Fault Detection for Automotive Shock Absorber

NASA Astrophysics Data System (ADS)

Hernandez-Alcantara, Diana; Morales-Menendez, Ruben; Amezquita-Brooks, Luis

2015-11-01

Fault detection for automotive semi-active shock absorbers is a challenge due to the non-linear dynamics and the strong influence of the disturbances such as the road profile. First obstacle for this task, is the modeling of the fault, which has been shown to be of multiplicative nature. Many of the most widespread fault detection schemes consider additive faults. Two model-based fault algorithms for semiactive shock absorber are compared: an observer-based approach and a parameter identification approach. The performance of these schemes is validated and compared using a commercial vehicle model that was experimentally validated. Early results shows that a parameter identification approach is more accurate, whereas an observer-based approach is less sensible to parametric uncertainty.

An adaptive deep learning approach for PPG-based identification.

PubMed

Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

2016-08-01

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

Identification Approach to Alleviate Effects of Unmeasured Heat Gains for MIMO Building Thermal Systems

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

Cai, Jie; Kim, Donghun; Braun, James E.

It is important to have practical methods for constructing a good mathematical model for a building's thermal system for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. Identification approaches based on semi-physical model structures are popular in building science for those purposes. However conventional gray box identification approaches applied to thermal networks would fail when significant unmeasured heat gains present in estimation data. Although this situation is very common and practical, there has been little research to tackle this issue in building science. This paper presents an overall identification approach to alleviate influences of unmeasured disturbances,more » and hence to obtain improved gray-box building models. The approach was applied to an existing open space building and the performance is demonstrated.« less

An AI-based approach to structural damage identification by modal analysis

NASA Technical Reports Server (NTRS)

Glass, B. J.; Hanagud, S.

1990-01-01

Flexible-structure damage is presently addressed by a combined model- and parameter-identification approach which employs the AI methodologies of classification, heuristic search, and object-oriented model knowledge representation. The conditions for model-space search convergence to the best model are discussed in terms of search-tree organization and initial model parameter error. In the illustrative example of a truss structure presented, the use of both model and parameter identification is shown to lead to smaller parameter corrections than would be required by parameter identification alone.

Dynamic neural networks based on-line identification and control of high performance motor drives

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Kotaru, Raj

1995-01-01

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

Strategies for de-identification and anonymization of electronic health record data for use in multicenter research studies.

PubMed

Kushida, Clete A; Nichols, Deborah A; Jadrnicek, Rik; Miller, Ric; Walsh, James K; Griffin, Kara

2012-07-01

De-identification and anonymization are strategies that are used to remove patient identifiers in electronic health record data. The use of these strategies in multicenter research studies is paramount in importance, given the need to share electronic health record data across multiple environments and institutions while safeguarding patient privacy. Systematic literature search using keywords of de-identify, deidentify, de-identification, deidentification, anonymize, anonymization, data scrubbing, and text scrubbing. Search was conducted up to June 30, 2011 and involved 6 different common literature databases. A total of 1798 prospective citations were identified, and 94 full-text articles met the criteria for review and the corresponding articles were obtained. Search results were supplemented by review of 26 additional full-text articles; a total of 120 full-text articles were reviewed. A final sample of 45 articles met inclusion criteria for review and discussion. Articles were grouped into text, images, and biological sample categories. For text-based strategies, the approaches were segregated into heuristic, lexical, and pattern-based systems versus statistical learning-based systems. For images, approaches that de-identified photographic facial images and magnetic resonance image data were described. For biological samples, approaches that managed the identifiers linked with these samples were discussed, particularly with respect to meeting the anonymization requirements needed for Institutional Review Board exemption under the Common Rule. Current de-identification strategies have their limitations, and statistical learning-based systems have distinct advantages over other approaches for the de-identification of free text. True anonymization is challenging, and further work is needed in the areas of de-identification of datasets and protection of genetic information.

Target identification of small molecules based on chemical biology approaches.

PubMed

Futamura, Yushi; Muroi, Makoto; Osada, Hiroyuki

2013-05-01

Recently, a phenotypic approach-screens that assess the effects of compounds on cells, tissues, or whole organisms-has been reconsidered and reintroduced as a complementary strategy of a target-based approach for drug discovery. Although the finding of novel bioactive compounds from large chemical libraries has become routine, the identification of their molecular targets is still a time-consuming and difficult process, making this step rate-limiting in drug development. In the last decade, we and other researchers have amassed a large amount of phenotypic data through progress in omics research and advances in instrumentation. Accordingly, the profiling methodologies using these datasets expertly have emerged to identify and validate specific molecular targets of drug candidates, attaining some progress in current drug discovery (e.g., eribulin). In the case of a compound that shows an unprecedented phenotype likely by inhibiting a first-in-class target, however, such phenotypic profiling is invalid. Under the circumstances, a photo-crosslinking affinity approach should be beneficial. In this review, we describe and summarize recent progress in both affinity-based (direct) and phenotypic profiling (indirect) approaches for chemical biology target identification.

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

Space-Based Identification of Archaeological Illegal Excavations and a New Automatic Method for Looting Feature Extraction in Desert Areas

NASA Astrophysics Data System (ADS)

Lasaponara, Rosa; Masini, Nicola

2018-06-01

The identification and quantification of disturbance of archaeological sites has been generally approached by visual inspection of optical aerial or satellite pictures. In this paper, we briefly summarize the state of the art of the traditionally satellite-based approaches for looting identification and propose a new automatic method for archaeological looting feature extraction approach (ALFEA). It is based on three steps: the enhancement using spatial autocorrelation, unsupervised classification, and segmentation. ALFEA has been applied to Google Earth images of two test areas, selected in desert environs in Syria (Dura Europos), and in Peru (Cahuachi-Nasca). The reliability of ALFEA was assessed through field surveys in Peru and visual inspection for the Syrian case study. Results from the evaluation procedure showed satisfactory performance from both of the two analysed test cases with a rate of success higher than 90%.

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

PubMed Central

Seo, Minseok; Shin, Su-kyung; Kwon, Eun-Young; Kim, Sung-Eun; Bae, Yun-Jung; Lee, Seungyeoun; Sung, Mi-Kyung; Choi, Myung-Sook; Park, Taesung

2016-01-01

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates. PMID:26964035

Biometric identification based on feature fusion with PCA and SVM

NASA Astrophysics Data System (ADS)

Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina

2018-04-01

Biometric identification is gaining ground compared to traditional identification methods. Many biometric measurements may be used for secure human identification. The most reliable among them is the iris pattern because of its uniqueness, stability, unforgeability and inalterability over time. The approach presented in this paper is a fusion of different feature descriptor methods such as HOG, LIOP, LBP, used for extracting iris texture information. The classifiers obtained through the SVM and PCA methods demonstrate the effectiveness of our system applied to one and both irises. The performances measured are highly accurate and foreshadow a fusion system with a rate of identification approaching 100% on the UPOL database.

Agent tracking: a psycho-historical theory of the identification of living and social agents.

PubMed

Bullot, Nicolas J

To explain agent-identification behaviours, universalist theories in the biological and cognitive sciences have posited mental mechanisms thought to be universal to all humans, such as agent detection and face recognition mechanisms. These universalist theories have paid little attention to how particular sociocultural or historical contexts interact with the psychobiological processes of agent-identification. In contrast to universalist theories, contextualist theories appeal to particular historical and sociocultural contexts for explaining agent-identification. Contextualist theories tend to adopt idiographic methods aimed at recording the heterogeneity of human behaviours across history, space, and cultures. Defenders of the universalist approach tend to criticise idiographic methods because such methods can lead to relativism or may lack generality. To overcome explanatory limitations of proposals that adopt either universalist or contextualist approaches in isolation, I propose a philosophical model that integrates contributions from both traditions: the psycho-historical theory of agent-identification. This theory investigates how the tracking processes that humans use for identifying agents interact with the unique socio-historical contexts that support agent-identification practices. In integrating hypotheses about the history of agents with psychological and epistemological principles regarding agent-identification, the theory can generate novel hypotheses regarding the distinction between recognition-based, heuristic-based, and explanation-based agent-identification.

Performance characterization of material identification systems

NASA Astrophysics Data System (ADS)

Brown, Christopher D.; Green, Robert L.

2006-10-01

In recent years a number of analytical devices have been proposed and marketed specifically to enable field-based material identification. Technologies reliant on mass, near- and mid-infrared, and Raman spectroscopies are available today, and other platforms are imminent. These systems tend to perform material recognition based on an on-board library of material signatures. While figures of merit for traditional quantitative analytical sensors are broadly established (e.g., SNR, selectivity, sensitivity, limit of detection/decision), measures of performance for material identification systems have not been systematically discussed. In this paper we present an approach to performance characterization similar in spirit to ROC curves, but including elements of precision-recall curves and specialized for the intended-use of material identification systems. Important experimental considerations are discussed, including study design, sources of bias, uncertainty estimation, and cross-validation and the approach as a whole is illustrated using a commercially available handheld Raman material identification system.

Gait Characteristic Analysis and Identification Based on the iPhone's Accelerometer and Gyrometer

PubMed Central

Sun, Bing; Wang, Yang; Banda, Jacob

2014-01-01

Gait identification is a valuable approach to identify humans at a distance. In this paper, gait characteristics are analyzed based on an iPhone's accelerometer and gyrometer, and a new approach is proposed for gait identification. Specifically, gait datasets are collected by the triaxial accelerometer and gyrometer embedded in an iPhone. Then, the datasets are processed to extract gait characteristic parameters which include gait frequency, symmetry coefficient, dynamic range and similarity coefficient of characteristic curves. Finally, a weighted voting scheme dependent upon the gait characteristic parameters is proposed for gait identification. Four experiments are implemented to validate the proposed scheme. The attitude and acceleration solutions are verified by simulation. Then the gait characteristics are analyzed by comparing two sets of actual data, and the performance of the weighted voting identification scheme is verified by 40 datasets of 10 subjects. PMID:25222034

Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Optical/digital identification/verification system based on digital watermarking technology

NASA Astrophysics Data System (ADS)

Herrigel, Alexander; Voloshynovskiy, Sviatoslav V.; Hrytskiv, Zenon D.

2000-06-01

This paper presents a new approach for the secure integrity verification of driver licenses, passports or other analogue identification documents. The system embeds (detects) the reference number of the identification document with the DCT watermark technology in (from) the owner photo of the identification document holder. During verification the reference number is extracted and compared with the reference number printed in the identification document. The approach combines optical and digital image processing techniques. The detection system must be able to scan an analogue driver license or passport, convert the image of this document into a digital representation and then apply the watermark verification algorithm to check the payload of the embedded watermark. If the payload of the watermark is identical with the printed visual reference number of the issuer, the verification was successful and the passport or driver license has not been modified. This approach constitutes a new class of application for the watermark technology, which was originally targeted for the copyright protection of digital multimedia data. The presented approach substantially increases the security of the analogue identification documents applied in many European countries.

Fractional System Identification: An Approach Using Continuous Order-Distributions

NASA Technical Reports Server (NTRS)

Hartley, Tom T.; Lorenzo, Carl F.

1999-01-01

This paper discusses the identification of fractional- and integer-order systems using the concept of continuous order-distribution. Based on the ability to define systems using continuous order-distributions, it is shown that frequency domain system identification can be performed using least squares techniques after discretizing the order-distribution.

A kinase-focused compound collection: compilation and screening strategy.

PubMed

Sun, Dongyu; Chuaqui, Claudio; Deng, Zhan; Bowes, Scott; Chin, Donovan; Singh, Juswinder; Cullen, Patrick; Hankins, Gretchen; Lee, Wen-Cherng; Donnelly, Jason; Friedman, Jessica; Josiah, Serene

2006-06-01

Lead identification by high-throughput screening of large compound libraries has been supplemented with virtual screening and focused compound libraries. To complement existing approaches for lead identification at Biogen Idec, a kinase-focused compound collection was designed, developed and validated. Two strategies were adopted to populate the compound collection: a ligand shape-based virtual screening and a receptor-based approach (structural interaction fingerprint). Compounds selected with the two approaches were cherry-picked from an existing high-throughput screening compound library, ordered from suppliers and supplemented with specific medicinal compounds from internal programs. Promising hits and leads have been generated from the kinase-focused compound collection against multiple kinase targets. The principle of the collection design and screening strategy was validated and the use of the kinase-focused compound collection for lead identification has been added to existing strategies.

A Clock Fingerprints-Based Approach for Wireless Transmitter Identification

NASA Astrophysics Data System (ADS)

Zhao, Caidan; Xie, Liang; Huang, Lianfen; Yao, Yan

Cognitive radio (CR) was proposed as one of the promising solutions for low spectrum utilization. However, security problems such as the primary user emulation (PUE) attack severely limit its applications. In this paper, we propose a clock fingerprints-based authentication approach to prevent PUE attacks in CR networks with the help of curve fitting and classifier. An experimental setup was constructed using the WLAN cards and software radio devices, and the corresponding results show that satisfied identification can be achieved for wireless transmitters.

An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm.

PubMed

Pal, Partha S; Kar, R; Mandal, D; Ghoshal, S P

2015-11-01

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamic programming-based hot spot identification approach for pedestrian crashes.

PubMed

Medury, Aditya; Grembek, Offer

2016-08-01

Network screening techniques are widely used by state agencies to identify locations with high collision concentration, also referred to as hot spots. However, most of the research in this regard has focused on identifying highway segments that are of concern to automobile collisions. In comparison, pedestrian hot spot detection has typically focused on analyzing pedestrian crashes in specific locations, such as at/near intersections, mid-blocks, and/or other crossings, as opposed to long stretches of roadway. In this context, the efficiency of the some of the widely used network screening methods has not been tested. Hence, in order to address this issue, a dynamic programming-based hot spot identification approach is proposed which provides efficient hot spot definitions for pedestrian crashes. The proposed approach is compared with the sliding window method and an intersection buffer-based approach. The results reveal that the dynamic programming method generates more hot spots with a higher number of crashes, while providing small hot spot segment lengths. In comparison, the sliding window method is shown to suffer from shortcomings due to a first-come-first-serve approach vis-à-vis hot spot identification and a fixed hot spot window length assumption. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Support Vector Machine-Based Gender Identification Using Speech Signal

NASA Astrophysics Data System (ADS)

Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

An IR-Based Approach Utilizing Query Expansion for Plagiarism Detection in MEDLINE.

