; Adams, Peter; Stephen, Stuart; Mattick, John S

2008-05-01

Evolutionary conservation is an important indicator of function and a major component of bioinformatic methods to identify non-protein-coding genes. We present a new Bayesian method for segmenting pairwise alignments of eukaryotic genomes while simultaneously classifying segments into slowly and rapidly evolving fractions. We also describe an information criterion similar to the Akaike Information Criterion (AIC) for determining the number of classes. Working with pairwise alignments enables detection of differences in conservation patterns among closely related species. We analyzed three whole-genome and three partial-genome pairwise alignments among eight Drosophila species. Three distinct classes of conservation level were detected. Sequences comprising the most slowly evolving component were consistent across a range of species pairs, and constituted approximately 62-66% of the D. melanogaster genome. Almost all (>90%) of the aligned protein-coding sequence is in this fraction, suggesting much of it (comprising the majority of the Drosophila genome, including approximately 56% of non-protein-coding sequences) is functional. The size and content of the most rapidly evolving component was species dependent, and varied from 1.6% to 4.8%. This fraction is also enriched for protein-coding sequence (while containing significant amounts of non-protein-coding sequence), suggesting it is under positive selection. We also classified segments according to conservation and GC content simultaneously. This analysis identified numerous sub-classes of those identified on the basis of conservation alone, but was nevertheless consistent with that classification. Software, data, and results available at www.maths.qut.edu.au/-keithj/. Genomic segments comprising the conservation classes available in BED format.

Quasiclassical theory of disordered multi-channel Majorana quantum wires

NASA Astrophysics Data System (ADS)

Neven, Patrick; Bagrets, Dmitry; Altland, Alexander

2013-05-01

Multi-channel spin-orbit quantum wires, when subjected to a magnetic field and proximity coupled to an s-wave superconductor, may support Majorana states. We study what happens to these systems in the presence of disorder. Inspired by the widely established theoretical methods of mesoscopic superconductivity, we develop á la Eilenberger a quasiclassical approach to topological nanowires valid in the limit of strong spin-orbit coupling. We find that the ‘Majorana number’ {\\cal M} , distinguishing between the state with Majorana fermions (symmetry class B) and no Majorana fermions (class D), is given by the product of two Pfaffians of gapped quasiclassical Green's functions fixed by the right and left terminals connected to the wire. A numerical solution of the Eilenberger equations reveals that the class D disordered quantum wires are prone to the formation of the zero-energy anomaly (class D impurity spectral peak) in the local density of states that shares the key features of the Majorana peak. In this way, we confirm the robustness of our previous conclusions (Bagrets and Altland 2012 Phys. Rev. Lett. 109 227005) on a more restrictive system setup. Generally speaking, we find that the quasiclassical approach provides a highly efficient means to address disordered class D superconductors both in the presence and in the absence of topological structures.

A Multi-modal, Discriminative and Spatially Invariant CNN for RGB-D Object Labeling.

PubMed

Asif, Umar; Bennamoun, Mohammed; Sohel, Ferdous

2017-08-30

While deep convolutional neural networks have shown a remarkable success in image classification, the problems of inter-class similarities, intra-class variances, the effective combination of multimodal data, and the spatial variability in images of objects remain to be major challenges. To address these problems, this paper proposes a novel framework to learn a discriminative and spatially invariant classification model for object and indoor scene recognition using multimodal RGB-D imagery. This is achieved through three postulates: 1) spatial invariance - this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations, rotations, and scale changes, 2) high discriminative capability - this is achieved by introducing Fisher encoding within the CNN architecture to learn features which have small inter-class similarities and large intra-class compactness, and 3) multimodal hierarchical fusion - this is achieved through the regularization of semantic segmentation to a multi-modal CNN architecture, where class probabilities are estimated at different hierarchical levels (i.e., imageand pixel-levels), and fused into a Conditional Random Field (CRF)- based inference hypothesis, the optimization of which produces consistent class labels in RGB-D images. Extensive experimental evaluations on RGB-D object and scene datasets, and live video streams (acquired from Kinect) show that our framework produces superior object and scene classification results compared to the state-of-the-art methods.

Molecular detection assay of five Salmonella serotypes of public interest: Typhimurium, Enteritidis, Newport, Heidelberg, and Hadar.

PubMed

Bugarel, M; Tudor, A; Loneragan, G H; Nightingale, K K

2017-03-01

Foodborne illnesses due to Salmonella represent an important public-health concern worldwide. In the United States, a majority of Salmonella infections are associated with a small number of serotypes. Furthermore, some serotypes that are overrepresented among human disease are also associated with multi-drug resistance phenotypes. Rapid detection of serotypes of public-health concern might help reduce the burden of salmonellosis cases and limit exposure to multi-drug resistant Salmonella. We developed a two-step real-time PCR-based rapid method for the identification and detection of five Salmonella serotypes that are either overrepresented in human disease or frequently associated with multi-drug resistance, including serotypes Enteritidis, Typhimurium, Newport, Hadar, and Heidelberg. Two sets of four markers were developed to detect and differentiate the five serotypes. The first set of markers was developed as a screening step to detect the five serotypes; whereas, the second set was used to further distinguish serotypes Heidelberg, Newport and Hadar. The utilization of these markers on a two-step investigation strategy provides a diagnostic specificity of 97% for the detection of Typhimurium, Enteritidis, Heidelberg, Infantis, Newport and Hadar. The diagnostic sensitivity of the detection makers is >96%. The availability of this two-step rapid method will facilitate specific detection of Salmonella serotypes that contribute to a significant proportion of human disease and carry antimicrobial resistance. Published by Elsevier B.V.

Rapid estimation of concentration of aromatic classes in middistillate fuels by high-performance liquid chromatography

NASA Technical Reports Server (NTRS)

Otterson, D. A.; Seng, G. T.

1985-01-01

An high performance liquid chromatography (HPLC) method to estimate four aromatic classes in middistillate fuels is presented. Average refractive indices are used in a correlation to obtain the concentrations of each of the aromatic classes from HPLC data. The aromatic class concentrations can be obtained in about 15 min when the concentration of the aromatic group is known. Seven fuels with a wide range of compositions were used to test the method. Relative errors in the concentration of the two major aromatic classes were not over 10 percent. Absolute errors of the minor classes were all less than 0.3 percent. The data show that errors in group-type analyses using sulfuric acid derived standards are greater for fuels containing high concentrations of polycyclic aromatics. Corrections are based on the change in refractive index of the aromatic fraction which can occur when sulfuric acid and the fuel react. These corrections improved both the precision and the accuracy of the group-type results.

77 FR 14269 - Amendment of Class C Airspace; Springfield, MO; Lincoln, NE; Grand Rapids, MI

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-09

.... SUMMARY: This action corrects the Class C airspace designation for Gerald R. Ford International Airport... coordinates of Gerald R. Ford International Airport, Grand Rapids, MI, to match the FAA's aeronautical... legal description for the Gerald R. Ford International Airport, Grand Rapids, MI, Class C airspace, as...

Morphologic changes of the nasal cavity induced by rapid maxillary expansion: a study on 3-dimensional computed tomography models.

PubMed

Haralambidis, Adam; Ari-Demirkaya, Arzu; Acar, Ahu; Küçükkeleş, Nazan; Ateş, Mustafa; Ozkaya, Selin

2009-12-01

The aim of this study was to evaluate the effect of rapid maxillary expansion on the volume of the nasal cavity by using computed tomography. The sample consisted of 24 patients (10 boys, 14 girls) in the permanent dentition who had maxillary constriction and bilateral posterior crossbite. Ten patients had skeletal Class I and 14 had Class II relationships. Skeletal maturity was assessed with the modified cervical vertebral maturation method. Computed tomograms were taken before expansion and at the end of the 3-month retention period, after active expansion. The tomograms were analyzed by Mimics software (version 10.11, Materialise Medical Co, Leuven, Belgium) to reconstruct 3-dimensional images and calculate the volume of the nasal cavities before and after expansion. A significant (P = 0.000) average increase of 11.3% in nasal volume was found. Sex, growth, and skeletal relationship did not influence measurements or response to treatment. A significant difference was found in the volume increase between the Class I and Class II patients, but it was attributed to the longer expansion period of the latter. Therefore, rapid maxillary expansion induces a significant average increase of the nasal volume and consequently can increase nasal permeability and establish a predominant nasal respiration pattern.

Solid-phase extraction of multi-class pharmaceuticals from environmental water samples onto modified multi-walled carbon nanotubes followed by LC-MS/MS.

PubMed

Lalović, Bojana; Đurkić, Tatjana; Vukčević, Marija; Janković-Častvan, Ivona; Kalijadis, Ana; Laušević, Zoran; Laušević, Mila

2017-09-01

In this paper, pristine and chemically treated multi-walled carbon nanotubes (MWCNTs) were employed as solid-phase extraction sorbents for the isolation and enrichment of multi-class pharmaceuticals from the surface water and groundwater, prior to liquid chromatography-tandem mass spectrometry analysis. Thirteen pharmaceuticals that belong to different therapeutical classes (erythromycin, azithromycin, sulfamethoxazole, diazepam, lorazepam, carbamazepine, metoprolol, bisoprolol, enalapril, cilazapril, simvastatin, clopidogrel, diclofenac) and two metabolites of metamizole (4-acetylaminoantipyrine and 4-formylaminoantipyrine) were selected for this study. The influence of chemical treatment on MWCNT surface characteristics and extraction efficiency was studied, and it was shown that HCl treatment of MWCNT leads to a decrease in the amount of surface oxygen groups and at the same time favorably affects the efficiency toward extraction of selected pharmaceuticals. After the optimization of the SPE procedure, the following conditions were chosen: 50 mg of HCl-treated MCWNT as a sorbent, 100 mL of water sample at pH 6, and 15 mL of the methanol-dichloromethane mixture (1:1, v/v) as eluent. Under optimal conditions, high recoveries (79-119%), as well as low detection (0.2 to 103 ng L -1 ) and quantitation (0.5-345 ng L -1 ) limits, were obtained. The optimized method was applied to the analysis of five surface water and two groundwater samples, and three pharmaceuticals were detected, the antiepileptic drug carbamazepine and two metabolites of antipyretic metamizole.

A Novel Multi-Class Ensemble Model for Classifying Imbalanced Biomedical Datasets

NASA Astrophysics Data System (ADS)

Bikku, Thulasi; Sambasiva Rao, N., Dr; Rao, Akepogu Ananda, Dr

2017-08-01

This paper mainly focuseson developing aHadoop based framework for feature selection and classification models to classify high dimensionality data in heterogeneous biomedical databases. Wide research has been performing in the fields of Machine learning, Big data and Data mining for identifying patterns. The main challenge is extracting useful features generated from diverse biological systems. The proposed model can be used for predicting diseases in various applications and identifying the features relevant to particular diseases. There is an exponential growth of biomedical repositories such as PubMed and Medline, an accurate predictive model is essential for knowledge discovery in Hadoop environment. Extracting key features from unstructured documents often lead to uncertain results due to outliers and missing values. In this paper, we proposed a two phase map-reduce framework with text preprocessor and classification model. In the first phase, mapper based preprocessing method was designed to eliminate irrelevant features, missing values and outliers from the biomedical data. In the second phase, a Map-Reduce based multi-class ensemble decision tree model was designed and implemented in the preprocessed mapper data to improve the true positive rate and computational time. The experimental results on the complex biomedical datasets show that the performance of our proposed Hadoop based multi-class ensemble model significantly outperforms state-of-the-art baselines.

Nonlinear viscoplasticity in ASPECT: benchmarking and applications to subduction

NASA Astrophysics Data System (ADS)

Glerum, Anne; Thieulot, Cedric; Fraters, Menno; Blom, Constantijn; Spakman, Wim

2018-03-01

ASPECT (Advanced Solver for Problems in Earth's ConvecTion) is a massively parallel finite element code originally designed for modeling thermal convection in the mantle with a Newtonian rheology. The code is characterized by modern numerical methods, high-performance parallelism and extensibility. This last characteristic is illustrated in this work: we have extended the use of ASPECT from global thermal convection modeling to upper-mantle-scale applications of subduction.

Subduction modeling generally requires the tracking of multiple materials with different properties and with nonlinear viscous and viscoplastic rheologies. To this end, we implemented a frictional plasticity criterion that is combined with a viscous diffusion and dislocation creep rheology. Because ASPECT uses compositional fields to represent different materials, all material parameters are made dependent on a user-specified number of fields.

The goal of this paper is primarily to describe and verify our implementations of complex, multi-material rheology by reproducing the results of four well-known two-dimensional benchmarks: the indentor benchmark, the brick experiment, the sandbox experiment and the slab detachment benchmark. Furthermore, we aim to provide hands-on examples for prospective users by demonstrating the use of multi-material viscoplasticity with three-dimensional, thermomechanical models of oceanic subduction, putting ASPECT on the map as a community code for high-resolution, nonlinear rheology subduction modeling.

A Multi-Institution Study on the Effectiveness of ClassAction to Promote Student Understanding in Astro 101

NASA Astrophysics Data System (ADS)

Lee, Kevin M.; French, R. S.; Hands, D. R.; Loranz, D. R.; Martino, D.; Rudolph, A. L.; Wysong, J.; Young, T. S.; Prather, E. E.; CATS

2010-01-01

ClassAction is a computer database of materials designed to enhance the conceptual understanding and reasoning abilities of Astro 101 students by promoting interactive engagement and providing rapid feedback. The main focus is dynamic conceptual questions largely based upon graphics that can be projected in the classroom. Instructors have the capability to select, order, and recast these questions into alternate permutations based on their own preferences and student responses. Instructors may also provide feedback through extensive resources including outlines, graphics, and simulations. The Light and Spectroscopy Concept Inventory (LSCI) is a multiple-choice assessment instrument which focuses on the electromagnetic spectrum, Doppler shift, Wien's Law, Stefan-Boltzmann Law, and Kirchhoff's Laws. Illustrative examples of how these concepts are targeted by the questions and resources of the ClassAction module are shown. ClassAction materials covering light and spectra concepts were utilized in multiple classrooms at 6 different institutions and the LSCI was delivered as a pretest and posttest to measure the gains in student understanding. A comparison of the gains achieved in these classes will be made against the national LSCI data. We will report on our investigation into correlations between gain and the extent of ClassAction usage. ClassAction materials are publicly available at http://astro.unl.edu. We would like to thank the NSF for funding under Grant Nos. 0404988 and 0715517, a CCLI Phase III Grant for the Collaboration of Astronomy Teaching Scholars (CATS) Program.

Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning.

PubMed

Mei, Suyu

2012-10-07

Recent years have witnessed much progress in computational modeling for protein subcellular localization. However, there are far few computational models for predicting plant protein subcellular multi-localization. In this paper, we propose a multi-label multi-kernel transfer learning model for predicting multiple subcellular locations of plant proteins (MLMK-TLM). The method proposes a multi-label confusion matrix and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which we further extend our published work MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for plant protein subcellular multi-localization. By proper homolog knowledge transfer, MLMK-TLM is applicable to novel plant protein subcellular localization in multi-label learning scenario. The experiments on plant protein benchmark dataset show that MLMK-TLM outperforms the baseline model. Unlike the existing models, MLMK-TLM also reports its misleading tendency, which is important for comprehensive survey of model's multi-labeling performance. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multiattribute selection of acute stroke imaging software platform for Extending the Time for Thrombolysis in Emergency Neurological Deficits (EXTEND) clinical trial.

PubMed

Churilov, Leonid; Liu, Daniel; Ma, Henry; Christensen, Soren; Nagakane, Yoshinari; Campbell, Bruce; Parsons, Mark W; Levi, Christopher R; Davis, Stephen M; Donnan, Geoffrey A

2013-04-01

The appropriateness of a software platform for rapid MRI assessment of the amount of salvageable brain tissue after stroke is critical for both the validity of the Extending the Time for Thrombolysis in Emergency Neurological Deficits (EXTEND) Clinical Trial of stroke thrombolysis beyond 4.5 hours and for stroke patient care outcomes. The objective of this research is to develop and implement a methodology for selecting the acute stroke imaging software platform most appropriate for the setting of a multi-centre clinical trial. A multi-disciplinary decision making panel formulated the set of preferentially independent evaluation attributes. Alternative Multi-Attribute Value Measurement methods were used to identify the best imaging software platform followed by sensitivity analysis to ensure the validity and robustness of the proposed solution. Four alternative imaging software platforms were identified. RApid processing of PerfusIon and Diffusion (RAPID) software was selected as the most appropriate for the needs of the EXTEND trial. A theoretically grounded generic multi-attribute selection methodology for imaging software was developed and implemented. The developed methodology assured both a high quality decision outcome and a rational and transparent decision process. This development contributes to stroke literature in the area of comprehensive evaluation of MRI clinical software. At the time of evaluation, RAPID software presented the most appropriate imaging software platform for use in the EXTEND clinical trial. The proposed multi-attribute imaging software evaluation methodology is based on sound theoretical foundations of multiple criteria decision analysis and can be successfully used for choosing the most appropriate imaging software while ensuring both robust decision process and outcomes. © 2012 The Authors. International Journal of Stroke © 2012 World Stroke Organization.

A multiplex immunochromatographic test using gold nanoparticles for the rapid and simultaneous detection of four nitrofuran metabolites in fish samples.

PubMed

Wang, Quan; Liu, Yingchun; Wang, Mingyan; Chen, Yongjun; Jiang, Wei

2018-01-01

There is an urgent need for the rapid and simultaneous detection of multiple analytes present in a sample matrix. Here, a multiplex immunochromatographic test (multi-ICT) was developed that successfully allowed for the rapid and simultaneous detection of four major nitrofuran metabolites, i.e., 3-amino-2-oxazolidinone (AOZ), semicarbazide (SEM), 3-amino-5-methylmorpholino-2-oxazolidinone (AMOZ), and 1-aminohydantoin (AHD), in fish samples. Four different antigens were separately immobilized in four test lines on a nitrocellulose membrane. Goat anti-mouse immunoglobulin (IgG) was used as a control. Sensitive and specific monoclonal antibodies (mAbs) that recognize the corresponding antigens were selected for the assay, and no cross-reactivity between the antibodies in the detection assay was observed. The free analytes in samples or standards were pre-incubated with freeze-dried mAb-gold conjugates to improve the sensitivity of the detection assay. The multi-ICT detection was accomplished in less than 15 min by the naked eye. The cutoff values for the strip test were 0.5 ng/mL for AOZ and 0.75 ng/mL for AHD, SEM, and AMOZ, which were all below the maximum residue levels set by the European Union and China. A high degree of consistency was observed between the multi-ICT method and commercially available enzyme-linked immunosorbent assay (ELISA) kits using spiked, incurred, and "blind" fish samples, indicating the accuracy, reproducibility, and reliability of the novel test strip. This newly developed multi-ICT strip assay is suitable for the rapid and high-throughput screening of four nitrofuran metabolites in fish samples on-site, with no treatment or devices required. Graphical abstract A multiplex immunochromatographic test (multi-ICT) was developed that successfully allowed for the rapid and simultaneous detection of four major nitrofuran metabolites (AOZ, SEM, AMOZ, and AHD) in fish samples.

A Generalized Mixture Framework for Multi-label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input–output and output–output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods. PMID:26613069

SIMS: A Hybrid Method for Rapid Conformational Analysis

PubMed Central

Gipson, Bryant; Moll, Mark; Kavraki, Lydia E.

2013-01-01

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (sims), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. sims can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that sims is applied to and demonstrate a rapid solution for each. These include the automatic determination of “active” residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems. PMID:23935893

Optimal Control-Based Adaptive NN Design for a Class of Nonlinear Discrete-Time Block-Triangular Systems.

PubMed

Liu, Yan-Jun; Tong, Shaocheng

2016-11-01

In this paper, we propose an optimal control scheme-based adaptive neural network design for a class of unknown nonlinear discrete-time systems. The controlled systems are in a block-triangular multi-input-multi-output pure-feedback structure, i.e., there are both state and input couplings and nonaffine functions to be included in every equation of each subsystem. The design objective is to provide a control scheme, which not only guarantees the stability of the systems, but also achieves optimal control performance. The main contribution of this paper is that it is for the first time to achieve the optimal performance for such a class of systems. Owing to the interactions among subsystems, making an optimal control signal is a difficult task. The design ideas are that: 1) the systems are transformed into an output predictor form; 2) for the output predictor, the ideal control signal and the strategic utility function can be approximated by using an action network and a critic network, respectively; and 3) an optimal control signal is constructed with the weight update rules to be designed based on a gradient descent method. The stability of the systems can be proved based on the difference Lyapunov method. Finally, a numerical simulation is given to illustrate the performance of the proposed scheme.

Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

DTIC Science & Technology

2008-09-01

B.H. (2000). STEGANOGRAPHY: Hidden Images, A New Challenge in the Fight Against Child Porn . UPDATE, Volume 13, Number 2, pp. 1-4, Retrieved June 3...Other crimes involving the use of steganography include child pornography where the stego files are used to hide a predator’s location when posting

On the Relation Between Spherical Harmonics and Simplified Spherical Harmonics Methods

NASA Astrophysics Data System (ADS)

Coppa, G. G. M.; Giusti, V.; Montagnini, B.; Ravetto, P.

2010-03-01

The purpose of the paper is, first, to recall the proof that the AN method and, therefore, the SP2N-1 method (of which AN was shown to be a variant) are equivalent to the odd order P2N-1, at least for a particular class of multi-region problems; namely the problems for which the total cross section has the same value for all the regions and the scattering is supposed to be isotropic. By virtue of the introduction of quadrature formulas representing first collision probabilities, this class is then enlarged in order to encompass the systems in which the regions may have different total cross sections. Some examples are reported to numerically validate the procedure.

Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature

PubMed Central

2011-01-01

Background The selection of relevant articles for curation, and linking those articles to experimental techniques confirming the findings became one of the primary subjects of the recent BioCreative III contest. The contest’s Protein-Protein Interaction (PPI) task consisted of two sub-tasks: Article Classification Task (ACT) and Interaction Method Task (IMT). ACT aimed to automatically select relevant documents for PPI curation, whereas the goal of IMT was to recognise the methods used in experiments for identifying the interactions in full-text articles. Results We proposed and compared several classification-based methods for both tasks, employing rich contextual features as well as features extracted from external knowledge sources. For IMT, a new method that classifies pair-wise relations between every text phrase and candidate interaction method obtained promising results with an F1 score of 64.49%, as tested on the task’s development dataset. We also explored ways to combine this new approach and more conventional, multi-label document classification methods. For ACT, our classifiers exploited automatically detected named entities and other linguistic information. The evaluation results on the BioCreative III PPI test datasets showed that our systems were very competitive: one of our IMT methods yielded the best performance among all participants, as measured by F1 score, Matthew’s Correlation Coefficient and AUC iP/R; whereas for ACT, our best classifier was ranked second as measured by AUC iP/R, and also competitive according to other metrics. Conclusions Our novel approach that converts the multi-class, multi-label classification problem to a binary classification problem showed much promise in IMT. Nevertheless, on the test dataset the best performance was achieved by taking the union of the output of this method and that of a multi-class, multi-label document classifier, which indicates that the two types of systems complement each other in terms of recall. For ACT, our system exploited a rich set of features and also obtained encouraging results. We examined the features with respect to their contributions to the classification results, and concluded that contextual words surrounding named entities, as well as the MeSH headings associated with the documents were among the main contributors to the performance. PMID:22151769

Application of Ontology Technology in Health Statistic Data Analysis.

PubMed

Guo, Minjiang; Hu, Hongpu; Lei, Xingyun

2017-01-01

Research Purpose: establish health management ontology for analysis of health statistic data. Proposed Methods: this paper established health management ontology based on the analysis of the concepts in China Health Statistics Yearbook, and used protégé to define the syntactic and semantic structure of health statistical data. six classes of top-level ontology concepts and their subclasses had been extracted and the object properties and data properties were defined to establish the construction of these classes. By ontology instantiation, we can integrate multi-source heterogeneous data and enable administrators to have an overall understanding and analysis of the health statistic data. ontology technology provides a comprehensive and unified information integration structure of the health management domain and lays a foundation for the efficient analysis of multi-source and heterogeneous health system management data and enhancement of the management efficiency.

Observer-based output consensus of a class of heterogeneous multi-agent systems with unmatched disturbances

NASA Astrophysics Data System (ADS)

Zhang, Jiancheng; Zhu, Fanglai

2018-03-01

In this paper, the output consensus of a class of linear heterogeneous multi-agent systems with unmatched disturbances is considered. Firstly, based on the relative output information among neighboring agents, we propose an asymptotic sliding-mode based consensus control scheme, under which, the output consensus error can converge to zero by removing the disturbances from output channels. Secondly, in order to reach the consensus goal, we design a novel high-order unknown input observer for each agent. It can estimate not only each agent's states and disturbances, but also the disturbances' high-order derivatives, which are required in the control scheme aforementioned above. The observer-based consensus control laws and the convergence analysis of the consensus error dynamics are given. Finally, a simulation example is provided to verify the validity of our methods.

Malaria surveillance in the Democratic Republic of the Congo: comparison of microscopy, PCR, and rapid diagnostic test.

PubMed

Doctor, Stephanie M; Liu, Yunhao; Whitesell, Amy; Thwai, Kyaw L; Taylor, Steve M; Janko, Mark; Emch, Michael; Kashamuka, Melchior; Muwonga, Jérémie; Tshefu, Antoinette; Meshnick, Steven R

2016-05-01

Malaria surveillance is critical for control efforts, but diagnostic methods frequently disagree. Here, we compare microscopy, PCR, and a rapid diagnostic test in 7137 samples from children in the Democratic Republic of the Congo using latent class analysis. PCR had the highest sensitivity (94.6%) and microscopy had the lowest (76.7%). Copyright © 2016 Elsevier Inc. All rights reserved.

Multi-Temporal Multi-Sensor Analysis of Urbanization and Environmental/Climate Impact in China for Sustainable Urban Development

NASA Astrophysics Data System (ADS)

Ban, Yifang; Gong, Peng; Gamba, Paolo; Taubenbock, Hannes; Du, Peijun

2016-08-01

The overall objective of this research is to investigate multi-temporal, multi-scale, multi-sensor satellite data for analysis of urbanization and environmental/climate impact in China to support sustainable planning. Multi- temporal multi-scale SAR and optical data have been evaluated for urban information extraction using innovative methods and algorithms, including KTH- Pavia Urban Extractor, Pavia UEXT, and an "exclusion- inclusion" framework for urban extent extraction, and KTH-SEG, a novel object-based classification method for detailed urban land cover mapping. Various pixel- based and object-based change detection algorithms were also developed to extract urban changes. Several Chinese cities including Beijing, Shanghai and Guangzhou are selected as study areas. Spatio-temporal urbanization patterns and environmental impact at regional, metropolitan and city core were evaluated through ecosystem service, landscape metrics, spatial indices, and/or their combinations. The relationship between land surface temperature and land-cover classes was also analyzed.The urban extraction results showed that urban areas and small towns could be well extracted using multitemporal SAR data with the KTH-Pavia Urban Extractor and UEXT. The fusion of SAR data at multiple scales from multiple sensors was proven to improve urban extraction. For urban land cover mapping, the results show that the fusion of multitemporal SAR and optical data could produce detailed land cover maps with improved accuracy than that of SAR or optical data alone. Pixel-based and object-based change detection algorithms developed with the project were effective to extract urban changes. Comparing the urban land cover results from mulitemporal multisensor data, the environmental impact analysis indicates major losses for food supply, noise reduction, runoff mitigation, waste treatment and global climate regulation services through landscape structural changes in terms of decreases in service area, edge contamination and fragmentation. In terms ofclimate impact, the results indicate that land surface temperature can be related to land use/land cover classes.

Scientific Visualization and Simulation for Multi-dimensional Marine Environment Data

NASA Astrophysics Data System (ADS)

Su, T.; Liu, H.; Wang, W.; Song, Z.; Jia, Z.

2017-12-01

As higher attention on the ocean and rapid development of marine detection, there are increasingly demands for realistic simulation and interactive visualization of marine environment in real time. Based on advanced technology such as GPU rendering, CUDA parallel computing and rapid grid oriented strategy, a series of efficient and high-quality visualization methods, which can deal with large-scale and multi-dimensional marine data in different environmental circumstances, has been proposed in this paper. Firstly, a high-quality seawater simulation is realized by FFT algorithm, bump mapping and texture animation technology. Secondly, large-scale multi-dimensional marine hydrological environmental data is virtualized by 3d interactive technologies and volume rendering techniques. Thirdly, seabed terrain data is simulated with improved Delaunay algorithm, surface reconstruction algorithm, dynamic LOD algorithm and GPU programming techniques. Fourthly, seamless modelling in real time for both ocean and land based on digital globe is achieved by the WebGL technique to meet the requirement of web-based application. The experiments suggest that these methods can not only have a satisfying marine environment simulation effect, but also meet the rendering requirements of global multi-dimension marine data. Additionally, a simulation system for underwater oil spill is established by OSG 3D-rendering engine. It is integrated with the marine visualization method mentioned above, which shows movement processes, physical parameters, current velocity and direction for different types of deep water oil spill particle (oil spill particles, hydrates particles, gas particles, etc.) dynamically and simultaneously in multi-dimension. With such application, valuable reference and decision-making information can be provided for understanding the progress of oil spill in deep water, which is helpful for ocean disaster forecasting, warning and emergency response.

Rapid 3D NMR using the filter diagonalization method: application to oligosaccharides derivatized with 13C-labeled acetyl groups

NASA Astrophysics Data System (ADS)

Armstrong, Geoffrey S.; Cano, Kristin E.; Mandelshtam, Vladimir A.; Shaka, A. J.; Bendiak, Brad

2004-09-01

Rapid 3D NMR spectroscopy of oligosaccharides having isotopically labeled acetyl "isotags" was made possible with high resolution in the indirect dimensions using the filter diagonalization method (FDM). A pulse sequence was designed for the optimal correlation of acetyl methyl protons, methyl carbons, and carbonyl carbons. The multi-dimensional nature of the FDM, coupled with the advantages of constant-time evolution periods, resulted in marked improvements over Fourier transform (FT) and mirror-image linear prediction (MI-LP) processing methods. The three methods were directly compared using identical data sets. A highly resolved 3D spectrum was achieved with the FDM using a very short experimental time (28 min).

Rapid 3D NMR using the filter diagonalization method: application to oligosaccharides derivatized with 13C-labeled acetyl groups.

PubMed

Armstrong, Geoffrey S; Cano, Kristin E; Mandelshtam, Vladimir A; Shaka, A J; Bendiak, Brad

2004-09-01

Rapid 3D NMR spectroscopy of oligosaccharides having isotopically labeled acetyl "isotags" was made possible with high resolution in the indirect dimensions using the filter diagonalization method (FDM). A pulse sequence was designed for the optimal correlation of acetyl methyl protons, methyl carbons, and carbonyl carbons. The multi-dimensional nature of the FDM, coupled with the advantages of constant-time evolution periods, resulted in marked improvements over Fourier transform (FT) and mirror-image linear prediction (MI-LP) processing methods. The three methods were directly compared using identical data sets. A highly resolved 3D spectrum was achieved with the FDM using a very short experimental time (28 min).

Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data.

PubMed

Yang, Yong; Christakos, George; Huang, Wei; Lin, Chengda; Fu, Peihong; Mei, Yang

2016-04-12

Because of the rapid economic growth in China, many regions are subjected to severe particulate matter pollution. Thus, improving the methods of determining the spatiotemporal distribution and uncertainty of air pollution can provide considerable benefits when developing risk assessments and environmental policies. The uncertainty assessment methods currently in use include the sequential indicator simulation (SIS) and indicator kriging techniques. However, these methods cannot be employed to assess multi-temporal data. In this work, a spatiotemporal sequential indicator simulation (STSIS) based on a non-separable spatiotemporal semivariogram model was used to assimilate multi-temporal data in the mapping and uncertainty assessment of PM2.5 distributions in a contaminated atmosphere. PM2.5 concentrations recorded throughout 2014 in Shandong Province, China were used as the experimental dataset. Based on the number of STSIS procedures, we assessed various types of mapping uncertainties, including single-location uncertainties over one day and multiple days and multi-location uncertainties over one day and multiple days. A comparison of the STSIS technique with the SIS technique indicate that a better performance was obtained with the STSIS method.

Chemiluminometric determination of phenothiazines by means of a combined multi-commutated/multi-pumped flow assembly.

PubMed

Halaburda, P; Mateo, J V García

2012-07-15

A rapid, sensitive and fully automated chemiluminometric method is described for determination of five phenothiazine derivatives, namely, trifluoperazine, fluphenazine, perphenazine, thioridazine and chlorpromazine. The method is based on the chemiluminescence (CL) induced by the oxidation of drugs with Ce(IV) in nitric acid. A flow manifold based on the association of multi-commutation and multi-pumping flow methodologies is proposed. The active operated solenoid devices consisted of a micro-pump (propelling 50μL per stroke) and a six ports solenoid valve. Reconfiguration of the flow manifold was performed by using software settings, without physical alteration of the instrument manifold. It permits the design of flexible miniaturized networks for flow analysis based on a time-pulse-counting strategy. The proposed method allows the determination of the drugs at the ngmL(-1) level with a sample throughput of 38h(-1) (chlorpromazine) and 40h(-1) (trifluoperazine, fluphenazine, perphenazine, and thioridazine). The method was successfully applied to the determination of the phenothiazine derivatives in pharmaceuticals formulations. Copyright © 2012 Elsevier B.V. All rights reserved.

Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data

NASA Astrophysics Data System (ADS)

Yang, Yong; Christakos, George; Huang, Wei; Lin, Chengda; Fu, Peihong; Mei, Yang

2016-04-01

Because of the rapid economic growth in China, many regions are subjected to severe particulate matter pollution. Thus, improving the methods of determining the spatiotemporal distribution and uncertainty of air pollution can provide considerable benefits when developing risk assessments and environmental policies. The uncertainty assessment methods currently in use include the sequential indicator simulation (SIS) and indicator kriging techniques. However, these methods cannot be employed to assess multi-temporal data. In this work, a spatiotemporal sequential indicator simulation (STSIS) based on a non-separable spatiotemporal semivariogram model was used to assimilate multi-temporal data in the mapping and uncertainty assessment of PM2.5 distributions in a contaminated atmosphere. PM2.5 concentrations recorded throughout 2014 in Shandong Province, China were used as the experimental dataset. Based on the number of STSIS procedures, we assessed various types of mapping uncertainties, including single-location uncertainties over one day and multiple days and multi-location uncertainties over one day and multiple days. A comparison of the STSIS technique with the SIS technique indicate that a better performance was obtained with the STSIS method.

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

DOE PAGES

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

2018-01-01

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

Detection of lettuce discoloration using hyperspectral reflectance imaging

USDA-ARS?s Scientific Manuscript database

Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to classify the discoloration of lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectra...

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

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

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

The LSST Data Mining Research Agenda

NASA Astrophysics Data System (ADS)

Borne, K.; Becla, J.; Davidson, I.; Szalay, A.; Tyson, J. A.

2008-12-01

We describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night) multi-resolution methods for exploration of petascale databases; indexing of multi-attribute multi-dimensional astronomical databases (beyond spatial indexing) for rapid querying of petabyte databases; and more.

A Novel HURRAH Protocol Reveals High Numbers of Monomorphic MHC Class II Loci and Two Asymmetric Multi-Locus Haplotypes in the Père David's Deer

PubMed Central

Wan, Qiu-Hong; Zhang, Pei; Ni, Xiao-Wei; Wu, Hai-Long; Chen, Yi-Yan; Kuang, Ye-Ye; Ge, Yun-Fa; Fang, Sheng-Guo

2011-01-01

The Père David's deer is a highly inbred, but recovered, species, making it interesting to consider their adaptive molecular evolution from an immunological perspective. Prior to this study, genomic sequencing was the only method for isolating all functional MHC genes within a certain species. Here, we report a novel protocol for isolating MHC class II loci from a species, and its use to investigate the adaptive evolution of this endangered deer at the level of multi-locus haplotypes. This protocol was designated “HURRAH” based on its various steps and used to estimate the total number of MHC class II loci. We confirmed the validity of this novel protocol in the giant panda and then used it to examine the Père David's deer. Our results revealed that the Père David's deer possesses nine MHC class II loci and therefore has more functional MHC class II loci than the eight genome-sequenced mammals for which full MHC data are currently available. This could potentially account at least in part for the strong survival ability of this species in the face of severe bottlenecking. The results from the HURRAH protocol also revealed that: (1) All of the identified MHC class II loci were monomorphic at their antigen-binding regions, although DRA was dimorphic at its cytoplasmic tail; and (2) these genes constituted two asymmetric functional MHC class II multi-locus haplotypes: DRA1*01 ∼ DRB1 ∼ DRB3 ∼ DQA1 ∼ DQB2 (H1) and DRA1*02 ∼ DRB2 ∼ DRB4 ∼ DQA2 ∼ DQB1 (H2). The latter finding indicates that the current members of the deer species have lost the powerful ancestral MHC class II haplotypes of nine or more loci, and have instead fixed two relatively weak haplotypes containing five genes. As a result, the Père David's deer are currently at risk for increased susceptibility to infectious pathogens. PMID:21267075

Trace analysis of multi-class pesticide residues in Chinese medicinal health wines using gas chromatography with electron capture detection

PubMed Central

Kong, Wei-Jun; Liu, Qiu-Tao; Kong, Dan-Dan; Liu, Qian-Zhen; Ma, Xin-Ping; Yang, Mei-Hua

2016-01-01

A method is described for multi-residue, high-throughput determination of trace levels of 22 organochlorine pesticides (OCPs) and 5 pyrethroid pesticides (PYPs) in Chinese medicinal (CM) health wines using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) based extraction method and gas chromatography-electron capture detection (GC-ECD). Several parameters were optimized to improve preparation and separation time while still maintaining high sensitivity. Validation tests of spiked samples showed good linearities for 27 pesticides (R = 0.9909–0.9996) over wide concentration ranges. Limits of detection (LODs) and quantification (LOQs) were measured at ng/L levels, 0.06–2 ng/L and 0.2–6 ng/L for OCPs and 0.02–3 ng/L and 0.06–7 ng/L for PYPs, respectively. Inter- and intra-day precision tests showed variations of 0.65–9.89% for OCPs and 0.98–13.99% for PYPs, respectively. Average recoveries were in the range of 47.74–120.31%, with relative standard deviations below 20%. The developed method was then applied to analyze 80 CM wine samples. Beta-BHC (Benzene hexachloride) was the most frequently detected pesticide at concentration levels of 5.67–31.55 mg/L, followed by delta-BHC, trans-chlordane, gamma-BHC, and alpha-BHC. The validated method is simple and economical, with adequate sensitivity for trace levels of multi-class pesticides. It could be adopted by laboratories for this and other types of complex matrices analysis. PMID:26883080

Trace analysis of multi-class pesticide residues in Chinese medicinal health wines using gas chromatography with electron capture detection

NASA Astrophysics Data System (ADS)

Kong, Wei-Jun; Liu, Qiu-Tao; Kong, Dan-Dan; Liu, Qian-Zhen; Ma, Xin-Ping; Yang, Mei-Hua

2016-02-01

A method is described for multi-residue, high-throughput determination of trace levels of 22 organochlorine pesticides (OCPs) and 5 pyrethroid pesticides (PYPs) in Chinese medicinal (CM) health wines using a QuEChERS (quick, easy, cheap, effective, rugged, and safe) based extraction method and gas chromatography-electron capture detection (GC-ECD). Several parameters were optimized to improve preparation and separation time while still maintaining high sensitivity. Validation tests of spiked samples showed good linearities for 27 pesticides (R = 0.9909-0.9996) over wide concentration ranges. Limits of detection (LODs) and quantification (LOQs) were measured at ng/L levels, 0.06-2 ng/L and 0.2-6 ng/L for OCPs and 0.02-3 ng/L and 0.06-7 ng/L for PYPs, respectively. Inter- and intra-day precision tests showed variations of 0.65-9.89% for OCPs and 0.98-13.99% for PYPs, respectively. Average recoveries were in the range of 47.74-120.31%, with relative standard deviations below 20%. The developed method was then applied to analyze 80 CM wine samples. Beta-BHC (Benzene hexachloride) was the most frequently detected pesticide at concentration levels of 5.67-31.55 mg/L, followed by delta-BHC, trans-chlordane, gamma-BHC, and alpha-BHC. The validated method is simple and economical, with adequate sensitivity for trace levels of multi-class pesticides. It could be adopted by laboratories for this and other types of complex matrices analysis.

75 FR 13668 - Amendment of Class E Airspace; Cedar Rapids, IA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-23

...-0916; Airspace Docket No. 09-ACE-12] Amendment of Class E Airspace; Cedar Rapids, IA AGENCY: Federal... Cedar Rapids, IA, to accommodate Area Navigation (RNAV) Standard Instrument Approach Procedures (SIAPs) at The Eastern Iowa Airport, Cedar Rapids, IA. The FAA is taking this action to enhance the safety...

Reliable, Low-Cost, Low-Weight, Non-Hermetic Coating for MCM Applications

NASA Technical Reports Server (NTRS)

Jones, Eric W.; Licari, James J.

2000-01-01

Through an Air Force Research Laboratory sponsored STM program, reliable, low-cost, low-weight, non-hermetic coatings for multi-chip-module(MCK applications were developed. Using the combination of Sandia Laboratory ATC-01 test chips, AvanTeco's moisture sensor chips(MSC's), and silicon slices, we have shown that organic and organic/inorganic overcoatings are reliable and practical non-hermetic moisture and oxidation barriers. The use of the MSC and unpassivated ATC-01 test chips provided rapid test results and comparison of moisture barrier quality of the overcoatings. The organic coatings studied were Parylene and Cyclotene. The inorganic coatings were Al2O3 and SiO2. The choice of coating(s) is dependent on the environment that the device(s) will be exposed to. We have defined four(4) classes of environments: Class I(moderate temperature/moderate humidity). Class H(high temperature/moderate humidity). Class III(moderate temperature/high humidity). Class IV(high temperature/high humidity). By subjecting the components to adhesion, FTIR, temperature-humidity(TH), pressure cooker(PCT), and electrical tests, we have determined that it is possible to reduce failures 50-70% for organic/inorganic coated components compared to organic coated components. All materials and equipment used are readily available commercially or are standard in most semiconductor fabrication lines. It is estimated that production cost for the developed technology would range from $1-10/module, compared to $20-200 for hermetically sealed packages.

Retinex Preprocessing for Improved Multi-Spectral Image Classification

NASA Technical Reports Server (NTRS)

Thompson, B.; Rahman, Z.; Park, S.

2000-01-01

The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed before classification, so as to reduce the adverse effects of image formation. In this paper, we discuss the overall impact on multi-spectral image classification when the retinex image enhancement algorithm is used to preprocess multi-spectral images. The retinex is a multi-purpose image enhancement algorithm that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The retinex has been successfully applied to the enhancement of many different types of grayscale and color images. We show in this paper that retinex preprocessing improves the spatial structure of multi-spectral images and thus provides better within-class variations than would otherwise be obtained without the preprocessing. For a series of multi-spectral images obtained with diffuse and direct lighting, we show that without retinex preprocessing the class spectral signatures vary substantially with the lighting conditions. Whereas multi-dimensional clustering without preprocessing produced one-class homogeneous regions, the classification on the preprocessed images produced multi-class non-homogeneous regions. This lack of homogeneity is explained by the interaction between different agronomic treatments applied to the regions: the preprocessed images are closer to ground truth. The principle advantage that the retinex offers is that for different lighting conditions classifications derived from the retinex preprocessed images look remarkably "similar", and thus more consistent, whereas classifications derived from the original images, without preprocessing, are much less similar.

Simulating carbon sequestration using cellular automata and land use assessment for Karaj, Iran

NASA Astrophysics Data System (ADS)

Khatibi, Ali; Pourebrahim, Sharareh; Mokhtar, Mazlin Bin

2018-06-01

Carbon sequestration has been proposed as a means of slowing the atmospheric and marine accumulation of greenhouse gases. This study used observed and simulated land use/cover changes to investigate and predict carbon sequestration rates in the city of Karaj. Karaj, a metropolis of Iran, has undergone rapid population expansion and associated changes in recent years, and these changes make it suitable for use as a case study for rapidly expanding urban areas. In particular, high quality agricultural space, green space and gardens have rapidly transformed into industrial, residential and urban service areas. Five classes of land use/cover (residential, agricultural, rangeland, forest and barren areas) were considered in the study; vegetation and soil samples were taken from 20 randomly selected locations. The level of carbon sequestration was determined for the vegetation samples by calculating the amount of organic carbon present using the dry plant weight method, and for soil samples by using the method of Walkley and Black. For each area class, average values of carbon sequestration in vegetation and soil samples were calculated to give a carbon sequestration index. A cellular automata approach was used to simulate changes in the classes. Finally, the carbon sequestration indices were combined with simulation results to calculate changes in carbon sequestration for each class. It is predicted that, in the 15 year period from 2014 to 2029, much agricultural land will be transformed into residential land, resulting in a severe reduction in the level of carbon sequestration. Results from this study indicate that expansion of forest areas in urban counties would be an effective means of increasing the levels of carbon sequestration. Finally, future opportunities to include carbon sequestration into the simulation of land use/cover changes are outlined.

The microdopant effects of surfactant elements on structure-phase transitions during the rapid quenched crystallization of Fe-C-based melts

NASA Astrophysics Data System (ADS)

Polukhin, V. A.; Belyakova, R. M.; Rigmant, L. K.

2008-02-01

The nature of microdopant effects of surfactant Te and H2 reagents on structure-phase transitions in rapidly quenched and crystallized eutectic Fe-C-based melts were studied by experimental and computer methods. On the base of results of statistic-geometrical analysis the new information about the structure changes in multi-scaling systems -from meso- to nano-ones were obtained.

Numerical analysis for trajectory controllability of a coupled multi-order fractional delay differential system via the shifted Jacobi method

NASA Astrophysics Data System (ADS)

Priya, B. Ganesh; Muthukumar, P.

2018-02-01

This paper deals with the trajectory controllability for a class of multi-order fractional linear systems subject to a constant delay in state vector. The solution for the coupled fractional delay differential equation is established by the Mittag-Leffler function. The necessary and sufficient condition for the trajectory controllability is formulated and proved by the generalized Gronwall's inequality. The approximate trajectory for the proposed system is obtained through the shifted Jacobi operational matrix method. The numerical simulation of the approximate solution shows the theoretical results. Finally, some remarks and comments on the existing results of constrained controllability for the fractional dynamical system are also presented.

Environmental Assessment for the Construction and Operation of a New Shoppette/Gas Station, Class Six Store, and Name-Brand Fast Food Store at Joint Base Andrews Camp Springs, Prince George’s County, Maryland

DTIC Science & Technology

2010-02-01

approximately 3.0-acre site. The facility would include retail gasoline sales through the installation of three 20,000-gallon double -walled tanks; 16 multi...construction activities; soil erosion control methods and best management practices would reduce potential for effects; additional impervious surfaces...through the installation of three 20,000-gallon, double -walled tanks; 16 multi- product dispensers with 32 fuel dispenser nozzles; a canopy roofing

Rapid Multi-Tracer PET Tumor Imaging With F-FDG and Secondary Shorter-Lived Tracers.

PubMed

Black, Noel F; McJames, Scott; Kadrmas, Dan J

2009-10-01

Rapid multi-tracer PET, where two to three PET tracers are rapidly scanned with staggered injections, can recover certain imaging measures for each tracer based on differences in tracer kinetics and decay. We previously showed that single-tracer imaging measures can be recovered to a certain extent from rapid dual-tracer (62)Cu - PTSM (blood flow) + (62)Cu - ATSM (hypoxia) tumor imaging. In this work, the feasibility of rapidly imaging (18)F-FDG plus one or two of these shorter-lived secondary tracers was evaluated in the same tumor model. Dynamic PET imaging was performed in four dogs with pre-existing tumors, and the raw scan data was combined to emulate 60 minute long dual- and triple-tracer scans, using the single-tracer scans as gold standards. The multi-tracer data were processed for static (SUV) and kinetic (K(1), K(net)) endpoints for each tracer, followed by linear regression analysis of multi-tracer versus single-tracer results. Static and quantitative dynamic imaging measures of FDG were both accurately recovered from the multi-tracer scans, closely matching the single-tracer FDG standards (R > 0.99). Quantitative blood flow information, as measured by PTSM K(1) and SUV, was also accurately recovered from the multi-tracer scans (R = 0.97). Recovery of ATSM kinetic parameters proved more difficult, though the ATSM SUV was reasonably well recovered (R = 0.92). We conclude that certain additional information from one to two shorter-lived PET tracers may be measured in a rapid multi-tracer scan alongside FDG without compromising the assessment of glucose metabolism. Such additional and complementary information has the potential to improve tumor characterization in vivo, warranting further investigation of rapid multi-tracer techniques.

Rapid Multi-Tracer PET Tumor Imaging With 18F-FDG and Secondary Shorter-Lived Tracers

PubMed Central

Black, Noel F.; McJames, Scott; Kadrmas, Dan J.

2009-01-01

Rapid multi-tracer PET, where two to three PET tracers are rapidly scanned with staggered injections, can recover certain imaging measures for each tracer based on differences in tracer kinetics and decay. We previously showed that single-tracer imaging measures can be recovered to a certain extent from rapid dual-tracer 62Cu – PTSM (blood flow) + 62Cu — ATSM (hypoxia) tumor imaging. In this work, the feasibility of rapidly imaging 18F-FDG plus one or two of these shorter-lived secondary tracers was evaluated in the same tumor model. Dynamic PET imaging was performed in four dogs with pre-existing tumors, and the raw scan data was combined to emulate 60 minute long dual- and triple-tracer scans, using the single-tracer scans as gold standards. The multi-tracer data were processed for static (SUV) and kinetic (K1, Knet) endpoints for each tracer, followed by linear regression analysis of multi-tracer versus single-tracer results. Static and quantitative dynamic imaging measures of FDG were both accurately recovered from the multi-tracer scans, closely matching the single-tracer FDG standards (R > 0.99). Quantitative blood flow information, as measured by PTSM K1 and SUV, was also accurately recovered from the multi-tracer scans (R = 0.97). Recovery of ATSM kinetic parameters proved more difficult, though the ATSM SUV was reasonably well recovered (R = 0.92). We conclude that certain additional information from one to two shorter-lived PET tracers may be measured in a rapid multi-tracer scan alongside FDG without compromising the assessment of glucose metabolism. Such additional and complementary information has the potential to improve tumor characterization in vivo, warranting further investigation of rapid multi-tracer techniques. PMID:20046800

Multiple directed graph large-class multi-spectral processor

NASA Technical Reports Server (NTRS)

Casasent, David; Liu, Shiaw-Dong; Yoneyama, Hideyuki

1988-01-01

Numerical analysis techniques for the interpretation of high-resolution imaging-spectrometer data are described and demonstrated. The method proposed involves the use of (1) a hierarchical classifier with a tree structure generated automatically by a Fisher linear-discriminant-function algorithm and (2) a novel multiple-directed-graph scheme which reduces the local maxima and the number of perturbations required. Results for a 500-class test problem involving simulated imaging-spectrometer data are presented in tables and graphs; 100-percent-correct classification is achieved with an improvement factor of 5.

Method for Rapid Purification of Class IIa Bacteriocins and Comparison of Their Activities

PubMed Central

Guyonnet, D.; Fremaux, C.; Cenatiempo, Y.; Berjeaud, J. M.

2000-01-01

A three-step method was developed for the purification of mesentericin Y105 (60% yield) from the culture supernatant of Leuconostoc mesenteroides Y105. The same procedure was successfully applied to the purification of five other anti-Listeria bacteriocins identified by mass spectrometry. Specific activities of the purified bacteriocins were compared. PMID:10742275

Research Methods in Environmental Studies: A County Planning Application in Colorado.

ERIC Educational Resources Information Center

Gruntfest, Eve C.

To obtain practical experience, a research methods class at the University of Colorado (Colorado Springs) undertook a special project to help a nearby county (Park County), assess its planning needs. The county was chosen for its characteristics as a rapidly growing rural area faced with the problems created by mounting population pressure on…

Fabrication and Cytocompatibility of In Situ Crosslinked Carbon Nanomaterial Films

PubMed Central

Patel, Sunny C.; Lalwani, Gaurav; Grover, Kartikey; Qin, Yi-Xian; Sitharaman, Balaji

2015-01-01

Assembly of carbon nanomaterials into two-dimensional (2D) coatings and films that harness their unique physiochemical properties may lead to high impact energy capture/storage, sensors, and biomedical applications. For potential biomedical applications, the suitability of current techniques such as chemical vapor deposition, spray and dip coating, and vacuum filtration, employed to fabricate macroscopic 2D all carbon coatings or films still requires thorough examination. Each of these methods presents challenges with regards to scalability, suitability for a large variety of substrates, mechanical stability of coatings or films, or biocompatibility. Herein we report a coating process that allow for rapid, in situ chemical crosslinking of multi-walled carbon nanotubes (MWCNTs) into macroscopic all carbon coatings. The resultant coatings were found to be continuous, electrically conductive, significantly more robust, and cytocompatible to human adipose derived stem cells. The results lay groundwork for 3D layer-on-layer nanomaterial assemblies (including various forms of graphene) and also opens avenues to further explore the potential of MWCNT films as a novel class of nano-fibrous mats for tissue engineering and regenerative medicine. PMID:26018775

Hydrangea-like magneto-fluorescent nanoparticles through thiol-inducing assembly

NASA Astrophysics Data System (ADS)

Chen, Shun; Zhang, Junjun; Song, Shaokun; Xiong, Chuanxi; Dong, Lijie

2017-01-01

Magneto-fluorescent nanoparticles (NPs), recognized as an emerging class of materials, have drawn much attention because of their potential applications. Due to surface functionalization and thiol-metal bonds, a simple method has been put forward for fabricating hydrangea-like magneto-fluorescent Fe3O4-SH@QD NPs, through assembling thiol-modified Fe3O4 NPs with sub-size multi-layer core/shell CdSe/CdS/ZnS QDs. After a refined but controllable silane hydrolysis process, thiol-modified Fe3O4 was fabricated, resulting in Fe3O4-SH@QD NPs with QDs, while preventing the quenching of the QDs. As a result, the core Fe3O4 NPs were 18 nm in diameter, while the scattered CdSe/CdS/ZnS QDs were 7 nm in diameter. The resultant magneto-fluorescent Fe3O4-SH@QD NPs exhibit efficient fluorescence, superparamagnetism at room temperature, and rapid response to the external field, which make them ideal candidates for difunctional probes in MRI and bio-labels, targeting and photodynamic therapy, and cell tracking and separation.

Reciprocal and Complementary Sibling Interactions, Relationship Quality and Socio-Emotional Problem Solving

ERIC Educational Resources Information Center

Karos, Leigh Karavasilis; Howe, Nina; Aquan-Assee, Jasmin

2007-01-01

Associations between reciprocal and complementary sibling interactions, sibling relationship quality, and children's socio-emotional problem solving were examined in 40 grade 5-6 children (M age = 11.5 years) from middle class, Caucasian, Canadian families using a multi-method approach (i.e. interviews, self-report questionnaires, daily diary…

The Oklahoma's Promise Program: A National Model to Promote College Persistence

ERIC Educational Resources Information Center

Mendoza, Pilar; Mendez, Jesse P.

2013-01-01

Using a multi-method approach involving fixed effects and logistic regressions, this study examined the effect of the Oklahoma's Promise Program on student persistence in relation to the Pell and Stafford federal programs and according to socio-economic characteristics and class level. The Oklahoma's Promise is a hybrid state program that pays…

The Efficacy of Collaborative Strategic Reading in Middle School Science and Social Studies Classes

ERIC Educational Resources Information Center

Boardman, Alison G.; Klingner, Janette K.; Buckley, Pamela; Annamma, Subini; Lasser, Cristin J.

2015-01-01

This study investigated the efficacy of a multi-component reading comprehension instructional approach, Collaborative Strategic Reading (CSR), compared to business-as-usual instructional methods with 19 teachers and 1074 students in middle school social studies and science classrooms in a large urban district. Researchers collaborated with school…

Strain and Torsion Quantification in Mouse Hearts under Dobutamine Stimulation using 2D Multi-Phase MR DENSE

PubMed Central

Zhong, Jia; Yu, Xin

2010-01-01

In the current study, a 2D multi-phase MR displacement encoding with stimulated echoes (DENSE) imaging and analysis method was developed for direct quantification of Lagrangian strain in the mouse heart. Using the proposed method, less than 10 ms temporal resolution and 0.56 mm in-plane resolution were achieved. A validation study that compared strain calculation by DENSE and by MR tagging showed high correlation between the two methods (R2 > 0.80). Regional ventricular wall strain and twist were characterized in mouse hearts at baseline and under dobutamine stimulation. Dobutamine stimulation induced significant increase in radial and circumferential strains and torsion at peak-systole. A rapid untwisting was also observed during early diastole. This work demonstrates the capability of characterizing cardiac functional response to dobutamine stimulation in the mouse heart using 2D multi-phase MR DENSE. PMID:20740659

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

PubMed

Duong, Bach Phi; Kim, Jong-Myon

2018-04-07

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

PubMed

Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

2014-07-01

In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

[Applications of meta-analysis in multi-omics].

PubMed

Han, Mingfei; Zhu, Yunping

2014-07-01

As a statistical method integrating multi-features and multi-data, meta-analysis was introduced to the field of life science in the 1990s. With the rapid advances in high-throughput technologies, life omics, the core of which are genomics, transcriptomics and proteomics, is becoming the new hot spot of life science. Although the fast output of massive data has promoted the development of omics study, it results in excessive data that are difficult to integrate systematically. In this case, meta-analysis is frequently applied to analyze different types of data and is improved continuously. Here, we first summarize the representative meta-analysis methods systematically, and then study the current applications of meta-analysis in various omics fields, finally we discuss the still-existing problems and the future development of meta-analysis.

Structure of rapidity divergences in multi-parton scattering soft factors

NASA Astrophysics Data System (ADS)

Vladimirov, Alexey

2018-04-01

We discuss the structure of rapidity divergences that are presented in the soft factors of transverse momentum dependent (TMD) factorization theorems. To provide the discussion on the most general level we consider soft factors for multi-parton scattering. We show that the rapidity divergences are result of the gluon exchanges with the distant transverse plane, and are structurally equivalent to the ultraviolet divergences. It allows to formulate and to prove the renormalization theorem for rapidity divergences. The proof is made with the help the conformal transformation which maps rapidity divergences to ultraviolet divergences. The theorem is the systematic form of the factorization of rapidity divergences, which is required for the definition of TMD parton distributions. In particular, the definition of multi parton distributions is presented. The equivalence of ultraviolet and rapidity divergences leads to the exact relation between soft and rapidity anomalous dimensions. Using this relation we derive the rapidity anomalous dimension at the three-loop order.

Di-codon Usage for Gene Classification

NASA Astrophysics Data System (ADS)

Nguyen, Minh N.; Ma, Jianmin; Fogel, Gary B.; Rajapakse, Jagath C.

Classification of genes into biologically related groups facilitates inference of their functions. Codon usage bias has been described previously as a potential feature for gene classification. In this paper, we demonstrate that di-codon usage can further improve classification of genes. By using both codon and di-codon features, we achieve near perfect accuracies for the classification of HLA molecules into major classes and sub-classes. The method is illustrated on 1,841 HLA sequences which are classified into two major classes, HLA-I and HLA-II. Major classes are further classified into sub-groups. A binary SVM using di-codon usage patterns achieved 99.95% accuracy in the classification of HLA genes into major HLA classes; and multi-class SVM achieved accuracy rates of 99.82% and 99.03% for sub-class classification of HLA-I and HLA-II genes, respectively. Furthermore, by combining codon and di-codon usages, the prediction accuracies reached 100%, 99.82%, and 99.84% for HLA major class classification, and for sub-class classification of HLA-I and HLA-II genes, respectively.

Label-aligned Multi-task Feature Learning for Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zu, Chen; Jie, Biao; Liu, Mingxia; Chen, Songcan

2015-01-01

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI. PMID:26572145

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

Challenges at Petascale for Pseudo-Spectral Methods on Spheres (A Last Hurrah?)

NASA Technical Reports Server (NTRS)

Clune, Thomas

2011-01-01

Conclusions: a) Proper software abstractions should enable rapid-exploration of platform-specific optimizations/ tradeoffs. b) Pseudo-spectra! methods are marginally viable for at least some classes of petascaie problems. i.e., GPU based machine with good bisection would be best. c) Scalability at exascale is possible, but the necessary resolution will make algorithm prohibitively expensive. Efficient implementations of realistic global transposes are mtricate and tedious in MPI. PS at petascaie requires exploration of a variety of strategies for spreading local and remote communic3tions. PGAS allows far simpler implementation and thus rapid exploration of variants.

Controller design for a class of nonlinear MIMO coupled system using multiple models and second level adaptation.

PubMed

Pandey, Vinay Kumar; Kar, Indrani; Mahanta, Chitralekha

2017-07-01

In this paper, an adaptive control method using multiple models with second level adaptation is proposed for a class of nonlinear multi-input multi-output (MIMO) coupled systems. Multiple estimation models are used to tune the unknown parameters at the first level. The second level adaptation provides a single parameter vector for the controller. A feedback linearization technique is used to design a state feedback control. The efficacy of the designed controller is validated by conducting real time experiment on a laboratory setup of twin rotor MIMO system (TRMS). The TRMS setup is discussed in detail and the experiments were performed for regulation and tracking problem for pitch and yaw control using different reference signals. An Extended Kalman Filter (EKF) has been used to observe the unavailable states of the TRMS. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Existence of multi-bump solutions for a class of Kirchhoff type problems in R{sup 3}

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

Liang, Sihua, E-mail: liangsihua@126.com; College of Mathematics, Jilin University, Changchun 130012; Shi, Shaoyun, E-mail: shisy@mail.jlu.edu.cn

2013-12-15

Using variational methods, we establish existence of multi-bump solutions for a class of Kirchhoff type problems −(a+b∫{sub R{sup 3}}|∇u|{sup 2}dx)Δu+λV(x)u=f(u), where f is a continuous function with subcritical growth, V(x) is a critical frequency in the sense that inf{sub x∈R{sup 3}}V(x)=0. We show that if the zero set of V(x) has several isolated connected components Ω{sub 1}, …, Ω{sub k} such that the interior of Ω{sub i} is not empty and ∂Ω{sub i} is smooth, then for λ > 0 large there exists, for any non-empty subset J ⊂ (1, …, k), a bump solution is trapped in a neighborhoodmore » of ∪{sub j∈J}Ω{sub j}.« less

Modelling Multi Hazard Mapping in Semarang City Using GIS-Fuzzy Method

NASA Astrophysics Data System (ADS)

Nugraha, A. L.; Awaluddin, M.; Sasmito, B.

2018-02-01

One important aspect of disaster mitigation planning is hazard mapping. Hazard mapping can provide spatial information on the distribution of locations that are threatened by disaster. Semarang City as the capital of Central Java Province is one of the cities with high natural disaster intensity. Frequent natural disasters Semarang city is tidal flood, floods, landslides, and droughts. Therefore, Semarang City needs spatial information by doing multi hazard mapping to support disaster mitigation planning in Semarang City. Multi Hazards map modelling can be derived from parameters such as slope maps, rainfall, land use, and soil types. This modelling is done by using GIS method with scoring and overlay technique. However, the accuracy of modelling would be better if the GIS method is combined with Fuzzy Logic techniques to provide a good classification in determining disaster threats. The Fuzzy-GIS method will build a multi hazards map of Semarang city can deliver results with good accuracy and with appropriate threat class spread so as to provide disaster information for disaster mitigation planning of Semarang city. from the multi-hazard modelling using GIS-Fuzzy can be known type of membership that has a good accuracy is the type of membership Gauss with RMSE of 0.404 the smallest of the other membership and VAF value of 72.909% of the largest of the other membership.

Comparative analysis and validation of the malachite green assay for the high throughput biochemical characterization of terpene synthases

PubMed Central

Vardakou, Maria; Salmon, Melissa; Faraldos, Juan A.; O’Maille, Paul E.

2014-01-01

Terpenes are the largest group of natural products with important and diverse biological roles, while of tremendous economic value as fragrances, flavours and pharmaceutical agents. Class-I terpene synthases (TPSs), the dominant type of TPS enzymes, catalyze the conversion of prenyl diphosphates to often structurally diverse bioactive terpene hydrocarbons, and inorganic pyrophosphate (PPi). To measure their kinetic properties, current bio-analytical methods typically rely on the direct detection of hydrocarbon products by radioactivity measurements or gas chromatography–mass spectrometry (GC–MS). In this study we employed an established, rapid colorimetric assay, the pyrophosphate/malachite green assay (MG), as an alternative means for the biochemical characterization of class I TPSs activity.•We describe the adaptation of the MG assay for turnover and catalytic efficiency measurements of TPSs.•We validate the method by direct comparison with established assays. The agreement of kcat/KM among methods makes this adaptation optimal for rapid evaluation of TPSs.•We demonstrate the application of the MG assay for the high-throughput screening of TPS gene libraries. PMID:26150952

Comparative analysis and validation of the malachite green assay for the high throughput biochemical characterization of terpene synthases.

PubMed

Vardakou, Maria; Salmon, Melissa; Faraldos, Juan A; O'Maille, Paul E

2014-01-01

Terpenes are the largest group of natural products with important and diverse biological roles, while of tremendous economic value as fragrances, flavours and pharmaceutical agents. Class-I terpene synthases (TPSs), the dominant type of TPS enzymes, catalyze the conversion of prenyl diphosphates to often structurally diverse bioactive terpene hydrocarbons, and inorganic pyrophosphate (PPi). To measure their kinetic properties, current bio-analytical methods typically rely on the direct detection of hydrocarbon products by radioactivity measurements or gas chromatography-mass spectrometry (GC-MS). In this study we employed an established, rapid colorimetric assay, the pyrophosphate/malachite green assay (MG), as an alternative means for the biochemical characterization of class I TPSs activity.•We describe the adaptation of the MG assay for turnover and catalytic efficiency measurements of TPSs.•We validate the method by direct comparison with established assays. The agreement of k cat/K M among methods makes this adaptation optimal for rapid evaluation of TPSs.•We demonstrate the application of the MG assay for the high-throughput screening of TPS gene libraries.

A multi-wavelength census of star formation activity in the young embedded cluster around Serpens/G3-G6

NASA Astrophysics Data System (ADS)

Djupvik, A. A.; André, Ph.; Bontemps, S.; Motte, F.; Olofsson, G.; Gålfalk, M.; Florén, H.-G.