PubMed

Nawab, Rao Muhammad Adeel; Stevenson, Mark; Clough, Paul

2017-01-01

The identification of duplicated and plagiarized passages of text has become an increasingly active area of research. In this paper, we investigate methods for plagiarism detection that aim to identify potential sources of plagiarism from MEDLINE, particularly when the original text has been modified through the replacement of words or phrases. A scalable approach based on Information Retrieval is used to perform candidate document selection-the identification of a subset of potential source documents given a suspicious text-from MEDLINE. Query expansion is performed using the ULMS Metathesaurus to deal with situations in which original documents are obfuscated. Various approaches to Word Sense Disambiguation are investigated to deal with cases where there are multiple Concept Unique Identifiers (CUIs) for a given term. Results using the proposed IR-based approach outperform a state-of-the-art baseline based on Kullback-Leibler Distance.

Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches.

PubMed

Abu, Arpah; Leow, Lee Kien; Ramli, Rosli; Omar, Hasmahzaiti

2016-12-22

Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs. At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community. This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

Effects-Based Operations: Useful or Useless

DTIC Science & Technology

2010-05-03

the effects -based approach largely irrelevant. 30 The idea of Consequence Identification, however, is not to identify all outcomes , but rather to... effects -based thinking could provide operational planners and commanders with a valuable consequence identification tool. It further argues that System...to achieve specific effects that contribute directly to desired military and political outcomes .” 14 Air Force Brig Gen David Deptula further writes

System Identification of Mistuned Bladed Disks from Traveling Wave Response Measurements

NASA Technical Reports Server (NTRS)

Feiner, D. M.; Griffin, J. H.; Jones, K. W.; Kenyon, J. A.; Mehmed, O.; Kurkov, A. P.

2003-01-01

A new approach to modal analysis is presented. By applying this technique to bladed disk system identification methods, one can determine the mistuning in a rotor based on its response to a traveling wave excitation. This allows system identification to be performed under rotating conditions, and thus expands the applicability of existing mistuning identification techniques from integrally bladed rotors to conventional bladed disks.

Regression-Based Identification of Behavior-Encoding Neurons During Large-Scale Optical Imaging of Neural Activity at Cellular Resolution

PubMed Central

Miri, Andrew; Daie, Kayvon; Burdine, Rebecca D.; Aksay, Emre

2011-01-01

The advent of methods for optical imaging of large-scale neural activity at cellular resolution in behaving animals presents the problem of identifying behavior-encoding cells within the resulting image time series. Rapid and precise identification of cells with particular neural encoding would facilitate targeted activity measurements and perturbations useful in characterizing the operating principles of neural circuits. Here we report a regression-based approach to semiautomatically identify neurons that is based on the correlation of fluorescence time series with quantitative measurements of behavior. The approach is illustrated with a novel preparation allowing synchronous eye tracking and two-photon laser scanning fluorescence imaging of calcium changes in populations of hindbrain neurons during spontaneous eye movement in the larval zebrafish. Putative velocity-to-position oculomotor integrator neurons were identified that showed a broad spatial distribution and diversity of encoding. Optical identification of integrator neurons was confirmed with targeted loose-patch electrical recording and laser ablation. The general regression-based approach we demonstrate should be widely applicable to calcium imaging time series in behaving animals. PMID:21084686

Pattern recognition tool based on complex network-based approach

NASA Astrophysics Data System (ADS)

Casanova, Dalcimar; Backes, André Ricardo; Martinez Bruno, Odemir

2013-02-01

This work proposed a generalization of the method proposed by the authors: 'A complex network-based approach for boundary shape analysis'. Instead of modelling a contour into a graph and use complex networks rules to characterize it, here, we generalize the technique. This way, the work proposes a mathematical tool for characterization signals, curves and set of points. To evaluate the pattern description power of the proposal, an experiment of plat identification based on leaf veins image are conducted. Leaf vein is a taxon characteristic used to plant identification proposes, and one of its characteristics is that these structures are complex, and difficult to be represented as a signal or curves and this way to be analyzed in a classical pattern recognition approach. Here, we model the veins as a set of points and model as graphs. As features, we use the degree and joint degree measurements in a dynamic evolution. The results demonstrates that the technique has a good power of discrimination and can be used for plant identification, as well as other complex pattern recognition tasks.

Network-Based Approaches in Drug Discovery and Early Development

PubMed Central

Harrold, JM; Ramanathan, M; Mager, DE

2015-01-01

Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target–drug combination with high potential for yielding clinical success within the efficacy–toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification. PMID:24025802

A New High-Throughput Approach to Genotype Ancient Human Gastrointestinal Parasites.

PubMed

Côté, Nathalie M L; Daligault, Julien; Pruvost, Mélanie; Bennett, E Andrew; Gorgé, Olivier; Guimaraes, Silvia; Capelli, Nicolas; Le Bailly, Matthieu; Geigl, Eva-Maria; Grange, Thierry

2016-01-01

Human gastrointestinal parasites are good indicators for hygienic conditions and health status of past and present individuals and communities. While microscopic analysis of eggs in sediments of archeological sites often allows their taxonomic identification, this method is rarely effective at the species level, and requires both the survival of intact eggs and their proper identification. Genotyping via PCR-based approaches has the potential to achieve a precise species-level taxonomic determination. However, so far it has mostly been applied to individual eggs isolated from archeological samples. To increase the throughput and taxonomic accuracy, as well as reduce costs of genotyping methods, we adapted a PCR-based approach coupled with next-generation sequencing to perform precise taxonomic identification of parasitic helminths directly from archeological sediments. Our study of twenty-five 100 to 7,200 year-old archeological samples proved this to be a powerful, reliable and efficient approach for species determination even in the absence of preserved eggs, either as a stand-alone method or as a complement to microscopic studies.

Forensic identification of resampling operators: A semi non-intrusive approach.

PubMed

Cao, Gang; Zhao, Yao; Ni, Rongrong

2012-03-10

Recently, several new resampling operators have been proposed and successfully invalidate the existing resampling detectors. However, the reliability of such anti-forensic techniques is unaware and needs to be investigated. In this paper, we focus on the forensic identification of digital image resampling operators including the traditional type and the anti-forensic type which hides the trace of traditional resampling. Various resampling algorithms involving geometric distortion (GD)-based, dual-path-based and postprocessing-based are investigated. The identification is achieved in the manner of semi non-intrusive, supposing the resampling software could be accessed. Given an input pattern of monotone signal, polarity aberration of GD-based resampled signal's first derivative is analyzed theoretically and measured by effective feature metric. Dual-path-based and postprocessing-based resampling can also be identified by feeding proper test patterns. Experimental results on various parameter settings demonstrate the effectiveness of the proposed approach. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Approach to identifying pollutant source and matching flow field

NASA Astrophysics Data System (ADS)

Liping, Pang; Yu, Zhang; Hongquan, Qu; Tao, Hu; Wei, Wang

2013-07-01

Accidental pollution events often threaten people's health and lives, and it is necessary to identify a pollutant source rapidly so that prompt actions can be taken to prevent the spread of pollution. But this identification process is one of the difficulties in the inverse problem areas. This paper carries out some studies on this issue. An approach using single sensor information with noise was developed to identify a sudden continuous emission trace pollutant source in a steady velocity field. This approach first compares the characteristic distance of the measured concentration sequence to the multiple hypothetical measured concentration sequences at the sensor position, which are obtained based on a source-three-parameter multiple hypotheses. Then we realize the source identification by globally searching the optimal values with the objective function of the maximum location probability. Considering the large amount of computation load resulting from this global searching, a local fine-mesh source search method based on priori coarse-mesh location probabilities is further used to improve the efficiency of identification. Studies have shown that the flow field has a very important influence on the source identification. Therefore, we also discuss the impact of non-matching flow fields with estimation deviation on identification. Based on this analysis, a method for matching accurate flow field is presented to improve the accuracy of identification. In order to verify the practical application of the above method, an experimental system simulating a sudden pollution process in a steady flow field was set up and some experiments were conducted when the diffusion coefficient was known. The studies showed that the three parameters (position, emission strength and initial emission time) of the pollutant source in the experiment can be estimated by using the method for matching flow field and source identification.

Linear control of oscillator and amplifier flows*

NASA Astrophysics Data System (ADS)

Schmid, Peter J.; Sipp, Denis

2016-08-01

Linear control applied to fluid systems near an equilibrium point has important applications for many flows of industrial or fundamental interest. In this article we give an exposition of tools and approaches for the design of control strategies for globally stable or unstable flows. For unstable oscillator flows a feedback configuration and a model-based approach is proposed, while for stable noise-amplifier flows a feedforward setup and an approach based on system identification is advocated. Model reduction and robustness issues are addressed for the oscillator case; statistical learning techniques are emphasized for the amplifier case. Effective suppression of global and convective instabilities could be demonstrated for either case, even though the system-identification approach results in a superior robustness to off-design conditions.

Data Driven Model Development for the Supersonic Semispan Transport (S(sup 4)T)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2011-01-01

We investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models and a data-driven system identification procedure. It is shown via analysis of experimental Super- Sonic SemiSpan Transport (S4T) wind-tunnel data using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

The application of Legendre-tau approximation to parameter identification for delay and partial differential equations

NASA Technical Reports Server (NTRS)

Ito, K.

1983-01-01

Approximation schemes based on Legendre-tau approximation are developed for application to parameter identification problem for delay and partial differential equations. The tau method is based on representing the approximate solution as a truncated series of orthonormal functions. The characteristic feature of the Legendre-tau approach is that when the solution to a problem is infinitely differentiable, the rate of convergence is faster than any finite power of 1/N; higher accuracy is thus achieved, making the approach suitable for small N.

A New Paradigm for Known Metabolite Identification in Metabonomics/Metabolomics: Metabolite Identification Efficiency

PubMed Central

Everett, Jeremy R.

2015-01-01

A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed including: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field. PMID:25750701

A new paradigm for known metabolite identification in metabonomics/metabolomics: metabolite identification efficiency.

PubMed

Everett, Jeremy R

2015-01-01

A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed including: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field.

An experimental study of nonlinear dynamic system identification

NASA Technical Reports Server (NTRS)

Stry, Greselda I.; Mook, D. Joseph

1990-01-01

A technique for robust identification of nonlinear dynamic systems is developed and illustrated using both simulations and analog experiments. The technique is based on the Minimum Model Error optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature of the current work is the ability to identify nonlinear dynamic systems without prior assumptions regarding the form of the nonlinearities, in constrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

HAZARDOUS WASTE IDENTIFICATION

EPA Science Inventory

This research is in direct support of the regulatory reform efforts under the Hazarous Waste Identification (HWIR) and is related to the development of national "exit levels" based on sound scientific data and models. Research focuses on developing a systems approach to modelin...

In Silico Identification Software (ISIS): A Machine Learning Approach to Tandem Mass Spectral Identification of Lipids

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

Kangas, Lars J.; Metz, Thomas O.; Isaac, Georgis

2012-05-15

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissocia-tion tandem mass spectrometry. A preliminary test of the algorithm with 45 lipidsmore » from a subset of lipid classes shows both high sensitivity and specificity.« less

DNA barcode-based molecular identification system for fish species.

PubMed

Kim, Sungmin; Eo, Hae-Seok; Koo, Hyeyoung; Choi, Jun-Kil; Kim, Won

2010-12-01

In this study, we applied DNA barcoding to identify species using short DNA sequence analysis. We examined the utility of DNA barcoding by identifying 53 Korean freshwater fish species, 233 other freshwater fish species, and 1339 saltwater fish species. We successfully developed a web-based molecular identification system for fish (MISF) using a profile hidden Markov model. MISF facilitates efficient and reliable species identification, overcoming the limitations of conventional taxonomic approaches. MISF is freely accessible at http://bioinfosys.snu.ac.kr:8080/MISF/misf.jsp .

Influence of Geographical Origin and Flour Type on Diversity of Lactic Acid Bacteria in Traditional Belgian Sourdoughs▿ †

PubMed Central

Scheirlinck, Ilse; Van der Meulen, Roel; Van Schoor, Ann; Vancanneyt, Marc; De Vuyst, Luc; Vandamme, Peter; Huys, Geert

2007-01-01

A culture-based approach was used to investigate the diversity of lactic acid bacteria (LAB) in Belgian traditional sourdoughs and to assess the influence of flour type, bakery environment, geographical origin, and technological characteristics on the taxonomic composition of these LAB communities. For this purpose, a total of 714 LAB from 21 sourdoughs sampled at 11 artisan bakeries throughout Belgium were subjected to a polyphasic identification approach. The microbial composition of the traditional sourdoughs was characterized by bacteriological culture in combination with genotypic identification methods, including repetitive element sequence-based PCR fingerprinting and phenylalanyl-tRNA synthase (pheS) gene sequence analysis. LAB from Belgian sourdoughs belonged to the genera Lactobacillus, Pediococcus, Leuconostoc, Weissella, and Enterococcus, with the heterofermentative species Lactobacillus paralimentarius, Lactobacillus sanfranciscensis, Lactobacillus plantarum, and Lactobacillus pontis as the most frequently isolated taxa. Statistical analysis of the identification data indicated that the microbial composition of the sourdoughs is mainly affected by the bakery environment rather than the flour type (wheat, rye, spelt, or a mixture of these) used. In conclusion, the polyphasic approach, based on rapid genotypic screening and high-resolution, sequence-dependent identification, proved to be a powerful tool for studying the LAB diversity in traditional fermented foods such as sourdough. PMID:17675431

Influence of geographical origin and flour type on diversity of lactic acid bacteria in traditional Belgian sourdoughs.

PubMed

Scheirlinck, Ilse; Van der Meulen, Roel; Van Schoor, Ann; Vancanneyt, Marc; De Vuyst, Luc; Vandamme, Peter; Huys, Geert

2007-10-01

A culture-based approach was used to investigate the diversity of lactic acid bacteria (LAB) in Belgian traditional sourdoughs and to assess the influence of flour type, bakery environment, geographical origin, and technological characteristics on the taxonomic composition of these LAB communities. For this purpose, a total of 714 LAB from 21 sourdoughs sampled at 11 artisan bakeries throughout Belgium were subjected to a polyphasic identification approach. The microbial composition of the traditional sourdoughs was characterized by bacteriological culture in combination with genotypic identification methods, including repetitive element sequence-based PCR fingerprinting and phenylalanyl-tRNA synthase (pheS) gene sequence analysis. LAB from Belgian sourdoughs belonged to the genera Lactobacillus, Pediococcus, Leuconostoc, Weissella, and Enterococcus, with the heterofermentative species Lactobacillus paralimentarius, Lactobacillus sanfranciscensis, Lactobacillus plantarum, and Lactobacillus pontis as the most frequently isolated taxa. Statistical analysis of the identification data indicated that the microbial composition of the sourdoughs is mainly affected by the bakery environment rather than the flour type (wheat, rye, spelt, or a mixture of these) used. In conclusion, the polyphasic approach, based on rapid genotypic screening and high-resolution, sequence-dependent identification, proved to be a powerful tool for studying the LAB diversity in traditional fermented foods such as sourdough.