2006-11-01

Aims.The aim of this paper is to characterise the star formation activity in the poorly studied embedded cluster Serpens/G3-G6, located ~45 arcmin (3 pc) to the south of the Serpens Cloud Core, and to determine the luminosity and mass functions of its population of Young Stellar Objects (YSOs). Methods: .Multi-wavelength broadband photometry was obtained to sample the near and mid-IR spectral energy distributions to separate YSOs from field stars and classify the YSO evolutionary stage. ISOCAM mapping in the two filters LW2 (5-8.5 μm) and LW3 (12-18 μm) of a 19 arcmin × 16 arcmin field was combined with JHKS data from 2MASS, KS data from Arnica/NOT, and L arcmin data from SIRCA/NOT. Continuum emission at 1.3 mm (IRAM) and 3.6 cm (VLA) was mapped to study the cloud structure and the coldest/youngest sources. Deep narrow band imaging at the 2.12 μm S(1) line of H2 from NOTCam/NOT was obtained to search for signs of bipolar outflows. Results: .We have strong evidence for a stellar population of 31 Class II sources, 5 flat-spectrum sources, 5 Class I sources, and two Class 0 sources. Our method does not sample the Class III sources. The cloud is composed of two main dense clumps aligned along a ridge over ~0.5 pc plus a starless core coinciding with absorption features seen in the ISOCAM maps. We find two S-shaped bipolar collimated flows embedded in the NE clump, and propose the two driving sources to be a Class 0 candidate (MMS3) and a double Class I (MMS2). For the Class II population we find a best age of ~2 Myr and compatibility with recent Initial Mass Functions (IMFs) by comparing the observed Class II luminosity function (LF), which is complete to 0.08 L⊙, to various model LFs with different star formation scenarios and input IMFs.

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

Application of ICME Methods for the Development of Rapid Manufacturing Technologies

NASA Astrophysics Data System (ADS)

Maiwald-Immer, T.; Göhler, T.; Fischersworring-Bunk, A.; Körner, C.; Osmanlic, F.; Bauereiß, A.

Rapid manufacturing technologies are lately gaining interest as alternative manufacturing method. Due to the large parameter sets applicable in these manufacturing methods and their impact on achievable material properties and quality, support of the manufacturing process development by the use of simulation is highly attractive. This is especially true for aerospace applications with their high quality demands and controlled scatter in the resulting material properties. The applicable simulation techniques to these manufacturing methods are manifold. The paper will focus on the melt pool simulation for a SLM (selective laser melting) process which was originally developed for EBM (electron beam melting). It will be discussed in the overall context of a multi-scale simulation within a virtual process chain.

Development of an UPLC-MS/MS Sulfonamide Multi-residue Method and its Application to Water, Manure Slurry, and Soils from Swine Rearing Facilities

USDA-ARS?s Scientific Manuscript database

An analytical method was developed using ultra performance liquid chromatography-triple quadrupole-tandem mass spectrometry (UPLC-TQ-MS/MS) to simultaneously analyze 14 sulfonamides (SA) in six minutes. Despite the rapidity of the assay the system was properly re-equilibrated in this time. No carryo...

[Electrical acupoint stimulation increases athletes' rapid strength].

PubMed

Yang, Hua-yuan; Liu, Tang-yi; Kuai, Le; Gao, Ming

2006-05-01

To search for a stimulation method for increasing athletes' performance. One hundred and fifty athletes were randomly divided into a trial group and a control group, 75 athletes in each group. Acupoints were stimulated with audio frequency pulse modulated wave and multi-blind method were used to investigate effects of the electric stimulation of acupoints on 30-meter running, standing long jumping and Cybex isokinetic testing index. The acupoint electric stimulation method could significantly increase athlete's performance (P < 0.05), and the biomechanical indexes, maximal peak moment of force (P < 0.05), force moment accelerating energy (P < 0.05) and average power (P < 0.05). Electrical acupoint stimulation can enhance athlete's rapid strength.

Macro-fingerprint analysis-through-separation of licorice based on FT-IR and 2DCOS-IR

NASA Astrophysics Data System (ADS)

Wang, Yang; Wang, Ping; Xu, Changhua; Yang, Yan; Li, Jin; Chen, Tao; Li, Zheng; Cui, Weili; Zhou, Qun; Sun, Suqin; Li, Huifen

2014-07-01

In this paper, a step-by-step analysis-through-separation method under the navigation of multi-step IR macro-fingerprint (FT-IR integrated with second derivative IR (SD-IR) and 2DCOS-IR) was developed for comprehensively characterizing the hierarchical chemical fingerprints of licorice from entirety to single active components. Subsequently, the chemical profile variation rules of three parts (flavonoids, saponins and saccharides) in the separation process were holistically revealed and the number of matching peaks and correlation coefficients with standards of pure compounds was increasing along the extracting directions. The findings were supported by UPLC results and a verification experiment of aqueous separation process. It has been demonstrated that the developed multi-step IR macro-fingerprint analysis-through-separation approach could be a rapid, effective and integrated method not only for objectively providing comprehensive chemical characterization of licorice and all its separated parts, but also for rapidly revealing the global enrichment trend of the active components in licorice separation process.

New diagnostic methods for laser plasma- and microwave-enhanced combustion

PubMed Central

Miles, Richard B; Michael, James B; Limbach, Christopher M; McGuire, Sean D; Chng, Tat Loon; Edwards, Matthew R; DeLuca, Nicholas J; Shneider, Mikhail N; Dogariu, Arthur

2015-01-01

The study of pulsed laser- and microwave-induced plasma interactions with atmospheric and higher pressure combusting gases requires rapid diagnostic methods that are capable of determining the mechanisms by which these interactions are taking place. New rapid diagnostics are presented here extending the capabilities of Rayleigh and Thomson scattering and resonance-enhanced multi-photon ionization (REMPI) detection and introducing femtosecond laser-induced velocity and temperature profile imaging. Spectrally filtered Rayleigh scattering provides a method for the planar imaging of temperature fields for constant pressure interactions and line imaging of velocity, temperature and density profiles. Depolarization of Rayleigh scattering provides a measure of the dissociation fraction, and multi-wavelength line imaging enables the separation of Thomson scattering from Rayleigh scattering. Radar REMPI takes advantage of high-frequency microwave scattering from the region of laser-selected species ionization to extend REMPI to atmospheric pressures and implement it as a stand-off detection method for atomic and molecular species in combusting environments. Femtosecond laser electronic excitation tagging (FLEET) generates highly excited molecular species and dissociation through the focal zone of the laser. The prompt fluorescence from excited molecular species yields temperature profiles, and the delayed fluorescence from recombining atomic fragments yields velocity profiles. PMID:26170432

Spectral and spatial resolution analysis of multi sensor satellite data for coral reef mapping: Tioman Island, Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet; Kabiri, Keivan

2012-07-01

This paper describes an assessment of coral reef mapping using multi sensor satellite images such as Landsat ETM, SPOT and IKONOS images for Tioman Island, Malaysia. The study area is known to be one of the best Islands in South East Asia for its unique collection of diversified coral reefs and serves host to thousands of tourists every year. For the coral reef identification, classification and analysis, Landsat ETM, SPOT and IKONOS images were collected processed and classified using hierarchical classification schemes. At first, Decision tree classification method was implemented to separate three main land cover classes i.e. water, rural and vegetation and then maximum likelihood supervised classification method was used to classify these main classes. The accuracy of the classification result is evaluated by a separated test sample set, which is selected based on the fieldwork survey and view interpretation from IKONOS image. Few types of ancillary data in used are: (a) DGPS ground control points; (b) Water quality parameters measured by Hydrolab DS4a; (c) Sea-bed substrates spectrum measured by Unispec and; (d) Landcover observation photos along Tioman island coastal area. The overall accuracy of the final classification result obtained was 92.25% with the kappa coefficient is 0.8940. Key words: Coral reef, Multi-spectral Segmentation, Pixel-Based Classification, Decision Tree, Tioman Island

Modelica buildings library

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

Wetter, Michael; Zuo, Wangda; Nouidui, Thierry S.

This paper describes the Buildings library, a free open-source library that is implemented in Modelica, an equation-based object-oriented modeling language. The library supports rapid prototyping, as well as design and operation of building energy and control systems. First, we describe the scope of the library, which covers HVAC systems, multi-zone heat transfer and multi-zone airflow and contaminant transport. Next, we describe differentiability requirements and address how we implemented them. We describe the class hierarchy that allows implementing component models by extending partial implementations of base models of heat and mass exchangers, and by instantiating basic models for conservation equations andmore » flow resistances. We also describe associated tools for pre- and post-processing, regression tests, co-simulation and real-time data exchange with building automation systems. Furthermore, the paper closes with an example of a chilled water plant, with and without water-side economizer, in which we analyzed the system-level efficiency for different control setpoints.« less

Modelica buildings library

DOE PAGES

Wetter, Michael; Zuo, Wangda; Nouidui, Thierry S.; ...

2013-03-13

This paper describes the Buildings library, a free open-source library that is implemented in Modelica, an equation-based object-oriented modeling language. The library supports rapid prototyping, as well as design and operation of building energy and control systems. First, we describe the scope of the library, which covers HVAC systems, multi-zone heat transfer and multi-zone airflow and contaminant transport. Next, we describe differentiability requirements and address how we implemented them. We describe the class hierarchy that allows implementing component models by extending partial implementations of base models of heat and mass exchangers, and by instantiating basic models for conservation equations andmore » flow resistances. We also describe associated tools for pre- and post-processing, regression tests, co-simulation and real-time data exchange with building automation systems. Furthermore, the paper closes with an example of a chilled water plant, with and without water-side economizer, in which we analyzed the system-level efficiency for different control setpoints.« less

Multi-level methods and approximating distribution functions

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

Wilson, D., E-mail: daniel.wilson@dtc.ox.ac.uk; Baker, R. E.

2016-07-15

Biochemical reaction networks are often modelled using discrete-state, continuous-time Markov chains. System statistics of these Markov chains usually cannot be calculated analytically and therefore estimates must be generated via simulation techniques. There is a well documented class of simulation techniques known as exact stochastic simulation algorithms, an example of which is Gillespie’s direct method. These algorithms often come with high computational costs, therefore approximate stochastic simulation algorithms such as the tau-leap method are used. However, in order to minimise the bias in the estimates generated using them, a relatively small value of tau is needed, rendering the computational costs comparablemore » to Gillespie’s direct method. The multi-level Monte Carlo method (Anderson and Higham, Multiscale Model. Simul. 10:146–179, 2012) provides a reduction in computational costs whilst minimising or even eliminating the bias in the estimates of system statistics. This is achieved by first crudely approximating required statistics with many sample paths of low accuracy. Then correction terms are added until a required level of accuracy is reached. Recent literature has primarily focussed on implementing the multi-level method efficiently to estimate a single system statistic. However, it is clearly also of interest to be able to approximate entire probability distributions of species counts. We present two novel methods that combine known techniques for distribution reconstruction with the multi-level method. We demonstrate the potential of our methods using a number of examples.« less

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

An intelligent framework for medical image retrieval using MDCT and multi SVM.

PubMed

Balan, J A Alex Rajju; Rajan, S Edward

2014-01-01

Volumes of medical images are rapidly generated in medical field and to manage them effectively has become a great challenge. This paper studies the development of innovative medical image retrieval based on texture features and accuracy. The objective of the paper is to analyze the image retrieval based on diagnosis of healthcare management systems. This paper traces the development of innovative medical image retrieval to estimate both the image texture features and accuracy. The texture features of medical images are extracted using MDCT and multi SVM. Both the theoretical approach and the simulation results revealed interesting observations and they were corroborated using MDCT coefficients and SVM methodology. All attempts to extract the data about the image in response to the query has been computed successfully and perfect image retrieval performance has been obtained. Experimental results on a database of 100 trademark medical images show that an integrated texture feature representation results in 98% of the images being retrieved using MDCT and multi SVM. Thus we have studied a multiclassification technique based on SVM which is prior suitable for medical images. The results show the retrieval accuracy of 98%, 99% for different sets of medical images with respect to the class of image.

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

PubMed

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

Accurate chemical master equation solution using multi-finite buffers

DOE PAGES

Cao, Youfang; Terebus, Anna; Liang, Jie

2016-06-29

Here, the discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multiscale nature of many networks where reaction rates have a large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multifinite buffers for reducing the state spacemore » by $O(n!)$, exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be precomputed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multiscale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks.« less

Accurate chemical master equation solution using multi-finite buffers

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

Cao, Youfang; Terebus, Anna; Liang, Jie

Here, the discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multiscale nature of many networks where reaction rates have a large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multifinite buffers for reducing the state spacemore » by $O(n!)$, exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be precomputed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multiscale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks.« less

Finite-time consensus for controlled dynamical systems in network

NASA Astrophysics Data System (ADS)

Zoghlami, Naim; Mlayeh, Rhouma; Beji, Lotfi; Abichou, Azgal

2018-04-01

The key challenges in networked dynamical systems are the component heterogeneities, nonlinearities, and the high dimension of the formulated vector of state variables. In this paper, the emphasise is put on two classes of systems in network include most controlled driftless systems as well as systems with drift. For each model structure that defines homogeneous and heterogeneous multi-system behaviour, we derive protocols leading to finite-time consensus. For each model evolving in networks forming a homogeneous or heterogeneous multi-system, protocols integrating sufficient conditions are derived leading to finite-time consensus. Likewise, for the networking topology, we make use of fixed directed and undirected graphs. To prove our approaches, finite-time stability theory and Lyapunov methods are considered. As illustrative examples, the homogeneous multi-unicycle kinematics and the homogeneous/heterogeneous multi-second order dynamics in networks are studied.

Enhanced iris recognition method based on multi-unit iris images

NASA Astrophysics Data System (ADS)

Shin, Kwang Yong; Kim, Yeong Gon; Park, Kang Ryoung

2013-04-01

For the purpose of biometric person identification, iris recognition uses the unique characteristics of the patterns of the iris; that is, the eye region between the pupil and the sclera. When obtaining an iris image, the iris's image is frequently rotated because of the user's head roll toward the left or right shoulder. As the rotation of the iris image leads to circular shifting of the iris features, the accuracy of iris recognition is degraded. To solve this problem, conventional iris recognition methods use shifting of the iris feature codes to perform the matching. However, this increases the computational complexity and level of false acceptance error. To solve these problems, we propose a novel iris recognition method based on multi-unit iris images. Our method is novel in the following five ways compared with previous methods. First, to detect both eyes, we use Adaboost and a rapid eye detector (RED) based on the iris shape feature and integral imaging. Both eyes are detected using RED in the approximate candidate region that consists of the binocular region, which is determined by the Adaboost detector. Second, we classify the detected eyes into the left and right eyes, because the iris patterns in the left and right eyes in the same person are different, and they are therefore considered as different classes. We can improve the accuracy of iris recognition using this pre-classification of the left and right eyes. Third, by measuring the angle of head roll using the two center positions of the left and right pupils, detected by two circular edge detectors, we obtain the information of the iris rotation angle. Fourth, in order to reduce the error and processing time of iris recognition, adaptive bit-shifting based on the measured iris rotation angle is used in feature matching. Fifth, the recognition accuracy is enhanced by the score fusion of the left and right irises. Experimental results on the iris open database of low-resolution images showed that the averaged equal error rate of iris recognition using the proposed method was 4.3006%, which is lower than that of other methods.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

NASA Astrophysics Data System (ADS)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Rapid screening of heavy metals and trace elements in environmental samples using portable X-ray fluorescence spectrometer, A comparative study

PubMed Central

McComb, Jacqueline Q.; Rogers, Christian; Han, Fengxiang X.; Tchounwou, Paul B.

2014-01-01

With industrialization, great amounts of trace elements and heavy metals have been excavated and released on the surface of the earth and dissipated into the environments. Rapid screening technology for detecting major and trace elements as well as heavy metals in variety of environmental samples is most desired. The objectives of this study were to determine the detection limits, accuracy, repeatability and efficiency of a X-ray fluorescence spectrometer (Niton XRF analyzer) in comparison with the traditional analytical methods, inductively coupled plasma optical emission spectrometer (ICP-OES) and inductively coupled plasma optical emission spectrometer (ICP-MS) in screening of major and trace elements of environmental samples including estuary soils and sediments, contaminated soils, and biological samples. XRF is a fast and non-destructive method in measuring the total concentration of multi--elements simultaneously. Contrary to ICP-OES and ICP-MS, XRF analyzer is characterized by the limited preparation required for solid samples, non-destructive analysis, increased total speed and high throughout, the decreased production of hazardous waste and the low running costs as well as multi-elemental determination and portability in the fields. The current comparative study demonstrates that XRF is a good rapid non-destructive method for contaminated soils, sediments and biological samples containing higher concentrations of major and trace elements. Unfortunately, XRF does not have sensitive detection limits of most major and trace elements as ICP-OES or ICP-MS but it may serve as a rapid screening tool for locating hot spots of uncontaminated field soils and sediments. PMID:25861136

Participant Analysis of a Multi-Class, Multi-State, On-Line, Discussion List.

ERIC Educational Resources Information Center

Knupfer, Nancy Nelson; And Others

As interest in distance education increases, many university instructors are experimenting with listserv discussion within their courses. Six educational technology professors at four universities initiated a listserv discussion group within their various classes for 52 graduate students. Discussion topics were four general themes within…

Multi-ingredients determination and fingerprint analysis of leaves from Ilex latifolia using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry.

PubMed

Fan, Chunlin; Deng, Jiewei; Yang, Yunyun; Liu, Junshan; Wang, Ying; Zhang, Xiaoqi; Fai, Kuokchiu; Zhang, Qingwen; Ye, Wencai

2013-10-01

An ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) method integrating multi-ingredients determination and fingerprint analysis has been established for quality assessment and control of leaves from Ilex latifolia. The method possesses the advantages of speediness, efficiency, accuracy, and allows the multi-ingredients determination and fingerprint analysis in one chromatographic run within 13min. Multi-ingredients determination was performed based on the extracted ion chromatograms of the exact pseudo-molecular ions (with a 0.01Da window), and fingerprint analysis was performed based on the base peak chromatograms, obtained by negative-ion electrospray ionization QTOF-MS. The method validation results demonstrated our developed method possessing desirable specificity, linearity, precision and accuracy. The method was utilized to analyze 22 I. latifolia samples from different origins. The quality assessment was achieved by using both similarity analysis (SA) and principal component analysis (PCA), and the results from SA were consistent with those from PCA. Our experimental results demonstrate that the strategy integrated multi-ingredients determination and fingerprint analysis using UPLC-QTOF-MS technique is a useful approach for rapid pharmaceutical analysis, with promising prospects for the differentiation of origin, the determination of authenticity, and the overall quality assessment of herbal medicines. Copyright © 2013 Elsevier B.V. All rights reserved.

Comparison of Evolutionary (Genetic) Algorithm and Adjoint Methods for Multi-Objective Viscous Airfoil Optimizations

NASA Technical Reports Server (NTRS)

Pulliam, T. H.; Nemec, M.; Holst, T.; Zingg, D. W.; Kwak, Dochan (Technical Monitor)

2002-01-01

A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.

Application of multi-agent coordination methods to the design of space debris mitigation tours

NASA Astrophysics Data System (ADS)

Stuart, Jeffrey; Howell, Kathleen; Wilson, Roby

2016-04-01

The growth in the number of defunct and fragmented objects near to the Earth poses a growing hazard to launch operations as well as existing on-orbit assets. Numerous studies have demonstrated the positive impact of active debris mitigation campaigns upon the growth of debris populations, but comparatively fewer investigations incorporate specific mission scenarios. Furthermore, while many active mitigation methods have been proposed, certain classes of debris objects are amenable to mitigation campaigns employing chaser spacecraft with existing chemical and low-thrust propulsive technologies. This investigation incorporates an ant colony optimization routing algorithm and multi-agent coordination via auctions into a debris mitigation tour scheme suitable for preliminary mission design and analysis as well as spacecraft flight operations.

High throughput screening technologies for ion channels

PubMed Central

Yu, Hai-bo; Li, Min; Wang, Wei-ping; Wang, Xiao-liang

2016-01-01

Ion channels are involved in a variety of fundamental physiological processes, and their malfunction causes numerous human diseases. Therefore, ion channels represent a class of attractive drug targets and a class of important off-targets for in vitro pharmacological profiling. In the past decades, the rapid progress in developing functional assays and instrumentation has enabled high throughput screening (HTS) campaigns on an expanding list of channel types. Chronologically, HTS methods for ion channels include the ligand binding assay, flux-based assay, fluorescence-based assay, and automated electrophysiological assay. In this review we summarize the current HTS technologies for different ion channel classes and their applications. PMID:26657056

Probabilistic Open Set Recognition

NASA Astrophysics Data System (ADS)

Jain, Lalit Prithviraj

Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary support vector machines. Building from the success of statistical EVT based recognition methods such as PI-SVM and W-SVM on the open set problem, we present a new general supervised learning algorithm for multi-class classification and multi-class open set recognition called the Extreme Value Local Basis (EVLB). The design of this algorithm is motivated by the observation that extrema from known negative class distributions are the closest negative points to any positive sample during training, and thus should be used to define the parameters of a probabilistic decision model. In the EVLB, the kernel distribution for each positive training sample is estimated via an EVT distribution fit over the distances to the separating hyperplane between positive training sample and closest negative samples, with a subset of the overall positive training data retained to form a probabilistic decision boundary. Using this subset as a frame of reference, the probability of a sample at test time decreases as it moves away from the positive class. Possessing this property, the EVLB is well-suited to open set recognition problems where samples from unknown or novel classes are encountered at test. Our experimental evaluation shows that the EVLB provides a substantial improvement in scalability compared to standard radial basis function kernel machines, as well as P I-SVM and W-SVM, with improved accuracy in many cases. We evaluate our algorithm on open set variations of the standard visual learning benchmarks, as well as with an open subset of classes from Caltech 256 and ImageNet. Our experiments show that PI-SVM, WSVM and EVLB provide significant advances over the previous state-of-the-art solutions for the same tasks.

Chaotic map clustering algorithm for EEG analysis

NASA Astrophysics Data System (ADS)

Bellotti, R.; De Carlo, F.; Stramaglia, S.

2004-03-01

The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.

Identification of aerosol composition from multi-wavelength lidar measurements

NASA Technical Reports Server (NTRS)

Wood, S. A.

1984-01-01

This paper seeks to develop the potential of lidar for the identification of the chemical composition of atmospheric aerosols. Available numerical computations suggest that aerosols can be identified by the wavelength dependence of aerosol optical properties. Since lidar can derive the volume backscatter coefficient as a function of wavelength, a multi-wavelength lidar system may be able to provide valuable information on the composition of aerosols. This research theoretically investigates the volume backscatter coefficients for the aerosol classes, sea-salts, and sulfates, as a function of wavelength. The results show that these aerosol compositions can be characterized and identified by their backscatter wavelength dependence. A method to utilize multi-wavelength lidar measurements to discriminate between compositionally different thin aerosol layers is discussed.

The prevalence of antiretroviral multidrug resistance in highly active antiretroviral therapy-treated patients with HIV/AIDS between 2004 and 2009 in South Korea.

PubMed

Choi, Ju-yeon; Kwon, Oh-Kyung; Choi, Byeong-Sun; Kee, Mee-Kyung; Park, Mina; Kim, Sung Soon

2014-06-01

Highly active antiretroviral therapy (HAART) including protease inhibitors (PIs) has been used in South Korea since 1997. Currently, more than 20 types of antiretroviral drugs are used in the treatment of human immunodeficiency virus-infected/acquired immune deficiency syndrome patients in South Korea. Despite the rapid development of various antiretroviral drugs, many drug-resistant variants have been reported after initiating HAART, and the efficiency of HAART is limited by these variants. To investigate and estimate the annual antiretroviral drug resistance and prevalence of antiretroviral multi-class drug resistance in Korean patients with experience of treatment. The amplified HIV-1 pol gene in 535 patients requested for genotypic drug resistance testing from 2004 to 2009 by the Korea Centers for Disease Control and Prevention was sequenced and analyzed annually and totally. The prevalence of antiretroviral drug resistance was estimated based on "SIR" interpretation of the Stanford sequence database. Of viruses derived from 787 specimens, 380 samples (48.3%) showed at least one drug class-related resistance. Predicted NRTI drug resistance was highest at 41.9%. NNRTI showed 27.2% resistance with 23.3% for PI. The percent of annual drug resistance showed similar pattern and slightly declined except 2004 and 2005. The prevalence of multi-class drug resistance against each drug class was: NRTI/NNRTI/PI, 9.8%; NRTI/PI, 21.9%; NNRTI/PI, 10.4%; and NRTI/NNRTI, 21.5%. About 50% and less than 10% of patients infected with HIV-1 have multidrug and multiclass resistance linked to 16 antiretroviral drugs, respectively. The significance of this study lies in its larger-scale examination of the prevalence of drug-resistant variants and multidrug resistance in HAART-experienced patients in South Korea. Copyright © 2014 Elsevier B.V. All rights reserved.

GIS/RS-based Rapid Reassessment for Slope Land Capability Classification

NASA Astrophysics Data System (ADS)

Chang, T. Y.; Chompuchan, C.

2014-12-01

Farmland resources in Taiwan are limited because about 73% is mountainous and slope land. Moreover, the rapid urbanization and dense population resulted in the highly developed flat area. Therefore, the utilization of slope land for agriculture is more needed. In 1976, "Slope Land Conservation and Utilization Act" was promulgated to regulate the slope land utilization. Consequently, slope land capability was categorized into Class I-IV according to 4 criteria, i.e., average land slope, effective soil depth, degree of soil erosion, and parent rock. The slope land capability Class I-VI are suitable for cultivation and pasture. Whereas, Class V should be used for forestry purpose and Class VI should be the conservation land which requires intensive conservation practices. The field survey was conducted to categorize each land unit as the classification scheme. The landowners may not allow to overuse land capability limitation. In the last decade, typhoons and landslides frequently devastated in Taiwan. The rapid post-disaster reassessment of the slope land capability classification is necessary. However, the large-scale disaster on slope land is the constraint of field investigation. This study focused on using satellite remote sensing and GIS as the rapid re-evaluation method. Chenyulan watershed in Nantou County, Taiwan was selected to be a case study area. Grid-based slope derivation, topographic wetness index (TWI) and USLE soil loss calculation were used to classify slope land capability. The results showed that GIS-based classification give an overall accuracy of 68.32%. In addition, the post-disaster areas of Typhoon Morakot in 2009, which interpreted by SPOT satellite imageries, were suggested to classify as the conservation lands. These tools perform better in the large coverage post-disaster update for slope land capability classification and reduce time-consuming, manpower and material resources to the field investigation.

Multi-bacteria multi-antibiotic testing using surface enhanced Raman spectroscopy (SERS) for urinary tract infection (UTI) diagnosis

NASA Astrophysics Data System (ADS)

Hadjigeorgiou, Katerina; Kastanos, Evdokia; Pitris, Costas

2013-06-01

The inappropriate use of antibiotics leads to antibiotic resistance, which is a major health care problem. The current method for determination of bacterial susceptibility to antibiotics requires overnight cultures. However most of the infections cannot wait for the results to receive treatment, so physicians administer general spectrum antibiotics. This results in ineffective treatments and aggravates the rising problem of antibiotic resistance. In this work, a rapid method for diagnosis and antibiogram for a bacterial infection was developed using Surface Enhanced Raman Spectroscopy (SERS) with silver nanoparticles. The advantages of this novel method include its rapidness and efficiency which will potentially allow doctors to prescribe the most appropriate antibiotic for an infection. SERS spectra of three species of gram negative bacteria, Escherichia coli, Proteus spp., and Klebsiella spp. were obtained after 0 and 4 hour exposure to the seven different antibiotics. Bacterial strains were diluted in order to reach the concentration of (2x105 cfu/ml), cells/ml which is equivalent to the minimum concentration found in urine samples from UTIs. Even though the concentration of bacteria was low, species classification was achieved with 94% accuracy using spectra obtained at 0 hours. Sensitivity or resistance to antibiotics was predicted with 81%-100% accuracy from spectra obtained after 4 hours of exposure to the different antibiotics. This technique can be applied directly to urine samples, and with the enhancement provided by SERS, this method has the potential to be developed into a rapid method for same day UTI diagnosis and antibiogram.

Global solutions to a class of multi-species reaction-diffusion systems with cross-diffusions arising in population dynamics

NASA Astrophysics Data System (ADS)

Wen, Zijuan; Fu, Shengmao

2009-08-01

In this paper, an n-species strongly coupled cooperating diffusive system is considered in a bounded smooth domain, subject to homogeneous Neumann boundary conditions. Employing the method of energy estimates, we obtain some conditions on the diffusion matrix and inter-specific cooperatives to ensure the global existence and uniform boundedness of a nonnegative solution. The globally asymptotical stability of the constant positive steady state is also discussed. As a consequence, all the results hold true for multi-species Lotka-Volterra type competition model and prey-predator model.

Revisiting and Extending Interface Penalties for Multi-Domain Summation-by-Parts Operators

NASA Technical Reports Server (NTRS)

Carpenter, Mark H.; Nordstrom, Jan; Gottlieb, David

2007-01-01

General interface coupling conditions are presented for multi-domain collocation methods, which satisfy the summation-by-parts (SBP) spatial discretization convention. The combined interior/interface operators are proven to be L2 stable, pointwise stable, and conservative, while maintaining the underlying accuracy of the interior SBP operator. The new interface conditions resemble (and were motivated by) those used in the discontinuous Galerkin finite element community, and maintain many of the same properties. Extensive validation studies are presented using two classes of high-order SBP operators: 1) central finite difference, and 2) Legendre spectral collocation.

Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs.

PubMed

Shamim, Mohammad Tabrez Anwar; Anwaruddin, Mohammad; Nagarajaram, H A

2007-12-15

Fold recognition is a key step in the protein structure discovery process, especially when traditional sequence comparison methods fail to yield convincing structural homologies. Although many methods have been developed for protein fold recognition, their accuracies remain low. This can be attributed to insufficient exploitation of fold discriminatory features. We have developed a new method for protein fold recognition using structural information of amino acid residues and amino acid residue pairs. Since protein fold recognition can be treated as a protein fold classification problem, we have developed a Support Vector Machine (SVM) based classifier approach that uses secondary structural state and solvent accessibility state frequencies of amino acids and amino acid pairs as feature vectors. Among the individual properties examined secondary structural state frequencies of amino acids gave an overall accuracy of 65.2% for fold discrimination, which is better than the accuracy by any method reported so far in the literature. Combination of secondary structural state frequencies with solvent accessibility state frequencies of amino acids and amino acid pairs further improved the fold discrimination accuracy to more than 70%, which is approximately 8% higher than the best available method. In this study we have also tested, for the first time, an all-together multi-class method known as Crammer and Singer method for protein fold classification. Our studies reveal that the three multi-class classification methods, namely one versus all, one versus one and Crammer and Singer method, yield similar predictions. Dataset and stand-alone program are available upon request.

Validation of rapid assessment methods to determine streamflow duration classes in the Pacific Northwest, USA

EPA Science Inventory

U.S. Supreme Court rulings have created uncertainty regarding federal Clean Water Act (CWA) authority over certain waters, including ephemeral and intermittent streams, and established new data and analytical requirements for determining whether a water body is covered under the ...

CSI 2264: simultaneous optical and infrared light curves of young disk-bearing stars in NGC 2264 with CoRoT and Spitzer—evidence for multiple origins of variability

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

Cody, Ann Marie; Stauffer, John; Rebull, Luisa M.

2014-04-01

We present the Coordinated Synoptic Investigation of NGC 2264, a continuous 30 day multi-wavelength photometric monitoring campaign on more than 1000 young cluster members using 16 telescopes. The unprecedented combination of multi-wavelength, high-precision, high-cadence, and long-duration data opens a new window into the time domain behavior of young stellar objects. Here we provide an overview of the observations, focusing on results from Spitzer and CoRoT. The highlight of this work is detailed analysis of 162 classical T Tauri stars for which we can probe optical and mid-infrared flux variations to 1% amplitudes and sub-hour timescales. We present a morphological variabilitymore » census and then use metrics of periodicity, stochasticity, and symmetry to statistically separate the light curves into seven distinct classes, which we suggest represent different physical processes and geometric effects. We provide distributions of the characteristic timescales and amplitudes and assess the fractional representation within each class. The largest category (>20%) are optical 'dippers' with discrete fading events lasting ∼1-5 days. The degree of correlation between the optical and infrared light curves is positive but weak; notably, the independently assigned optical and infrared morphology classes tend to be different for the same object. Assessment of flux variation behavior with respect to (circum)stellar properties reveals correlations of variability parameters with Hα emission and with effective temperature. Overall, our results point to multiple origins of young star variability, including circumstellar obscuration events, hot spots on the star and/or disk, accretion bursts, and rapid structural changes in the inner disk.« less

On mixed derivatives type high dimensional multi-term fractional partial differential equations approximate solutions

NASA Astrophysics Data System (ADS)

Talib, Imran; Belgacem, Fethi Bin Muhammad; Asif, Naseer Ahmad; Khalil, Hammad

2017-01-01

In this research article, we derive and analyze an efficient spectral method based on the operational matrices of three dimensional orthogonal Jacobi polynomials to solve numerically the mixed partial derivatives type multi-terms high dimensions generalized class of fractional order partial differential equations. We transform the considered fractional order problem to an easily solvable algebraic equations with the aid of the operational matrices. Being easily solvable, the associated algebraic system leads to finding the solution of the problem. Some test problems are considered to confirm the accuracy and validity of the proposed numerical method. The convergence of the method is ensured by comparing our Matlab software simulations based obtained results with the exact solutions in the literature, yielding negligible errors. Moreover, comparative results discussed in the literature are extended and improved in this study.