An integrated metagenome and -proteome analysis of the microbial community residing in a biogas production plant.

PubMed

Ortseifen, Vera; Stolze, Yvonne; Maus, Irena; Sczyrba, Alexander; Bremges, Andreas; Albaum, Stefan P; Jaenicke, Sebastian; Fracowiak, Jochen; Pühler, Alfred; Schlüter, Andreas

2016-08-10

To study the metaproteome of a biogas-producing microbial community, fermentation samples were taken from an agricultural biogas plant for microbial cell and protein extraction and corresponding metagenome analyses. Based on metagenome sequence data, taxonomic community profiling was performed to elucidate the composition of bacterial and archaeal sub-communities. The community's cytosolic metaproteome was represented in a 2D-PAGE approach. Metaproteome databases for protein identification were compiled based on the assembled metagenome sequence dataset for the biogas plant analyzed and non-corresponding biogas metagenomes. Protein identification results revealed that the corresponding biogas protein database facilitated the highest identification rate followed by other biogas-specific databases, whereas common public databases yielded insufficient identification rates. Proteins of the biogas microbiome identified as highly abundant were assigned to the pathways involved in methanogenesis, transport and carbon metabolism. Moreover, the integrated metagenome/-proteome approach enabled the examination of genetic-context information for genes encoding identified proteins by studying neighboring genes on the corresponding contig. Exemplarily, this approach led to the identification of a Methanoculleus sp. contig encoding 16 methanogenesis-related gene products, three of which were also detected as abundant proteins within the community's metaproteome. Thus, metagenome contigs provide additional information on the genetic environment of identified abundant proteins. Copyright © 2016 Elsevier B.V. All rights reserved.

Surgical instrument similarity metrics and tray analysis for multi-sensor instrument identification

NASA Astrophysics Data System (ADS)

Glaser, Bernhard; Schellenberg, Tobias; Franke, Stefan; Dänzer, Stefan; Neumuth, Thomas

2015-03-01

A robust identification of the instrument currently used by the surgeon is crucial for the automatic modeling and analysis of surgical procedures. Various approaches for intra-operative surgical instrument identification have been presented, mostly based on radio-frequency identification (RFID) or endoscopic video analysis. A novel approach is to identify the instruments on the instrument table of the scrub nurse with a combination of video and weight information. In a previous article, we successfully followed this approach and applied it to multiple instances of an ear, nose and throat (ENT) procedure and the surgical tray used therein. In this article, we present a metric for the suitability of the instruments of a surgical tray for identification by video and weight analysis and apply it to twelve trays of four different surgical domains (abdominal surgery, neurosurgery, orthopedics and urology). The used trays were digitized at the central sterile services department of the hospital. The results illustrate that surgical trays differ in their suitability for the approach. In general, additional weight information can significantly contribute to the successful identification of surgical instruments. Additionally, for ten different surgical instruments, ten exemplars of each instrument were tested for their weight differences. The samples indicate high weight variability in instruments with identical brand and model number. The results present a new metric for approaches aiming towards intra-operative surgical instrument detection and imply consequences for algorithms exploiting video and weight information for identification purposes.

Identification of inelastic parameters based on deep drawing forming operations using a global-local hybrid Particle Swarm approach

NASA Astrophysics Data System (ADS)

Vaz, Miguel; Luersen, Marco A.; Muñoz-Rojas, Pablo A.; Trentin, Robson G.

2016-04-01

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress-strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder-Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global-local PSO-Nelder-Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder-Mead algorithm to obtain the minimum itself.

New approach for point pollution source identification in rivers based on the backward probability method.

PubMed

Wang, Jiabiao; Zhao, Jianshi; Lei, Xiaohui; Wang, Hao

2018-06-13

Pollution risk from the discharge of industrial waste or accidental spills during transportation poses a considerable threat to the security of rivers. The ability to quickly identify the pollution source is extremely important to enable emergency disposal of pollutants. This study proposes a new approach for point source identification of sudden water pollution in rivers, which aims to determine where (source location), when (release time) and how much pollutant (released mass) was introduced into the river. Based on the backward probability method (BPM) and the linear regression model (LR), the proposed LR-BPM converts the ill-posed problem of source identification into an optimization model, which is solved using a Differential Evolution Algorithm (DEA). The decoupled parameters of released mass are not dependent on prior information, which improves the identification efficiency. A hypothetical case study with a different number of pollution sources was conducted to test the proposed approach, and the largest relative errors for identified location, release time, and released mass in all tests were not greater than 10%. Uncertainty in the LR-BPM is mainly due to a problem with model equifinality, but averaging the results of repeated tests greatly reduces errors. Furthermore, increasing the gauging sections further improves identification results. A real-world case study examines the applicability of the LR-BPM in practice, where it is demonstrated to be more accurate and time-saving than two existing approaches, Bayesian-MCMC and basic DEA. Copyright © 2018 Elsevier Ltd. All rights reserved.

Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Tolpekin, Valentyn A.; Bijker, Wietske; Stein, Alfred

2011-11-01

Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches.

The confidence-accuracy relationship in eyewitness identification: effects of lineup instructions, foil similarity, and target-absent base rates.

PubMed

Brewer, Neil; Wells, Gary L

2006-03-01

Discriminating accurate from mistaken eyewitness identifications is a major issue facing criminal justice systems. This study examined whether eyewitness confidence assists such decisions under a variety of conditions using a confidence-accuracy (CA) calibration approach. Participants (N = 1,200) viewed a simulated crime and attempted 2 separate identifications from 8-person target-present or target-absent lineups. Confidence and accuracy were calibrated for choosers (but not nonchoosers) for both targets under all conditions. Lower overconfidence was associated with higher diagnosticity, lower target-absent base rates, and shorter identification latencies. Although researchers agree that courtroom expressions of confidence are uninformative, our findings indicate that confidence assessments obtained immediately after a positive identification can provide a useful guide for investigators about the likely accuracy of an identification.

Efficient Bayesian experimental design for contaminant source identification

NASA Astrophysics Data System (ADS)

Zhang, Jiangjiang; Zeng, Lingzao; Chen, Cheng; Chen, Dingjiang; Wu, Laosheng

2015-01-01

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.

Ontology-based malaria parasite stage and species identification from peripheral blood smear images.

PubMed

Makkapati, Vishnu V; Rao, Raghuveer M

2011-01-01

The diagnosis and treatment of malaria infection requires detecting the presence of the malaria parasite in the patient as well as identification of the parasite species. We present an image processing-based approach to detect parasites in microscope images of a blood smear and an ontology-based classification of the stage of the parasite for identifying the species of infection. This approach is patterned after the diagnosis approach adopted by a pathologist for visual examination, and hence, is expected to deliver similar results. We formulate several rules based on the morphology of the basic components of a parasite, namely, chromatin dot(s) and cytoplasm, to identify the parasite stage and species. Numerical results are presented for data taken from various patients. A sensitivity of 88% and a specificity of 95% is reported by evaluation of the scheme on 55 images.

Microfluidic microscopy-assisted label-free approach for cancer screening: automated microfluidic cytology for cancer screening.

PubMed

Jagannadh, Veerendra Kalyan; Gopakumar, G; Subrahmanyam, Gorthi R K Sai; Gorthi, Sai Siva

2017-05-01

Each year, about 7-8 million deaths occur due to cancer around the world. More than half of the cancer-related deaths occur in the less-developed parts of the world. Cancer mortality rate can be reduced with early detection and subsequent treatment of the disease. In this paper, we introduce a microfluidic microscopy-based cost-effective and label-free approach for identification of cancerous cells. We outline a diagnostic framework for the same and detail an instrumentation layout. We have employed classical computer vision techniques such as 2D principal component analysis-based cell type representation followed by support vector machine-based classification. Analogous to criminal face recognition systems implemented with help of surveillance cameras, a signature-based approach for cancerous cell identification using microfluidic microscopy surveillance is demonstrated. Such a platform would facilitate affordable mass screening camps in the developing countries and therefore help decrease cancer mortality rate.

Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book.

PubMed

Sadygov, Rovshan G; Cociorva, Daniel; Yates, John R

2004-12-01

Database searching is an essential element of large-scale proteomics. Because these methods are widely used, it is important to understand the rationale of the algorithms. Most algorithms are based on concepts first developed in SEQUEST and PeptideSearch. Four basic approaches are used to determine a match between a spectrum and sequence: descriptive, interpretative, stochastic and probability-based matching. We review the basic concepts used by most search algorithms, the computational modeling of peptide identification and current challenges and limitations of this approach for protein identification.

Data Driven Model Development for the SuperSonic SemiSpan Transport (S(sup 4)T)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2011-01-01

In this report, we will investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models, and a data-driven system identification procedure. It is shown via analysis of experimental SuperSonic SemiSpan Transport (S4T) wind-tunnel data that by using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Device-Free Passive Identity Identification via WiFi Signals.

PubMed

Lv, Jiguang; Yang, Wu; Man, Dapeng

2017-11-02

Device-free passive identity identification attracts much attention in recent years, and it is a representative application in sensorless sensing. It can be used in many applications such as intrusion detection and smart building. Previous studies show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human's gait is unique from each other similar to fingerprint and iris. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human's gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract several entities' gait features from both time and frequency domain, and select the most effective features according to information gain. Based on these features, Wii realizes stranger recognition through Gaussian Mixture Model (GMM) and identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated on a dataset with more than 1500 gait instances collected from eight subjects walking in a room. The results indicate that Wii can effectively recognize strangers and can achieves high identification accuracy with low computational cost. As a result, Wii has the potential to work in typical home security systems.

Device-Free Passive Identity Identification via WiFi Signals

PubMed Central

Yang, Wu; Man, Dapeng

2017-01-01

Device-free passive identity identification attracts much attention in recent years, and it is a representative application in sensorless sensing. It can be used in many applications such as intrusion detection and smart building. Previous studies show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human’s gait is unique from each other similar to fingerprint and iris. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human’s gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract several entities’ gait features from both time and frequency domain, and select the most effective features according to information gain. Based on these features, Wii realizes stranger recognition through Gaussian Mixture Model (GMM) and identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated on a dataset with more than 1500 gait instances collected from eight subjects walking in a room. The results indicate that Wii can effectively recognize strangers and can achieves high identification accuracy with low computational cost. As a result, Wii has the potential to work in typical home security systems. PMID:29099091

Non-parametric identification of multivariable systems: A local rational modeling approach with application to a vibration isolation benchmark

NASA Astrophysics Data System (ADS)

Voorhoeve, Robbert; van der Maas, Annemiek; Oomen, Tom

2018-05-01

Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF identification of lightly damped mechanical systems with improved speed and accuracy. The proposed method is based on local rational models, which can efficiently handle the lightly-damped resonant dynamics. A key aspect herein is the freedom in the multivariable rational model parametrizations. Several choices for such multivariable rational model parametrizations are proposed and investigated. For systems with many inputs and outputs the required number of model parameters can rapidly increase, adversely affecting the performance of the local modeling approach. Therefore, low-order model structures are investigated. The structure of these low-order parametrizations leads to an undesired directionality in the identification problem. To address this, an iterative local rational modeling algorithm is proposed. As a special case recently developed SISO algorithms are recovered. The proposed approach is successfully demonstrated on simulations and on an active vibration isolation system benchmark, confirming good performance of the method using significantly less parameters compared with alternative approaches.

Identification of watershed priority management areas under water quality constraints: A simulation-optimization approach with ideal load reduction

NASA Astrophysics Data System (ADS)

Dong, Feifei; Liu, Yong; Wu, Zhen; Chen, Yihui; Guo, Huaicheng

2018-07-01

Targeting nonpoint source (NPS) pollution hot spots is of vital importance for placement of best management practices (BMPs). Although physically-based watershed models have been widely used to estimate nutrient emissions, connections between nutrient abatement and compliance of water quality standards have been rarely considered in NPS hotspot ranking, which may lead to ineffective decision-making. It's critical to develop a strategy to identify priority management areas (PMAs) based on water quality response to nutrient load mitigation. A water quality constrained PMA identification framework was thereby proposed in this study, based on the simulation-optimization approach with ideal load reduction (ILR-SO). It integrates the physically-based Soil and Water Assessment Tool (SWAT) model and an optimization model under constraints of site-specific water quality standards. To our knowledge, it was the first effort to identify PMAs with simulation-based optimization. The SWAT model was established to simulate temporal and spatial nutrient loading and evaluate effectiveness of pollution mitigation. A metamodel was trained to establish a quantitative relationship between sources and water quality. Ranking of priority areas is based on required nutrient load reduction in each sub-watershed targeting to satisfy water quality standards in waterbodies, which was calculated with genetic algorithm (GA). The proposed approach was used for identification of PMAs on the basis of diffuse total phosphorus (TP) in Lake Dianchi Watershed, one of the three most eutrophic large lakes in China. The modeling results demonstrated that 85% of diffuse TP came from 30% of the watershed area. Compared with the two conventional targeting strategies based on overland nutrient loss and instream nutrient loading, the ILR-SO model identified distinct PMAs and narrowed down the coverage of management areas. This study addressed the urgent need to incorporate water quality response into PMA identification and showed that the ILR-SO approach is effective to guide watershed management for aquatic ecosystem restoration.

Repositioning the substrate activity screening (SAS) approach as a fragment-based method for identification of weak binders.

PubMed

Gladysz, Rafaela; Cleenewerck, Matthias; Joossens, Jurgen; Lambeir, Anne-Marie; Augustyns, Koen; Van der Veken, Pieter

2014-10-13

Fragment-based drug discovery (FBDD) has evolved into an established approach for "hit" identification. Typically, most applications of FBDD depend on specialised cost- and time-intensive biophysical techniques. The substrate activity screening (SAS) approach has been proposed as a relatively cheap and straightforward alternative for identification of fragments for enzyme inhibitors. We have investigated SAS for the discovery of inhibitors of oncology target urokinase (uPA). Although our results support the key hypotheses of SAS, we also encountered a number of unreported limitations. In response, we propose an efficient modified methodology: "MSAS" (modified substrate activity screening). MSAS circumvents the limitations of SAS and broadens its scope by providing additional fragments and more coherent SAR data. As well as presenting and validating MSAS, this study expands existing SAR knowledge for the S1 pocket of uPA and reports new reversible and irreversible uPA inhibitor scaffolds. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

NASA Astrophysics Data System (ADS)

Acernese, F.; Barone, F.; de Rosa, M.; De Rosa, R.; Eleuteri, A.; Milano, L.; Tagliaferri, R.

2002-06-01

In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

Dynamic Parameter Identification of Subject-Specific Body Segment Parameters Using Robotics Formalism: Case Study Head Complex.

PubMed

Díaz-Rodríguez, Miguel; Valera, Angel; Page, Alvaro; Besa, Antonio; Mata, Vicente

2016-05-01

Accurate knowledge of body segment inertia parameters (BSIP) improves the assessment of dynamic analysis based on biomechanical models, which is of paramount importance in fields such as sport activities or impact crash test. Early approaches for BSIP identification rely on the experiments conducted on cadavers or through imaging techniques conducted on living subjects. Recent approaches for BSIP identification rely on inverse dynamic modeling. However, most of the approaches are focused on the entire body, and verification of BSIP for dynamic analysis for distal segment or chain of segments, which has proven to be of significant importance in impact test studies, is rarely established. Previous studies have suggested that BSIP should be obtained by using subject-specific identification techniques. To this end, our paper develops a novel approach for estimating subject-specific BSIP based on static and dynamics identification models (SIM, DIM). We test the validity of SIM and DIM by comparing the results using parameters obtained from a regression model proposed by De Leva (1996, "Adjustments to Zatsiorsky-Seluyanov's Segment Inertia Parameters," J. Biomech., 29(9), pp. 1223-1230). Both SIM and DIM are developed considering robotics formalism. First, the static model allows the mass and center of gravity (COG) to be estimated. Second, the results from the static model are included in the dynamics equation allowing us to estimate the moment of inertia (MOI). As a case study, we applied the approach to evaluate the dynamics modeling of the head complex. Findings provide some insight into the validity not only of the proposed method but also of the application proposed by De Leva (1996, "Adjustments to Zatsiorsky-Seluyanov's Segment Inertia Parameters," J. Biomech., 29(9), pp. 1223-1230) for dynamic modeling of body segments.

From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity.

PubMed

Cristescu, Melania E

2014-10-01

DNA-based species identification, known as barcoding, transformed the traditional approach to the study of biodiversity science. The field is transitioning from barcoding individuals to metabarcoding communities. This revolution involves new sequencing technologies, bioinformatics pipelines, computational infrastructure, and experimental designs. In this dynamic genomics landscape, metabarcoding studies remain insular and biodiversity estimates depend on the particular methods used. In this opinion article, I discuss the need for a coordinated advancement of DNA-based species identification that integrates taxonomic and barcoding information. Such an approach would facilitate access to almost 3 centuries of taxonomic knowledge and 1 decade of building repository barcodes. Conservation projects are time sensitive, research funding is becoming restricted, and informed decisions depend on our ability to embrace integrative approaches to biodiversity science. Copyright © 2014 Elsevier Ltd. All rights reserved.

A biometric identification system based on eigenpalm and eigenfinger features.

PubMed

Ribaric, Slobodan; Fratric, Ivan

2005-11-01

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

An FEM-based AI approach to model parameter identification for low vibration modes of wind turbine composite rotor blades

NASA Astrophysics Data System (ADS)

Navadeh, N.; Goroshko, I. O.; Zhuk, Y. A.; Fallah, A. S.

2017-11-01

An approach to construction of a beam-type simplified model of a horizontal axis wind turbine composite blade based on the finite element method is proposed. The model allows effective and accurate description of low vibration bending modes taking into account the effects of coupling between flapwise and lead-lag modes of vibration transpiring due to the non-uniform distribution of twist angle in the blade geometry along its length. The identification of model parameters is carried out on the basis of modal data obtained by more detailed finite element simulations and subsequent adoption of the 'DIRECT' optimisation algorithm. Stable identification results were obtained using absolute deviations in frequencies and in modal displacements in the objective function and additional a priori information (boundedness and monotony) on the solution properties.

Taxonomy of Mechanitis (f.) (Lepidoptera: Nymphalidae) from the west Colombian Andes: an integrative approach.

PubMed

Giraldo, C E; Uribe, S I

2012-12-01

Species identification in the butterfly genus Mechanitis (F.) (Lepidoptera: Nymphalidae) becomes difficult when it is based only on wing color patterns, a common practice in butterfly taxonomy. Difficulties in Mechanitis taxonomy are related to the widespread mimicry and polymorphism among species belonging to this genus. Species recognition and inventories of Mechanitis genus in geographic areas as the Andean region of Colombia are of particular interest and the use of more than one character for taxonomic identification is desirable. In this study, we included morphological, ecological, and mitochondrial DNA data to identify the occurring species in this region. Species of Mechanitis were studied from ecological, morphological, and molecular perspectives considering host plant identification, oviposition behavior, and life cycles under laboratory conditions. Immature morphology, patterns of wing color, and genital structures of adults were also studied. The genetic barcoding region of the cytochrome oxidase I mitochondrial gene was sequenced and used to verify the limits between species previously defined by the other characters and to validate its usefulness for species delimitation in this particular genus. The integrative approach combining independent datasets successfully allowed species identification as compared to the approach based on a single dataset. Three well-differentiated species were found in the studied region, Mechanitis menapis (Hewitson), Mechanitis polymnia (Linnaeus), and Mechanitis lysimnia (Fabricius). New valuable characters that could improve taxonomic identification in this genus are considered.

Detection and identification of concealed weapons using matrix pencil

NASA Astrophysics Data System (ADS)

Adve, Raviraj S.; Thayaparan, Thayananthan

2011-06-01

The detection and identification of concealed weapons is an extremely hard problem due to the weak signature of the target buried within the much stronger signal from the human body. This paper furthers the automatic detection and identification of concealed weapons by proposing the use of an effective approach to obtain the resonant frequencies in a measurement. The technique, based on Matrix Pencil, a scheme for model based parameter estimation also provides amplitude information, hence providing a level of confidence in the results. Of specific interest is the fact that Matrix Pencil is based on a singular value decomposition, making the scheme robust against noise.

An adaptive optimal control for smart structures based on the subspace tracking identification technique

NASA Astrophysics Data System (ADS)

Ripamonti, Francesco; Resta, Ferruccio; Borroni, Massimo; Cazzulani, Gabriele

2014-04-01

A new method for the real-time identification of mechanical system modal parameters is used in order to design different adaptive control logics aiming to reduce the vibrations in a carbon fiber plate smart structure. It is instrumented with three piezoelectric actuators, three accelerometers and three strain gauges. The real-time identification is based on a recursive subspace tracking algorithm whose outputs are elaborated by an ARMA model. A statistical approach is finally applied to choose the modal parameter correct values. These are given in input to model-based control logics such as a gain scheduling and an adaptive LQR control.

Differential identification of Candida species and other yeasts by analysis of (/sup 35/S)methionine-labeled polypeptide profiles

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

Shen, H.D.; Choo, K.B.; Tsai, W.C.

1988-12-01

This paper describes a scheme for differential identification of Candida species and other yeasts based on autoradiographic analysis of protein profiles of (/sup 35/S)methionine-labeled cellular proteins separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Using ATCC strains as references, protein profile analysis showed that different Candida and other yeast species produced distinctively different patterns. Good agreement in results obtained with this approach and with other conventional systems was observed. Being accurate and reproducible, this approach provides a basis for the development of an alternative method for the identification of yeasts isolated from clinical specimens.

Indel-seq: a fast-forward genetics approach for identification of trait-associated putative candidate genomic regions and its application in pigeonpea (Cajanus cajan).

PubMed

Singh, Vikas K; Khan, Aamir W; Saxena, Rachit K; Sinha, Pallavi; Kale, Sandip M; Parupalli, Swathi; Kumar, Vinay; Chitikineni, Annapurna; Vechalapu, Suryanarayana; Sameer Kumar, Chanda Venkata; Sharma, Mamta; Ghanta, Anuradha; Yamini, Kalinati Narasimhan; Muniswamy, Sonnappa; Varshney, Rajeev K

2017-07-01

Identification of candidate genomic regions associated with target traits using conventional mapping methods is challenging and time-consuming. In recent years, a number of single nucleotide polymorphism (SNP)-based mapping approaches have been developed and used for identification of candidate/putative genomic regions. However, in the majority of these studies, insertion-deletion (Indel) were largely ignored. For efficient use of Indels in mapping target traits, we propose Indel-seq approach, which is a combination of whole-genome resequencing (WGRS) and bulked segregant analysis (BSA) and relies on the Indel frequencies in extreme bulks. Deployment of Indel-seq approach for identification of candidate genomic regions associated with fusarium wilt (FW) and sterility mosaic disease (SMD) resistance in pigeonpea has identified 16 Indels affecting 26 putative candidate genes. Of these 26 affected putative candidate genes, 24 genes showed effect in the upstream/downstream of the genic region and two genes showed effect in the genes. Validation of these 16 candidate Indels in other FW- and SMD-resistant and FW- and SMD-susceptible genotypes revealed a significant association of five Indels (three for FW and two for SMD resistance). Comparative analysis of Indel-seq with other genetic mapping approaches highlighted the importance of the approach in identification of significant genomic regions associated with target traits. Therefore, the Indel-seq approach can be used for quick and precise identification of candidate genomic regions for any target traits in any crop species. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics

PubMed Central

Stanley, Jeffrey R.; Adkins, Joshua N.; Slysz, Gordon W.; Monroe, Matthew E.; Purvine, Samuel O.; Karpievitch, Yuliya V.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.

2011-01-01

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, as this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referred to as Statistical Tools for AMT tag Confidence (STAC). STAC additionally provides a Uniqueness Probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download as both a command line and a Windows graphical application. PMID:21692516

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Krishnanathan, Kirubhakaran; Anderson, Sean R.; Billings, Stephen A.; Kadirkamanathan, Visakan

2016-11-01

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation.

On using the Hilbert transform for blind identification of complex modes: A practical approach

NASA Astrophysics Data System (ADS)

Antunes, Jose; Debut, Vincent; Piteau, Pilippe; Delaune, Xavier; Borsoi, Laurent

2018-01-01

The modal identification of dynamical systems under operational conditions, when subjected to wide-band unmeasured excitations, is today a viable alternative to more traditional modal identification approaches based on processing sets of measured FRFs or impulse responses. Among current techniques for performing operational modal identification, the so-called blind identification methods are the subject of considerable investigation. In particular, the SOBI (Second-Order Blind Identification) method was found to be quite efficient. SOBI was originally developed for systems with normal modes. To address systems with complex modes, various extension approaches have been proposed, in particular: (a) Using a first-order state-space formulation for the system dynamics; (b) Building complex analytic signals from the measured responses using the Hilbert transform. In this paper we further explore the latter option, which is conceptually interesting while preserving the model order and size. Focus is on applicability of the SOBI technique for extracting the modal responses from analytic signals built from a set of vibratory responses. The novelty of this work is to propose a straightforward computational procedure for obtaining the complex cross-correlation response matrix to be used for the modal identification procedure. After clarifying subtle aspects of the general theoretical framework, we demonstrate that the correlation matrix of the analytic responses can be computed through a Hilbert transform of the real correlation matrix, so that the actual time-domain responses are no longer required for modal identification purposes. The numerical validation of the proposed technique is presented based on time-domain simulations of a conceptual physical multi-modal system, designed to display modes ranging from normal to highly complex, while keeping modal damping low and nearly independent of the modal complexity, and which can prove very interesting in test bench applications. Numerical results for complex modal identifications are presented, and the quality of the identified modal matrix and modal responses, extracted using the complex SOBI technique and implementing the proposed formulation, is assessed.

Use of rbcL and trnL-F as a Two-Locus DNA Barcode for Identification of NW-European Ferns: An Ecological Perspective

PubMed Central

de Groot, G. Arjen; During, Heinjo J.; Maas, Jan W.; Schneider, Harald; Vogel, Johannes C.; Erkens, Roy H. J.

2011-01-01

Although consensus has now been reached on a general two-locus DNA barcode for land plants, the selected combination of markers (rbcL + matK) is not applicable for ferns at the moment. Yet especially for ferns, DNA barcoding is potentially of great value since fern gametophytes—while playing an essential role in fern colonization and reproduction—generally lack the morphological complexity for morphology-based identification and have therefore been underappreciated in ecological studies. We evaluated the potential of a combination of rbcL with a noncoding plastid marker, trnL-F, to obtain DNA-identifications for fern species. A regional approach was adopted, by creating a reference database of trusted rbcL and trnL-F sequences for the wild-occurring homosporous ferns of NW-Europe. A combination of parsimony analyses and distance-based analyses was performed to evaluate the discriminatory power of the two-region barcode. DNA was successfully extracted from 86 tiny fern gametophytes and was used as a test case for the performance of DNA-based identification. Primer universality proved high for both markers. Based on the combined rbcL + trnL-F dataset, all genera as well as all species with non-equal chloroplast genomes formed their own well supported monophyletic clade, indicating a high discriminatory power. Interspecific distances were larger than intraspecific distances for all tested taxa. Identification tests on gametophytes showed a comparable result. All test samples could be identified to genus level, species identification was well possible unless they belonged to a pair of Dryopteris species with completely identical chloroplast genomes. Our results suggest a high potential of the combined use of rbcL and trnL-F as a two-locus cpDNA barcode for identification of fern species. A regional approach may be preferred for ecological tests. We here offer such a ready-to-use barcoding approach for ferns, which opens the way for answering a whole range of questions previously unaddressed in fern gametophyte ecology. PMID:21298108

Species and hybrid identification of sturgeon caviar: a new molecular approach to detect illegal trade.