MultiBLUP: improved SNP-based prediction for complex traits.

PubMed

Speed, Doug; Balding, David J

2014-09-01

BLUP (best linear unbiased prediction) is widely used to predict complex traits in plant and animal breeding, and increasingly in human genetics. The BLUP mathematical model, which consists of a single random effect term, was adequate when kinships were measured from pedigrees. However, when genome-wide SNPs are used to measure kinships, the BLUP model implicitly assumes that all SNPs have the same effect-size distribution, which is a severe and unnecessary limitation. We propose MultiBLUP, which extends the BLUP model to include multiple random effects, allowing greatly improved prediction when the random effects correspond to classes of SNPs with distinct effect-size variances. The SNP classes can be specified in advance, for example, based on SNP functional annotations, and we also provide an adaptive procedure for determining a suitable partition of SNPs. We apply MultiBLUP to genome-wide association data from the Wellcome Trust Case Control Consortium (seven diseases), and from much larger studies of celiac disease and inflammatory bowel disease, finding that it consistently provides better prediction than alternative methods. Moreover, MultiBLUP is computationally very efficient; for the largest data set, which includes 12,678 individuals and 1.5 M SNPs, the total analysis can be run on a single desktop PC in less than a day and can be parallelized to run even faster. Tools to perform MultiBLUP are freely available in our software LDAK. © 2014 Speed and Balding; Published by Cold Spring Harbor Laboratory Press.

Natural scene logo recognition by joint boosting feature selection in salient regions

NASA Astrophysics Data System (ADS)

Fan, Wei; Sun, Jun; Naoi, Satoshi; Minagawa, Akihiro; Hotta, Yoshinobu

2011-01-01

Logos are considered valuable intellectual properties and a key component of the goodwill of a business. In this paper, we propose a natural scene logo recognition method which is segmentation-free and capable of processing images extremely rapidly and achieving high recognition rates. The classifiers for each logo are trained jointly, rather than independently. In this way, common features can be shared across multiple classes for better generalization. To deal with large range of aspect ratio of different logos, a set of salient regions of interest (ROI) are extracted to describe each class. We ensure the selected ROIs to be both individually informative and two-by-two weakly dependant by a Class Conditional Entropy Maximization criteria. Experimental results on a large logo database demonstrate the effectiveness and efficiency of our proposed method.

Biological implications of lab-on-a-chip devices fabricated using multi-jet modelling and stereolithography processes

NASA Astrophysics Data System (ADS)

Zhu, Feng; Macdonald, Niall; Skommer, Joanna; Wlodkowic, Donald

2015-06-01

Current microfabrication methods are often restricted to two-dimensional (2D) or two and a half dimensional (2.5D) structures. Those fabrication issues can be potentially addressed by emerging additive manufacturing technologies. Despite rapid growth of additive manufacturing technologies in tissue engineering, microfluidics has seen relatively little developments with regards to adopting 3D printing for rapid fabrication of complex chip-based devices. This has been due to two major factors: lack of sufficient resolution of current rapid-prototyping methods (usually >100 μm ) and optical transparency of polymers to allow in vitro imaging of specimens. We postulate that adopting innovative fabrication processes can provide effective solutions for prototyping and manufacturing of chip-based devices with high-aspect ratios (i.e. above ration of 20:1). This work provides a comprehensive investigation of commercially available additive manufacturing technologies as an alternative for rapid prototyping of complex monolithic Lab-on-a-Chip devices for biological applications. We explored both multi-jet modelling (MJM) and several stereolithography (SLA) processes with five different 3D printing resins. Compared with other rapid prototyping technologies such as PDMS soft lithography and infrared laser micromachining, we demonstrated that selected SLA technologies had superior resolution and feature quality. We also for the first time optimised the post-processing protocols and demonstrated polymer features under scanning electronic microscope (SEM). Finally we demonstrate that selected SLA polymers have optical properties enabling high-resolution biological imaging. A caution should be, however, exercised as more work is needed to develop fully bio-compatible and non-toxic polymer chemistries.

Carriage of Class 1 and 2 Integrons in Quinolone, Extended-Spectrum-β-Lactamase-Producing and Multi Drug Resistant E.coli and K.pneumoniae: High Burden of Antibiotic Resistance

PubMed Central

Shams, Froogh; Hasani, Alka; Ahangarzadeh Rezaee, Mohammad; Nahaie, Mohammad Reza; Hasani, Akbar; Soroush Bar Haghi, Mohammad Hossein; Pormohammad, Ali; Elli Arbatan, Asghar

2015-01-01

Purpose: The study aimed at assessing any association between quinolone resistance, MDR and ESBL production and their relation with the presence of integrons in Esherichia coli and Klebsiella pneumoniae. Methods: E.coli and K.pneumoniae isolated from various clinical infections were fully identified and analyzed for being quinolone resistant. These isolates were further tested for ESBL production, multi drug resistance and carriage of integrons. Results: In total, 135 isolates were confirmed as quinolone resistant. K.pneumoniae was observed as potent ESBL producer in comparison to E.coli. Ciprofloxacin resistance in both organisms was related significantly with the presence of integron class 1, co-presence of class 1 and 2 as well as to the presence of ESBL production (p< 0.001). However, nalidixic acid resistance was related significantly (p< 0.01) with only integron class 1 and to the presence of ESBL production. Class 1 and 2 integrons were found in 73.5% of MDR isolates with 13.2% of them possessing both intI1 and intI2 genes. Conclusion: Prevalence of quinolone resistance together with ESBL production and MDR in E.coli and K.pneumoniae has contributed to the emergence of antibacterial resistance burden. The higher integron prevalence in our isolates advocates the potentiality of these isolates as a source for dissemination of resistance determinants. PMID:26504755

Design synthesis and optimization of permanent magnet synchronous machines based on computationally-efficient finite element analysis

NASA Astrophysics Data System (ADS)

Sizov, Gennadi Y.

In this dissertation, a model-based multi-objective optimal design of permanent magnet ac machines, supplied by sine-wave current regulated drives, is developed and implemented. The design procedure uses an efficient electromagnetic finite element-based solver to accurately model nonlinear material properties and complex geometric shapes associated with magnetic circuit design. Application of an electromagnetic finite element-based solver allows for accurate computation of intricate performance parameters and characteristics. The first contribution of this dissertation is the development of a rapid computational method that allows accurate and efficient exploration of large multi-dimensional design spaces in search of optimum design(s). The computationally efficient finite element-based approach developed in this work provides a framework of tools that allow rapid analysis of synchronous electric machines operating under steady-state conditions. In the developed modeling approach, major steady-state performance parameters such as, winding flux linkages and voltages, average, cogging and ripple torques, stator core flux densities, core losses, efficiencies and saturated machine winding inductances, are calculated with minimum computational effort. In addition, the method includes means for rapid estimation of distributed stator forces and three-dimensional effects of stator and/or rotor skew on the performance of the machine. The second contribution of this dissertation is the development of the design synthesis and optimization method based on a differential evolution algorithm. The approach relies on the developed finite element-based modeling method for electromagnetic analysis and is able to tackle large-scale multi-objective design problems using modest computational resources. Overall, computational time savings of up to two orders of magnitude are achievable, when compared to current and prevalent state-of-the-art methods. These computational savings allow one to expand the optimization problem to achieve more complex and comprehensive design objectives. The method is used in the design process of several interior permanent magnet industrial motors. The presented case studies demonstrate that the developed finite element-based approach practically eliminates the need for using less accurate analytical and lumped parameter equivalent circuit models for electric machine design optimization. The design process and experimental validation of the case-study machines are detailed in the dissertation.

MRJP1-containing glycoproteins isolated from honey, a novel antibacterial drug candidate with broad spectrum activity against multi-drug resistant clinical isolates

PubMed Central

Brudzynski, Katrina; Sjaarda, Calvin; Lannigan, Robert

2015-01-01

The emergence of extended- spectrum β-lactamase (ESBL) is the underlying cause of growing antibiotic resistance among Gram-negative bacteria to β-lactam antibiotics. We recently reported the discovery of honey glycoproteins (glps) that exhibited a rapid, concentration-dependent antibacterial activity against both Gram-positive Bacillus subtilis and Gram-negative Escherichia coli that resembled action of cell wall-active β-lactam drugs. Glps showed sequence identity with the Major Royal Jelly Protein 1 (MRJP1) precursor that harbors three antimicrobial peptides: Jelleins 1, 2, and 4. Here, we used semi-quantitative radial diffusion assay and broth microdilution assay to evaluate susceptibility of a number of multi-drug resistant (MDR) clinical isolates to the MRJP1-contaning honey glycoproteins. The MDR bacterial strains comprised three methicillin-resistant Staphylococcus aureus (MRSA), four Pseudomonas aeruginosa, two Klebsiella pneumoniae, two vancomycin-resistant Enterococci (VRE), and five ESBL identified as one Proteus mirabilis, three E. coli, and one E. coli NDM. Their resistance to different classes of antibiotics was confirmed using automated system Vitek 2. MDR isolates differed in their susceptibility to glps with MIC90 values ranging from 4.8 μg/ml against B. subtilis to 14.4 μg/ml against ESBL K. pneumoniae, Klebsiella spp. ESBL and E. coli and up to 33 μg/ml against highly resistant strains of P. aeruginosa. Glps isolated from different honeys showed a similar ability to overcome bacterial resistance to β-lactams suggesting that (a) their mode of action is distinct from other classes of β-lactams and that (b) the common glps structure was the lead structure responsible for the activity. The results of the current study together with our previous evidence of a rapid bactericidal activity of glps demonstrate that glps possess suitable characteristics to be considered a novel antibacterial drug candidate. PMID:26217333

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

PubMed Central

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-01-01

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design. PMID:27958331

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.

PubMed

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-12-13

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

Multiplex screening of persistent organic pollutants in fish using spectrally encoded microspheres

USDA-ARS?s Scientific Manuscript database

Persistent organic pollutants (POPs) are food contaminants of global public health concern and known to be carcinogenic and endocrine disruptors. Their monitoring is essential and an easy-to-use, rapid and affordable multi-analyte screening method with simplified sample preparation can be a valuable...

It is Time to Ban Rapid Weight Loss from Combat Sports.

PubMed

Artioli, Guilherme G; Saunders, Bryan; Iglesias, Rodrigo T; Franchini, Emerson

2016-11-01

Most competitions in combat sports are divided into weight classes, theoretically allowing for fairer and more evenly contested disputes between athletes of similar body size, strength and agility. It has been well documented that most athletes, regardless of the combat sports discipline, reduce significant amounts of body weight in the days prior to competition to qualify for lighter weight classes. Rapid weight loss is characterised by the reduction of a significant amount of body weight (typically 2-10 %, although larger reductions are often seen) in a few days prior to weigh-in (mostly in the last 2-3 days) achieved by a combination of methods that include starvation, severe restriction of fluid intake and intentional sweating. In doing so, athletes try to gain a competitive advantage against lighter, smaller and weaker opponents. Such a drastic and rapid weight reduction is only achievable via a combination of aggressive strategies that lead to hypohydration and starvation. The negative impact of these procedures on health is well described in the literature. Although the impact of rapid weight loss on performance is debated, there remains robust evidence showing that rapid weight loss may not impair performance, and translates into an actual competitive advantage. In addition to the health and performance implications, rapid weight loss clearly breaches fair play and stands against the spirit of the sport because an athlete unwilling to compete having rapidly reduced weight would face unfair contests against opponents who are 'artificially' bigger and stronger. The World Anti-Doping Agency Code states that a prohibited method must meet at least two of the following criteria: (1) enhances performance; (2) endangers an athlete's health; and (3) violates the spirit of the sport. We herein argue that rapid weight loss clearly meets all three criteria and, therefore, should be banned from the sport. To quote the World Anti-Doping Agency Code, this would "protect the athletes' fundamental right to participate in a doping-free sport and thus promote health, fairness and equality".

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

PubMed

Liu, Min; Wang, Xueping; Zhang, Hongzhong

2018-03-01

In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

Multi-class texture analysis in colorectal cancer histology

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Weis, Cleo-Aron; Bianconi, Francesco; Melchers, Susanne M.; Schad, Lothar R.; Gaiser, Timo; Marx, Alexander; Zöllner, Frank Gerrit

2016-06-01

Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.

Additional information for “TREMOR: A Wireless, MEMS Accelerograph for Dense Arrays” (Evans et al., 2003)

USGS Publications Warehouse

Evans, John R.; Hamstra, Robert H.; Spudich, Paul; Kundig, Christoph; Camina, Patrick; Rogers, John A.

2003-01-01

The length of Evans et al. (2003) necessitated transfer of several less germane sections to this alternate forum to meet that venue’s needs. These sections include a description of the development of Figure 1, the plot of spatial variability so critical to the argument for dense arrays of strong-motion instruments; the description of the rapid, integer, computational method for PGV used in the TREMOR instrument (the Oakland instrument, the commercial prototype, and the commercial instrument); siting methods and strategies used for Class B TREMOR instruments and those that can be used for Class C instruments to preserve the cost advantages of such systems; and some general discussion of MEMS accelerometers, including a comparative Table with representative examples of Class A, B and C MEMS devices. (“MEMS” means “Micro-ElectroMechanical” Systems—“micromachined” sensors, generally of silicon. Classes A, B, and C are defined in Table 1.)

RapidIO as a multi-purpose interconnect

NASA Astrophysics Data System (ADS)

Baymani, Simaolhoda; Alexopoulos, Konstantinos; Valat, Sébastien

2017-10-01

RapidIO (http://rapidio.org/) technology is a packet-switched high-performance fabric, which has been under active development since 1997. Originally meant to be a front side bus, it developed into a system level interconnect which is today used in all 4G/LTE base stations world wide. RapidIO is often used in embedded systems that require high reliability, low latency and scalability in a heterogeneous environment - features that are highly interesting for several use cases, such as data analytics and data acquisition (DAQ) networks. We will present the results of evaluating RapidIO in a data analytics environment, from setup to benchmark. Specifically, we will share the experience of running ROOT and Hadoop on top of RapidIO. To demonstrate the multi-purpose characteristics of RapidIO, we will also present the results of investigating RapidIO as a technology for high-speed DAQ networks using a generic multi-protocol event-building emulation tool. In addition we will present lessons learned from implementing native ports of CERN applications to RapidIO.

SU-F-T-312: Identifying Distinct Radiation Therapy Plan Classes Through Multi-Dimensional Analysis of Plan Complexity Metrics

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

Desai, V; Labby, Z; Culberson, W

Purpose: To determine whether body site-specific treatment plans form unique “plan class” clusters in a multi-dimensional analysis of plan complexity metrics such that a single beam quality correction determined for a representative plan could be universally applied within the “plan class”, thereby increasing the dosimetric accuracy of a detector’s response within a subset of similarly modulated nonstandard deliveries. Methods: We collected 95 clinical volumetric modulated arc therapy (VMAT) plans from four body sites (brain, lung, prostate, and spine). The lung data was further subdivided into SBRT and non-SBRT data for a total of five plan classes. For each control pointmore » in each plan, a variety of aperture-based complexity metrics were calculated and stored as unique characteristics of each patient plan. A multiple comparison of means analysis was performed such that every plan class was compared to every other plan class for every complexity metric in order to determine which groups could be considered different from one another. Statistical significance was assessed after correcting for multiple hypothesis testing. Results: Six out of a possible 10 pairwise plan class comparisons were uniquely distinguished based on at least nine out of 14 of the proposed metrics (Brain/Lung, Brain/SBRT lung, Lung/Prostate, Lung/SBRT Lung, Lung/Spine, Prostate/SBRT Lung). Eight out of 14 of the complexity metrics could distinguish at least six out of the possible 10 pairwise plan class comparisons. Conclusion: Aperture-based complexity metrics could prove to be useful tools to quantitatively describe a distinct class of treatment plans. Certain plan-averaged complexity metrics could be considered unique characteristics of a particular plan. A new approach to generating plan-class specific reference (pcsr) fields could be established through a targeted preservation of select complexity metrics or a clustering algorithm that identifies plans exhibiting similar modulation characteristics. Measurements and simulations will better elucidate potential plan-class specific dosimetry correction factors.« less

Artificial Gravity: Will it Preserve Bone Health on Long-Duration Missions?

NASA Technical Reports Server (NTRS)

Davis-Street, Janis; Paloski, William H.

2005-01-01

Prolonged microgravity exposure disrupts bone, muscle, and cardiovascular homeostasis, sensory-motor coordination, immune function, and behavioral performance. Bone loss, in particular, remains a serious impediment to the success of exploration-class missions by increasing the risks of bone fracture and renal stone formation for crew members. Current countermeasures, consisting primarily of resistive and aerobic exercise, have not yet proven fully successful for preventing bone loss during long-duration spaceflight. While other bone-specific countermeasures, such as pharmacological therapy and dietary modifications, are under consideration, countermeasure approaches that simultaneously address multiple physiologic systems may be more desirable for exploration-class missions, particularly if they can provide effective protection at reduced mission resource requirements (up-mass, power, crew time, etc). The most robust of the multi-system approaches under consideration, artificial gravity (AG), could prevent all of the microgravity-related physiological changes from occurring. The potential methods for realizing an artificial gravity countermeasure are reviewed, as well as selected animal and human studies evaluating the effects of artificial gravity on bone function. Future plans for the study of the multi-system effects of artificial gravity include a joint, cooperative international effort that will systematically seek an optimal prescription for intermittent AG to preserve bone, muscle, and cardiovascular function in human subjects deconditioned by 6 degree head-down-tilt-bed rest. It is concluded that AG has great promise as a multi-system countermeasure, but that further research is required to determine the appropriate parameters for implementation of such a countermeasure for exploration-class missions.

Predicting honey bee sensitivity based on the conservation of the pesticide molecular initiating event

EPA Science Inventory

Concern surrounding the potential adverse impacts of pesticides to honey bee colonies has led to the need for rapid/cost efficient methods for aiding decision making relative to the protection of this important pollinator species. Neonicotinoids represent a class of pesticides th...

Monoclonal antibody-based broad-specificity immunoassay for monitoring organophosphorus pesticides in environmental water samples

USDA-ARS?s Scientific Manuscript database

The extensive use of organophosphorus pesticides (OPs) in agriculture and domestic settings can result in widespread water contamination. The development of easy-to-use and rapid-screening immunoassay methods in a class-selective manner is a topic of considerable environmental interest. In this wo...

Enhanced risk management by an emerging multi-agent architecture

NASA Astrophysics Data System (ADS)

Lin, Sin-Jin; Hsu, Ming-Fu

2014-07-01

Classification in imbalanced datasets has attracted much attention from researchers in the field of machine learning. Most existing techniques tend not to perform well on minority class instances when the dataset is highly skewed because they focus on minimising the forecasting error without considering the relative distribution of each class. This investigation proposes an emerging multi-agent architecture, grounded on cooperative learning, to solve the class-imbalanced classification problem. Additionally, this study deals further with the obscure nature of the multi-agent architecture and expresses comprehensive rules for auditors. The results from this study indicate that the presented model performs satisfactorily in risk management and is able to tackle a highly class-imbalanced dataset comparatively well. Furthermore, the knowledge visualised process, supported by real examples, can assist both internal and external auditors who must allocate limited detecting resources; they can take the rules as roadmaps to modify the auditing programme.

Automatic classification and detection of clinically relevant images for diabetic retinopathy

NASA Astrophysics Data System (ADS)

Xu, Xinyu; Li, Baoxin

2008-03-01

We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

Multi-dimensional tunnelling and complex momentum

NASA Technical Reports Server (NTRS)

Bowcock, Peter; Gregory, Ruth

1991-01-01

The problem of modeling tunneling phenomena in more than one dimension is examined. It is found that existing techniques are inadequate in a wide class of situations, due to their inability to deal with concurrent classical motion. The generalization of these methods to allow for complex momenta is shown, and improved techniques are demonstrated with a selection of illustrative examples. Possible applications are presented.

Detection of Multi-drug Resistant Acinetobacter Lwoffii Isolated from Soil of Mink Farm.

PubMed

Sun, Na; Wen, Yong Jun; Zhang, Shu Qin; Zhu, Hong Wei; Guo, Li; Wang, Feng Xue; Chen, Qiang; Ma, Hong Xia; Cheng, Shi Peng

2016-07-01

There were 4 Acinetobacter lwoffii obtained from soil samples. The antimicrobial susceptibility of the strains to 16 antimicrobial agents was investigated using K-B method. Three isolates showed the multi-drug resistance. The presence of resistance genes and integrons was determined using PCR. The aadA1, aac(3')-IIc, aph(3')-VII, aac(6')-Ib, sul2, cat2, floR, and tet(K) genes were detected, respectively. Three class 1 integrons were obtained. The arr-3-aacA4 and blaPSE-1 gene cassette, which cause resistance to aminoglycoside and beta-lactamase antibiotics. Our results reported the detection of multi-drug resistant and carried resistant genes Acinetobacter lwoffii from soil. The findings suggested that we should pay close attention to the prevalence of multi-drug resistant bacterial species of environment. Copyright © 2016 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.

Deep Hashing for Scalable Image Search.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Jie

2017-05-01

In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for scalable image search. Unlike most existing binary codes learning methods, which usually seek a single linear projection to map each sample into a binary feature vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the non-linear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the developed deep network: 1) the loss between the compact real-valued code and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) and multi-label SDH by including a discriminative term into the objective function of DH, which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes with the single-label and multi-label settings, respectively. Extensive experimental results on eight widely used image search data sets show that our proposed methods achieve very competitive results with the state-of-the-arts.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

PubMed Central

Kim, Jong-Myon

2018-01-01

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466

Using Latent Class Analysis to Model Temperament Types.

PubMed

Loken, Eric

2004-10-01

Mixture models are appropriate for data that arise from a set of qualitatively different subpopulations. In this study, latent class analysis was applied to observational data from a laboratory assessment of infant temperament at four months of age. The EM algorithm was used to fit the models, and the Bayesian method of posterior predictive checks was used for model selection. Results show at least three types of infant temperament, with patterns consistent with those identified by previous researchers who classified the infants using a theoretically based system. Multiple imputation of group memberships is proposed as an alternative to assigning subjects to the latent class with maximum posterior probability in order to reflect variance due to uncertainty in the parameter estimation. Latent class membership at four months of age predicted longitudinal outcomes at four years of age. The example illustrates issues relevant to all mixture models, including estimation, multi-modality, model selection, and comparisons based on the latent group indicators.

Object-based classification of global undersea topography and geomorphological features from the SRTM30_PLUS data

NASA Astrophysics Data System (ADS)

Dekavalla, Maria; Argialas, Demetre

2017-07-01

The analysis of undersea topography and geomorphological features provides necessary information to related disciplines and many applications. The development of an automated knowledge-based classification approach of undersea topography and geomorphological features is challenging due to their multi-scale nature. The aim of the study is to develop and evaluate an automated knowledge-based OBIA approach to: i) decompose the global undersea topography to multi-scale regions of distinct morphometric properties, and ii) assign the derived regions to characteristic geomorphological features. First, the global undersea topography was decomposed through the SRTM30_PLUS bathymetry data to the so-called morphometric objects of discrete morphometric properties and spatial scales defined by data-driven methods (local variance graphs and nested means) and multi-scale analysis. The derived morphometric objects were combined with additional relative topographic position information computed with a self-adaptive pattern recognition method (geomorphons), and auxiliary data and were assigned to characteristic undersea geomorphological feature classes through a knowledge base, developed from standard definitions. The decomposition of the SRTM30_PLUS data to morphometric objects was considered successful for the requirements of maximizing intra-object and inter-object heterogeneity, based on the near zero values of the Moran's I and the low values of the weighted variance index. The knowledge-based classification approach was tested for its transferability in six case studies of various tectonic settings and achieved the efficient extraction of 11 undersea geomorphological feature classes. The classification results for the six case studies were compared with the digital global seafloor geomorphic features map (GSFM). The 11 undersea feature classes and their producer's accuracies in respect to the GSFM relevant areas were Basin (95%), Continental Shelf (94.9%), Trough (88.4%), Plateau (78.9%), Continental Slope (76.4%), Trench (71.2%), Abyssal Hill (62.9%), Abyssal Plain (62.4%), Ridge (49.8%), Seamount (48.8%) and Continental Rise (25.4%). The knowledge-based OBIA classification approach was considered transferable since the percentages of spatial and thematic agreement between the most of the classified undersea feature classes and the GSFM exhibited low deviations across the six case studies.

Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models

NASA Astrophysics Data System (ADS)

Ahmadian, Narjes; Khosravi, Alireza; Sarhadi, Pouria

2017-08-01

In this paper, a multiple model adaptive control (MMAC) method is proposed for a gas turbine engine. The model of a twin spool turbo-shaft engine driving a variable pitch propeller includes various operating points. Variations in fuel flow and propeller pitch inputs produce different operating conditions which force the controller to be adopted rapidly. Important operating points are three idle, cruise and full thrust cases for the entire flight envelope. A multi-input multi-output (MIMO) version of second level adaptation using multiple models is developed. Also, stability analysis using Lyapunov method is presented. The proposed method is compared with two conventional first level adaptation and model reference adaptive control techniques. Simulation results for JetCat SPT5 turbo-shaft engine demonstrate the performance and fidelity of the proposed method.

Measurement and correction of transverse chromatic offsets for multi-wavelength retinal microscopy in the living eye.

PubMed

Harmening, Wolf M; Tiruveedhula, Pavan; Roorda, Austin; Sincich, Lawrence C

2012-09-01

A special challenge arises when pursuing multi-wavelength imaging of retinal tissue in vivo, because the eye's optics must be used as the main focusing elements, and they introduce significant chromatic dispersion. Here we present an image-based method to measure and correct for the eye's transverse chromatic aberrations rapidly, non-invasively, and with high precision. We validate the technique against hyperacute psychophysical performance and the standard chromatic human eye model. In vivo correction of chromatic dispersion will enable confocal multi-wavelength images of the living retina to be aligned, and allow targeted chromatic stimulation of the photoreceptor mosaic to be performed accurately with sub-cellular resolution.

Measurement and correction of transverse chromatic offsets for multi-wavelength retinal microscopy in the living eye

PubMed Central

Harmening, Wolf M.; Tiruveedhula, Pavan; Roorda, Austin; Sincich, Lawrence C.

2012-01-01

A special challenge arises when pursuing multi-wavelength imaging of retinal tissue in vivo, because the eye’s optics must be used as the main focusing elements, and they introduce significant chromatic dispersion. Here we present an image-based method to measure and correct for the eye’s transverse chromatic aberrations rapidly, non-invasively, and with high precision. We validate the technique against hyperacute psychophysical performance and the standard chromatic human eye model. In vivo correction of chromatic dispersion will enable confocal multi-wavelength images of the living retina to be aligned, and allow targeted chromatic stimulation of the photoreceptor mosaic to be performed accurately with sub-cellular resolution. PMID:23024901

Modeling of a production system using the multi-agent approach

NASA Astrophysics Data System (ADS)

Gwiazda, A.; Sękala, A.; Banaś, W.

2017-08-01

The method that allows for the analysis of complex systems is a multi-agent simulation. The multi-agent simulation (Agent-based modeling and simulation - ABMS) is modeling of complex systems consisting of independent agents. In the case of the model of the production system agents may be manufactured pieces set apart from other types of agents like machine tools, conveyors or replacements stands. Agents are magazines and buffers. More generally speaking, the agents in the model can be single individuals, but you can also be defined as agents of collective entities. They are allowed hierarchical structures. It means that a single agent could belong to a certain class. Depending on the needs of the agent may also be a natural or physical resource. From a technical point of view, the agent is a bundle of data and rules describing its behavior in different situations. Agents can be autonomous or non-autonomous in making the decision about the types of classes of agents, class sizes and types of connections between elements of the system. Multi-agent modeling is a very flexible technique for modeling and model creating in the convention that could be adapted to any research problem analyzed from different points of views. One of the major problems associated with the organization of production is the spatial organization of the production process. Secondly, it is important to include the optimal scheduling. For this purpose use can approach multi-purposeful. In this regard, the model of the production process will refer to the design and scheduling of production space for four different elements. The program system was developed in the environment NetLogo. It was also used elements of artificial intelligence. The main agent represents the manufactured pieces that, according to previously assumed rules, generate the technological route and allow preprint the schedule of that line. Machine lines, reorientation stands, conveyors and transport devices also represent the other type of agent that are utilized in the described simulation. The article presents the idea of an integrated program approach and shows the resulting production layout as a virtual model. This model was developed in the NetLogo multi-agent program environment.

Is alternate rapid maxillary expansion and constriction an effective protocol in the treatment of Class III malocclusion? A systematic review

PubMed Central

Pithon, Matheus Melo; Santos, Nathalia de Lima; dos Santos, Camila Rangel Barreto; Baião, Felipe Carvalho Souza; Pinheiro, Murilo Costa Rangel; Matos, Manoel; Souza, Ianderlei Andrade; de Paula, Rafael Pereira

2016-01-01

ABSTRACT Introduction: the treatment of Class III malocclusion in early age is one of the greatest challenges for orthodontists, and the establishment of more effective treatment method is a constant concern for these professionals. Thus, the objective of this systematic review is to verify the effectiveness of the therapy protocol for alternate rapid maxillary expansion and constriction (Alt-RAMEC) in the early treatment of Class III malocclusion. Methods: searches were performed in the following electronic databases: Cochrane Library, Medline (EBSCO and PubMed), SciELO, LILACS and Scopus. The following inclusion criteria were used: in vivo studies conducted with early intervention (patient in craniofacial development phase) with the use of the Alt-RAMEC protocol. Reviews, case reports, editorials, and studies with syndromic patients or under use of systemic drug were excluded. Duplicates were also excluded. The studies were assessed for methodological quality using the Cochrane tool for assessment of risk of bias, and classified as high or low risk of bias. Results: 53 articles were found. Duplicates exclusion was thus performed and 35 articles remained. After inclusion analysis, only 5 matched the criteria. Two articles were classified as low risk of bias and three as high risk of bias. It was observed that the Alt-RAMEC enable protraction in less time and with better results, promoting greater effectiveness in the protraction treatment of Class III malocclusion. Conclusions: Although there is positive evidence of the effectiveness of early treatment with the Alt-RAMEC protocol in patients with Class III malocclusion, further studies are needed to confirm its effectiveness using long-term methodology. PMID:28125138

Three-Level Models for Indirect Effects in School- and Class-Randomized Experiments in Education

ERIC Educational Resources Information Center

Pituch, Keenan A.; Murphy, Daniel L.; Tate, Richard L.

2009-01-01

Due to the clustered nature of field data, multi-level modeling has become commonly used to analyze data arising from educational field experiments. While recent methodological literature has focused on multi-level mediation analysis, relatively little attention has been devoted to mediation analysis when three levels (e.g., student, class,…

Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses.

PubMed

Karunathilaka, Sanjeewa R; Kia, Ali-Reza Fardin; Srigley, Cynthia; Chung, Jin Kyu; Mossoba, Magdi M

2016-10-01

A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

A 0.4-2.3 GHz broadband power amplifier extended continuous class-F design technology

NASA Astrophysics Data System (ADS)

Chen, Peng; He, Songbai

2015-08-01

A 0.4-2.3 GHz broadband power amplifier (PA) extended continuous class-F design technology is proposed in this paper. Traditional continuous class-F PA performs in high-efficiency only in one octave bandwidth. With the increasing development of wireless communication, the PA is in demand to cover the mainstream communication standards' working frequencies from 0.4 GHz to 2.2 GHz. In order to achieve this objective, the bandwidths of class-F and continuous class-F PA are analysed and discussed by Fourier series. Also, two criteria, which could reduce the continuous class-F PA's implementation complexity, are presented and explained to investigate the overlapping area of the transistor's current and voltage waveforms. The proposed PA design technology is based on the continuous class-F design method and divides the bandwidth into two parts: the first part covers the bandwidth from 1.3 GHz to 2.3 GHz, where the impedances are designed by the continuous class-F method; the other part covers the bandwidth from 0.4 GHz to 1.3 GHz, where the impedance to guarantee PA to be in high-efficiency over this bandwidth is selected and controlled. The improved particle swarm optimisation is employed for realising the multi-impedances of output and input network. A PA based on a commercial 10 W GaN high electron mobility transistor is designed and fabricated to verify the proposed design method. The simulation and measurement results show that the proposed PA could deliver 40-76% power added efficiency and more than 11 dB power gain with more than 40 dBm output power over the bandwidth from 0.4-2.3 GHz.

Alkaloid profiling of the traditional Chinese medicine Rhizoma corydalis using high performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry

PubMed Central

Sun, Mingqian; Liu, Jianxun; Lin, Chengren; Miao, Lan; Lin, Li

2014-01-01

Since alkaloids are the major active constituents of Rhizoma corydalis (RC), a convenient and accurate analytical method is needed for their identification and characterization. Here we report a method to profile the alkaloids in RC based on liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (LC–Q-TOF-MS/MS). A total of 16 alkaloids belonging to four different classes were identified by comparison with authentic standards. The fragmentation pathway of each class of alkaloid was clarified and their differences were elucidated. Furthermore, based on an analysis of fragmentation pathways and alkaloid profiling, a rapid and accurate method for the identification of unknown alkaloids in RC is proposed. The method could also be useful for the quality control of RC. PMID:26579385

Improved Filon-type asymptotic methods for highly oscillatory differential equations with multiple time scales

NASA Astrophysics Data System (ADS)

Wang, Bin; Wu, Xinyuan

2014-11-01

In this paper we consider multi-frequency highly oscillatory second-order differential equations x″ (t) + Mx (t) = f (t , x (t) ,x‧ (t)) where high-frequency oscillations are generated by the linear part Mx (t), and M is positive semi-definite (not necessarily nonsingular). It is known that Filon-type methods are effective approach to numerically solving highly oscillatory problems. Unfortunately, however, existing Filon-type asymptotic methods fail to apply to the highly oscillatory second-order differential equations when M is singular. We study and propose an efficient improvement on the existing Filon-type asymptotic methods, so that the improved Filon-type asymptotic methods can be able to numerically solving this class of multi-frequency highly oscillatory systems with a singular matrix M. The improved Filon-type asymptotic methods are designed by combining Filon-type methods with the asymptotic methods based on the variation-of-constants formula. We also present one efficient and practical improved Filon-type asymptotic method which can be performed at lower cost. Accompanying numerical results show the remarkable efficiency.

Rapid molecular diagnostics for multi-drug resistant tuberculosis in India.

PubMed

Ramachandran, Rajeswari; Muniyandi, M

2018-03-01

Rapid molecular diagnostic methods help in the detection of TB and Rifampicin resistance. These methods detect TB early, are accurate and play a crucial role in reducing the burden of drug resistant tuberculosis. Areas covered: This review analyses rapid molecular diagnostic tools used in the diagnosis of MDR-TB in India, such as the Line Probe Assay and GeneXpert. We have discussed the burden of MDR-TB and the impact of recent diagnostic tools on case detection and treatment outcomes. This review also discusses the costs involved in establishing these new techniques in India. Expert commentary: Molecular methods have considerable advantages for the programmatic management of drug resistant TB. These include speed, standardization of testing, potentially high throughput and reduced laboratory biosafety requirements. There is a desperate need for India to adopt modern, rapid, molecular tools with point-of-care tests being currently evaluated. New molecular diagnostic tests appear to be cost effective and also help in detecting missing cases. There is enough evidence to support the scaling up of these new tools in India.

Carotid stenosis assessment with multi-detector CT angiography: comparison between manual and automatic segmentation methods.

PubMed

Zhu, Chengcheng; Patterson, Andrew J; Thomas, Owen M; Sadat, Umar; Graves, Martin J; Gillard, Jonathan H

2013-04-01

Luminal stenosis is used for selecting the optimal management strategy for patients with carotid artery disease. The aim of this study is to evaluate the reproducibility of carotid stenosis quantification using manual and automated segmentation methods using submillimeter through-plane resolution Multi-Detector CT angiography (MDCTA). 35 patients having carotid artery disease with >30 % luminal stenosis as identified by carotid duplex imaging underwent contrast enhanced MDCTA. Two experienced CT readers quantified carotid stenosis from axial source images, reconstructed maximum intensity projection (MIP) and 3D-carotid geometry which was automatically segmented by an open-source toolkit (Vascular Modelling Toolkit, VMTK) using NASCET criteria. Good agreement among the measurement using axial images, MIP and automatic segmentation was observed. Automatic segmentation methods show better inter-observer agreement between the readers (intra-class correlation coefficient (ICC): 0.99 for diameter stenosis measurement) than manual measurement of axial (ICC = 0.82) and MIP (ICC = 0.86) images. Carotid stenosis quantification using an automatic segmentation method has higher reproducibility compared with manual methods.

Credibilistic multi-period portfolio optimization based on scenario tree

NASA Astrophysics Data System (ADS)

Mohebbi, Negin; Najafi, Amir Abbas

2018-02-01

In this paper, we consider a multi-period fuzzy portfolio optimization model with considering transaction costs and the possibility of risk-free investment. We formulate a bi-objective mean-VaR portfolio selection model based on the integration of fuzzy credibility theory and scenario tree in order to dealing with the markets uncertainty. The scenario tree is also a proper method for modeling multi-period portfolio problems since the length and continuity of their horizon. We take the return and risk as well cardinality, threshold, class, and liquidity constraints into consideration for further compliance of the model with reality. Then, an interactive dynamic programming method, which is based on a two-phase fuzzy interactive approach, is employed to solve the proposed model. In order to verify the proposed model, we present an empirical application in NYSE under different circumstances. The results show that the consideration of data uncertainty and other real-world assumptions lead to more practical and efficient solutions.

Serving the army as secretaries: intersectionality, multi-level contract and subjective experience of citizenship.

PubMed

Lomsky-Feder, Edna; Sasson-Levy, Orna

2015-03-01

With the growing elusiveness of the state apparatus in late modernity, military service is one of the last institutions to be clearly identified with the state, its ideologies and its policies. Therefore, negotiations between the military and its recruits produce acting subjects of citizenship with long-lasting consequences. Arguing that these negotiations are regulated by multi-level (civic, group, and individual) contracts, we explore the various meanings that these contracts obtain at the intersectionality of gender, class, and ethnicity; and examine how they shape the subjective experience of soldierhood and citizenship. More particularly, we analyse the meaning of military service in the retrospective life stories of Israeli Jewish women from various ethno-class backgrounds who served as army secretaries - a low-status, feminine gender-typed occupation within a hyper-masculine organization. Findings reveal that for women of the lower class, the organizing cultural schema of the multi-level contract is that of achieving respectability through military service, which means being included in the national collective. Conversely, for middle-class women, it is the sense of entitlement that shapes their contract with the military, which they expect to signify and maintain their privileged status. Thus, while for the lower class, the multi-level contract is about inclusion within the boundaries of the national collective, for the dominant groups, this contract is about reproducing social class hierarchies within national boundaries. © London School of Economics and Political Science 2014.

Most Probable Number Rapid Viability PCR Method to Detect Viable Spores of Bacillus anthracis in Swab Samples

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

Letant, S E; Kane, S R; Murphy, G A

2008-05-30

This note presents a comparison of Most-Probable-Number Rapid Viability (MPN-RV) PCR and traditional culture methods for the quantification of Bacillus anthracis Sterne spores in macrofoam swabs generated by the Centers for Disease Control and Prevention (CDC) for a multi-center validation study aimed at testing environmental swab processing methods for recovery, detection, and quantification of viable B. anthracis spores from surfaces. Results show that spore numbers provided by the MPN RV-PCR method were in statistical agreement with the CDC conventional culture method for all three levels of spores tested (10{sup 4}, 10{sup 2}, and 10 spores) even in the presence ofmore » dirt. In addition to detecting low levels of spores in environmental conditions, the MPN RV-PCR method is specific, and compatible with automated high-throughput sample processing and analysis protocols.« less

Discriminative motif discovery via simulated evolution and random under-sampling.

PubMed

Song, Tao; Gu, Hong

2014-01-01

Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is applied to solve the multi-class imbalance problem at the stage of data preprocessing, and at the stage of Hidden Markov Models (HMMs) training, a random under-sampling method is introduced for the imbalance between the positive and negative datasets. It is shown that, in the task of discovering targeting motifs of nine subcellular compartments, the motifs found by our method are more conserved than the methods without considering data imbalance problem and recover the most known targeting motifs from Minimotif Miner and InterPro. Meanwhile, we use the found motifs to predict protein subcellular localization and achieve higher prediction precision and recall for the minority classes.

Blind Linguistic Steganalysis against Translation Based Steganography

NASA Astrophysics Data System (ADS)

Chen, Zhili; Huang, Liusheng; Meng, Peng; Yang, Wei; Miao, Haibo

Translation based steganography (TBS) is a kind of relatively new and secure linguistic steganography. It takes advantage of the "noise" created by automatic translation of natural language text to encode the secret information. Up to date, there is little research on the steganalysis against this kind of linguistic steganography. In this paper, a blind steganalytic method, which is named natural frequency zoned word distribution analysis (NFZ-WDA), is presented. This method has improved on a previously proposed linguistic steganalysis method based on word distribution which is targeted for the detection of linguistic steganography like nicetext and texto. The new method aims to detect the application of TBS and uses none of the related information about TBS, its only used resource is a word frequency dictionary obtained from a large corpus, or a so called natural frequency dictionary, so it is totally blind. To verify the effectiveness of NFZ-WDA, two experiments with two-class and multi-class SVM classifiers respectively are carried out. The experimental results show that the steganalytic method is pretty promising.

Cropping Pattern Detection and Change Analysis in Central Luzon, Philippines Using Multi-Temporal MODIS Imagery and Artificial Neural Network Classifier

NASA Astrophysics Data System (ADS)

dela Torre, D. M.; Perez, G. J. P.

2016-12-01

Cropping practices in the Philippines has been intensifying with greater demand for food and agricultural supplies in view of an increasing population and advanced technologies for farming. This has not been monitored regularly using traditional methods but alternative methods using remote sensing has been promising yet underutilized. This study employed multi-temporal data from MODIS and neural network classifier to map annual land use in agricultural areas from 2001-2014 in Central Luzon, the primary rice growing area of the Philippines. Land use statistics derived from these maps were compared with historical El Nino events to examine how land area is affected by drought events. Fourteen maps of agricultural land use was produced, with the primary classes being single-cropping, double-cropping and perennial crops with secondary classes of forests, urban, bare, water and other classes. Primary classes were produced from the neural network classifier while secondary classes were derived from NDVI threshold masks. The overall accuracy for the 2014 map was 62.05% and a kappa statistic of 0.45. 155.56% increase in single-cropping systems from 2001 to 2014 was observed while double cropping systems decreased by 14.83%. Perennials increased by 76.21% while built-up areas decreased by 12.22% within the 14-year interval. There are several sources of error including mixed-pixels, scale-conversion problems and limited ground reference data. An analysis including El Niño events in 2004 and 2010 demonstrated that marginally irrigated areas that usually planted twice in a year resorted to single cropping, indicating that scarcity of water limited the intensification allowable in the area. Findings from this study can be used to predict future use of agricultural land in the country and also examine how farmlands have responded to climatic factors and stressors.

Locomotor Adaptation Improves Balance Control, Multitasking Ability and Reduces the Metabolic Cost of Postural Instability

NASA Technical Reports Server (NTRS)

Bloomberg, J. J.; Peters, B. T.; Mulavara, A. P.; Brady, R. A.; Batson, C. D.; Miller, C. A.; Ploutz-Snyder, R. J.; Guined, J. R.; Buxton, R. E.; Cohen, H. S.

2011-01-01

During exploration-class missions, sensorimotor disturbances may lead to disruption in the ability to ambulate and perform functional tasks during the initial introduction to a novel gravitational environment following a landing on a planetary surface. The overall goal of our current project is to develop a sensorimotor adaptability training program to facilitate rapid adaptation to these environments. We have developed a unique training system comprised of a treadmill placed on a motion-base facing a virtual visual scene. It provides an unstable walking surface combined with incongruent visual flow designed to enhance sensorimotor adaptability. Greater metabolic cost incurred during balance instability means more physical work is required during adaptation to new environments possibly affecting crewmembers? ability to perform mission critical tasks during early surface operations on planetary expeditions. The goal of this study was to characterize adaptation to a discordant sensory challenge across a number of performance modalities including locomotor stability, multi-tasking ability and metabolic cost. METHODS: Subjects (n=15) walked (4.0 km/h) on a treadmill for an 8 -minute baseline walking period followed by 20-minutes of walking (4.0 km/h) with support surface motion (0.3 Hz, sinusoidal lateral motion, peak amplitude 25.4 cm) provided by the treadmill/motion-base system. Stride frequency and auditory reaction time were collected as measures of locomotor stability and multi-tasking ability, respectively. Metabolic data (VO2) were collected via a portable metabolic gas analysis system. RESULTS: At the onset of lateral support surface motion, subj ects walking on our treadmill showed an increase in stride frequency and auditory reaction time indicating initial balance and multi-tasking disturbances. During the 20-minute adaptation period, balance control and multi-tasking performance improved. Similarly, throughout the 20-minute adaptation period, VO2 gradually decreased following an initial increase after the onset of support surface motion. DISCUSSION: Resu lts confirmed that walking in discordant conditions not only compromises locomotor stability and the ability to multi-task, but comes at a quantifiable metabolic cost. Importantly, like locomotor stability and multi-tasking ability, metabolic expenditure while walking in discordant sensory conditions improved during adaptation. This confirms that sensorimotor adaptability training can benefit multiple performance parameters central to the successful completion of critical mission tasks.

Multi-residue determination of 171 pesticides in cowpea using modified QuEChERS method with multi-walled carbon nanotubes as reversed-dispersive solid-phase extraction materials.

PubMed

Han, Yongtao; Song, Le; Zou, Nan; Chen, Ronghua; Qin, Yuhong; Pan, Canping

2016-09-15

A rapid and sensitive method for the determination of 171 pesticides in cowpea was developed using multi-walled carbon nanotubes (MWCNTs) as reversed-dispersive solid-phase (r-DSPE) extraction materials. The clean-up performance of MWCNTs was proved to be obviously superior to PSA and GCB. This method was validated on cowpea spiked at 0.01 and 0.1mgkg(-1) with five replicates. The mean recoveries for 169 pesticides ranged from 74% to 129% with relative standard deviations (RSDs) (n=5) lower than 16.4%, except diflufenican and quizalofop-ethyl. Good linearity for all pesticides was obtained with the calibration curve coefficients (R(2)) larger than 0.9970. The limit of detection (LODs) and limit of quantification (LOQs) for the 171 pesticides ranged from 0.001 to 0.003mgkg(-1) and from 0.002 to 0.009mgkg(-1), respectively. The method was demonstrated to be reliable and sensitive for the routine monitoring of the 171 pesticides in cowpea samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Trabecular bone class mapping across resolutions: translating methods from HR-pQCT to clinical CT

NASA Astrophysics Data System (ADS)

Valentinitsch, Alexander; Fischer, Lukas; Patsch, Janina M.; Bauer, Jan; Kainberger, Franz; Langs, Georg; DiFranco, Matthew

2015-03-01

Quantitative assessment of 3D bone microarchitecture in high-resolution peripheral quantitative computed tomography (HR-pQCT) has shown promise in fracture risk assessment and biomechanics, but is limited to the distal radius and tibia. Trabecular microarchitecture classes (TMACs), based on voxel-wise clustering texture and structure tensor features in HRpQCT, is extended in this paper to quantify trabecular bone classes in clinical multi-detector CT (MDCT) images. Our comparison of TMACs in 12 cadaver radii imaged using both HRpQCT and MDCT yields a mean Dice score of up to 0.717+/-0.40 and visually concordant bone quality maps. Further work to develop clinically viable bone quantitative imaging using HR-pQCT validation could have a significant impact on overall bone health assessment.

Structures and construction of nuclear power plants on lunar surface

NASA Astrophysics Data System (ADS)

Shimizu, Katsunori; Kobatake, Masuhiko; Ogawa, Sachio; Kanamori, Hiroshi; Okada, Yasuhiko; Mano, Hideyuki; Takagi, Kenji

1991-07-01

The best structure and construction techniques of nuclear power plants in the severe environments on the lunar surface are studied. Facility construction types (functional conditions such as stable structure, shield thickness, maintainability, safety distances, and service life), construction conditions (such as construction methods, construction equipment, number of personnel, time required for construction, external power supply, and required transportation) and construction feasibility (construction method, reactor transportation between the moon and the earth, ground excavation for installation, loading and unloading, transportation, and installation, filling up the ground, electric power supply of plant S (300 kW class) and plant L (3000 kW class)) are outlined. Items to pay attention to in construction are (1) automation and robotization of construction; (2) cost reduction by multi functional robots; and (3) methods of supplying power to robots. A precast concrete block manufacturing plant is also outlined.

[Research on Rapid Discrimination of Edible Oil by ATR Infrared Spectroscopy].

PubMed

Ma, Xiao; Yuan, Hong-fu; Song, Chun-feng; Hu, Ai-qin; Li, Xiao-yu; Zhao, Zhong; Li, Xiu-qin; Guo Zhen; Zhu, Zhi-qiang

2015-07-01

A rapid discrimination method of edible oils, KL-BP model, was proposed by attenuated total reflectance infrared spectroscopy. The model extracts the characteristic of classification from source data by KL and reduces data dimension at the same time. Then the neural network model is constructed by the new data which as the input of the model. 84 edible oil samples which include sesame oil, corn oil, canola oil, blend oil, sunflower oil, peanut oil, olive oil, soybean oil and tea seed oil, were collected and their infrared spectra determined using an ATR FT-IR spectrometer. In order to compare the method performance, principal component analysis (PCA) direct-classification model, KL direct-classification model, PLS-DA model, PCA-BP model and KL-BP model are constructed in this paper. The results show that the recognition rates of PCA, PCA-BP, KL, PLS-DA and KL-BP are 59.1%, 68.2%, 77.3%, 77.3% and 90.9% for discriminating the 9 kinds of edible oils, respectively. KL extracts the eigenvector which make the distance between different class and distance of every class ratio is the largest. So the method can get much more classify information than PCA. BP neural network can effectively enhance the classification ability and accuracy. Taking full of the advantages of KL in extracting more category information in dimension reducing and the features of BP neural network in self-learning, adaptive, nonlinear, the KL-BP method has the best classification ability and recognition accuracy and great importance for rapidly recognizing edible oil in practice.

Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

PubMed

Liu, Fang; Zhou, Zhaoye; Jang, Hyungseok; Samsonov, Alexey; Zhao, Gengyan; Kijowski, Richard

2018-04-01

To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

COMPARISON OF REAL-TIME PCR FECAL BACTERIA MEASUREMENTS IN RECREATIONAL WATERS USING DIFFERENT INSTRUMENTS AND REAGENT SYSTEMS

EPA Science Inventory

U.S. EPA guidance on the safety of surface waters for recreational use is currently based on concentrations of culturable fecal indicator bacteria. Attention is now shifting to more rapid molecular monitoring methods. A multi-year epidemiological study is in progress to determine...

ARRA-funded VS30 measurements using multi-technique approach at strong-motion stations in California and central-eastern United States

USGS Publications Warehouse

Yong, Alan; Martin, Antony; Stokoe, Kenneth; Diehl, John

2013-01-01

Funded by the 2009 American Recovery and Reinvestment Act (ARRA), we conducted geophysical site characterizations at 191 strong-motion stations: 187 in California and 4 in the Central-Eastern United States (CEUS). The geophysical methods used at each site included passive and active surface-wave and body-wave techniques. Multiple techniques were used at most sites, with the goal of robustly determining VS (shear-wave velocity) profiles and VS30 (the time-averaged shear-wave velocity in the upper 30 meters depth). These techniques included: horizontal-to-vertical spectral ratio (HVSR), two-dimensional (2-D) array microtremor (AM), refraction microtremor (ReMi™), spectral analysis of surface wave (SASW), multi-channel analysis of surface waves (Rayleigh wave: MASRW; and Love wave: MASLW), and compressional- and shear-wave refraction. Of the selected sites, 47 percent have crystalline, volcanic, or sedimentary rock at the surface or at relatively shallow depth, and 53 percent are of Quaternary sediments located in either rural or urban environments. Calculated values of VS30 span almost the full range of the National Earthquake Hazards Reduction Program (NEHRP) Site Classes, from D (stiff soils) to B (rock). The NEHRP Site Classes based on VS30 range from being consistent with the Class expected from analysis of surficial geology, to being one or two Site Classes below expected. In a few cases where differences between the observed and expected Site Class occurred, it was the consequence of inaccurate or coarse geologic mapping, as well as considerable degradation of the near-surface rock. Additionally, several sites mapped as rock have Site Class D (stiff soil) velocities, which is due to the extensive weathering of the surficial rock.

Rapid detection of ssDNA and RNA using multi-walled carbon nanotubes modified screen-printed carbon electrode.

PubMed

Ye, Yongkang; Ju, Huangxian

2005-11-15

A method for rapid sensitive detection of DNA or RNA was designed using a composite screen-printed carbon electrode modified with multi-walled carbon nanotubes (MWNTs). MWNTs showed catalytic characteristics for the direct electrochemical oxidation of guanine or adenine residues of signal strand DNA (ssDNA) and adenine residues of RNA, leading to indicator-free detection of ssDNA and RNA concentrations. With an accumulation time of 5 min, the proposed method could be used for detection of calf thymus ssDNA ranging from 17.0 to 345 microg ml(-1) with a detection limit of 2.0 microg ml(-1) at 3 sigma and yeast tRNA ranging from 8.2 microg ml(-1) to 4.1 mg ml(-1). AC impedance was employed to characterize the surface of modified electrodes. The advantages of convenient fabrication, low-cost detection, short analysis time and combination with nanotechnology for increasing the sensitivity made the subject worthy of special emphasis in the research programs and sources of new commercial products.

HPSLPred: An Ensemble Multi-Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source.

PubMed

Wan, Shixiang; Duan, Yucong; Zou, Quan

2017-09-01

Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine learning approaches to predict the subcellular location of proteins. There are two main challenges among the state-of-the-art prediction methods. First, most of the existing techniques are designed to deal with multi-class rather than multi-label classification, which ignores connections between multiple labels. In reality, multiple locations of particular proteins imply that there are vital and unique biological significances that deserve special focus and cannot be ignored. Second, techniques for handling imbalanced data in multi-label classification problems are necessary, but never employed. For solving these two issues, we have developed an ensemble multi-label classifier called HPSLPred, which can be applied for multi-label classification with an imbalanced protein source. For convenience, a user-friendly webserver has been established at http://server.malab.cn/HPSLPred. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Camouflage treatment of skeletal class III malocclusion with asymmetry using a bone-borne rapid maxillary expander.

PubMed

Seo, Yu-Jin; Chung, Kyu-Rhim; Kim, Seong-Hun; Nelson, Gerald

2015-03-01

This case report presents the successful use of palatal mini-implants for rapid maxillary expansion and mandibular distalization in a skeletal Class III malocclusion. The patient was a 13-year-old girl with the chief complaint of facial asymmetry and a protruded chin. Camouflage orthodontic treatment was chosen, acknowledging the possibility of need for orthognathic surgery after completion of her growth. A bone-borne rapid expander (BBRME) was used to correct the transverse discrepancy and was then used as indirect anchorage for distalization of the lower dentition with Class III elastics. As a result, a Class I occlusion with favorable inclination of the upper teeth was achieved without any adverse effects. The total treatment period was 25 months. Therefore, BBRME can be considered an alternative treatment in skeletal Class III malocclusion.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Machine vision system for measuring conifer seedling morphology

NASA Astrophysics Data System (ADS)

Rigney, Michael P.; Kranzler, Glenn A.

1995-01-01

A PC-based machine vision system providing rapid measurement of bare-root tree seedling morphological features has been designed. The system uses backlighting and a 2048-pixel line- scan camera to acquire images with transverse resolutions as high as 0.05 mm for precise measurement of stem diameter. Individual seedlings are manually loaded on a conveyor belt and inspected by the vision system in less than 0.25 seconds. Designed for quality control and morphological data acquisition by nursery personnel, the system provides a user-friendly, menu-driven graphical interface. The system automatically locates the seedling root collar and measures stem diameter, shoot height, sturdiness ratio, root mass length, projected shoot and root area, shoot-root area ratio, and percent fine roots. Sample statistics are computed for each measured feature. Measurements for each seedling may be stored for later analysis. Feature measurements may be compared with multi-class quality criteria to determine sample quality or to perform multi-class sorting. Statistical summary and classification reports may be printed to facilitate the communication of quality concerns with grading personnel. Tests were conducted at a commercial forest nursery to evaluate measurement precision. Four quality control personnel measured root collar diameter, stem height, and root mass length on each of 200 conifer seedlings. The same seedlings were inspected four times by the machine vision system. Machine stem diameter measurement precision was four times greater than that of manual measurements. Machine and manual measurements had comparable precision for shoot height and root mass length.

A multi-segment soft actuator for biomedical applications based on IPMCs

NASA Astrophysics Data System (ADS)

Zhao, Dongxu; Wang, Yanjie; Liu, Jiayu; Luo, Meng; Li, Dichen; Chen, Hualing

2015-04-01

With rapid progress of biomedical devices towards miniaturization, flexibility, multifunction and low cost, the restrictions of traditional mechanical structures become particularly apparent, while soft materials become research focus in broad fields. As one of the most attractive soft materials, Ionic Polymer-Metal Composite (IPMC) is widely used as artificial muscles and actuators, with the advantages of low driving-voltage, high efficiency of electromechanical transduction and functional stabilization. In this paper, a new intuitive control method was presented to achieve the omnidirectional bending movements and was applied on a representative actuation structure of a multi-degree-offreedom soft actuator composed of two segments bar-shaped IPMC with a square cross section. Firstly, the bar-shaped IPMCs were fabricated by the solution casting method, reducing plating, autocatalytic plating method and cut into shapes successively. The connectors of the multi-segment IPMC actuator were fabricated by 3D printing. Then, a new control method was introduced to realize the intuitive mapping relationship between the actuator and the joystick manipulator. The control circuit was designed and tested. Finally, the multi-degree-of-freedom actuator of 2 segments bar-shaped IPMCs was implemented and omnidirectional bending movements were achieved, which could be a promising actuator for biomedical applications, such as endoscope, catheterism, laparoscopy and the surgical resection of tumors.

Multi-colored immunochromatography using nanobeads for rapid and sensitive typing of seasonal influenza viruses.

PubMed

Sakurai, Akira; Takayama, Katsuyoshi; Nomura, Namiko; Yamamoto, Naoki; Sakoda, Yoshihiro; Kobayashi, Yukuharu; Kida, Hiroshi; Shibasaki, Futoshi

2014-12-01

Immunochromatography (IC) is an antigen-detection assay that plays an important role in the rapid diagnosis of influenza viruses because of its rapid turnaround and ease of use. Despite the usefulness of IC, the limit of detection of common IC kits is as high as 10(3)-10(4) plaque forming units (pfu) per reaction, resulting in their limited sensitivities. Early diagnosis within 24h would provide more appropriate timing of treatment. In this study, a multi-colored NanoAct™ bead IC was established to detect seasonal influenza viruses. This method has approximately 10-fold higher sensitivity than that of colloidal gold or colored latex bead IC assays, and does not require specific instruments. More notably, NanoAct™ bead IC can distinguish influenza A and B viruses from clinical samples with a straightforward readout composed of colored lines. Our results will provide new strategies for the diagnosis, treatment, and a chance to survey of influenza viruses in developing countries and in the field research. Copyright © 2014 Elsevier B.V. All rights reserved.

Coping with Multi-Level Classes Effectively and Creatively.

ERIC Educational Resources Information Center

Strasheim, Lorraine A.

This paper includes a discussion of the problem of multilevel Latin classes, a description of various techniques and perspectives the teacher might use in dealing with these classes, and copies of materials and exercises that have proved useful in multilevel classes. Because the reasons for the existence of such classes are varied, it is suggested…

Learning Science in Small Multi-Age Groups: The Role of Age Composition

ERIC Educational Resources Information Center

Kallery, Maria; Loupidou, Thomais

2016-01-01

The present study examines how the overall cognitive achievements in science of the younger children in a class where the students work in small multi-age groups are influenced by the number of older children in the groups. The context of the study was early-years education. The study has two parts: The first part involved classes attended by…

49 CFR 179.300 - General specifications applicable to multi-unit tank car tanks designed to be removed from car...

Code of Federal Regulations, 2012 CFR

2012-10-01

... tank car tanks designed to be removed from car structure for filling and emptying (Classes DOT-106A and...) PIPELINE AND HAZARDOUS MATERIALS SAFETY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) SPECIFICATIONS FOR TANK CARS Specifications for Multi-Unit Tank Car Tanks (Classes DOT-106A and 110AW) § 179.300...

49 CFR 179.300 - General specifications applicable to multi-unit tank car tanks designed to be removed from car...

Code of Federal Regulations, 2011 CFR

2011-10-01

... tank car tanks designed to be removed from car structure for filling and emptying (Classes DOT-106A and...) PIPELINE AND HAZARDOUS MATERIALS SAFETY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) SPECIFICATIONS FOR TANK CARS Specifications for Multi-Unit Tank Car Tanks (Classes DOT-106A and 110AW) § 179.300...

Project Based Learning Multi Life Skill for Collaborative Skills and Technological Skills of Senior High School Students

NASA Astrophysics Data System (ADS)

Susilawati; Ardhyani, S.; Masturi; Wijayanto; Khoiri, N.

2017-04-01

This work aims to determine the effect of Project Based Learning containing Multi Life-Skills on collaborative and technology skills of senior high school (SMA) students, especially on thestatic fluid subject. The research design was aquasi-experiment using Posttest-Only Control Design. This work was conducted in SMA Negeri 1 Bae Kudus, with the population is all students of class X, while the sample is students of class X MIA 2 as an experimental class and X MIA 3 as a control class. The data were obtained by observation, test, and documentation. The results showed this model significantly affects the collaborative and technology skills of students of SMA 1 Bae Kudus, where the average result of collaborative and technology skills for the experimental class is higher than that of the control class. This is also supported by the remark of the post-test experimental class is higher than that of the control class.

Classification of Urban Feature from Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

NASA Astrophysics Data System (ADS)

Modiri, M.; Salehabadi, A.; Mohebbi, M.; Hashemi, A. M.; Masumi, M.

2015-12-01

The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

Characterization of multidrug-resistant Acinetobacter ssp. strains isolated from medical intensive care units in Cali - Colombia.

PubMed Central

Castillo, Andres; Chávez-Vivas, Mónica

2017-01-01

Abstract Introduction: The extensive use of antibiotics has led to the emergence of multi-resistant strains in some species of the genus Acinetobacter. Objective: To investigate the molecular characteristics of multidrug-resistant of Acinetobacter ssp. strains isolated from 52 patients collected between March 2009 and July 2010 in medical intensive care units in Cali - Colombia. Methods: The susceptibility to various classes of antibiotics was determined by disc diffusion method, and the determination of the genomic species was carried out using amplified ribosomal DNA restriction analysis (ARDRA) and by sequencing of the 16s rDNA gene. Also, the genes of beta-lactamases as well as, integrases IntI1 and IntI2 were analyzed by PCR method. Results: The phenotypic identification showed that the isolates belong mainly to A. calcoaceticus- A. baumannii complex. All of them were multi-resistant to almost the whole antibiotics except to tigecycline and sulperazon, and they were grouped into five (I to V) different antibiotypes, being the antibiotype I the most common (50.0%). The percent of beta-lactamases detected was: blaTEM (17.3%), blaCTX-M (9.6%), blaVIM (21.2%), blaIMP (7.7%), blaOXA-58 (21.2%), and blaOXA-51 (21.2%). The phylogenetic tree analysis showed that the isolates were clustering to A. baumannii (74.1%), A. nosocomialis (11.1%) and A. calcoaceticus (7.4 %). Besides, the integron class 1 and class 2 were detected in 23.1% and 17.3% respectively. Conclusion: The isolates were identified to species A. baumanii mainly, and they were multiresistant. The resistance to beta-lactams may be by for presence of beta-lactamases in the majority of the isolates. PMID:29662260

Taipei's Use of a Multi-Channel Mass Risk Communication Program to Rapidly Reverse an Epidemic of Highly Communicable Disease

PubMed Central

Yen, Muh-Yong; Wu, Tsung-Shu Joseph; Chiu, Allen Wen-Hsiang; Wong, Wing-Wai; Wang, Po-En; Chan, Ta-Chien; King, Chwan-Chuen

2009-01-01

Background In September 2007, an outbreak of acute hemorrhagic conjunctivitis (AHC) occurred in Keelung City and spread to Taipei City. In response to the epidemic, a new crisis management program was implemented and tested in Taipei. Methodology and Principal Findings Having noticed that transmission surged on weekends during the Keelung epidemic, Taipei City launched a multi-channel mass risk communications program that included short message service (SMS) messages sent directly to approximately 2.2 million Taipei residents on Friday, October 12th, 2007. The public was told to keep symptomatic students from schools and was provided guidelines for preventing the spread of the disease at home. Epidemiological characteristics of Taipei's outbreak were analyzed from 461 sampled AHC cases. Median time from exposure to onset of the disease was 1 day. This was significantly shorter for cases occurring in family clusters than in class clusters (mean±SD: 2.6±3.2 vs. 4.39±4.82 days, p = 0.03), as well as for cases occurring in larger family clusters as opposed to smaller ones (1.2±1.7 days vs. 3.9±4.0 days, p<0.01). Taipei's program had a significant impact on patient compliance. Home confinement of symptomatic children increased from 10% to 60% (p<0.05) and helped curb the spread of AHC. Taipei experienced a rapid decrease in AHC cases between the Friday of the SMS announcement and the following Monday, October 15, (0.70% vs. 0.36%). By October 26, AHC cases reduced to 0.01%. The success of this risk communication program in Taipei (as compared to Keelung) is further reflected through rapid improvements in three epidemic indicators: (1) significantly lower crude attack rates (1.95% vs. 14.92%, p<0.001), (2) a short epidemic period of AHC (13 vs. 34 days), and (3) a quick drop in risk level (1∼2 weeks) in Taipei districts that border Keelung (the original domestic epicenter). Conclusions and Significance The timely launch of this systematic, communication-based intervention proved effective at preventing a dangerous spike in AHC and was able to bring this high-risk disease under control. We recommend that public health officials incorporate similar methods into existing guidelines for preventing pandemic influenza and other emerging infectious diseases. PMID:19956722

Quantitative on-line preconcentration-liquid chromatography coupled with tandem mass spectrometry method for the determination of pharmaceutical compounds in water.