PubMed

Boscari, E; Barmintseva, A; Pujolar, J M; Doukakis, P; Mugue, N; Congiu, L

2014-05-01

Overexploitation of wild populations due to the high economic value of caviar has driven sturgeons to near extinction. The high prices commanded by caviar on world markets have made it a magnet for illegal and fraudulent caviar trade, often involving low-value farmed caviar being sold as top-quality caviar. We present a new molecular approach for the identification of pure sturgeon species and hybrids that are among the most commercialized species in Europe and North America. Our test is based on the discovery of species-specific single nucleotide polymorphisms (SNPs) in the ribosomal protein S7, supplemented with the Vimentin gene and the mitochondrial D-loop. Test validations performed in 702 specimens of target and nontarget sturgeon species demonstrated a 100% identification success for Acipenser naccarii, A. fulvescens, A. stellatus, A. sinensis and A. transmontanus. In addition to species identification, our approach allows the identification of Bester and AL hybrids, two of the most economically important hybrids in the world, with 80% and 100% success, respectively. Moreover, the approach has the potential to identify many other existing sturgeon hybrids. The development of a standardized sturgeon identification tool will directly benefit trade law enforcement, providing the tools to monitor and regulate the legal trade of caviar and protect sturgeon stocks from illicit producers and traders, hence contributing to safeguarding this group of heavily threatened species. © 2013 John Wiley & Sons Ltd.

A grass molecular identification system for forensic botany: a critical evaluation of the strengths and limitations.

PubMed

Ward, Jodie; Gilmore, Simon R; Robertson, James; Peakall, Rod

2009-11-01

Plant material is frequently encountered in criminal investigations but often overlooked as potential evidence. We designed a DNA-based molecular identification system for 100 Australian grasses that consisted of a series of polymerase chain reaction assays that enabled the progressive identification of grasses to different taxonomic levels. The identification system was based on DNA sequence variation at four chloroplast and two mitochondrial loci. Seventeen informative indels and 68 single-nucleotide polymorphisms were utilized as molecular markers for subfamily to species-level identification. To identify an unknown sample to subfamily level required a minimum of four markers or nine markers for species identification. The accuracy of the system was confirmed by blind tests. We have demonstrated "proof of concept" of a molecular identification system for trace botanical samples. Our evaluation suggests that the adoption of a system that combines this approach with DNA sequencing could assist the morphological identification of grasses found as forensic evidence.

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

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

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized inmore » the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.« less

The mathematics of a successful deconvolution: a quantitative assessment of mixture-based combinatorial libraries screened against two formylpeptide receptors.

PubMed

Santos, Radleigh G; Appel, Jon R; Giulianotti, Marc A; Edwards, Bruce S; Sklar, Larry A; Houghten, Richard A; Pinilla, Clemencia

2013-05-30

In the past 20 years, synthetic combinatorial methods have fundamentally advanced the ability to synthesize and screen large numbers of compounds for drug discovery and basic research. Mixture-based libraries and positional scanning deconvolution combine two approaches for the rapid identification of specific scaffolds and active ligands. Here we present a quantitative assessment of the screening of 32 positional scanning libraries in the identification of highly specific and selective ligands for two formylpeptide receptors. We also compare and contrast two mixture-based library approaches using a mathematical model to facilitate the selection of active scaffolds and libraries to be pursued for further evaluation. The flexibility demonstrated in the differently formatted mixture-based libraries allows for their screening in a wide range of assays.

Model of Emotional Expressions in Movements

ERIC Educational Resources Information Center

Rozaliev, Vladimir L.; Orlova, Yulia A.

2013-01-01

This paper presents a new approach to automated identification of human emotions based on analysis of body movements, a recognition of gestures and poses. Methodology, models and automated system for emotion identification are considered. To characterize the person emotions in the model, body movements are described with linguistic variables and a…

Modern control concepts in hydrology

NASA Technical Reports Server (NTRS)

Duong, N.; Johnson, G. R.; Winn, C. B.

1974-01-01

Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

New Technologies for Rapid Bacterial Identification and Antibiotic Resistance Profiling.

PubMed

Kelley, Shana O

2017-04-01

Conventional approaches to bacterial identification and drug susceptibility testing typically rely on culture-based approaches that take 2 to 7 days to return results. The long turnaround times contribute to the spread of infectious disease, negative patient outcomes, and the misuse of antibiotics that can contribute to antibiotic resistance. To provide new solutions enabling faster bacterial analysis, a variety of approaches are under development that leverage single-cell analysis, microfluidic concentration and detection strategies, and ultrasensitive readout mechanisms. This review discusses recent advances in this area and the potential of new technologies to enable more effective management of infectious disease.

MULTI-DIMENSIONAL MASS SPECTROMETRY-BASED SHOTGUN LIPIDOMICS AND NOVEL STRATEGIES FOR LIPIDOMIC ANALYSES

PubMed Central

Han, Xianlin; Yang, Kui; Gross, Richard W.

2011-01-01

Since our last comprehensive review on multi-dimensional mass spectrometry-based shotgun lipidomics (Mass Spectrom. Rev. 24 (2005), 367), many new developments in the field of lipidomics have occurred. These developments include new strategies and refinements for shotgun lipidomic approaches that use direct infusion, including novel fragmentation strategies, identification of multiple new informative dimensions for mass spectrometric interrogation, and the development of new bioinformatic approaches for enhanced identification and quantitation of the individual molecular constituents that comprise each cell’s lipidome. Concurrently, advances in liquid chromatography-based platforms and novel strategies for quantitative matrix-assisted laser desorption/ionization mass spectrometry for lipidomic analyses have been developed. Through the synergistic use of this repertoire of new mass spectrometric approaches, the power and scope of lipidomics has been greatly expanded to accelerate progress toward the comprehensive understanding of the pleiotropic roles of lipids in biological systems. PMID:21755525

Modeling and Model Identification of Autonomous Underwater Vehicles

DTIC Science & Technology

2015-06-01

setup, based on a quadrifilar pendulum , is developed to measure the moments of inertia of the vehicle. System identification techniques, based on...parametric models of the platforms: an individual channel excitation approach and a free decay pendulum test. The former is applied to THAUS, which can...excite the system in individual channels in four degrees of freedom. These results are verified in the free decay pendulum setup, which has the

Construction and identification of a D-Vine model applied to the probability distribution of modal parameters in structural dynamics

NASA Astrophysics Data System (ADS)

Dubreuil, S.; Salaün, M.; Rodriguez, E.; Petitjean, F.

2018-01-01

This study investigates the construction and identification of the probability distribution of random modal parameters (natural frequencies and effective parameters) in structural dynamics. As these parameters present various types of dependence structures, the retained approach is based on pair copula construction (PCC). A literature review leads us to choose a D-Vine model for the construction of modal parameters probability distributions. Identification of this model is based on likelihood maximization which makes it sensitive to the dimension of the distribution, namely the number of considered modes in our context. To this respect, a mode selection preprocessing step is proposed. It allows the selection of the relevant random modes for a given transfer function. The second point, addressed in this study, concerns the choice of the D-Vine model. Indeed, D-Vine model is not uniquely defined. Two strategies are proposed and compared. The first one is based on the context of the study whereas the second one is purely based on statistical considerations. Finally, the proposed approaches are numerically studied and compared with respect to their capabilities, first in the identification of the probability distribution of random modal parameters and second in the estimation of the 99 % quantiles of some transfer functions.

Automatic identification approach for high-performance liquid chromatography-multiple reaction monitoring fatty acid global profiling.

PubMed

Tie, Cai; Hu, Ting; Jia, Zhi-Xin; Zhang, Jin-Lan

2015-08-18

Fatty acids (FAs) are a group of lipid molecules that are essential to organisms. As potential biomarkers for different diseases, FAs have attracted increasing attention from both biological researchers and the pharmaceutical industry. A sensitive and accurate method for globally profiling and identifying FAs is required for biomarker discovery. The high selectivity and sensitivity of high-performance liquid chromatography-multiple reaction monitoring (HPLC-MRM) gives it great potential to fulfill the need to identify FAs from complicated matrices. This paper developed a new approach for global FA profiling and identification for HPLC-MRM FA data mining. Mathematical models for identifying FAs were simulated using the isotope-induced retention time (RT) shift (IRS) and peak area ratios between parallel isotope peaks for a series of FA standards. The FA structures were predicated using another model based on the RT and molecular weight. Fully automated FA identification software was coded using the Qt platform based on these mathematical models. Different samples were used to verify the software. A high identification efficiency (greater than 75%) was observed when 96 FA species were identified in plasma. This FAs identification strategy promises to accelerate FA research and applications.

Automatic identification of alpine mass movements based on seismic and infrasound signals

NASA Astrophysics Data System (ADS)

Schimmel, Andreas; Hübl, Johannes

2017-04-01

The automatic detection and identification of alpine mass movements like debris flows, debris floods or landslides gets increasing importance for mitigation measures in the densely populated and intensively used alpine regions. Since this mass movement processes emits characteristically seismic and acoustic waves in the low frequency range this events can be detected and identified based on this signals. So already several approaches for detection and warning systems based on seismic or infrasound signals has been developed. But a combination of both methods, which can increase detection probability and reduce false alarms is currently used very rarely and can serve as a promising method for developing an automatic detection and identification system. So this work presents an approach for a detection and identification system based on a combination of seismic and infrasound sensors, which can detect sediment related mass movements from a remote location unaffected by the process. The system is based on one infrasound sensor and one geophone which are placed co-located and a microcontroller where a specially designed detection algorithm is executed which can detect mass movements in real time directly at the sensor site. Further this work tries to get out more information from the seismic and infrasound spectrum produced by different sediment related mass movements to identify the process type and estimate the magnitude of the event. The system is currently installed and tested on five test sites in Austria, two in Italy and one in Switzerland as well as one in Germany. This high number of test sites is used to get a large database of very different events which will be the basis for a new identification method for alpine mass movements. These tests shows promising results and so this system provides an easy to install and inexpensive approach for a detection and warning system.

Development of a multilocus-based approach for sponge (phylum Porifera) identification: refinement and limitations.

PubMed

Yang, Qi; Franco, Christopher M M; Sorokin, Shirley J; Zhang, Wei

2017-02-02

For sponges (phylum Porifera), there is no reliable molecular protocol available for species identification. To address this gap, we developed a multilocus-based Sponge Identification Protocol (SIP) validated by a sample of 37 sponge species belonging to 10 orders from South Australia. The universal barcode COI mtDNA, 28S rRNA gene (D3-D5), and the nuclear ITS1-5.8S-ITS2 region were evaluated for their suitability and capacity for sponge identification. The highest Bit Score was applied to infer the identity. The reliability of SIP was validated by phylogenetic analysis. The 28S rRNA gene and COI mtDNA performed better than the ITS region in classifying sponges at various taxonomic levels. A major limitation is that the databases are not well populated and possess low diversity, making it difficult to conduct the molecular identification protocol. The identification is also impacted by the accuracy of the morphological classification of the sponges whose sequences have been submitted to the database. Re-examination of the morphological identification further demonstrated and improved the reliability of sponge identification by SIP. Integrated with morphological identification, the multilocus-based SIP offers an improved protocol for more reliable and effective sponge identification, by coupling the accuracy of different DNA markers.

Development of a multilocus-based approach for sponge (phylum Porifera) identification: refinement and limitations

PubMed Central

Yang, Qi; Franco, Christopher M. M.; Sorokin, Shirley J.; Zhang, Wei

2017-01-01

For sponges (phylum Porifera), there is no reliable molecular protocol available for species identification. To address this gap, we developed a multilocus-based Sponge Identification Protocol (SIP) validated by a sample of 37 sponge species belonging to 10 orders from South Australia. The universal barcode COI mtDNA, 28S rRNA gene (D3–D5), and the nuclear ITS1-5.8S-ITS2 region were evaluated for their suitability and capacity for sponge identification. The highest Bit Score was applied to infer the identity. The reliability of SIP was validated by phylogenetic analysis. The 28S rRNA gene and COI mtDNA performed better than the ITS region in classifying sponges at various taxonomic levels. A major limitation is that the databases are not well populated and possess low diversity, making it difficult to conduct the molecular identification protocol. The identification is also impacted by the accuracy of the morphological classification of the sponges whose sequences have been submitted to the database. Re-examination of the morphological identification further demonstrated and improved the reliability of sponge identification by SIP. Integrated with morphological identification, the multilocus-based SIP offers an improved protocol for more reliable and effective sponge identification, by coupling the accuracy of different DNA markers. PMID:28150727

Identification of sea ice types in spaceborne synthetic aperture radar data

NASA Technical Reports Server (NTRS)

Kwok, Ronald; Rignot, Eric; Holt, Benjamin; Onstott, R.

1992-01-01

This study presents an approach for identification of sea ice types in spaceborne SAR image data. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by a land-based scatterometer. Extensive scatterometer observations and experience accumulated in field campaigns during the last 10 yr were used to construct these look-up tables. The classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics are discussed. Results using both aircraft and simulated ERS-1 SAR data are presented and compared to limited field ice property measurements and coincident passive microwave imagery. The importance of an integrated postlaunch program for the validation and improvement of this approach is discussed.

A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets.