PubMed

Idder, Salima; Ley, Laurent; Mazellier, Patrick; Budzinski, Hélène

2013-12-17

One of the current environmental issues concerns the presence and fate of pharmaceuticals in water bodies as these compounds may represent a potential environmental problem. The characterization of pharmaceutical contamination requires powerful analytical method able to quantify these pollutants at very low concentration (few ng L(-1)). In this work, a multi-residue analytical methodology (on-line solid phase extraction-liquid chromatography-triple quadrupole mass spectrometry using positive and negative electrospray ionization) has been developed and validated for 40 multi-class pharmaceuticals and metabolites for tap and surface waters. This on-line SPE method was very convenient and efficient compared to classical off-line SPE method because of its shorter total run time including sample preparation and smaller sample volume (1 mL vs up to 1 L). The optimized method included several therapeutic classes as lipid regulators, antibiotics, beta-blockers, non-steroidal anti-inflammatories, antineoplastic, etc., with various physicochemical properties. Quantification has been achieved with the internal standards. The limits of detection are between 0.7 and 15 ng L(-1) for drinking waters and 2-15 ng L(-1) for surface waters. The inter-day precision values are below 20% for each studied level. The improvement and strength of the analytical method has been verified along a monitoring of these 40 pharmaceuticals in Isle River, a French stream located in the South West of France. During this survey, 16 pharmaceutical compounds have been detected. Copyright © 2013 Elsevier B.V. All rights reserved.

A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation.

PubMed

Yao, Lin; Sheng, Xinjun; Zhang, Dingguo; Jiang, Ning; Mrachacz-Kersting, Natalie; Zhu, Xiangyang; Farina, Dario

2017-09-01

Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this paper, we hypothesize that a combination of these two signal modalities provides improvements in a brain-computer interface (BCI) performance with respect to using the two methods separately, and generate novel types of multi-class BCI systems. Thirty two subjects were randomly divided into a Control-Group and a Hybrid-Group. In the Control-Group, the subjects performed left and right hand motor imagery (i.e., L-MI and R-MI). In the Hybrid-Group, the subjects performed the four mental tasks (i.e., L-MI, R-MI, L-SAO, and R-SAO). The results indicate that combining two of the tasks in a hybrid manner (such as L-SAO and R-MI) resulted in a significantly greater classification accuracy than when using two MI tasks. The hybrid modality reached 86.1% classification accuracy on average, with a 7.70% increase with respect to MI ( ), and 7.21% to SAO ( ) alone. Moreover, all 16 subjects in the hybrid modality reached at least 70% accuracy, which is considered the threshold for BCI illiteracy. In addition to the two-class results, the classification accuracy was 68.1% and 54.1% for the three-class and four-class hybrid BCI. Combining the induced brain signals from motor and somatosensory cortex, the proposed stimulus-independent hybrid BCI has shown improved performance with respect to individual modalities, reducing the portion of BCI-illiterate subjects, and provided novel types of multi-class BCIs.

Color Comprehension and Color Categories among Blind Students: A Multi-Sensory Approach in Implementing Concrete Language to Include All Students in Advanced Writing Classes

ERIC Educational Resources Information Center

Antarasena, Salinee

2009-01-01

This study investigates teaching methods regarding color comprehension and color categorization among blind students, as compared to their non-blind peers and whether they understand and represent the same color comprehension and color categories. Then after digit codes for color comprehension teaching and assistive technology for the blind had…

An introduction to planar chromatography and its application to natural products isolation.

PubMed

Gibbons, Simon

2012-01-01

Thin-layer chromatography (TLC) is an easy, inexpensive, rapid, and the most widely used method for the analysis and isolation of small organic natural and synthetic products. It also has use in the biological evaluation of organic compounds, particularly in the areas of antimicrobial and antioxidant metabolites and for the evaluation of acetylcholinesterase inhibitors which have utility in the treatment of Alzheimer's disease. The ease and inexpensiveness of use of this technique, coupled with the ability to rapidly develop separation and bioassay protocols will ensure that TLC will be used for some considerable time alongside conventional instrumental methods. This chapter deals with the basic principles of TLC and describes methods for the analysis and isolation of natural products. Examples of methods for isolation of several classes of natural product are detailed and protocols for TLC bioassays are given.

Use of Multi-class Empirical Orthogonal Function for Identification of Hydrogeological Parameters and Spatiotemporal Pattern of Multiple Recharges in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Huang, C. L.; Hsu, N. S.; Yeh, W. W. G.; Hsieh, I. H.

2017-12-01

This study develops an innovative calibration method for regional groundwater modeling by using multi-class empirical orthogonal functions (EOFs). The developed method is an iterative approach. Prior to carrying out the iterative procedures, the groundwater storage hydrographs associated with the observation wells are calculated. The combined multi-class EOF amplitudes and EOF expansion coefficients of the storage hydrographs are then used to compute the initial gauss of the temporal and spatial pattern of multiple recharges. The initial guess of the hydrogeological parameters are also assigned according to in-situ pumping experiment. The recharges include net rainfall recharge and boundary recharge, and the hydrogeological parameters are riverbed leakage conductivity, horizontal hydraulic conductivity, vertical hydraulic conductivity, storage coefficient, and specific yield. The first step of the iterative algorithm is to conduct the numerical model (i.e. MODFLOW) by the initial guess / adjusted values of the recharges and parameters. Second, in order to determine the best EOF combination of the error storage hydrographs for determining the correction vectors, the objective function is devised as minimizing the root mean square error (RMSE) of the simulated storage hydrographs. The error storage hydrograph are the differences between the storage hydrographs computed from observed and simulated groundwater level fluctuations. Third, adjust the values of recharges and parameters and repeat the iterative procedures until the stopping criterion is reached. The established methodology was applied to the groundwater system of Ming-Chu Basin, Taiwan. The study period is from January 1st to December 2ed in 2012. Results showed that the optimal EOF combination for the multiple recharges and hydrogeological parameters can decrease the RMSE of the simulated storage hydrographs dramatically within three calibration iterations. It represents that the iterative approach that using EOF techniques can capture the groundwater flow tendency and detects the correction vector of the simulated error sources. Hence, the established EOF-based methodology can effectively and accurately identify the multiple recharges and hydrogeological parameters.

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

PubMed Central

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C. M.

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases. PMID:25692885

Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.

PubMed

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C M

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.

The cost-effectiveness of NBPTS teacher certification.

PubMed

Yeh, Stuart S

2010-06-01

A cost-effectiveness analysis of the National Board for Professional Teaching Standards (NBPTS) program suggests that Board certification is less cost-effective than a range of alternative approaches for raising student achievement, including comprehensive school reform, class size reduction, a 10% increase in per pupil expenditure, the use of value-added statistical methods to identify effective teachers, and the implementation of systems where student performance in math and reading is rapidly assessed 2-5 times per week. The most cost-effective approach, rapid assessment, is three magnitudes as cost-effective as Board certification.

Research and Development of Hazardous/Toxic Waste Analytical Screening Procedures. Available Field Methods for Rapid Screening of Hazardous Waste Materials at Waste Sites (Class A Poisons).

DTIC Science & Technology

1982-01-01

bromide is listed as a positive interference. Nitric oxide and nitrogen dioxide can be detected by using the Draeger nitrous fumes detector tube. A... fumes exhibit a delay from the time of exposure to the onset of symptoms. This time delay would not be conducive for a rapid field screening test. It...Dangerous when strongly heated, emits highly toxic fumes . THRESHOLD LIMIT VALUE: No information available PHYSIOLOGICAL EFFECTS: A. Intensely irritating to

Randomized trial of the impact of a sun safety program on volunteers in outdoor venues.

PubMed

Cheng, Shaowei; Guan, Xin; Cao, Mei; Liu, Yalan; Zhai, Siwen

2011-04-01

A suitable sun safety educational program could help the public avoid sun exposure-induced skin damage. The objective of this study was to assess the impact of a sun safety program on volunteers in outdoor venues and explore the most effective sun safety education method in China. An intervention program was implemented to raise knowledge and behavior regarding sun exposure among volunteers in the outdoor competition venues in Beijing, China. Five intervention methods were used, including class education, free sunscreen samples, pamphlets, posters, and newsletters. The self-administered multiple-choice questionnaires were administered before and after the intervention. Two hundred and eighty-five subjects were enrolled, including 107 males (37.5%) and 178 females (62.5%). The intervention group showed improvement in sun safety knowledge and behavior. Other improvements were achieved in the field of sun safety awareness and intended behavior, with most of the items achieving no statistically significant differences. Subgroup A (multi-component interventions, including class education, free sunscreen samples, and written materials) achieved better results than subgroup B (written materials only) to improve sun safety knowledge and awareness. Sun safety education could improve volunteer 's sun safety knowledge and behavior in the outdoor venues in China. Multi-component interventions proved to be the most effective sun safety education method. © 2011 John Wiley & Sons A/S.

Multi-rate DPSK optical transceivers for free-space applications

NASA Astrophysics Data System (ADS)

Caplan, D. O.; Carney, J. J.; Fitzgerald, J. J.; Gaschits, I.; Kaminsky, R.; Lund, G.; Hamilton, S. A.; Magliocco, R. J.; Murphy, R. J.; Rao, H. G.; Spellmeyer, N. W.; Wang, J. P.

2014-03-01

We describe a flexible high-sensitivity laser communication transceiver design that can significantly benefit performance and cost of NASA's satellite-based Laser Communications Relay Demonstration. Optical communications using differential phase shift keying, widely deployed for use in long-haul fiber-optic networks, is well known for its superior sensitivity and link performance over on-off keying, while maintaining a relatively straightforward design. However, unlike fiber-optic links, free-space applications often require operation over a wide dynamic range of power due to variations in link distance and channel conditions, which can include rapid kHz-class fading when operating through the turbulent atmosphere. Here we discuss the implementation of a robust, near-quantum-limited multi-rate DPSK transceiver, co-located transmitter and receiver subsystems that can operate efficiently over the highly-variable free-space channel. Key performance features will be presented on the master oscillator power amplifier (MOPA) based TX, including a wavelength-stabilized master laser, high-extinction-ratio burst-mode modulator, and 0.5 W single polarization power amplifier, as well as low-noise optically preamplified DSPK receiver and built-in test capabilities.

Simultaneous determination of nandrolone, testosterone, and methyltestosterone by multi-immunoaffinity column and capillary electrophoresis.

PubMed

Qi, Xiao-Hua; Zhang, Li-Wei; Zhang, Xin-Xiang

2008-08-01

A multitarget antibody immunoaffinity column was proposed for the purification and enrichment of nandrolone, testosterone, and methyltestosterone from urine. Nandrolone-3-site substituted antigen was designed and synthesized and the polyclonal antibody was prepared with immunizing rabbits. The stationary phase of the immunoaffinity column was synthesized by covalently bonding the antibodies specific to nandrolone, testosterone, and methyltestosterone onto CNBr-actived Sepharose 4B. The analytes of interest were extracted with a methanol/water mixture in one step. The immunoaffinity column showed high affinity and high selectivity to a class of structurally related compounds. The elution was then transferred to a micellar electrokinetic CE system with a running buffer of sodium borate and sodium cholate for separation and determination. Recoveries of the three steroids from complex matrix were 88-94% with RSD values less than 5.2%. Optimization of the immunoaffinity column purification was achieved and the feasibility of the technique for the analysis of steroid hormone was discussed. The results indicated that the combination of multi-immunoaffinity column and CE was an effective technique, which was rapid, simple, and sensitive for the assay of steroids.

Small Rocket/Spacecraft Technology (SMART) Platform

NASA Technical Reports Server (NTRS)

Esper, Jaime; Flatley, Thomas P.; Bull, James B.; Buckley, Steven J.

2011-01-01

The NASA Goddard Space Flight Center (GSFC) and the Department of Defense Operationally Responsive Space (ORS) Office are exercising a multi-year collaborative agreement focused on a redefinition of the way space missions are designed and implemented. A much faster, leaner and effective approach to space flight requires the concerted effort of a multi-agency team tasked with developing the building blocks, both programmatically and technologically, to ultimately achieve flights within 7-days from mission call-up. For NASA, rapid mission implementations represent an opportunity to find creative ways for reducing mission life-cycle times with the resulting savings in cost. This in tum enables a class of missions catering to a broader audience of science participants, from universities to private and national laboratory researchers. To that end, the SMART (Small Rocket/Spacecraft Technology) micro-spacecraft prototype demonstrates an advanced avionics system with integrated GPS capability, high-speed plug-and-playable interfaces, legacy interfaces, inertial navigation, a modular reconfigurable structure, tunable thermal technology, and a number of instruments for environmental and optical sensing. Although SMART was first launched inside a sounding rocket, it is designed as a free-flyer.

Spatial accessibility to healthcare services in Shenzhen, China: improving the multi-modal two-step floating catchment area method by estimating travel time via online map APIs.

PubMed

Tao, Zhuolin; Yao, Zaoxing; Kong, Hui; Duan, Fei; Li, Guicai

2018-05-09

Shenzhen has rapidly grown into a megacity in the recent decades. It is a challenging task for the Shenzhen government to provide sufficient healthcare services. The spatial configuration of healthcare services can influence the convenience for the consumers to obtain healthcare services. Spatial accessibility has been widely adopted as a scientific measurement for evaluating the rationality of the spatial configuration of healthcare services. The multi-modal two-step floating catchment area (2SFCA) method is an important advance in the field of healthcare accessibility modelling, which enables the simultaneous assessment of spatial accessibility via multiple transport modes. This study further develops the multi-modal 2SFCA method by introducing online map APIs to improve the estimation of travel time by public transit or by car respectively. As the results show, the distribution of healthcare accessibility by multi-modal 2SFCA shows significant spatial disparity. Moreover, by dividing the multi-modal accessibility into car-mode and transit-mode accessibility, this study discovers that the transit-mode subgroup is disadvantaged in the competition for healthcare services with the car-mode subgroup. The disparity in transit-mode accessibility is the main reason of the uneven pattern of healthcare accessibility in Shenzhen. The findings suggest improving the public transit conditions for accessing healthcare services to reduce the disparity of healthcare accessibility. More healthcare services should be allocated in the eastern and western Shenzhen, especially sub-districts in Dapeng District and western Bao'an District. As these findings cannot be drawn by the traditional single-modal 2SFCA method, the advantage of the multi-modal 2SFCA method is significant to both healthcare studies and healthcare system planning.

Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network

PubMed Central

Lin, Kai; Wang, Di; Hu, Long

2016-01-01

With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods. PMID:27376302

Minimum impulse three-body trajectories.

NASA Technical Reports Server (NTRS)

D'Amario, L.; Edelbaum, T. N.

1973-01-01

A rapid and accurate method of calculating optimal impulsive transfers in the restricted problem of three bodies has been developed. The technique combines a multi-conic method of trajectory integration with primer vector theory and an accelerated gradient method of trajectory optimization. A unique feature is that the state transition matrix and the primer vector are found analytical without additional integrations or differentiations. The method has been applied to the determination of optimal two and three impulse transfers between the L2 libration point and circular orbits about both the earth and the moon.

EnzML: multi-label prediction of enzyme classes using InterPro signatures

PubMed Central

2012-01-01

Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN). PMID:22533924

The MPLEx Protocol for Multi-omic Analyses of Soil Samples

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

Nicora, Carrie D.; Burnum-Johnson, Kristin E.; Nakayasu, Ernesto S.

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for simultaneous metabolite, protein, and lipid extraction (MPLEx) from the exact same soil sample was created bymore » adapting a solvent-based approach. This MPLEx protocol has proven to be simple yet robust for many sample types and even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.« less

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

DOE PAGES

Mollah, Md Atiqul; Yuan, Xin; Pakin, Scott; ...

2017-08-29

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fairmore » rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.« less

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

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

Mollah, Md Atiqul; Yuan, Xin; Pakin, Scott

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fairmore » rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.« less

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Voxel-based plaque classification in coronary intravascular optical coherence tomography images using decision trees

NASA Astrophysics Data System (ADS)

Kolluru, Chaitanya; Prabhu, David; Gharaibeh, Yazan; Wu, Hao; Wilson, David L.

2018-02-01

Intravascular Optical Coherence Tomography (IVOCT) is a high contrast, 3D microscopic imaging technique that can be used to assess atherosclerosis and guide stent interventions. Despite its advantages, IVOCT image interpretation is challenging and time consuming with over 500 image frames generated in a single pullback volume. We have developed a method to classify voxel plaque types in IVOCT images using machine learning. To train and test the classifier, we have used our unique database of labeled cadaver vessel IVOCT images accurately registered to gold standard cryoimages. This database currently contains 300 images and is growing. Each voxel is labeled as fibrotic, lipid-rich, calcified or other. Optical attenuation, intensity and texture features were extracted for each voxel and were used to build a decision tree classifier for multi-class classification. Five-fold cross-validation across images gave accuracies of 96 % +/- 0.01 %, 90 +/- 0.02% and 90 % +/- 0.01 % for fibrotic, lipid-rich and calcified classes respectively. To rectify performance degradation seen in left out vessel specimens as opposed to left out images, we are adding data and reducing features to limit overfitting. Following spatial noise cleaning, important vascular regions were unambiguous in display. We developed displays that enable physicians to make rapid determination of calcified and lipid regions. This will inform treatment decisions such as the need for devices (e.g., atherectomy or scoring balloon in the case of calcifications) or extended stent lengths to ensure coverage of lipid regions prone to injury at the edge of a stent.

Material classification and automatic content enrichment of images using supervised learning and knowledge bases

NASA Astrophysics Data System (ADS)

Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

2011-02-01

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

Risk-taking behaviors and subgrouping of suicide in Iran: A latent class analysis of national registries data.

PubMed

Hajebi, Ahmad; Abbasi-Ghahramanloo, Abbas; Hashemian, Seyed Sepehr; Khatibi, Seyed Reza; Ghasemzade, Masomeh; Khodadost, Mahmoud

2017-09-01

Suicide is one the most important public health problem which is rapidly growing concerns. The aim of this study was to subgroup suicide using LCA method. This cross-sectional study was conducted in Iran based on 66990 records registered in Ministry of Health in 2014. A case report questionnaire in the form of software was used for case registries. Latent class analysis was used to achieve the research objectives. Four latent classes were identified; (a) Non-lethal attempters without a history of psychiatric disorders, (b) Non-lethal attempters with a history of psychiatric disorders, (c) Lethal attempters without a history of psychiatric disorders, (d) Lethal attempters with a history of psychiatric disorders. The probability of completed/an achieved suicide is high in lethal attempter classes. Being male increases the risk of inclusion in lethal attempters' classes (OR = 4.93). Also, being single (OR = 1.16), having an age lower than 25 years (OR = 1.14) and being a rural citizen (OR = 2.36) associate with lethal attempters classes. The males tend to use more violent methods and have more completed suicide. Majority of the individuals are non-lethal attempters who need to be addressed by implementing preventive interventions and mental support provision. Copyright © 2017. Published by Elsevier B.V.

Books Only Got Us so Far: The Need for Multi-Genre Inquiry

ERIC Educational Resources Information Center

Jewett, Pamela

2010-01-01

This study examines the instructional steps I took, based on gaps between what was happening in a graduate literacy class I taught and what I had intended to happen. This study describes the ways that I re-imagined the class and what came about when I created a pedagogical approach that featured multi-genre inquiry. I define inter-discursivity as…

Multi-Positioning Mathematics Class Size: Teachers' Views

ERIC Educational Resources Information Center

Handal, Boris; Watson, Kevin; Maher, Marguerite

2015-01-01

This paper explores mathematics teachers' perceptions about class size and the impact class size has on teaching and learning in secondary mathematics classrooms. It seeks to understand teachers' views about optimal class sizes and their thoughts about the education variables that influence these views. The paper draws on questionnaire responses…

Nanofabrication

DOEpatents

Tuominen, Mark; Bal, Mustafa; Russell, Thomas P.; Ursache, Andrei

2007-03-13

Pathways to rapid and reliable fabrication of three-dimensional nanostructures are provided. Simple methods are described for the production of well-ordered, multilevel nanostructures. This is accomplished by patterning block copolymer templates with selective exposure to a radiation source. The resulting multi-scale lithographic template can be treated with post-fabrication steps to produce multilevel, three-dimensional, integrated nanoscale media, devices, and systems.

Residue analysis of sixty pesticides in red swamp crayfish using QuEChERS with high-performance liquid chromatography-tandem mass spectrometry

USDA-ARS?s Scientific Manuscript database

In this study, a multi-residue analytical method using QuEChERS extraction and dispersive solid-phase extraction (d-SPE) cleanup followed by high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) was developed for rapid determination of 60 pesticide residues in whole crayfish a...

Momentum-Based Dynamics for Spacecraft with Chained Revolute Appendages

NASA Technical Reports Server (NTRS)

Queen, Steven; London, Ken; Gonzalez, Marcelo

2005-01-01

An efficient formulation is presented for a sub-class of multi-body dynamics problems that involve a six degree-of-freedom base body and a chain of N rigid linkages connected in series by single degree-of-freedom revolute joints. This general method is particularly well suited for simulations of spacecraft dynamics and control that include the modeling of an orbiting platform with or without internal degrees of freedom such as reaction wheels, dampers, and/or booms. In the present work, particular emphasis is placed on dynamic simulation of multi-linkage robotic manipulators. The differential equations of motion are explicitly given in terms of linear and angular momentum states, which can be evaluated recursively along a serial chain of linkages for an efficient real-time solution on par with the best of the O(N3) methods.

An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine

NASA Astrophysics Data System (ADS)

Zhao, Lili; Yin, Jianping; Yuan, Lihuan; Liu, Qiang; Li, Kuan; Qiu, Minghui

2017-07-01

Automatic detection of abnormal cells from cervical smear images is extremely demanded in annual diagnosis of women's cervical cancer. For this medical cell recognition problem, there are three different feature sections, namely cytology morphology, nuclear chromatin pathology and region intensity. The challenges of this problem come from feature combination s and classification accurately and efficiently. Thus, we propose an efficient abnormal cervical cell detection system based on multi-instance extreme learning machine (MI-ELM) to deal with above two questions in one unified framework. MI-ELM is one of the most promising supervised learning classifiers which can deal with several feature sections and realistic classification problems analytically. Experiment results over Herlev dataset demonstrate that the proposed method outperforms three traditional methods for two-class classification in terms of well accuracy and less time.

Deadlock-free class routes for collective communications embedded in a multi-dimensional torus network

DOEpatents

Chen, Dong; Eisley, Noel A.; Steinmacher-Burow, Burkhard; Heidelberger, Philip

2013-01-29

A computer implemented method and a system for routing data packets in a multi-dimensional computer network. The method comprises routing a data packet among nodes along one dimension towards a root node, each node having input and output communication links, said root node not having any outgoing uplinks, and determining at each node if the data packet has reached a predefined coordinate for the dimension or an edge of the subrectangle for the dimension, and if the data packet has reached the predefined coordinate for the dimension or the edge of the subrectangle for the dimension, determining if the data packet has reached the root node, and if the data packet has not reached the root node, routing the data packet among nodes along another dimension towards the root node.

The numerical methods for the development of the mixture region in the vapor explosion simulations

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

Yang, Y.; Ohashi, H.; Akiyama, M.

An attempt to numerically simulate the process of the vapor explosion with a general multi-component and multi-dimension code is being challenged. Because of the rapid change of the flow field and extremely nonuniform distribution of the components in the system of the vapor explosion, the numerical divergence and diffusion are subject to occur easily. A dispersed component model and a multiregion scheme, by which these difficulties can be effectively overcome, were proposed. The simulations have been performed for the processes of the premixing and the fragmentation propagation in the vapor explosion.

Two-Wavelength Multi-Gigahertz Frequency Comb-Based Interferometry for Full-Field Profilometry

NASA Astrophysics Data System (ADS)

Choi, Samuel; Kashiwagi, Ken; Kojima, Shuto; Kasuya, Yosuke; Kurokawa, Takashi

2013-10-01

The multi-gigahertz frequency comb-based interferometer exhibits only the interference amplitude peak without the phase fringes, which can produce a rapid axial scan for full-field profilometry and tomography. Despite huge technical advantages, there remain problems that the interference intensity undulations occurred depending on the interference phase. To avoid such problems, we propose a compensation technique of the interference signals using two frequency combs with slightly varied center wavelengths. The compensated full-field surface profile measurements of cover glass and onion skin were demonstrated experimentally to verify the advantages of the proposed method.

Rapid automated superposition of shapes and macromolecular models using spherical harmonics.

PubMed

Konarev, Petr V; Petoukhov, Maxim V; Svergun, Dmitri I

2016-06-01

A rapid algorithm to superimpose macromolecular models in Fourier space is proposed and implemented ( SUPALM ). The method uses a normalized integrated cross-term of the scattering amplitudes as a proximity measure between two three-dimensional objects. The reciprocal-space algorithm allows for direct matching of heterogeneous objects including high- and low-resolution models represented by atomic coordinates, beads or dummy residue chains as well as electron microscopy density maps and inhomogeneous multi-phase models ( e.g. of protein-nucleic acid complexes). Using spherical harmonics for the computation of the amplitudes, the method is up to an order of magnitude faster than the real-space algorithm implemented in SUPCOMB by Kozin & Svergun [ J. Appl. Cryst. (2001 ▸), 34 , 33-41]. The utility of the new method is demonstrated in a number of test cases and compared with the results of SUPCOMB . The spherical harmonics algorithm is best suited for low-resolution shape models, e.g . those provided by solution scattering experiments, but also facilitates a rapid cross-validation against structural models obtained by other methods.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

Multi-wavelength dye concentration determination for enzymatic assays: evaluation of chromogenic para-nitrophenol over a wide pH range.

PubMed

Max, Jean-Joseph; Meddeb-Mouelhi, Fatma; Beauregard, Marc; Chapados, Camille

2012-12-01

Enzymatic assays need robust, rapid colorimetric methods that can follow ongoing reactions. For this, we developed a highly accurate, multi-wavelength detection method that could be used for several systems. Here, it was applied to the detection of para-nitrophenol (pNP) in basic and acidic solutions. First, we confirmed by factor analysis that pNP has two forms, with unique spectral characteristics in the 240 to 600 nm range: Phenol in acidic conditions absorbs in the lower range, whereas phenolate in basic conditions absorbs in the higher range. Thereafter, the method was used for the determination of species concentration. For this, the intensity measurements were made at only two wavelengths with a microtiter plate reader. This yielded total dye concentration, species relative abundance, and solution pH value. The method was applied to an enzymatic assay. For this, a chromogenic substrate that generates pNP after hydrolysis catalyzed by a lipase from the fungus Yarrowia lipolytica was used. Over the pH range of 3-11, accurate amounts of acidic and basic pNP were determined at 340 and 405 nm, respectively. This method surpasses the commonly used single-wavelength assay at 405 nm, which does not detect pNP acidic species, leading to activity underestimations. Moreover, alleviation of this pH-related problem by neutralization is not necessary. On the whole, the method developed is readily applicable to rapid high-throughput of enzymatic activity measurements over a wide pH range.

Authentication of bee pollen grains in bright-field microscopy by combining one-class classification techniques and image processing.

PubMed

Chica, Manuel

2012-11-01

A novel method for authenticating pollen grains in bright-field microscopic images is presented in this work. The usage of this new method is clear in many application fields such as bee-keeping sector, where laboratory experts need to identify fraudulent bee pollen samples against local known pollen types. Our system is based on image processing and one-class classification to reject unknown pollen grain objects. The latter classification technique allows us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types, and the impossibility of modeling all of them. Different one-class classification paradigms are compared to study the most suitable technique for solving the problem. In addition, feature selection algorithms are applied to reduce the complexity and increase the accuracy of the models. For each local pollen type, a one-class classifier is trained and aggregated into a multiclassifier model. This multiclassification scheme combines the output of all the one-class classifiers in a unique final response. The proposed method is validated by authenticating pollen grains belonging to different Spanish bee pollen types. The overall accuracy of the system on classifying fraudulent microscopic pollen grain objects is 92.3%. The system is able to rapidly reject pollen grains, which belong to nonlocal pollen types, reducing the laboratory work and effort. The number of possible applications of this authentication method in the microscopy research field is unlimited. Copyright © 2012 Wiley Periodicals, Inc.

Hybrid Particle-Continuum Numerical Methods for Aerospace Applications

DTIC Science & Technology

2011-01-01

may require kinetic analysis. Another possible option that will enable high-mass, Mars missions is supersonic retro -propulsion [17], where a jet is...exploration missions [15]. 2.3 Plumes Another class of multi-scale ows of interest is rocket exhaust plumes. Ecient and accurate predictions of...atmospheric exhaust plumes at high altitudes are necessary to ensure that the chemical rocket maintains eciency while also assuring that the vehicle heating

SDI-Flex: a new technique of allocating growing stock for developing treatment prescriptions in uneven-aged forest stands

Treesearch

Wayne D. Shepperd

2007-01-01

One of the difficulties of apportioning growing stock across diameter classes in multi- or uneven-aged forests is estimating how closely the target stocking value compares to the maximum stocking that could occur in a particular forest type and eco-region. Although the BDQ method had been used to develop uneven-aged prescriptions, it is not inherently related to any...

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2014-09-30

floor 1176 Howell St Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR...APPROACH Odontocete click detection and classification. A multi-class support vector machine (SVM) classifier was previously developed ( Jarvis ...beaked whales, Risso’s dolphins, short-finned pilot whales, and sperm whales. Here Moretti’s group, particularly S. Jarvis , is improving the SVM

Analysis of multi-class preservatives in leave-on and rinse-off cosmetics by matrix solid-phase dispersion.

PubMed

Sanchez-Prado, Lucia; Alvarez-Rivera, Gerardo; Lamas, J Pablo; Lores, Marta; Garcia-Jares, Carmen; Llompart, Maria

2011-12-01

Matrix solid-phase extraction has been successfully applied for the determination of multi-class preservatives in a wide variety of cosmetic samples including rinse-off and leave-on products. After extraction, derivatization with acetic anhydride, and gas chromatography-mass spectrometry analysis were performed. Optimization studies were done on real non-spiked and spiked leave-on and rinse-off cosmetic samples. The selection of the most suitable extraction conditions was made using statistical tools such as ANOVA, as well as factorial experimental designs. The final optimized conditions were common for both groups of cosmetics and included the dispersion of the sample with Florisil (1:4), and the elution of the MSPD column with 5 mL of hexane/acetone (1:1). After derivatization, the extract was analyzed without any further clean-up or concentration step. Accuracy, precision, linearity and detection limits were evaluated to assess the performance of the proposed method. The recovery studies on leave-on and rinse-off cosmetics gave satisfactory values (>78% for all analytes in all the samples) with an average relative standard deviation value of 4.2%. The quantification limits were well below those set by the international cosmetic regulations, making this multi-component analytical method suitable for routine control. The analysis of a broad range of cosmetics including body milk, moisturizing creams, anti-stretch marks creams, hand creams, deodorant, shampoos, liquid soaps, makeup, sun milk, hand soaps, among others, demonstrated the high use of most of the target preservatives, especially butylated hydroxytoluene, methylparaben, propylparaben, and butylparaben.

Rapid Identification of unstable acyl glucoside flavonoids of Oxytropis racemosa Turcz by high-performance liquid chromatography-diode array detection-electrospray ionisation/multi-stage mass spectrometry.

PubMed

Song, Shuang; Zheng, Xiu-Ping; Liu, Wei-Dong; Du, Rui-Fang; Feng, Zi-Ming; Zhang, Pei-Cheng; Bi, Li-Fu

2013-02-01

Oxytropis racemosa Turcz is an important minority medicine that is used mainly to improve children's indigestion, especially in inner Mongolia and Tibet. Previous studies indicated that the characteristic constituents of this plant are acylated flavonoids. Rapidly identify the characteristic chemical constituents of O. racemosa by high-performance liquid chromatography-diode array detection-electrospray ionisation/multi-stage mass spectrometry (HPLC-DAD-ESI/MS(n) ) and suggest a useful method to control the quality of this medicinal plant. In the HPLC fingerprint, 32 flavonoids were tentatively identified by a detailed analysis of their mass spectra, UV spectra and retention times. Furthermore, 13 flavonoids were confirmed by comparison with previously isolated compounds obtained from O. racemosa. In total, 32 flavonoids, including 13 flavonoids with 3-hydroxy-3-methylglutaric acid (HMG) moieties and four flavonoids with 3-malonyl moieties, were identified in the extract of O. racemosa. Among the compounds identified, 10 were characterised as new compounds for their particular acylated sugar moieties. The method described is effective for obtaining a comprehensive phytochemical profile of plants containing unstable acylated flavonoids. The method is also useful for constructing the chromatographic fingerprint of the minority medicine -O. racemosa Turcz for quality control. Copyright © 2012 John Wiley & Sons, Ltd.