PubMed

Savitski, Mikhail M; Wilhelm, Mathias; Hahne, Hannes; Kuster, Bernhard; Bantscheff, Marcus

2015-09-01

Calculating the number of confidently identified proteins and estimating false discovery rate (FDR) is a challenge when analyzing very large proteomic data sets such as entire human proteomes. Biological and technical heterogeneity in proteomic experiments further add to the challenge and there are strong differences in opinion regarding the conceptual validity of a protein FDR and no consensus regarding the methodology for protein FDR determination. There are also limitations inherent to the widely used classic target-decoy strategy that particularly show when analyzing very large data sets and that lead to a strong over-representation of decoy identifications. In this study, we investigated the merits of the classic, as well as a novel target-decoy-based protein FDR estimation approach, taking advantage of a heterogeneous data collection comprised of ∼19,000 LC-MS/MS runs deposited in ProteomicsDB (https://www.proteomicsdb.org). The "picked" protein FDR approach treats target and decoy sequences of the same protein as a pair rather than as individual entities and chooses either the target or the decoy sequence depending on which receives the highest score. We investigated the performance of this approach in combination with q-value based peptide scoring to normalize sample-, instrument-, and search engine-specific differences. The "picked" target-decoy strategy performed best when protein scoring was based on the best peptide q-value for each protein yielding a stable number of true positive protein identifications over a wide range of q-value thresholds. We show that this simple and unbiased strategy eliminates a conceptual issue in the commonly used "classic" protein FDR approach that causes overprediction of false-positive protein identification in large data sets. The approach scales from small to very large data sets without losing performance, consistently increases the number of true-positive protein identifications and is readily implemented in proteomics analysis software. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets

PubMed Central

Savitski, Mikhail M.; Wilhelm, Mathias; Hahne, Hannes; Kuster, Bernhard; Bantscheff, Marcus

2015-01-01

Calculating the number of confidently identified proteins and estimating false discovery rate (FDR) is a challenge when analyzing very large proteomic data sets such as entire human proteomes. Biological and technical heterogeneity in proteomic experiments further add to the challenge and there are strong differences in opinion regarding the conceptual validity of a protein FDR and no consensus regarding the methodology for protein FDR determination. There are also limitations inherent to the widely used classic target–decoy strategy that particularly show when analyzing very large data sets and that lead to a strong over-representation of decoy identifications. In this study, we investigated the merits of the classic, as well as a novel target–decoy-based protein FDR estimation approach, taking advantage of a heterogeneous data collection comprised of ∼19,000 LC-MS/MS runs deposited in ProteomicsDB (https://www.proteomicsdb.org). The “picked” protein FDR approach treats target and decoy sequences of the same protein as a pair rather than as individual entities and chooses either the target or the decoy sequence depending on which receives the highest score. We investigated the performance of this approach in combination with q-value based peptide scoring to normalize sample-, instrument-, and search engine-specific differences. The “picked” target–decoy strategy performed best when protein scoring was based on the best peptide q-value for each protein yielding a stable number of true positive protein identifications over a wide range of q-value thresholds. We show that this simple and unbiased strategy eliminates a conceptual issue in the commonly used “classic” protein FDR approach that causes overprediction of false-positive protein identification in large data sets. The approach scales from small to very large data sets without losing performance, consistently increases the number of true-positive protein identifications and is readily implemented in proteomics analysis software. PMID:25987413

An Oracle-based co-training framework for writer identification in offline handwriting

NASA Astrophysics Data System (ADS)

Porwal, Utkarsh; Rajan, Sreeranga; Govindaraju, Venu

2012-01-01

State-of-the-art techniques for writer identification have been centered primarily on enhancing the performance of the system for writer identification. Machine learning algorithms have been used extensively to improve the accuracy of such system assuming sufficient amount of data is available for training. Little attention has been paid to the prospect of harnessing the information tapped in a large amount of un-annotated data. This paper focuses on co-training based framework that can be used for iterative labeling of the unlabeled data set exploiting the independence between the multiple views (features) of the data. This paradigm relaxes the assumption of sufficiency of the data available and tries to generate labeled data from unlabeled data set along with improving the accuracy of the system. However, performance of co-training based framework is dependent on the effectiveness of the algorithm used for the selection of data points to be added in the labeled set. We propose an Oracle based approach for data selection that learns the patterns in the score distribution of classes for labeled data points and then predicts the labels (writers) of the unlabeled data point. This method for selection statistically learns the class distribution and predicts the most probable class unlike traditional selection algorithms which were based on heuristic approaches. We conducted experiments on publicly available IAM dataset and illustrate the efficacy of the proposed approach.

Aerodynamic parameter estimation via Fourier modulating function techniques

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1995-01-01

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.

Automatic limb identification and sleeping parameters assessment for pressure ulcer prevention.

PubMed

Baran Pouyan, Maziyar; Birjandtalab, Javad; Nourani, Mehrdad; Matthew Pompeo, M D

2016-08-01

Pressure ulcers (PUs) are common among vulnerable patients such as elderly, bedridden and diabetic. PUs are very painful for patients and costly for hospitals and nursing homes. Assessment of sleeping parameters on at-risk limbs is critical for ulcer prevention. An effective assessment depends on automatic identification and tracking of at-risk limbs. An accurate limb identification can be used to analyze the pressure distribution and assess risk for each limb. In this paper, we propose a graph-based clustering approach to extract the body limbs from the pressure data collected by a commercial pressure map system. A robust signature-based technique is employed to automatically label each limb. Finally, an assessment technique is applied to evaluate the experienced stress by each limb over time. The experimental results indicate high performance and more than 94% average accuracy of the proposed approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identification of metabolic pathways using pathfinding approaches: a systematic review.

PubMed

Abd Algfoor, Zeyad; Shahrizal Sunar, Mohd; Abdullah, Afnizanfaizal; Kolivand, Hoshang

2017-03-01

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A dual PCR-based sequencing approach for the identification and discrimination of Echinococcus and Taenia taxa.

PubMed

Boubaker, Ghalia; Marinova, Irina; Gori, Francesca; Hizem, Amani; Müller, Norbert; Casulli, Adriano; Jerez Puebla, Luis Enrique; Babba, Hamouda; Gottstein, Bruno; Spiliotis, Markus

2016-08-01

Reliable and rapid molecular tools for the genetic identification and differentiation of Echinococcus species and/or genotypes are crucial for studying spatial and temporal transmission dynamics. Here, we describe a novel dual PCR targeting regions in the small (rrnS) and large (rrnL) subunits of mitochondrial ribosomal RNA (rRNA) genes, which enables (i) the specific identification of species and genotypes of Echinococcus (rrnS + L-PCR) and/or (ii) the identification of a range of taeniid cestodes, including different species of Echinococcus, Taenia and some others (17 species of diphyllidean helminths). This dual PCR approach was highly sensitive, with an analytical detection limit of 1 pg for genomic DNA of Echinococcus. Using concatenated sequence data derived from the two gene markers (1225 bp), we identified five unique and geographically informative single nucleotide polymorphisms (SNPs) that allowed genotypes (G1 and G3) of Echinococcus granulosus sensu stricto to be distinguished, and 25 SNPs that allowed differentiation within Echinococcus canadensis (G6/7/8/10). In conclusion, we propose that this dual PCR-based sequencing approach can be used for molecular epidemiological studies of Echinococcus and other taeniid cestodes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mould routine identification in the clinical laboratory by matrix-assisted laser desorption ionization time-of-flight mass spectrometry.

PubMed

Cassagne, Carole; Ranque, Stéphane; Normand, Anne-Cécile; Fourquet, Patrick; Thiebault, Sandrine; Planard, Chantal; Hendrickx, Marijke; Piarroux, Renaud

2011-01-01

MALDI-TOF MS recently emerged as a valuable identification tool for bacteria and yeasts and revolutionized the daily clinical laboratory routine. But it has not been established for routine mould identification. This study aimed to validate a standardized procedure for MALDI-TOF MS-based mould identification in clinical laboratory. First, pre-extraction and extraction procedures were optimized. With this standardized procedure, a 143 mould strains reference spectra library was built. Then, the mould isolates cultured from sequential clinical samples were prospectively subjected to this MALDI-TOF MS based-identification assay. MALDI-TOF MS-based identification was considered correct if it was concordant with the phenotypic identification; otherwise, the gold standard was DNA sequence comparison-based identification. The optimized procedure comprised a culture on sabouraud-gentamicin-chloramphenicol agar followed by a chemical extraction of the fungal colonies with formic acid and acetonitril. The identification was done using a reference database built with references from at least four culture replicates. For five months, 197 clinical isolates were analyzed; 20 were excluded because they were not identified at the species level. MALDI-TOF MS-based approach correctly identified 87% (154/177) of the isolates analyzed in a routine clinical laboratory activity. It failed in 12% (21/177), whose species were not represented in the reference library. MALDI-TOF MS-based identification was correct in 154 out of the remaining 156 isolates. One Beauveria bassiana was not identified and one Rhizopus oryzae was misidentified as Mucor circinelloides. This work's seminal finding is that a standardized procedure can also be used for MALDI-TOF MS-based identification of a wide array of clinically relevant mould species. It thus makes it possible to identify moulds in the routine clinical laboratory setting and opens new avenues for the development of an integrated MALDI-TOF MS-based solution for the identification of any clinically relevant microorganism.

MODAL TRACKING of A Structural Device: A Subspace Identification Approach

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

Candy, J. V.; Franco, S. N.; Ruggiero, E. L.

Mechanical devices operating in an environment contaminated by noise, uncertainties, and extraneous disturbances lead to low signal-to-noise-ratios creating an extremely challenging processing problem. To detect/classify a device subsystem from noisy data, it is necessary to identify unique signatures or particular features. An obvious feature would be resonant (modal) frequencies emitted during its normal operation. In this report, we discuss a model-based approach to incorporate these physical features into a dynamic structure that can be used for such an identification. The approach we take after pre-processing the raw vibration data and removing any extraneous disturbances is to obtain a representation ofmore » the structurally unknown device along with its subsystems that capture these salient features. One approach is to recognize that unique modal frequencies (sinusoidal lines) appear in the estimated power spectrum that are solely characteristic of the device under investigation. Therefore, the objective of this effort is based on constructing a black box model of the device that captures these physical features that can be exploited to “diagnose” whether or not the particular device subsystem (track/detect/classify) is operating normally from noisy vibrational data. Here we discuss the application of a modern system identification approach based on stochastic subspace realization techniques capable of both (1) identifying the underlying black-box structure thereby enabling the extraction of structural modes that can be used for analysis and modal tracking as well as (2) indicators of condition and possible changes from normal operation.« less

Personalized medicine and chronic obstructive pulmonary disease.

PubMed

Wouters, E F M; Wouters, B B R A F; Augustin, I M L; Franssen, F M E

2017-05-01

The current review summarizes ongoing developments in personalized medicine and precision medicine in chronic obstructive pulmonary disease (COPD). Our current approach is far away of personalized management algorithms as current recommendations for COPD are largely based on a reductionist disease description, operationally defined by results of spirometry. Besides precision medicine developments, a personalized medicine approach in COPD is described based on a holistic approach of the patient and considering illness as the consequence of dynamic interactions within and between multiple interacting and self-adjusting systems. Pulmonary rehabilitation is described as a model of personalized medicine. Largely based on current understanding of inflammatory processes in COPD, targeted interventions in COPD are reviewed. Augmentation therapy for α-1-antitrypsine deficiency is described as model of precision medicine in COPD based in profound understanding of the related genetic endotype. Future developments of precision medicine in COPD require identification of relevant endotypes combined with proper identification of phenotypes involved in the complex and heterogeneous manifestations of COPD.

A hybrid approach to parameter identification of linear delay differential equations involving multiple delays

NASA Astrophysics Data System (ADS)

Marzban, Hamid Reza

2018-05-01

In this paper, we are concerned with the parameter identification of linear time-invariant systems containing multiple delays. The approach is based upon a hybrid of block-pulse functions and Legendre's polynomials. The convergence of the proposed procedure is established and an upper error bound with respect to the L2-norm associated with the hybrid functions is derived. The problem under consideration is first transformed into a system of algebraic equations. The least squares technique is then employed for identification of the desired parameters. Several multi-delay systems of varying complexity are investigated to evaluate the performance and capability of the proposed approximation method. It is shown that the proposed approach is also applicable to a class of nonlinear multi-delay systems. It is demonstrated that the suggested procedure provides accurate results for the desired parameters.

Adaptive convex combination approach for the identification of improper quaternion processes.

PubMed

Ujang, Bukhari Che; Jahanchahi, Cyrus; Took, Clive Cheong; Mandic, Danilo P

2014-01-01

Data-adaptive optimal modeling and identification of real-world vector sensor data is provided by combining the fractional tap-length (FT) approach with model order selection in the quaternion domain. To account rigorously for the generality of such processes, both second-order circular (proper) and noncircular (improper), the proposed approach in this paper combines the FT length optimization with both the strictly linear quaternion least mean square (QLMS) and widely linear QLMS (WL-QLMS). A collaborative approach based on QLMS and WL-QLMS is shown to both identify the type of processes (proper or improper) and to track their optimal parameters in real time. Analysis shows that monitoring the evolution of the convex mixing parameter within the collaborative approach allows us to track the improperness in real time. Further insight into the properties of those algorithms is provided by establishing a relationship between the steady-state error and optimal model order. The approach is supported by simulations on model order selection and identification of both strictly linear and widely linear quaternion-valued systems, such as those routinely used in renewable energy (wind) and human-centered computing (biomechanics).

Patterns of Cognitive Strengths and Weaknesses: Identification Rates, Agreement, and Validity for Learning Disabilities Identification

PubMed Central

Miciak, Jeremy; Fletcher, Jack M.; Stuebing, Karla; Vaughn, Sharon; Tolar, Tammy D.

2014-01-01

Purpose Few empirical investigations have evaluated LD identification methods based on a pattern of cognitive strengths and weaknesses (PSW). This study investigated the reliability and validity of two proposed PSW methods: the concordance/discordance method (C/DM) and cross battery assessment (XBA) method. Methods Cognitive assessment data for 139 adolescents demonstrating inadequate response to intervention was utilized to empirically classify participants as meeting or not meeting PSW LD identification criteria using the two approaches, permitting an analysis of: (1) LD identification rates; (2) agreement between methods; and (3) external validity. Results LD identification rates varied between the two methods depending upon the cut point for low achievement, with low agreement for LD identification decisions. Comparisons of groups that met and did not meet LD identification criteria on external academic variables were largely null, raising questions of external validity. Conclusions This study found low agreement and little evidence of validity for LD identification decisions based on PSW methods. An alternative may be to use multiple measures of academic achievement to guide intervention. PMID:24274155

Learning Behaviour and Learning Outcomes: The Roles for Social Influence and Field of Study

ERIC Educational Resources Information Center

Smyth, Lillian; Mavor, Kenneth I.; Platow, Michael J.

2017-01-01

Research has demonstrated a significant role of discipline social identification in predicting learning approaches, even controlling for individual differences. Smyth et al. ("Educ Psychol" 35(1):53-72, 2015. doi:10.1080/01443410.2013.822962) suggest that learners share discipline-based social identifications, and that this…

TOXICITY-BASED IDENTIFICATION OF DRINKING WATER DISINFECTION BY-PRODUCTS USING LC/MS AND LC/MS/MS

EPA Science Inventory

The goal of this research is to use a bio-assay directed approach to focus identification work on the most toxicologically important disinfection by-products. To this end, drinking water is being collected from full-scale treatment plants that use chlorine, ozone, chlorine dioxi...

TOXICITY-BASED IDENTIFICATION OF DRINKING WATER DISINFECTION BY-PRODUCTS USING ESI-MS AND ESI-MS/MS

EPA Science Inventory

The goal of this research is to use a bio-assay directed approach to focus identification work on the most toxicologically important disinfection by-products. To this end, drinking water is being collected from full-scale treatment plants that use chlorine, ozone, chlorine dioxi...

Simple and fast multiplex PCR method for detection of species origin in meat products.