Urinary tract infection (UTI) multi-bacteria multi-antibiotic testing using surface enhanced Raman spectroscopy (SERS)

NASA Astrophysics Data System (ADS)

Hadjigeorgiou, Katerina; Kastanos, Evdokia; Pitris, Costas

2013-02-01

Antibiotic resistance is a major health care problem mostly caused by the inappropriate use of antibiotics. At the root of the problem lies the current method for determination of bacterial susceptibility to antibiotics which requires overnight cultures. Physicians suspecting an infection usually prescribe an antibiotic without waiting for the results. This practice aggravates the problem of bacterial resistance. In this work, a rapid method of diagnosis and antibiogram for a bacterial infection was developed using Surface Enhanced Raman Spectroscopy (SERS) with silver nanoparticles. SERS spectra of three species of gram negative bacteria, Escherichia coli, Proteus spp., and Klebsiella spp. were obtained after 0 and 4 hour exposure to the seven different antibiotics. Even though the concentration of bacteria was low (2x105 cfu/ml), species classification was achieved with 94% accuracy using spectra obtained at 0 hours. Sensitivity or resistance to antibiotics was predicted with 81%-100% accuracy from spectra obtained after 4 hours of exposure to the different antibiotics. With the enhancement provided by SERS, the technique can be applied directly to urine or blood samples, bypassing the need for overnight cultures. This technology can lead to the development of rapid methods of diagnosis and antibiogram for a variety of bacterial infections.

Land cover's refined classification based on multi source of remote sensing information fusion: a case study of national geographic conditions census in China

NASA Astrophysics Data System (ADS)

Cheng, Tao; Zhang, Jialong; Zheng, Xinyan; Yuan, Rujin

2018-03-01

The project of The First National Geographic Conditions Census developed by Chinese government has designed the data acquisition content and indexes, and has built corresponding classification system mainly based on the natural property of material. However, the unified standard for land cover classification system has not been formed; the production always needs converting to meet the actual needs. Therefore, it proposed a refined classification method based on multi source of remote sensing information fusion. It takes the third-level classes of forest land and grassland for example, and has collected the thematic data of Vegetation Map of China (1:1,000,000), attempts to develop refined classification utilizing raster spatial analysis model. Study area is selected, and refined classification is achieved by using the proposed method. The results show that land cover within study area is divided principally among 20 classes, from subtropical broad-leaved forest (31131) to grass-forb community type of low coverage grassland (41192); what's more, after 30 years in the study area, climatic factors, developmental rhythm characteristics and vegetation ecological geographical characteristics have not changed fundamentally, only part of the original vegetation types have changed in spatial distribution range or land cover types. Research shows that refined classification for the third-level classes of forest land and grassland could make the results take on both the natural attributes of the original and plant community ecology characteristics, which could meet the needs of some industry application, and has certain practical significance for promoting the product of The First National Geographic Conditions Census.

Multi-Level Bitmap Indexes for Flash Memory Storage

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

Wu, Kesheng; Madduri, Kamesh; Canon, Shane

2010-07-23

Due to their low access latency, high read speed, and power-efficient operation, flash memory storage devices are rapidly emerging as an attractive alternative to traditional magnetic storage devices. However, tests show that the most efficient indexing methods are not able to take advantage of the flash memory storage devices. In this paper, we present a set of multi-level bitmap indexes that can effectively take advantage of flash storage devices. These indexing methods use coarsely binned indexes to answer queries approximately, and then use finely binned indexes to refine the answers. Our new methods read significantly lower volumes of data atmore » the expense of an increased disk access count, thus taking full advantage of the improved read speed and low access latency of flash devices. To demonstrate the advantage of these new indexes, we measure their performance on a number of storage systems using a standard data warehousing benchmark called the Set Query Benchmark. We observe that multi-level strategies on flash drives are up to 3 times faster than traditional indexing strategies on magnetic disk drives.« less

Nanomaterial-based x-ray sources

NASA Astrophysics Data System (ADS)

Cole, Matthew T.; Parmee, R. J.; Milne, William I.

2016-02-01

Following the recent global excitement and investment in the emerging, and rapidly growing, classes of one and two-dimensional nanomaterials, we here present a perspective on one of the viable applications of such materials: field electron emission based x-ray sources. These devices, which have a notable history in medicine, security, industry and research, to date have almost exclusively incorporated thermionic electron sources. Since the middle of the last century, field emission based cathodes were demonstrated, but it is only recently that they have become practicable. We outline some of the technological achievements of the past two decades, and describe a number of the seminal contributions. We explore the foremost market hurdles hindering their roll-out and broader industrial adoption and summarise the recent progress in miniaturised, pulsed and multi-source devices.

The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

2016-09-01

The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

Electrochemical Biosensors for Rapid Detection of Foodborne Salmonella: A Critical Overview

PubMed Central

Cinti, Stefano; Volpe, Giulia; Piermarini, Silvia; Delibato, Elisabetta; Palleschi, Giuseppe

2017-01-01

Salmonella has represented the most common and primary cause of food poisoning in many countries for at least over 100 years. Its detection is still primarily based on traditional microbiological culture methods which are labor-intensive, extremely time consuming, and not suitable for testing a large number of samples. Accordingly, great efforts to develop rapid, sensitive and specific methods, easy to use, and suitable for multi-sample analysis, have been made and continue. Biosensor-based technology has all the potentialities to meet these requirements. In this paper, we review the features of the electrochemical immunosensors, genosensors, aptasensors and phagosensors developed in the last five years for Salmonella detection, focusing on the critical aspects of their application in food analysis. PMID:28820458

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Research in Applied Mathematics Related to Mathematical System Theory.

DTIC Science & Technology

1977-06-01

This report deals with research results obtained in the field of mathematical system theory . Special emphasis was given to the following areas: (1...Linear system theory over a field: parametrization of multi-input, multi-output systems and the geometric structure of classes of systems of...constant dimension. (2) Linear systems over a ring: development of the theory for very general classes of rings. (3) Nonlinear system theory : basic

Multi-Cohort Stand Structural Classification: Ground- and LiDAR-based Approaches for Boreal Mixedwood and Black Spruce Forest Types of Northeastern Ontario

NASA Astrophysics Data System (ADS)

Kuttner, Benjamin George

Natural fire return intervals are relatively long in eastern Canadian boreal forests and often allow for the development of stands with multiple, successive cohorts of trees. Multi-cohort forest management (MCM) provides a strategy to maintain such multi-cohort stands that focuses on three broad phases of increasingly complex, post-fire stand development, termed "cohorts", and recommends different silvicultural approaches be applied to emulate different cohort types. Previous research on structural cohort typing has relied upon primarily subjective classification methods; in this thesis, I develop more comprehensive and objective methods for three common boreal mixedwood and black spruce forest types in northeastern Ontario. Additionally, I examine relationships between cohort types and stand age, productivity, and disturbance history and the utility of airborne LiDAR to retrieve ground-based classifications and to extend structural cohort typing from plot- to stand-levels. In both mixedwood and black spruce forest types, stand age and age-related deadwood features varied systematically with cohort classes in support of an age-based interpretation of increasing cohort complexity. However, correlations of stand age with cohort classes were surprisingly weak. Differences in site productivity had a significant effect on the accrual of increasingly complex multi-cohort stand structure in both forest types, especially in black spruce stands. The effects of past harvesting in predictive models of class membership were only significant when considered in isolation of age. As an age-emulation strategy, the three cohort model appeared to be poorly suited to black spruce forests where the accrual of structural complexity appeared to be more a function of site productivity than age. Airborne LiDAR data appear to be particularly useful in recovering plot-based cohort types and extending them to the stand-level. The main gradients of structural variability detected using LiDAR were similar between boreal mixedwood and black spruce forest types; the best LiDAR-based models of cohort type relied upon combinations of tree size, size heterogeneity, and tree density related variables. The methods described here to measure, classify, and predict cohort-related structural complexity assist in translating the conceptual three cohort model to a more precise, measurement-based management system. In addition, the approaches presented here to measure and classify stand structural complexity promise to significantly enhance the detail of structural information in operational forest inventories in support of a wide array of forest management and conservation applications.

Therapeutic targeting and rapid mobilization of endosteal HSC using a small molecule integrin antagonist

PubMed Central

Cao, Benjamin; Zhang, Zhen; Grassinger, Jochen; Williams, Brenda; Heazlewood, Chad K.; Churches, Quentin I.; James, Simon A.; Li, Songhui; Papayannopoulou, Thalia; Nilsson, Susan K.

2016-01-01

The inherent disadvantages of using granulocyte colony-stimulating factor (G-CSF) for hematopoietic stem cell (HSC) mobilization have driven efforts to identify alternate strategies based on single doses of small molecules. Here, we show targeting α9β1/α4β1 integrins with a single dose of a small molecule antagonist (BOP (N-(benzenesulfonyl)-L-prolyl-L-O-(1-pyrrolidinylcarbonyl)tyrosine)) rapidly mobilizes long-term multi-lineage reconstituting HSC. Synergistic engraftment augmentation is observed when BOP is co-administered with AMD3100. Impressively, HSC in equal volumes of peripheral blood (PB) mobilized with this combination effectively out-competes PB mobilized with G-CSF. The enhanced mobilization observed using BOP and AMD3100 is recapitulated in a humanized NODSCIDIL2Rγ−/− model, demonstrated by a significant increase in PB CD34+ cells. Using a related fluorescent analogue of BOP (R-BC154), we show that this class of antagonists preferentially bind human and mouse HSC and progenitors via endogenously primed/activated α9β1/α4β1 within the endosteal niche. These results support using dual α9β1/α4β1 inhibitors as effective, rapid and transient mobilization agents with promising clinical applications. PMID:26975966

Automatic threshold selection for multi-class open set recognition

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2017-05-01

Multi-class open set recognition is the problem of supervised classification with additional unknown classes encountered after a model has been trained. An open set classifer often has two core components. The first component is a base classifier which estimates the most likely class of a given example. The second component consists of open set logic which estimates if the example is truly a member of the candidate class. Such a system is operated in a feed-forward fashion. That is, a candidate label is first estimated by the base classifier, and the true membership of the example to the candidate class is estimated afterward. Previous works have developed an iterative threshold selection algorithm for rejecting examples from classes which were not present at training time. In those studies, a Platt-calibrated SVM was used as the base classifier, and the thresholds were applied to class posterior probabilities for rejection. In this work, we investigate the effectiveness of other base classifiers when paired with the threshold selection algorithm and compare their performance with the original SVM solution.

Multi-Task Learning with Low Rank Attribute Embedding for Multi-Camera Person Re-Identification.

PubMed

Su, Chi; Yang, Fan; Zhang, Shiliang; Tian, Qi; Davis, Larry Steven; Gao, Wen

2018-05-01

We propose Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE) to address the problem of person re-identification on multi-cameras. Re-identifications on different cameras are considered as related tasks, which allows the shared information among different tasks to be explored to improve the re-identification accuracy. The MTL-LORAE framework integrates low-level features with mid-level attributes as the descriptions for persons. To improve the accuracy of such description, we introduce the low-rank attribute embedding, which maps original binary attributes into a continuous space utilizing the correlative relationship between each pair of attributes. In this way, inaccurate attributes are rectified and missing attributes are recovered. The resulting objective function is constructed with an attribute embedding error and a quadratic loss concerning class labels. It is solved by an alternating optimization strategy. The proposed MTL-LORAE is tested on four datasets and is validated to outperform the existing methods with significant margins.

Rapid classification of heavy metal-exposed freshwater bacteria by infrared spectroscopy coupled with chemometrics using supervised method

NASA Astrophysics Data System (ADS)

Gurbanov, Rafig; Gozen, Ayse Gul; Severcan, Feride

2018-01-01

Rapid, cost-effective, sensitive and accurate methodologies to classify bacteria are still in the process of development. The major drawbacks of standard microbiological, molecular and immunological techniques call for the possible usage of infrared (IR) spectroscopy based supervised chemometric techniques. Previous applications of IR based chemometric methods have demonstrated outstanding findings in the classification of bacteria. Therefore, we have exploited an IR spectroscopy based chemometrics using supervised method namely Soft Independent Modeling of Class Analogy (SIMCA) technique for the first time to classify heavy metal-exposed bacteria to be used in the selection of suitable bacteria to evaluate their potential for environmental cleanup applications. Herein, we present the powerful differentiation and classification of laboratory strains (Escherichia coli and Staphylococcus aureus) and environmental isolates (Gordonia sp. and Microbacterium oxydans) of bacteria exposed to growth inhibitory concentrations of silver (Ag), cadmium (Cd) and lead (Pb). Our results demonstrated that SIMCA was able to differentiate all heavy metal-exposed and control groups from each other with 95% confidence level. Correct identification of randomly chosen test samples in their corresponding groups and high model distances between the classes were also achieved. We report, for the first time, the success of IR spectroscopy coupled with supervised chemometric technique SIMCA in classification of different bacteria under a given treatment.

Integrating Rapid Prototyping into Graphic Communications

ERIC Educational Resources Information Center

Xu, Renmei; Flowers, Jim

2015-01-01

Integrating different science, technology, engineering, and mathematics (STEM) areas can help students learn and leverage both the equipment and expertise at a single school. In comparing graphic communications classes with classes that involve rapid prototyping (RP) technologies like 3D printing, there are sufficient similarities between goals,…

Statistical Study of Rapid Penumbral Decay Associated with Flares

NASA Astrophysics Data System (ADS)

Chen, W.; Liu, C.; Wang, H.

2005-05-01

We present results of statistical study of rapid penumbral decay associated with flares. In total, we investigated 402 events from 05/09/98 to 07/17/04, including 40 X-class, 173 M-class and 189 C-class flares. We show strong evidence that penumbral segments decayed rapidly and permanently right after many flares. The rapid changes, which can be identified in the time profiles of white-light(WL) mean intensity are permanent, not transient, thus are not due to flare emissions. Our study shows that penumbral decay is more likely to be detected when associated with large solar flares. The larger the flare magnitude, the stronger the penumbral decay is. For X-class flares, almost 50% events show distinct decay. But for M- and C-class flares, this percentage drops to 16% and 10%, respectively. For all the events that clear decay can be observed, we find that the locations of penumbral decay are associated with flare emissions and are connected by prominent TRACE post-flare loops. To explain these observations, we propose a reconnection picture in that the penumbral fields change from a highly inclined to a more vertical configuration, leading to penumbral decay.

HIV-1 broadly neutralizing antibody precursor B cells revealed by germline-targeting immunogen

DOE PAGES

Jardine, Joseph G.; Kulp, Daniel W.; Havenar-Daughton, Colin; ...

2016-03-25

Induction of broadly neutralizing antibodies (bnAbs) is a major HIV vaccine goal. Germline-targeting immunogens aim to initiate bnAb induction by activating bnAb germline precursor B cells. Critical unmet challenges are to determine whether bnAb precursor naïve B cells bind germline-targeting immunogens and occur at sufficient frequency in humans for reliable vaccine responses. We employed deep mutational scanning and multi-target optimization to develop a germline-targeting immunogen (eOD-GT8) for diverse VRC01-class bnAbs. We then used the immunogen to isolate VRC01-class precursor naïve B cells from HIV-uninfected donors. Frequencies of true VRC01-class precursors, their structures, and their eOD-GT8 affinities support this immunogen asmore » a candidate human vaccine prime. Lastly, these methods could be applied to germline targeting for other classes of HIV bnAbs and for Abs to other pathogens.« less

Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah

2016-05-01

A novel hyperspectral target detection technique based on Fukunaga-Koontz transform (FKT) is presented. FKT offers significant properties for feature selection and ordering. However, it can only be used to solve multi-pattern classification problems. Target detection may be considered as a two-class classification problem, i.e., target versus background clutter. Nevertheless, background clutter typically contains different types of materials. That's why; target detection techniques are different than classification methods by way of modeling clutter. To avoid the modeling of the background clutter, we have improved one-class FKT (OC-FKT) for target detection. The statistical properties of target training samples are used to define tunnel-like boundary of the target class. Non-target samples are then created synthetically as to be outside of the boundary. Thus, only limited target samples become adequate for training of FKT. The hyperspectral image experiments confirm that the proposed OC-FKT technique provides an effective means for target detection.

HIV-1 broadly neutralizing antibody precursor B cells revealed by germline-targeting immunogen

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

Jardine, Joseph G.; Kulp, Daniel W.; Havenar-Daughton, Colin

Induction of broadly neutralizing antibodies (bnAbs) is a major HIV vaccine goal. Germline-targeting immunogens aim to initiate bnAb induction by activating bnAb germline precursor B cells. Critical unmet challenges are to determine whether bnAb precursor naïve B cells bind germline-targeting immunogens and occur at sufficient frequency in humans for reliable vaccine responses. We employed deep mutational scanning and multi-target optimization to develop a germline-targeting immunogen (eOD-GT8) for diverse VRC01-class bnAbs. We then used the immunogen to isolate VRC01-class precursor naïve B cells from HIV-uninfected donors. Frequencies of true VRC01-class precursors, their structures, and their eOD-GT8 affinities support this immunogen asmore » a candidate human vaccine prime. Lastly, these methods could be applied to germline targeting for other classes of HIV bnAbs and for Abs to other pathogens.« less

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

NASA Astrophysics Data System (ADS)

Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

2014-05-01

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.

Vorticity and symplecticity in multi-symplectic, Lagrangian gas dynamics

NASA Astrophysics Data System (ADS)

Webb, G. M.; Anco, S. C.

2016-02-01

The Lagrangian, multi-dimensional, ideal, compressible gas dynamic equations are written in a multi-symplectic form, in which the Lagrangian fluid labels, m i (the Lagrangian mass coordinates) and time t are the independent variables, and in which the Eulerian position of the fluid element {x}={x}({m},t) and the entropy S=S({m},t) are the dependent variables. Constraints in the variational principle are incorporated by means of Lagrange multipliers. The constraints are: the entropy advection equation S t = 0, the Lagrangian map equation {{x}}t={u} where {u} is the fluid velocity, and the mass continuity equation which has the form J=τ where J={det}({x}{ij}) is the Jacobian of the Lagrangian map in which {x}{ij}=\\partial {x}i/\\partial {m}j and τ =1/ρ is the specific volume of the gas. The internal energy per unit volume of the gas \\varepsilon =\\varepsilon (ρ ,S) corresponds to a non-barotropic gas. The Lagrangian is used to define multi-momenta, and to develop de Donder-Weyl Hamiltonian equations. The de Donder-Weyl equations are cast in a multi-symplectic form. The pullback conservation laws and the symplecticity conservation laws are obtained. One class of symplecticity conservation laws give rise to vorticity and potential vorticity type conservation laws, and another class of symplecticity laws are related to derivatives of the Lagrangian energy conservation law with respect to the Lagrangian mass coordinates m i . We show that the vorticity-symplecticity laws can be derived by a Lie dragging method, and also by using Noether’s second theorem and a fluid relabelling symmetry which is a divergence symmetry of the action. We obtain the Cartan-Poincaré form describing the equations and we discuss a set of differential forms representing the equation system.

Development of high through-put Sr isotope analysis for monitoring reservoir integrity for CO{sub 2} storage.

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

Wall, Andy; Jain, Jinesh; Stewart, Brian

2012-01-01

Recent innovations in multi-collector ICP-mass spectrometry (MC-ICP-MS) have allowed for rapid and precise measurements of isotope ratios in geological samples. Naturally occurring Sr isotopes has the potential for use in Monitoring, Verification, and Accounting (MVA) associated with geologic CO2 storage. Sr isotopes can be useful for: Sensitive tracking of brine migration; Determining seal rock leakage; Studying fluid/rock reactions. We have optimized separation chemistry procedures that will allow operators to prepare samples for Sr isotope analysis off site using rapid, low cost methods.

Comparability of river quality assessment using macrophytes: a multi-step procedure to overcome biogeographical differences.

PubMed

Aguiar, F C; Segurado, P; Urbanič, G; Cambra, J; Chauvin, C; Ciadamidaro, S; Dörflinger, G; Ferreira, J; Germ, M; Manolaki, P; Minciardi, M R; Munné, A; Papastergiadou, E; Ferreira, M T

2014-04-01

This paper exposes a new methodological approach to solve the problem of intercalibrating river quality national methods when a common metric is lacking and most of the countries share the same Water Framework Directive (WFD) assessment method. We provide recommendations for similar works in future concerning the assessment of ecological accuracy and highlight the importance of a good common ground to make feasible the scientific work beyond the intercalibration. The approach herein presented was applied to highly seasonal rivers of the Mediterranean Geographical Intercalibration Group for the Biological Quality Element Macrophytes. The Mediterranean Group of river macrophytes involved seven countries and two assessment methods with similar acquisition data and assessment concept: the Macrophyte Biological Index for Rivers (IBMR) for Cyprus, France, Greece, Italy, Portugal and Spain, and the River Macrophyte Index (RMI) for Slovenia. Database included 318 sites of which 78 were considered as benchmarks. The boundary harmonization was performed for common WFD-assessment methods (all countries except Slovenia) using the median of the Good/Moderate and High/Good boundaries of all countries. Then, whenever possible, the Slovenian method, RMI was computed for the entire database. The IBMR was also computed for the Slovenian sites and was regressed against RMI in order to check the relatedness of methods (R(2)=0.45; p<0.00001) and to convert RMI boundaries into the IBMR scale. The boundary bias of RMI was computed using direct comparison of classification and the median boundary values following boundary harmonization. The average absolute class differences after harmonization is 26% and the percentage of classifications differing by half of a quality class is also small (16.4%). This multi-step approach to the intercalibration was endorsed by the WFD Regulatory Committee. © 2013 Elsevier B.V. All rights reserved.

Statistical methods for convergence detection of multi-objective evolutionary algorithms.

PubMed

Trautmann, H; Wagner, T; Naujoks, B; Preuss, M; Mehnen, J

2009-01-01

In this paper, two approaches for estimating the generation in which a multi-objective evolutionary algorithm (MOEA) shows statistically significant signs of convergence are introduced. A set-based perspective is taken where convergence is measured by performance indicators. The proposed techniques fulfill the requirements of proper statistical assessment on the one hand and efficient optimisation for real-world problems on the other hand. The first approach accounts for the stochastic nature of the MOEA by repeating the optimisation runs for increasing generation numbers and analysing the performance indicators using statistical tools. This technique results in a very robust offline procedure. Moreover, an online convergence detection method is introduced as well. This method automatically stops the MOEA when either the variance of the performance indicators falls below a specified threshold or a stagnation of their overall trend is detected. Both methods are analysed and compared for two MOEA and on different classes of benchmark functions. It is shown that the methods successfully operate on all stated problems needing less function evaluations while preserving good approximation quality at the same time.

In vitro characterization of Multi-Drug Resistant HIV-1 Isolates from a Recently Infected Patient Associated with Dual Tropism and Rapid Disease Progression

PubMed Central

Mohri, Hiroshi; Markowitz, Martin

2013-01-01

Objective: Multi-drug resistant (MDR)-HIV-1 variants are thought to be less fit than wild type virus. In 2005 we reported a case of transmitted MDR-HIV-1 infection associated with dual tropism and rapid clinical progression. Here, we report the in vitro characterization of the virus isolates. Methods: Replication characteristics of bulk and clonal isolates from this case (MDR-1) were examined and compared with these to a panel of transmitted MDR and wild type viruses (MDR-2~4, WT-1, 2). Results: Infectivity and frequency of infectious virion of propagated isolates were high in MDR-1 biological clones (mean titer, 3.5×105 TCID50/ml; mean frequency of infectious virion, 1/2,444) and its bulk isolate (3.2×106TCID50/ml; 1/301), as compared to the other biological clones (7.3×103TCID50/ml; 1/21,320). Up-slope (log10p24/ml/d) of viral replication in PBMC culture was much higher in MDR-1 clones (1.30±0.30: mean±SD) than those of MDR-2~4 (0.75±0.08) or WT-1, -2 clones (0.82±0.03). The bulk isolate and dual tropic biological clones from MDR-1 depleted CD4+ T cells very rapidly in vitro compared to the other viruses tested. Conclusion: These findings support the hypothesis that multi-drug resistant HIV-1 can effectively evolve and compensate to not only retain high level replication but exhibit virulence associated with rapid disease progression. PMID:18645523

MLP: A Parallel Programming Alternative to MPI for New Shared Memory Parallel Systems

NASA Technical Reports Server (NTRS)

Taft, James R.

1999-01-01

Recent developments at the NASA AMES Research Center's NAS Division have demonstrated that the new generation of NUMA based Symmetric Multi-Processing systems (SMPs), such as the Silicon Graphics Origin 2000, can successfully execute legacy vector oriented CFD production codes at sustained rates far exceeding processing rates possible on dedicated 16 CPU Cray C90 systems. This high level of performance is achieved via shared memory based Multi-Level Parallelism (MLP). This programming approach, developed at NAS and outlined below, is distinct from the message passing paradigm of MPI. It offers parallelism at both the fine and coarse grained level, with communication latencies that are approximately 50-100 times lower than typical MPI implementations on the same platform. Such latency reductions offer the promise of performance scaling to very large CPU counts. The method draws on, but is also distinct from, the newly defined OpenMP specification, which uses compiler directives to support a limited subset of multi-level parallel operations. The NAS MLP method is general, and applicable to a large class of NASA CFD codes.

A generalized simplest equation method and its application to the Boussinesq-Burgers equation.

PubMed

Sudao, Bilige; Wang, Xiaomin

2015-01-01

In this paper, a generalized simplest equation method is proposed to seek exact solutions of nonlinear evolution equations (NLEEs). In the method, we chose a solution expression with a variable coefficient and a variable coefficient ordinary differential auxiliary equation. This method can yield a Bäcklund transformation between NLEEs and a related constraint equation. By dealing with the constraint equation, we can derive infinite number of exact solutions for NLEEs. These solutions include the traveling wave solutions, non-traveling wave solutions, multi-soliton solutions, rational solutions, and other types of solutions. As applications, we obtained wide classes of exact solutions for the Boussinesq-Burgers equation by using the generalized simplest equation method.

A Generalized Simplest Equation Method and Its Application to the Boussinesq-Burgers Equation

PubMed Central

Sudao, Bilige; Wang, Xiaomin

2015-01-01

In this paper, a generalized simplest equation method is proposed to seek exact solutions of nonlinear evolution equations (NLEEs). In the method, we chose a solution expression with a variable coefficient and a variable coefficient ordinary differential auxiliary equation. This method can yield a Bäcklund transformation between NLEEs and a related constraint equation. By dealing with the constraint equation, we can derive infinite number of exact solutions for NLEEs. These solutions include the traveling wave solutions, non-traveling wave solutions, multi-soliton solutions, rational solutions, and other types of solutions. As applications, we obtained wide classes of exact solutions for the Boussinesq-Burgers equation by using the generalized simplest equation method. PMID:25973605

Friendship networks of inner-city adults: a latent class analysis and multi-level regression of supporter types and the association of supporter latent class membership with supporter and recipient drug use.

PubMed

Bohnert, Amy S B; German, Danielle; Knowlton, Amy R; Latkin, Carl A

2010-03-01

Social support is a multi-dimensional construct that is important to drug use cessation. The present study identified types of supportive friends among the social network members in a community-based sample and examined the relationship of supporter-type classes with supporter, recipient, and supporter-recipient relationship characteristics. We hypothesized that the most supportive network members and their support recipients would be less likely to be current heroin/cocaine users. Participants (n=1453) were recruited from low-income neighborhoods with a high prevalence of drug use. Participants identified their friends via a network inventory, and all nominated friends were included in a latent class analysis and grouped based on their probability of providing seven types of support. These latent classes were included as the dependent variable in a multi-level regression of supporter drug use, recipient drug use, and other characteristics. The best-fitting latent class model identified five support patterns: friends who provided Little/No Support, Low/Moderate Support, High Support, Socialization Support, and Financial Support. In bivariate models, friends in the High, Low/Moderate, and Financial Support were less likely to use heroin or cocaine and had less conflict with and were more trusted by the support recipient than friends in the Low/No Support class. Individuals with supporters in those same support classes compared to the Low/No Support class were less likely to use heroin or cocaine, or to be homeless or female. Multivariable models suggested similar trends. Those with current heroin/cocaine use were less likely to provide or receive comprehensive support from friends. Published by Elsevier Ireland Ltd.

Automated compound classification using a chemical ontology.

PubMed

Bobach, Claudia; Böhme, Timo; Laube, Ulf; Püschel, Anett; Weber, Lutz

2012-12-29

Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated.

Automated compound classification using a chemical ontology

PubMed Central

2012-01-01

Background Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. Results In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. Conclusions A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated. PMID:23273256

Stratification of women's sport in contemporary China.

PubMed

Xiong, Huan

2011-01-01

Since economic reform in the 1980s, Chinese sport has undergone an extraordinary transformation. The most distinguishing phenomenon is the rapid growth of mass sport at the grassroots level with increasing demands for physical activities in women's daily lives. The rapid growth of women's sports participation at the grassroots is deeply embedded in the process of social stratification as a result of the urbanisation of Chinese society. The purpose of this paper is to use the socialist, feminist and theoretical framework to explore how Chinese women's different economic, educational, domestic and cultural situations shape their sports values and patterns of participation, marking social boundaries in Chinese urban communities. Semi-structured interviews and observations were conducted with 60 female physical exercisers in sports clubs, parks and neighbourhood playgrounds. Documentary research was also applied as a complement method to the interview. The findings indicate that within different classes (middle class, working class and a group who were unemployed), many different opportunities for and limitations on women to participate in sport are noticed. Chinese women have not fully and equally utilised sports opportunities created by urbanisation. Most Chinese women still live within patriarchal arrangements. Consequently, they do not completely fulfil their ambitions in sport.

Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle Multi-Pixel Polarimetric Measurements

NASA Technical Reports Server (NTRS)

Martin, William G.; Cairns, Brian; Bal, Guillaume

2014-01-01

This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earth?s atmosphere.

McMAC: Towards a MAC Protocol with Multi-Constrained QoS Provisioning for Diverse Traffic in Wireless Body Area Networks

PubMed Central

Monowar, Muhammad Mostafa; Hassan, Mohammad Mehedi; Bajaber, Fuad; Al-Hussein, Musaed; Alamri, Atif

2012-01-01

The emergence of heterogeneous applications with diverse requirements for resource-constrained Wireless Body Area Networks (WBANs) poses significant challenges for provisioning Quality of Service (QoS) with multi-constraints (delay and reliability) while preserving energy efficiency. To address such challenges, this paper proposes McMAC, a MAC protocol with multi-constrained QoS provisioning for diverse traffic classes in WBANs. McMAC classifies traffic based on their multi-constrained QoS demands and introduces a novel superframe structure based on the “transmit-whenever-appropriate” principle, which allows diverse periods for diverse traffic classes according to their respective QoS requirements. Furthermore, a novel emergency packet handling mechanism is proposed to ensure packet delivery with the least possible delay and the highest reliability. McMAC is also modeled analytically, and extensive simulations were performed to evaluate its performance. The results reveal that McMAC achieves the desired delay and reliability guarantee according to the requirements of a particular traffic class while achieving energy efficiency. PMID:23202224

Monitoring of rock glacier dynamics by multi-temporal UAV images

NASA Astrophysics Data System (ADS)

Morra di Cella, Umberto; Pogliotti, Paolo; Diotri, Fabrizio; Cremonese, Edoardo; Filippa, Gianluca; Galvagno, Marta

2015-04-01

During the last years several steps forward have been made in the comprehension of rock glaciers dynamics mainly for their potential evolution into rapid mass movements phenomena. Monitoring the surface movement of creeping mountain permafrost is important for understanding the potential effect of ongoing climate change on such a landforms. This study presents the reconstruction of two years of surface movements and DEM changes obtained by multi-temporal analysis of UAV images (provided by SenseFly Swinglet CAM drone). The movement rate obtained by photogrammetry are compared to those obtained by differential GNSS repeated campaigns on almost fifty points distributed on the rock glacier. Results reveals a very good agreements between both rates velocities obtained by the two methods and vertical displacements on fixed points. Strengths, weaknesses and shrewdness of this methods will be discussed. Such a method is very promising mainly for remote regions with difficult access.

Immunoanalysis Methods for the Detection of Dioxins and Related Chemicals

PubMed Central

Tian, Wenjing; Xie, Heidi Qunhui; Fu, Hualing; Pei, Xinhui; Zhao, Bin

2012-01-01

With the development of biotechnology, approaches based on antibodies, such as enzyme-linked immunosorbent assay (ELISA), active aryl hydrocarbon immunoassay (Ah-I) and other multi-analyte immunoassays, have been utilized as alternatives to the conventional techniques based on gas chromatography and mass spectroscopy for the analysis of dioxin and dioxin-like compounds in environmental and biological samples. These screening methods have been verified as rapid, simple and cost-effective. This paper provides an overview on the development and application of antibody-based approaches, such as ELISA, Ah-I, and multi-analyte immunoassays, covering the sample extraction and cleanup, antigen design, antibody preparation and immunoanalysis. However, in order to meet the requirements for on-site fast detection and relative quantification of dioxins in the environment, further optimization is needed to make these immuno-analytical methods more sensitive and easy to use. PMID:23443395

Shape-Constrained Segmentation Approach for Arctic Multiyear Sea Ice Floe Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Brucker, Ludovic; Ivanoff, Alvaro; Tilton, James C.

2013-01-01

The melting of sea ice is correlated to increases in sea surface temperature and associated climatic changes. Therefore, it is important to investigate how rapidly sea ice floes melt. For this purpose, a new Tempo Seg method for multi temporal segmentation of multi year ice floes is proposed. The microwave radiometer is used to track the position of an ice floe. Then,a time series of MODIS images are created with the ice floe in the image center. A Tempo Seg method is performed to segment these images into two regions: Floe and Background.First, morphological feature extraction is applied. Then, the central image pixel is marked as Floe, and shape-constrained best merge region growing is performed. The resulting tworegionmap is post-filtered by applying morphological operators.We have successfully tested our method on a set of MODIS images and estimated the area of a sea ice floe as afunction of time.