PubMed

Izadpanah, Mehrnaz; Mohebali, Nazanin; Elyasi Gorji, Zahra; Farzaneh, Parvaneh; Vakhshiteh, Faezeh; Shahzadeh Fazeli, Seyed Abolhassan

2018-02-01

Identification of animal species is one of the major concerns in food regulatory control and quality assurance system. Different approaches have been used for species identification in animal origin of feedstuff. This study aimed to develop a multiplex PCR approach to detect the origin of meat and meat products. Specific primers were designed based on the conserved region of mitochondrial Cytochrome C Oxidase subunit I ( COX1 ) gene. This method could successfully distinguish the origin of the pig, camel, sheep, donkey, goat, cow, and chicken in one single reaction. Since PCR products derived from each species represent unique molecular weight, the amplified products could be identified by electrophoresis and analyzed based on their size. Due to the synchronized amplification of segments within a single PCR reaction, multiplex PCR is considered to be a simple, fast, and inexpensive technique that can be applied for identification of meat products in food industries. Nowadays, this technique has been considered as a practical method to identify the species origin, which could further applied for animal feedstuffs identification.

Open-set speaker identification with diverse-duration speech data

NASA Astrophysics Data System (ADS)

Karadaghi, Rawande; Hertlein, Heinz; Ariyaeeinia, Aladdin

2015-05-01

The concern in this paper is an important category of applications of open-set speaker identification in criminal investigation, which involves operating with short and varied duration speech. The study presents investigations into the adverse effects of such an operating condition on the accuracy of open-set speaker identification, based on both GMMUBM and i-vector approaches. The experiments are conducted using a protocol developed for the identification task, based on the NIST speaker recognition evaluation corpus of 2008. In order to closely cover the real-world operating conditions in the considered application area, the study includes experiments with various combinations of training and testing data duration. The paper details the characteristics of the experimental investigations conducted and provides a thorough analysis of the results obtained.

Performance metrics for the evaluation of hyperspectral chemical identification systems

NASA Astrophysics Data System (ADS)

Truslow, Eric; Golowich, Steven; Manolakis, Dimitris; Ingle, Vinay

2016-02-01

Remote sensing of chemical vapor plumes is a difficult but important task for many military and civilian applications. Hyperspectral sensors operating in the long-wave infrared regime have well-demonstrated detection capabilities. However, the identification of a plume's chemical constituents, based on a chemical library, is a multiple hypothesis testing problem which standard detection metrics do not fully describe. We propose using an additional performance metric for identification based on the so-called Dice index. Our approach partitions and weights a confusion matrix to develop both the standard detection metrics and identification metric. Using the proposed metrics, we demonstrate that the intuitive system design of a detector bank followed by an identifier is indeed justified when incorporating performance information beyond the standard detection metrics.

Palm-Vein Classification Based on Principal Orientation Features

PubMed Central

Zhou, Yujia; Liu, Yaqin; Feng, Qianjin; Yang, Feng; Huang, Jing; Nie, Yixiao

2014-01-01

Personal recognition using palm–vein patterns has emerged as a promising alternative for human recognition because of its uniqueness, stability, live body identification, flexibility, and difficulty to cheat. With the expanding application of palm–vein pattern recognition, the corresponding growth of the database has resulted in a long response time. To shorten the response time of identification, this paper proposes a simple and useful classification for palm–vein identification based on principal direction features. In the registration process, the Gaussian-Radon transform is adopted to extract the orientation matrix and then compute the principal direction of a palm–vein image based on the orientation matrix. The database can be classified into six bins based on the value of the principal direction. In the identification process, the principal direction of the test sample is first extracted to ascertain the corresponding bin. One-by-one matching with the training samples is then performed in the bin. To improve recognition efficiency while maintaining better recognition accuracy, two neighborhood bins of the corresponding bin are continuously searched to identify the input palm–vein image. Evaluation experiments are conducted on three different databases, namely, PolyU, CASIA, and the database of this study. Experimental results show that the searching range of one test sample in PolyU, CASIA and our database by the proposed method for palm–vein identification can be reduced to 14.29%, 14.50%, and 14.28%, with retrieval accuracy of 96.67%, 96.00%, and 97.71%, respectively. With 10,000 training samples in the database, the execution time of the identification process by the traditional method is 18.56 s, while that by the proposed approach is 3.16 s. The experimental results confirm that the proposed approach is more efficient than the traditional method, especially for a large database. PMID:25383715

The Mathematics of a Successful Deconvolution: A Quantitative Assessment of Mixture-Based Combinatorial Libraries Screened Against Two Formylpeptide Receptors

PubMed Central

Santos, Radleigh G.; Appel, Jon R.; Giulianotti, Marc A.; Edwards, Bruce S.; Sklar, Larry A.; Houghten, Richard A.; Pinilla, Clemencia

2014-01-01

In the past 20 years, synthetic combinatorial methods have fundamentally advanced the ability to synthesize and screen large numbers of compounds for drug discovery and basic research. Mixture-based libraries and positional scanning deconvolution combine two approaches for the rapid identification of specific scaffolds and active ligands. Here we present a quantitative assessment of the screening of 32 positional scanning libraries in the identification of highly specific and selective ligands for two formylpeptide receptors. We also compare and contrast two mixture-based library approaches using a mathematical model to facilitate the selection of active scaffolds and libraries to be pursued for further evaluation. The flexibility demonstrated in the differently formatted mixture-based libraries allows for their screening in a wide range of assays. PMID:23722730

Developing an Educational Computer Game for Migratory Bird Identification Based on a Two-Tier Test Approach

ERIC Educational Resources Information Center

Chu, Hui-Chun; Chang, Shao-Chen

2014-01-01

Although educational computer games have been recognized as being a promising approach, previous studies have indicated that, without supportive models, students might only show temporary interest during the game-based learning process, and their learning performance is often not as good as expected. Therefore, in this paper, a two-tier test…

Efficient Discovery of De-identification Policies Through a Risk-Utility Frontier

PubMed Central

Xia, Weiyi; Heatherly, Raymond; Ding, Xiaofeng; Li, Jiuyong; Malin, Bradley

2014-01-01

Modern information technologies enable organizations to capture large quantities of person-specific data while providing routine services. Many organizations hope, or are legally required, to share such data for secondary purposes (e.g., validation of research findings) in a de-identified manner. In previous work, it was shown de-identification policy alternatives could be modeled on a lattice, which could be searched for policies that met a prespecified risk threshold (e.g., likelihood of re-identification). However, the search was limited in several ways. First, its definition of utility was syntactic - based on the level of the lattice - and not semantic - based on the actual changes induced in the resulting data. Second, the threshold may not be known in advance. The goal of this work is to build the optimal set of policies that trade-off between privacy risk (R) and utility (U), which we refer to as a R-U frontier. To model this problem, we introduce a semantic definition of utility, based on information theory, that is compatible with the lattice representation of policies. To solve the problem, we initially build a set of policies that define a frontier. We then use a probability-guided heuristic to search the lattice for policies likely to update the frontier. To demonstrate the effectiveness of our approach, we perform an empirical analysis with the Adult dataset of the UCI Machine Learning Repository. We show that our approach can construct a frontier closer to optimal than competitive approaches by searching a smaller number of policies. In addition, we show that a frequently followed de-identification policy (i.e., the Safe Harbor standard of the HIPAA Privacy Rule) is suboptimal in comparison to the frontier discovered by our approach. PMID:25520961

Efficient Discovery of De-identification Policies Through a Risk-Utility Frontier.

PubMed

Xia, Weiyi; Heatherly, Raymond; Ding, Xiaofeng; Li, Jiuyong; Malin, Bradley

2013-01-01

Modern information technologies enable organizations to capture large quantities of person-specific data while providing routine services. Many organizations hope, or are legally required, to share such data for secondary purposes (e.g., validation of research findings) in a de-identified manner. In previous work, it was shown de-identification policy alternatives could be modeled on a lattice, which could be searched for policies that met a prespecified risk threshold (e.g., likelihood of re-identification). However, the search was limited in several ways. First, its definition of utility was syntactic - based on the level of the lattice - and not semantic - based on the actual changes induced in the resulting data. Second, the threshold may not be known in advance. The goal of this work is to build the optimal set of policies that trade-off between privacy risk (R) and utility (U), which we refer to as a R-U frontier. To model this problem, we introduce a semantic definition of utility, based on information theory, that is compatible with the lattice representation of policies. To solve the problem, we initially build a set of policies that define a frontier. We then use a probability-guided heuristic to search the lattice for policies likely to update the frontier. To demonstrate the effectiveness of our approach, we perform an empirical analysis with the Adult dataset of the UCI Machine Learning Repository. We show that our approach can construct a frontier closer to optimal than competitive approaches by searching a smaller number of policies. In addition, we show that a frequently followed de-identification policy (i.e., the Safe Harbor standard of the HIPAA Privacy Rule) is suboptimal in comparison to the frontier discovered by our approach.

DNA barcoding: an efficient tool to overcome authentication challenges in the herbal market.

PubMed

Mishra, Priyanka; Kumar, Amit; Nagireddy, Akshitha; Mani, Daya N; Shukla, Ashutosh K; Tiwari, Rakesh; Sundaresan, Velusamy

2016-01-01

The past couple of decades have witnessed global resurgence of herbal-based health care. As a result, the trade of raw drugs has surged globally. Accurate and fast scientific identification of the plant(s) is the key to success for the herbal drug industry. The conventional approach is to engage an expert taxonomist, who uses a mix of traditional and modern techniques for precise plant identification. However, for bulk identification at industrial scale, the process is protracted and time-consuming. DNA barcoding, on the other hand, offers an alternative and feasible taxonomic tool box for rapid and robust species identification. For the success of DNA barcode, the barcode loci must have sufficient information to differentiate unambiguously between closely related plant species and discover new cryptic species. For herbal plant identification, matK, rbcL, trnH-psbA, ITS, trnL-F, 5S-rRNA and 18S-rRNA have been used as successful DNA barcodes. Emerging advances in DNA barcoding coupled with next-generation sequencing and high-resolution melting curve analysis have paved the way for successful species-level resolution recovered from finished herbal products. Further, development of multilocus strategy and its application has provided new vistas to the DNA barcode-based plant identification for herbal drug industry. For successful and acceptable identification of herbal ingredients and a holistic quality control of the drug, DNA barcoding needs to work harmoniously with other components of the systems biology approach. We suggest that for effectively resolving authentication challenges associated with the herbal market, DNA barcoding must be used in conjunction with metabolomics along with need-based transcriptomics and proteomics. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Real-time diagnostics of the reusable rocket engine using on-line system identification

NASA Technical Reports Server (NTRS)

Guo, T.-H.; Merrill, W.; Duyar, A.

1990-01-01

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

Semantic Image Based Geolocation Given a Map (Author’s Initial Manuscript)

DTIC Science & Technology

2016-09-01

novel technique for detection and identification of building facades from geo-tagged reference view using the map and geometry of the building facades. We...2D map of the environment, and geometry of building facades. We evaluate our approach for building identification and geo-localization on a new...location recognition and building identification is done by matching the query view to a reference set, followed by estimation of 3D building facades

Computer aided manual validation of mass spectrometry-based proteomic data.

PubMed

Curran, Timothy G; Bryson, Bryan D; Reigelhaupt, Michael; Johnson, Hannah; White, Forest M

2013-06-15

Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based on statistical analysis of the likelihood of false positive identifications in large-scale data sets. While helpful, these calculations do not consider the accuracy of each identification, thus creating a precarious situation for biologists relying on the data to inform experimental design. Manual validation is the gold standard approach to confirm accuracy of database identifications, but is extremely time-intensive. To palliate the increasing time required to manually validate large proteomic datasets, we provide computer aided manual validation software (CAMV) to expedite the process. Relevant spectra are collected, catalogued, and pre-labeled, allowing users to efficiently judge the quality of each identification and summarize applicable quantitative information. CAMV significantly reduces the burden associated with manual validation and will hopefully encourage broader adoption of manual validation in mass spectrometry-based proteomics. Copyright © 2013 Elsevier Inc. All rights reserved.

Writer identification on historical Glagolitic documents

NASA Astrophysics Data System (ADS)

Fiel, Stefan; Hollaus, Fabian; Gau, Melanie; Sablatnig, Robert

2013-12-01

This work aims at automatically identifying scribes of historical Slavonic manuscripts. The quality of the ancient documents is partially degraded by faded-out ink or varying background. The writer identification method used is based on image features, which are described with Scale Invariant Feature Transform (SIFT) features. A visual vocabulary is used for the description of handwriting characteristics, whereby the features are clustered using a Gaussian Mixture Model and employing the Fisher kernel. The writer identification approach is originally designed for grayscale images of modern handwritings. But contrary to modern documents, the historical manuscripts are partially corrupted by background clutter and water stains. As a result, SIFT features are also found on the background. Since the method shows also good results on binarized images of modern handwritings, the approach was additionally applied on binarized images of the ancient writings. Experiments show that this preprocessing step leads to a significant performance increase: The identification rate on binarized images is 98.9%, compared to an identification rate of 87.6% gained on grayscale images.

Direct identification of bacteria in blood culture by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry: a new methodological approach.

PubMed

Kroumova, Vesselina; Gobbato, Elisa; Basso, Elisa; Mucedola, Luca; Giani, Tommaso; Fortina, Giacomo

2011-08-15

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has recently been demonstrated to be a powerful tool for the rapid identification of bacteria from growing colonies. In order to speed up the identification of bacteria, several authors have evaluated the usefulness of this MALDI-TOF MS technology for the direct and quick identification bacteria from positive blood cultures. The results obtained so far have been encouraging but have also shown some limitations, mainly related to the bacterial growth and to the presence of interference substances belonging to the blood cultures. In this paper, we present a new methodological approach that we have developed to overcome these limitations, based mainly on an enrichment of the sample into a growing medium before the extraction process, prior to mass spectrometric analysis. The proposed method shows important advantages for the identification of bacterial strains, yielding an increased identification score, which gives higher confidence in the results. Copyright © 2011 John Wiley & Sons, Ltd.

Evaluation of a preschool nutrition education program based on the theory of multiple intelligences.

PubMed

Cason, K L

2001-01-01

This report describes the evaluation of a preschool nutrition education program based on the theory of multiple intelligences. Forty-six nutrition educators provided a series of 12 lessons to 6102 preschool-age children. The program was evaluated using a pretest/post-test design to assess differences in fruit and vegetable identification, healthy snack choices, willingness to taste foods, and eating behaviors. Subjects showed significant improvement in food identification and recognition, healthy snack identification, willingness to taste foods, and frequency of fruit, vegetable, meat, and dairy consumption. The evaluation indicates that the program was an effective approach for educating preschool children about nutrition.