Methods and systems for integrating fluid dispensing technology with stereolithography

DOEpatents

Medina, Francisco; Wicker, Ryan; Palmer, Jeremy A.; Davis, Don W.; Chavez, Bart D.; Gallegos, Phillip L.

2010-02-09

An integrated system and method of integrating fluid dispensing technologies (e.g., direct-write (DW)) with rapid prototyping (RP) technologies (e.g., stereolithography (SL)) without part registration comprising: an SL apparatus and a fluid dispensing apparatus further comprising a translation mechanism adapted to translate the fluid dispensing apparatus along the Z-, Y- and Z-axes. The fluid dispensing apparatus comprises: a pressurized fluid container; a valve mechanism adapted to control the flow of fluid from the pressurized fluid container; and a dispensing nozzle adapted to deposit the fluid in a desired location. To aid in calibration, the integrated system includes a laser sensor and a mechanical switch. The method further comprises building a second part layer on top of the fluid deposits and optionally accommodating multi-layered circuitry by incorporating a connector trace. Thus, the present invention is capable of efficiently building single and multi-material SL fabricated parts embedded with complex three-dimensional circuitry using DW.

Experimental Solubility Approach to Determine PDMS-Water Partition Constants and PDMS Activity Coefficients.

PubMed

Grant, Sharon; Schacht, Veronika J; Escher, Beate I; Hawker, Darryl W; Gaus, Caroline

2016-03-15

Freely dissolved aqueous concentration and chemical activity are important determinants of contaminant transport, fate, and toxic potential. Both parameters are commonly quantified using Solid Phase Micro-Extraction (SPME) based on a sorptive polymer such as polydimethylsiloxane (PDMS). This method requires the PDMS-water partition constants, KPDMSw, or activity coefficient to be known. For superhydrophobic contaminants (log KOW >6), application of existing methods to measure these parameters is challenging, and independent measures to validate KPDMSw values would be beneficial. We developed a simple, rapid method to directly measure PDMS solubilities of solid contaminants, SPDMS(S), which together with literature thermodynamic properties was then used to estimate KPDMSw and activity coefficients in PDMS. PDMS solubility for the test compounds (log KOW 7.2-8.3) ranged over 3 orders of magnitude (4.1-5700 μM), and was dependent on compound class. For polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins (PCDDs), solubility-derived KPDMSw increased linearly with hydrophobicity, consistent with trends previously reported for less chlorinated congeners. In contrast, subcooled liquid PDMS solubilities, SPDMS(L), were approximately constant within a compound class. SPDMS(S) and KPDMSw can therefore be predicted for a compound class with reasonable robustness based solely on the class-specific SPDMS(L) and a particular congener's entropy of fusion, melting point, and aqueous solubility.

On multi-site damage identification using single-site training data

NASA Astrophysics Data System (ADS)

Barthorpe, R. J.; Manson, G.; Worden, K.

2017-11-01

This paper proposes a methodology for developing multi-site damage location systems for engineering structures that can be trained using single-site damaged state data only. The methodology involves training a sequence of binary classifiers based upon single-site damage data and combining the developed classifiers into a robust multi-class damage locator. In this way, the multi-site damage identification problem may be decomposed into a sequence of binary decisions. In this paper Support Vector Classifiers are adopted as the means of making these binary decisions. The proposed methodology represents an advancement on the state of the art in the field of multi-site damage identification which require either: (1) full damaged state data from single- and multi-site damage cases or (2) the development of a physics-based model to make multi-site model predictions. The potential benefit of the proposed methodology is that a significantly reduced number of recorded damage states may be required in order to train a multi-site damage locator without recourse to physics-based model predictions. In this paper it is first demonstrated that Support Vector Classification represents an appropriate approach to the multi-site damage location problem, with methods for combining binary classifiers discussed. Next, the proposed methodology is demonstrated and evaluated through application to a real engineering structure - a Piper Tomahawk trainer aircraft wing - with its performance compared to classifiers trained using the full damaged-state dataset.

Overview of Multi-Kilowatt Free-Piston Stirling Power Conversion Research at Glenn Research Center

NASA Technical Reports Server (NTRS)

Geng, Steven M.; Mason, Lee S.; Dyson, Rodger W.; Penswick, L. Barry

2008-01-01

As a step towards development of Stirling power conversion for potential use in Fission Surface Power (FSP) systems, a pair of commercially available 1 kW class free-piston Stirling convertors and a pair of commercially available pressure wave generators (which will be plumbed together to create a high power Stirling linear alternator test rig) have been procured for in-house testing at Glenn Research Center (GRC). Delivery of both the Stirling convertors and the linear alternator test rig is expected by October 2007. The 1 kW class free-piston Stirling convertors will be tested at GRC to map and verify performance. The convertors will later be modified to operate with a NaK liquid metal pumped loop for thermal energy input. The high power linear alternator test rig will be used to map and verify high power Stirling linear alternator performance and to develop power management and distribution (PMAD) methods and techniques. This paper provides an overview of the multi-kilowatt free-piston Stirling power conversion work being performed at GRC.

Overview of Multi-kilowatt Free-Piston Stirling Power Conversion Research at GRC

NASA Technical Reports Server (NTRS)

Geng, Steven M.; Mason, Lee S.; Dyson, Rodger W.; Penswick, L. Barry

2008-01-01

As a step towards development of Stirling power conversion for potential use in Fission Surface Power (FSP) systems, a pair of commercially available 1 kW class free-piston Stirling convertors and a pair of commercially available pressure wave generators (which will be plumbed together to create a high power Stirling linear alternator test rig) have been procured for in-house testing at Glenn Research Center. Delivery of both the Stirling convertors and the linear alternator test rig is expected by October, 2007. The 1 kW class free-piston Stirling convertors will be tested at GRC to map and verify performance. The convertors will later be modified to operate with a NaK liquid metal pumped loop for thermal energy input. The high power linear alternator test rig will be used to map and verify high power Stirling linear alternator performance and to develop power management and distribution (PMAD) methods and techniques. This paper provides an overview of the multi-kilowatt free-piston Stirling power conversion work being performed at GRC.

Overview of Multi-Kilowatt Free-Piston Stirling Power Conversion Research at GRC

NASA Astrophysics Data System (ADS)

Geng, Steven M.; Mason, Lee S.; Dyson, Rodger W.; Penswick, L. Barry

2008-01-01

As a step towards development of Stirling power conversion for potential use in Fission Surface Power (FSP) systems, a pair of commercially available 1 kW class free-piston Stirling convertors and a pair of commercially available pressure wave generators (which will be plumbed together to create a high power Stirling linear alternator test rig) have been procured for in-house testing at Glenn Research Center. Delivery of both the Stirling convertors and the linear alternator test rig is expected by October, 2007. The 1 kW class free-piston Stirling convertors will be tested at GRC to map and verify performance. The convertors will later be modified to operate with a NaK liquid metal pumped loop for thermal energy input. The high power linear alternator test rig will be used to map and verify high power Stirling linear alternator performance and to develop power management and distribution (PMAD) methods and techniques. This paper provides an overview of the multi-kilowatt free-piston Stirling power conversion work being performed at GRC.

A multi-criteria inference approach for anti-desertification management.

PubMed

Tervonen, Tommi; Sepehr, Adel; Kadziński, Miłosz

2015-10-01

We propose an approach for classifying land zones into categories indicating their resilience against desertification. Environmental management support is provided by a multi-criteria inference method that derives a set of value functions compatible with the given classification examples, and applies them to define, for the rest of the zones, their possible classes. In addition, a representative value function is inferred to explain the relative importance of the criteria to the stakeholders. We use the approach for classifying 28 administrative regions of the Khorasan Razavi province in Iran into three equilibrium classes: collapsed, transition, and sustainable zones. The model is parameterized with enhanced vegetation index measurements from 2005 to 2012, and 7 other natural and anthropogenic indicators for the status of the region in 2012. Results indicate that grazing density and land use changes are the main anthropogenic factors affecting desertification in Khorasan Razavi. The inference procedure suggests that the classification model is underdetermined in terms of attributes, but the approach itself is promising for supporting the management of anti-desertification efforts. Copyright © 2015 Elsevier Ltd. All rights reserved.

Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set.

PubMed

Zhang, Jinshui; Yuan, Zhoumiqi; Shuai, Guanyuan; Pan, Yaozhong; Zhu, Xiufang

2017-04-26

This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD), to determine optimal parameters for support vector data description (SVDD) model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM) method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient ( C ) and kernel width ( s ), in mapping homogeneous specific land cover.

CSI 2264: Simultaneous Optical and Infrared Light Curves of Young Disk-bearing Stars in NGC 2264 with CoRoT and Spitzer—Evidence for Multiple Origins of Variability

NASA Astrophysics Data System (ADS)

Cody, Ann Marie; Stauffer, John; Baglin, Annie; Micela, Giuseppina; Rebull, Luisa M.; Flaccomio, Ettore; Morales-Calderón, María; Aigrain, Suzanne; Bouvier, Jèrôme; Hillenbrand, Lynne A.; Gutermuth, Robert; Song, Inseok; Turner, Neal; Alencar, Silvia H. P.; Zwintz, Konstanze; Plavchan, Peter; Carpenter, John; Findeisen, Krzysztof; Carey, Sean; Terebey, Susan; Hartmann, Lee; Calvet, Nuria; Teixeira, Paula; Vrba, Frederick J.; Wolk, Scott; Covey, Kevin; Poppenhaeger, Katja; Günther, Hans Moritz; Forbrich, Jan; Whitney, Barbara; Affer, Laura; Herbst, William; Hora, Joseph; Barrado, David; Holtzman, Jon; Marchis, Franck; Wood, Kenneth; Medeiros Guimarães, Marcelo; Lillo Box, Jorge; Gillen, Ed; McQuillan, Amy; Espaillat, Catherine; Allen, Lori; D'Alessio, Paola; Favata, Fabio

2014-04-01

We present the Coordinated Synoptic Investigation of NGC 2264, a continuous 30 day multi-wavelength photometric monitoring campaign on more than 1000 young cluster members using 16 telescopes. The unprecedented combination of multi-wavelength, high-precision, high-cadence, and long-duration data opens a new window into the time domain behavior of young stellar objects. Here we provide an overview of the observations, focusing on results from Spitzer and CoRoT. The highlight of this work is detailed analysis of 162 classical T Tauri stars for which we can probe optical and mid-infrared flux variations to 1% amplitudes and sub-hour timescales. We present a morphological variability census and then use metrics of periodicity, stochasticity, and symmetry to statistically separate the light curves into seven distinct classes, which we suggest represent different physical processes and geometric effects. We provide distributions of the characteristic timescales and amplitudes and assess the fractional representation within each class. The largest category (>20%) are optical "dippers" with discrete fading events lasting ~1-5 days. The degree of correlation between the optical and infrared light curves is positive but weak; notably, the independently assigned optical and infrared morphology classes tend to be different for the same object. Assessment of flux variation behavior with respect to (circum)stellar properties reveals correlations of variability parameters with Hα emission and with effective temperature. Overall, our results point to multiple origins of young star variability, including circumstellar obscuration events, hot spots on the star and/or disk, accretion bursts, and rapid structural changes in the inner disk. Based on data from the Spitzer and CoRoT missions. The CoRoT space mission was developed and is operated by the French space agency CNES, with participation of ESA's RSSD and Science Programmes, Austria, Belgium, Brazil, Germany, and Spain.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Energy Engineering Analysis Program. Lighting Survey of Selected Buildings, Pine Bluff Arsenal, Pine Bluff, Arkansas. Volume 2A: Appendices

DTIC Science & Technology

1995-06-01

Energy efficient, 30 and 40 watt ballasts are Rapid Start, thermally protected, automatic resetting. Class P, high or low power factor as required...BALLASTS Energy efficient, 30 ana 40 watt Rapic Start, thermally protected, automatic resetting. Class P. high power factor, CEM, sound rated A. unless...BALLASTS Energy efficient, 40 Watt Rapid Start, thermally protected, automatic resetting, Class P, high power factor, CBM, sound rated A, unless

A novel unsupervised spike sorting algorithm for intracranial EEG.

PubMed

Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

2011-01-01

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

Nontraditional Students' Perception of a Blended Course: Integrating Synchronous Online Discussion and Face-to-Face Instruction

ERIC Educational Resources Information Center

Blankson, Joseph; Kyei-Blankson, Lydia

2008-01-01

Nontraditional students are rapidly becoming the majority group on campuses across America. These students are often hard pressed for time and in order to provide them with equal learning opportunities many universities and colleges are currently responding by offering classes using a variety of delivery methods and formats. In this study,…

Off the Shelf and out of the Box: Saving Time, Meeting Outcomes and Reaching Students with Information Literacy Modules

ERIC Educational Resources Information Center

Kelley, Jennifer

2012-01-01

As institutions of higher learning rapidly expand their offerings of online, hybrid and other distance learning opportunities for their students, librarians must adapt, adopt and improve information literacy instruction methods to accommodate instructors they may never meet and classes they may never see. Many responses to these challenges, such…

The Effects of Online Courses for Student Success in Basic Skills Mathematics Classes at California Community Colleges

ERIC Educational Resources Information Center

Rey, Jason Goering

2010-01-01

Online education is a modality of teaching that has proliferated throughout higher education in such a rapid form and without any guidelines that its quality and merit is largely unknown, hotly debated, and still evolving. Institutions have used online education as a method of reducing costs and increasing enrollments and students have flocked to…

Serum protein measurement using a tapered fluorescent fibre-optic evanescent wave-based biosensor

NASA Astrophysics Data System (ADS)

Preejith, P. V.; Lim, C. S.; Chia, T. F.

2006-12-01

A novel method to measure the total serum protein concentration is described in this paper. The method is based on the principles of fibre-optic evanescent wave spectroscopy. The biosensor applies a fluorescent dye-immobilized porous glass coating on a multi-mode optical fibre. The evanescent wave's intensity at the fibre-optic core-cladding interface is used to monitor the protein-induced changes in the sensor element. The sensor offers a rapid, single-step method for quantifying protein concentrations without destroying the sample. This unique sensing method presents a sensitive and accurate platform for the quantification of protein.

An Improved Computational Technique for Calculating Electromagnetic Forces and Power Absorptions Generated in Spherical and Deformed Body in Levitation Melting Devices

NASA Technical Reports Server (NTRS)

Zong, Jin-Ho; Szekely, Julian; Schwartz, Elliot

1992-01-01

An improved computational technique for calculating the electromagnetic force field, the power absorption and the deformation of an electromagnetically levitated metal sample is described. The technique is based on the volume integral method, but represents a substantial refinement; the coordinate transformation employed allows the efficient treatment of a broad class of rotationally symmetrical bodies. Computed results are presented to represent the behavior of levitation melted metal samples in a multi-coil, multi-frequency levitation unit to be used in microgravity experiments. The theoretical predictions are compared with both analytical solutions and with the results or previous computational efforts for the spherical samples and the agreement has been very good. The treatment of problems involving deformed surfaces and actually predicting the deformed shape of the specimens breaks new ground and should be the major usefulness of the proposed method.

Multiple continuous coverage of the earth based on multi-satellite systems with linear structure

NASA Astrophysics Data System (ADS)

Saulskiy, V. K.

2009-04-01

A new and wider definition is given to multi-satellite systems with linear structure (SLS), and efficiency of their application to multiple continuous coverage of the Earth is substantiated. Owing to this widening, SLS have incorporated already well-recognized “polar systems” by L. Rider and W.S. Adams, “kinematically regular systems” by G.V. Mozhaev, and “delta-systems” by J.G. Walker, as well as “near-polar systems” by Yu.P. Ulybyshev, and some other satellite constellations unknown before. A universal method of SLS optimization is presented, valid for any values of coverage multiplicity and the number of satellites in a system. The method uses the criterion of minimum radius of a circle seen from a satellite on the surface of the globe. Among the best SLS found in this way there are both systems representing the well-known classes mentioned above and new orbit constellations of satellites.

Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.

PubMed

Dehghan, Azad; Kovacevic, Aleksandar; Karystianis, George; Keane, John A; Nenadic, Goran

2017-11-01

De-identification of clinical narratives is one of the main obstacles to making healthcare free text available for research. In this paper we describe our experience in expanding and tailoring two existing tools as part of the 2016 CEGS N-GRID Shared Tasks Track 1, which evaluated de-identification methods on a set of psychiatric evaluation notes for up to 25 different types of Protected Health Information (PHI). The methods we used rely on machine learning on either a large or small feature space, with additional strategies, including two-pass tagging and multi-class models, which both proved to be beneficial. The results show that the integration of the proposed methods can identify Health Information Portability and Accountability Act (HIPAA) defined PHIs with overall F 1 -scores of ∼90% and above. Yet, some classes (Profession, Organization) proved again to be challenging given the variability of expressions used to reference given information. Copyright © 2017. Published by Elsevier Inc.

A machine learning approach to the potential-field method for implicit modeling of geological structures

NASA Astrophysics Data System (ADS)

Gonçalves, Ítalo Gomes; Kumaira, Sissa; Guadagnin, Felipe

2017-06-01

Implicit modeling has experienced a rise in popularity over the last decade due to its advantages in terms of speed and reproducibility in comparison with manual digitization of geological structures. The potential-field method consists in interpolating a scalar function that indicates to which side of a geological boundary a given point belongs to, based on cokriging of point data and structural orientations. This work proposes a vector potential-field solution from a machine learning perspective, recasting the problem as multi-class classification, which alleviates some of the original method's assumptions. The potentials related to each geological class are interpreted in a compositional data framework. Variogram modeling is avoided through the use of maximum likelihood to train the model, and an uncertainty measure is introduced. The methodology was applied to the modeling of a sample dataset provided with the software Move™. The calculations were implemented in the R language and 3D visualizations were prepared with the rgl package.

Detection and Mapping of the Geomorphic Effects of Flooding Using UAV Photogrammetry

NASA Astrophysics Data System (ADS)

Langhammer, Jakub; Vacková, Tereza

2018-04-01

In this paper, we present a novel technique for the objective detection of the geomorphological effects of flooding in riverbeds and floodplains using imagery acquired by unmanned aerial vehicles (UAVs, also known as drones) equipped with an panchromatic camera. The proposed method is based on the fusion of the two key data products of UAV photogrammetry, the digital elevation model (DEM), and the orthoimage, as well as derived qualitative information, which together serve as the basis for object-based segmentation and the supervised classification of fluvial forms. The orthoimage is used to calculate textural features, enabling detection of the structural properties of the image area and supporting the differentiation of features with similar spectral responses but different surface structures. The DEM is used to derive a flood depth model and the terrain ruggedness index, supporting the detection of bank erosion. All the newly derived information layers are merged with the orthoimage to form a multi-band data set, which is used for object-based segmentation and the supervised classification of key fluvial forms resulting from flooding, i.e., fresh and old gravel accumulations, sand accumulations, and bank erosion. The method was tested on the effects of a snowmelt flood that occurred in December 2015 in a montane stream in the Sumava Mountains, Czech Republic, Central Europe. A multi-rotor UAV was used to collect images of a 1-km-long and 200-m-wide stretch of meandering stream with fresh traces of fluvial activity. The performed segmentation and classification proved that the fusion of 2D and 3D data with the derived qualitative layers significantly enhanced the reliability of the fluvial form detection process. The assessment accuracy for all of the detected classes exceeded 90%. The proposed technique proved its potential for application in rapid mapping and detection of the geomorphological effects of flooding.

Quantitative determination of carcinogenic mycotoxins in human and animal biological matrices and animal-derived foods using multi-mycotoxin and analyte-specific high performance liquid chromatography-tandem mass spectrometric methods.

PubMed

Cao, Xiaoqin; Li, Xiaofei; Li, Jian; Niu, Yunhui; Shi, Lu; Fang, Zhenfeng; Zhang, Tao; Ding, Hong

2018-01-15

A sensitive and reliable multi-mycotoxin-based method was developed to identify and quantify several carcinogenic mycotoxins in human blood and urine, as well as edible animal tissues, including muscle and liver tissue from swine and chickens, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). For the toxicokinetic studies with individual mycotoxins, highly sensitive analyte-specific LC-MS/MS methods were developed for rat plasma and urine. Sample purification consisted of a rapid 'dilute and shoot' approach in urine samples, a simple 'dilute, evaporate and shoot' approach in plasma samples and a 'QuEChERS' procedure in edible animal tissues. The multi-mycotoxin and analyte-specific methods were validated in-house: The limits of detection (LOD) for the multi-mycotoxin and analyte-specific methods ranged from 0.02 to 0.41 μg/kg (μg/L) and 0.01 to 0.19 μg/L, respectively, and limits of quantification (LOQ) between 0.10 to 1.02 μg/kg (μg/L) and 0.09 to 0.47 μg/L, respectively. Apparent recoveries of the samples spiked with 0.25 to 4 μg/kg (μg/L) ranged from 60.1% to 109.8% with relative standard deviations below 15%. The methods were successfully applied to real samples. To the best of our knowledge, this is the first study carried out using a small group of patients from the Chinese population with hepatocellular carcinoma to assess their exposure to carcinogenic mycotoxins using biomarkers. Finally, the multi-mycotoxin method is a useful analytical method for assessing exposure to mycotoxins edible in animal tissues. The analyte-specific methods could be useful during toxicokinetic and toxicological studies. Copyright © 2017. Published by Elsevier B.V.

Regular physical education class enhances sociality and physical fitness while reducing psychological problems in children of multicultural families

PubMed Central

Park, Jae-Wan; Park, Seong-Hwan; Koo, Chang-Mo; Eun, Denny; Kim, Kang-Ho; Lee, Chan-Bok; Ham, Joung-Hyun; Jang, Jeong-Hoon; Jee, Yong-Seok

2017-01-01

This study investigated the influence of physical education class (PEC) as an intervention method for aggression, sociality, stress, and physical fitness levels in children from multicultural families. The hypothesis was that participating in PEC would result in reduced aggression and stress and improved sociality and physical fitness in multicultural children. A three-item questionnaire, a body composition test, and physical fitness tests were given three times. Eighty-four subjects were divided into four groups: multicultural children who participated in PEC (multi-PEG, n=12), multicultural children who did not participate in PEC (multi-NPEG, n=13), single-cultural children who participated in PEC (sing-PEG, n=11), and single-cultural children who did not participate in PEC (sing-NPEG, n=12), respectively. Parametric and nonparametric statistical methods were conducted on the collected data with a significance level set a priori at P<0.05. After 8 weeks of PEC, fat mass (F=2.966, P=0.045) and body mass index (F=3.654, P=0.021) had significantly different interaction effects. In the aspect of interaction effects from physical fitness variables, cardiopulmonary endurance (F=21.961, P=0.001), flexibility (F=8.892, P=0.001), muscular endurance (F=31.996, P=0.001), muscular strength (F=4.570, P=0.008), and power (F=24.479, P=0.001) were significantly improved in the multi-PEG compared to those of the other three groups. Moreover, sociality (F=22.144, P=0.001) in the multi-PEG was enhanced, whereas aggression (F=6.745, P=0.001) and stress (F=3.242, P=0.033) levels were reduced. As conclusion, the PEC reduced aggression and stress levels, and improved sociality and physical fitness levels after 8 weeks. This study confirmed that PEC for children from multicultural families can improve psychosocial factors and physical health. PMID:28503529

Regular physical education class enhances sociality and physical fitness while reducing psychological problems in children of multicultural families.

PubMed

Park, Jae-Wan; Park, Seong-Hwan; Koo, Chang-Mo; Eun, Denny; Kim, Kang-Ho; Lee, Chan-Bok; Ham, Joung-Hyun; Jang, Jeong-Hoon; Jee, Yong-Seok

2017-04-01

This study investigated the influence of physical education class (PEC) as an intervention method for aggression, sociality, stress, and physical fitness levels in children from multicultural families. The hypothesis was that participating in PEC would result in reduced aggression and stress and improved sociality and physical fitness in multicultural children. A three-item questionnaire, a body composition test, and physical fitness tests were given three times. Eighty-four subjects were divided into four groups: multicultural children who participated in PEC (multi-PEG, n=12), multicultural children who did not participate in PEC (multi-NPEG, n=13), single-cultural children who participated in PEC (sing-PEG, n=11), and single-cultural children who did not participate in PEC (sing-NPEG, n=12), respectively. Parametric and nonparametric statistical methods were conducted on the collected data with a significance level set a priori at P <0.05. After 8 weeks of PEC, fat mass ( F =2.966, P =0.045) and body mass index ( F =3.654, P =0.021) had significantly different interaction effects. In the aspect of interaction effects from physical fitness variables, cardiopulmonary endurance ( F =21.961, P =0.001), flexibility ( F =8.892, P =0.001), muscular endurance ( F =31.996, P =0.001), muscular strength ( F =4.570, P =0.008), and power ( F =24.479, P =0.001) were significantly improved in the multi-PEG compared to those of the other three groups. Moreover, sociality ( F =22.144, P =0.001) in the multi-PEG was enhanced, whereas aggression ( F =6.745, P =0.001) and stress ( F =3.242, P =0.033) levels were reduced. As conclusion, the PEC reduced aggression and stress levels, and improved sociality and physical fitness levels after 8 weeks. This study confirmed that PEC for children from multicultural families can improve psychosocial factors and physical health.

Human cell structure-driven model construction for predicting protein subcellular location from biological images.

PubMed

Shao, Wei; Liu, Mingxia; Zhang, Daoqiang

2016-01-01

The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting. We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method. The dataset and code can be downloaded from https://github.com/shaoweinuaa/. dqzhang@nuaa.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies

NASA Astrophysics Data System (ADS)

Chen, Xiaogang; Wang, Yijun; Zhang, Shangen; Gao, Shangkai; Hu, Yong; Gao, Xiaorong

2017-04-01

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted. Approach. This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies. The additional modulation frequencies were generated on the basis of choosing desired flickering frequencies. The conventional frame-based ‘on/off’ stimulation method was used to realize the desired flickering frequencies. All visual stimulation was present on a conventional LCD screen. A 9-target SSVEP-BCI based on intermodulation frequencies was implemented for performance evaluation. To optimize the stimulation design, three approaches (C: chromatic; L: luminance; CL: chromatic and luminance) were evaluated by online testing and offline analysis. Main results. SSVEP-BCIs with different paradigms (C, L, and CL) enabled us not only to encode more targets, but also to reliably evoke intermodulation frequencies. The online accuracies for the three paradigms were 91.67% (C), 93.98% (L), and 96.41% (CL). The CL condition achieved the highest classification performance. Significance. These results demonstrated the efficacy of three approaches (C, L, and CL) for eliciting intermodulation frequencies for multi-class SSVEP-BCIs. The combination of chromatic and luminance characteristics of the visual stimuli is the most efficient way for the intermodulation frequency coding method.

Robust point cloud classification based on multi-level semantic relationships for urban scenes

NASA Astrophysics Data System (ADS)

Zhu, Qing; Li, Yuan; Hu, Han; Wu, Bo

2017-07-01

The semantic classification of point clouds is a fundamental part of three-dimensional urban reconstruction. For datasets with high spatial resolution but significantly more noises, a general trend is to exploit more contexture information to surmount the decrease of discrimination of features for classification. However, previous works on adoption of contexture information are either too restrictive or only in a small region and in this paper, we propose a point cloud classification method based on multi-level semantic relationships, including point-homogeneity, supervoxel-adjacency and class-knowledge constraints, which is more versatile and incrementally propagate the classification cues from individual points to the object level and formulate them as a graphical model. The point-homogeneity constraint clusters points with similar geometric and radiometric properties into regular-shaped supervoxels that correspond to the vertices in the graphical model. The supervoxel-adjacency constraint contributes to the pairwise interactions by providing explicit adjacent relationships between supervoxels. The class-knowledge constraint operates at the object level based on semantic rules, guaranteeing the classification correctness of supervoxel clusters at that level. International Society of Photogrammetry and Remote Sensing (ISPRS) benchmark tests have shown that the proposed method achieves state-of-the-art performance with an average per-area completeness and correctness of 93.88% and 95.78%, respectively. The evaluation of classification of photogrammetric point clouds and DSM generated from aerial imagery confirms the method's reliability in several challenging urban scenes.

A framework for global terrain classification using 250-m DEMs to predict geohazards

NASA Astrophysics Data System (ADS)

Iwahashi, J.; Matsuoka, M.; Yong, A.

2016-12-01

Geomorphology is key for identifying factors that control geohazards induced by landslides, liquefaction, and ground shaking. To systematically identify landforms that affect these hazards, Iwahashi and Pike (2007; IP07) introduced an automated terrain classification scheme using 1-km-scale Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs). The IP07 classes describe 16 categories of terrain types and were used as a proxy for predicting ground motion amplification (Yong et al., 2012; Seyhan et al., 2014; Stewart et al., 2014; Yong, 2016). These classes, however, were not sufficiently resolved because coarse-scaled SRTM DEMs were the basis for the categories (Yong, 2016). Thus, we develop a new framework consisting of more detailed polygonal global terrain classes to improve estimations of soil-type and material stiffness. We first prepare high resolution 250-m DEMs derived from the 2010 Global Multi-resolution Terrain Elevation Data (GMTED2010). As in IP07, we calculate three geometric signatures (slope, local convexity and surface texture) from the DEMs. We create additional polygons by using the same signatures and multi-resolution segmentation techniques on the GMTED2010. We consider two types of surface texture thresholds in different window sizes (3x3 and 13x13 pixels), in addition to slope and local convexity, to classify pixels within the DEM. Finally, we apply the k-means clustering and thresholding methods to the 250-m DEM and produce more detailed polygonal terrain classes. We compare the new terrain classification maps of Japan and California with geologic, aerial photography, and landslide distribution maps, and visually find good correspondence of key features. To predict ground motion amplification, we apply the Yong (2016) method for estimating VS30. The systematic classification of geomorphology has the potential to provide a better understanding of the susceptibility to geohazards, which is especially vital in populated areas.

A multi-level rapid prototyping drill guide template reduces the perforation risk of pedicle screw placement in the lumbar and sacral spine.

PubMed

Merc, Matjaz; Drstvensek, Igor; Vogrin, Matjaz; Brajlih, Tomaz; Recnik, Gregor

2013-07-01

The method of free-hand pedicle screw placement is generally safe although it carries potential risks. For this reason, several highly accurate computer-assisted systems were developed and are currently on the market. However, these devices have certain disadvantages. We have developed a method of pedicle screw placement in the lumbar and sacral region using a multi-level drill guide template, created with the rapid prototyping technology and have validated it in a clinical study. The aim of the study was to manufacture and evaluate the accuracy of a multi-level drill guide template for lumbar and first sacral pedicle screw placement and to compare it with the free-hand technique under fluoroscopy supervision. In 2011 and 2012, a randomized clinical trial was performed on 20 patients. 54 screws were implanted in the trial group using templates and 54 in the control group using the fluoroscopy-supervised free-hand technique. Furthermore, applicability for the first sacral level was tested. Preoperative CT-scans were taken and templates were designed using the selective laser sintering method. Postoperative evaluation and statistical analysis of pedicle violation, displacement, screw length and deviation were performed for both groups. The incidence of cortex perforation was significantly reduced in the template group; likewise, the deviation and displacement level of screws in the sagittal plane. In both groups there was no significantly important difference in deviation and displacement level in the transversal plane as not in pedicle screw length. The results for the first sacral level resembled the main investigated group. The method significantly lowers the incidence of cortex perforation and is therefore potentially applicable in clinical practice, especially in some selected cases. The applied method, however, carries a potential for errors during manufacturing and practical usage and therefore still requires further improvements.

Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

PubMed

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

2018-06-25

The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance: The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings. © 2018 IOP Publishing Ltd.

MUSTA fluxes for systems of conservation laws

NASA Astrophysics Data System (ADS)

Toro, E. F.; Titarev, V. A.

2006-08-01

This paper is about numerical fluxes for hyperbolic systems and we first present a numerical flux, called GFORCE, that is a weighted average of the Lax-Friedrichs and Lax-Wendroff fluxes. For the linear advection equation with constant coefficient, the new flux reduces identically to that of the Godunov first-order upwind method. Then we incorporate GFORCE in the framework of the MUSTA approach [E.F. Toro, Multi-Stage Predictor-Corrector Fluxes for Hyperbolic Equations. Technical Report NI03037-NPA, Isaac Newton Institute for Mathematical Sciences, University of Cambridge, UK, 17th June, 2003], resulting in a version that we call GMUSTA. For non-linear systems this gives results that are comparable to those of the Godunov method in conjunction with the exact Riemann solver or complete approximate Riemann solvers, noting however that in our approach, the solution of the Riemann problem in the conventional sense is avoided. Both the GFORCE and GMUSTA fluxes are extended to multi-dimensional non-linear systems in a straightforward unsplit manner, resulting in linearly stable schemes that have the same stability regions as the straightforward multi-dimensional extension of Godunov's method. The methods are applicable to general meshes. The schemes of this paper share with the family of centred methods the common properties of being simple and applicable to a large class of hyperbolic systems, but the schemes of this paper are distinctly more accurate. Finally, we proceed to the practical implementation of our numerical fluxes in the framework of high-order finite volume WENO methods for multi-dimensional non-linear hyperbolic systems. Numerical results are presented for the Euler equations and for the equations of magnetohydrodynamics.