Mitochondrial Telomeres as Molecular Markers for Identification of the Opportunistic Yeast Pathogen Candida parapsilosis

PubMed Central

Nosek, Jozef; Tomáška, L'ubomír; Ryčovská, Adriana; Fukuhara, Hiroshi

2002-01-01

Recent studies have demonstrated that a large number of organisms carry linear mitochondrial DNA molecules possessing specialized telomeric structures at their ends. Based on this specific structural feature of linear mitochondrial genomes, we have developed an approach for identification of the opportunistic yeast pathogen Candida parapsilosis. The strategy for identification of C. parapsilosis strains is based on PCR amplification of specific DNA sequences derived from the mitochondrial telomere region. This assay is complemented by immunodetection of a protein component of mitochondrial telomeres. The results demonstrate that mitochondrial telomeres represent specific molecular markers with potential applications in yeast diagnostics and taxonomy. PMID:11923346

An automated approach for extracting Barrier Island morphology from digital elevation models

NASA Astrophysics Data System (ADS)

Wernette, Phillipe; Houser, Chris; Bishop, Michael P.

2016-06-01

The response and recovery of a barrier island to extreme storms depends on the elevation of the dune base and crest, both of which can vary considerably alongshore and through time. Quantifying the response to and recovery from storms requires that we can first identify and differentiate the dune(s) from the beach and back-barrier, which in turn depends on accurate identification and delineation of the dune toe, crest and heel. The purpose of this paper is to introduce a multi-scale automated approach for extracting beach, dune (dune toe, dune crest and dune heel), and barrier island morphology. The automated approach introduced here extracts the shoreline and back-barrier shoreline based on elevation thresholds, and extracts the dune toe, dune crest and dune heel based on the average relative relief (RR) across multiple spatial scales of analysis. The multi-scale automated RR approach to extracting dune toe, dune crest, and dune heel based upon relative relief is more objective than traditional approaches because every pixel is analyzed across multiple computational scales and the identification of features is based on the calculated RR values. The RR approach out-performed contemporary approaches and represents a fast objective means to define important beach and dune features for predicting barrier island response to storms. The RR method also does not require that the dune toe, crest, or heel are spatially continuous, which is important because dune morphology is likely naturally variable alongshore.

Correlation techniques to determine model form in robust nonlinear system realization/identification

NASA Technical Reports Server (NTRS)

Stry, Greselda I.; Mook, D. Joseph

1991-01-01

The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME) optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature is the ability to identify nonlinear dynamic systems without prior assumption regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Spectral identification of a 90Sr source in the presence of masking nuclides using Maximum-Likelihood deconvolution

NASA Astrophysics Data System (ADS)

Neuer, Marcus J.

2013-11-01

A technique for the spectral identification of strontium-90 is shown, utilising a Maximum-Likelihood deconvolution. Different deconvolution approaches are discussed and summarised. Based on the intensity distribution of the beta emission and Geant4 simulations, a combined response matrix is derived, tailored to the β- detection process in sodium iodide detectors. It includes scattering effects and attenuation by applying a base material decomposition extracted from Geant4 simulations with a CAD model for a realistic detector system. Inversion results of measurements show the agreement between deconvolution and reconstruction. A detailed investigation with additional masking sources like 40K, 226Ra and 131I shows that a contamination of strontium can be found in the presence of these nuisance sources. Identification algorithms for strontium are presented based on the derived technique. For the implementation of blind identification, an exemplary masking ratio is calculated.

Entropy based file type identification and partitioning

DTIC Science & Technology

2017-06-01

energy spectrum,” Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, pp. 288–293, 2016...ABBREVIATIONS AES Advanced Encryption Standard ANN Artificial Neural Network ASCII American Standard Code for Information Interchange CWT...the identification of file types and file partitioning. This approach has applications in cybersecurity as it allows for a quick determination of

The Confidence-Accuracy Relationship in Eyewitness Identification: Effects of Lineup Instructions, Foil Similarity, and Target-Absent Base Rates

ERIC Educational Resources Information Center

Brewer, Neil; Wells, Gary L.

2006-01-01

Discriminating accurate from mistaken eyewitness identifications is a major issue facing criminal justice systems. This study examined whether eyewitness confidence assists such decisions under a variety of conditions using a confidence-accuracy (CA) calibration approach. Participants (N = 1,200) viewed a simulated crime and attempted 2 separate…

Developing Human-Machine Interfaces to Support Appropriate Trust and Reliance on Automated Combat Identification Systems (Developpement d’Interfaces Homme-Machine Pour Appuyer la Confiance dans les Systemes Automatises d’Identification au Combat)

DTIC Science & Technology

2008-03-31

on automation; the ‘response bias’ approach. This new approach is based on Signal Detection Theory (SDT) (Macmillan & Creelman , 1991; Wickens...SDT), response bias will vary with the expectation of the target probability, whereas their sensitivity will stay constant (Macmillan & Creelman ...measures, C has the simplest statistical properties (Macmillan & Creelman , 1991, p273), and it was also the measure used in Dzindolet et al.’s study

A microbial identification framework for risk assessment.

PubMed

Bernatchez, Stéphane; Anoop, Valar; Saikali, Zeina; Breton, Marie

2018-06-01

Micro-organisms are increasingly used in a variety of products for commercial uses, including cleaning products. Such microbial-based cleaning products (MBCP) are represented as a more environmentally-friendly alternative to chemically based cleaning products. The identity of the micro-organisms formulated into these products is often considered confidential business information and is not revealed or it is only partly revealed (i.e., identification to the genus, not to the species). That paucity of information complicates the evaluation of the risk associated with their use. The accurate taxonomic identification of those micro-organisms is important so that a suitable risk assessment of the products can be conducted. To alleviate difficulties associated with adequate identification of micro-organisms in MBCP and other products containing micro-organisms, a microbial identification framework for risk assessment (MIFRA) has been elaborated. It serves to provide guidance on a polyphasic tiered approach, combining the data obtained from the use of various methods (i.e., polyphasic approach) combined with the sequential selection of the methods (i.e., tiered) to achieve a satisfactory identity of the micro-organism to an acceptable taxonomic level. The MIFRA is suitable in various risk assessment contexts for micro-organisms used in any commercial product. Copyright © 2018. Published by Elsevier Ltd.

Tracking blood products in blood centres using radio frequency identification: a comprehensive assessment.

PubMed

Davis, Rodeina; Geiger, Bradley; Gutierrez, Alfonso; Heaser, Julie; Veeramani, Dharmaraj

2009-07-01

Radio frequency identification (RFID) can be a key enabler for enhancing productivity and safety of the blood product supply chain. This article describes a systematic approach developed by the RFID Blood Consortium for a comprehensive feasibility and impact assessment of RFID application in blood centre operations. Our comprehensive assessment approach incorporates process-orientated and technological perspectives as well as impact analysis. Assessment of RFID-enabled process redesign is based on generic core processes derived from the three participating blood centres. The technological assessment includes RFID tag readability and performance evaluation, testing of temperature and biological effects of RF energy on blood products, and RFID system architecture design and standards. The scope of this article is limited to blood centre processes (from donation to manufacturing/distribution) for selected mainstream blood products (red blood cells and platelets). Radio frequency identification can help overcome a number of common challenges and process inefficiencies associated with identification and tracking of blood products. High frequency-based RFID technology performs adequately and safely for red blood cell and platelet products. Productivity and quality improvements in RFID-enabled blood centre processes can recoup investment cost in a 4-year payback period. Radio frequency identification application has significant process-orientated and technological implications. It is feasible and economically justifiable to incorporate RFID into blood centre processes.

Molecular Identification of Ectomycorrhizal Mycelium in Soil Horizons

PubMed Central

Landeweert, Renske; Leeflang, Paula; Kuyper, Thom W.; Hoffland, Ellis; Rosling, Anna; Wernars, Karel; Smit, Eric

2003-01-01

Molecular identification techniques based on total DNA extraction provide a unique tool for identification of mycelium in soil. Using molecular identification techniques, the ectomycorrhizal (EM) fungal community under coniferous vegetation was analyzed. Soil samples were taken at different depths from four horizons of a podzol profile. A basidiomycete-specific primer pair (ITS1F-ITS4B) was used to amplify fungal internal transcribed spacer (ITS) sequences from total DNA extracts of the soil horizons. Amplified basidiomycete DNA was cloned and sequenced, and a selection of the obtained clones was analyzed phylogenetically. Based on sequence similarity, the fungal clone sequences were sorted into 25 different fungal groups, or operational taxonomic units (OTUs). Out of 25 basidiomycete OTUs, 7 OTUs showed high nucleotide homology (≥99%) with known EM fungal sequences and 16 were found exclusively in the mineral soil. The taxonomic positions of six OTUs remained unclear. OTU sequences were compared to sequences from morphotyped EM root tips collected from the same sites. Of the 25 OTUs, 10 OTUs had ≥98% sequence similarity with these EM root tip sequences. The present study demonstrates the use of molecular techniques to identify EM hyphae in various soil types. This approach differs from the conventional method of EM root tip identification and provides a novel approach to examine EM fungal communities in soil. PMID:12514012

Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data.

PubMed

Perez, Miguel A; Sudweeks, Jeremy D; Sears, Edie; Antin, Jonathan; Lee, Suzanne; Hankey, Jonathan M; Dingus, Thomas A

2017-06-01

Understanding causal factors for traffic safety-critical events (e.g., crashes and near-crashes) is an important step in reducing their frequency and severity. Naturalistic driving data offers unparalleled insight into these factors, but requires identification of situations where crashes are present within large volumes of data. Sensitivity and specificity of these identification approaches are key to minimizing the resources required to validate candidate crash events. This investigation used data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) and the Canada Naturalistic Driving Study (CNDS) to develop and validate different kinematic thresholds that can be used to detect crash events. Results indicate that the sensitivity of many of these approaches can be quite low, but can be improved by selecting particular threshold levels based on detection performance. Additional improvements in these approaches are possible, and may involve leveraging combinations of different detection approaches, including advanced statistical techniques and artificial intelligence approaches, additional parameter modifications, and automation of validation processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid Scanning Structure-Activity Relationships in Combinatorial Data Sets: Identification of Activity Switches

PubMed Central

Medina-Franco, José L.; Edwards, Bruce S.; Pinilla, Clemencia; Appel, Jon R.; Giulianotti, Marc A.; Santos, Radleigh G.; Yongye, Austin B.; Sklar, Larry A.; Houghten, Richard A.

2013-01-01

We present a general approach to describe the structure-activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses Dual-Activity Difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries. PMID:23705689

Comparison between the basic least squares and the Bayesian approach for elastic constants identification

NASA Astrophysics Data System (ADS)

Gogu, C.; Haftka, R.; LeRiche, R.; Molimard, J.; Vautrin, A.; Sankar, B.

2008-11-01

The basic formulation of the least squares method, based on the L2 norm of the misfit, is still widely used today for identifying elastic material properties from experimental data. An alternative statistical approach is the Bayesian method. We seek here situations with significant difference between the material properties found by the two methods. For a simple three bar truss example we illustrate three such situations in which the Bayesian approach leads to more accurate results: different magnitude of the measurements, different uncertainty in the measurements and correlation among measurements. When all three effects add up, the Bayesian approach can have a large advantage. We then compared the two methods for identification of elastic constants from plate vibration natural frequencies.

Interdisciplinary Approach to Understanding Literary Texts

ERIC Educational Resources Information Center

Dossanova, Altynay Zh.; Ismakova, Bibissara S.; Tapanova, Saule E.; Ayupova, Gulbagira K.; Gotting, Valentina V.; Kaltayeva, Gulnar K.

2016-01-01

The primary purpose is the implementation of the interdisciplinary approach to understanding and the construction of integrative models of understanding literary texts. The interdisciplinary methodological paradigm of studying text understanding, based on the principles of various sciences facilitating the identification of the text understanding…

Temporary disaster debris management site identification using binomial cluster analysis and GIS.

PubMed

Grzeda, Stanislaw; Mazzuchi, Thomas A; Sarkani, Shahram

2014-04-01

An essential component of disaster planning and preparation is the identification and selection of temporary disaster debris management sites (DMS). However, since DMS identification is a complex process involving numerous variable constraints, many regional, county and municipal jurisdictions initiate this process during the post-disaster response and recovery phases, typically a period of severely stressed resources. Hence, a pre-disaster approach in identifying the most likely sites based on the number of locational constraints would significantly contribute to disaster debris management planning. As disasters vary in their nature, location and extent, an effective approach must facilitate scalability, flexibility and adaptability to variable local requirements, while also being generalisable to other regions and geographical extents. This study demonstrates the use of binomial cluster analysis in potential DMS identification in a case study conducted in Hamilton County, Indiana. © 2014 The Author(s). Disasters © Overseas Development Institute, 2014.

Mark-resight abundance estimation under incomplete identification of marked individuals

USGS Publications Warehouse

McClintock, Brett T.; Hill, Jason M.; Fritz, Lowell; Chumbley, Kathryn; Luxa, Katie; Diefenbach, Duane R.

2014-01-01

Often less expensive and less invasive than conventional mark–recapture, so-called 'mark-resight' methods are popular in the estimation of population abundance. These methods are most often applied when a subset of the population of interest is marked (naturally or artificially), and non-invasive sighting data can be simultaneously collected for both marked and unmarked individuals. However, it can often be difficult to identify marked individuals with certainty during resighting surveys, and incomplete identification of marked individuals is potentially a major source of bias in mark-resight abundance estimators. Previously proposed solutions are ad hoc and will tend to underperform unless marked individual identification rates are relatively high (>90%) or individual sighting heterogeneity is negligible.Based on a complete data likelihood, we present an approach that properly accounts for uncertainty in marked individual detection histories when incomplete identifications occur. The models allow for individual heterogeneity in detection, sampling with (e.g. Poisson) or without (e.g. Bernoulli) replacement, and an unknown number of marked individuals. Using a custom Markov chain Monte Carlo algorithm to facilitate Bayesian inference, we demonstrate these models using two example data sets and investigate their properties via simulation experiments.We estimate abundance for grassland sparrow populations in Pennsylvania, USA when sampling was conducted with replacement and the number of marked individuals was either known or unknown. To increase marked individual identification probabilities, extensive territory mapping was used to assign incomplete identifications to individuals based on location. Despite marked individual identification probabilities as low as 67% in the absence of this territorial mapping procedure, we generally found little return (or need) for this time-consuming investment when using our proposed approach. We also estimate rookery abundance from Alaskan Steller sea lion counts when sampling was conducted without replacement, the number of marked individuals was unknown, and individual heterogeneity was suspected as non-negligible.In terms of estimator performance, our simulation experiments and examples demonstrated advantages of our proposed approach over previous methods, particularly when marked individual identification probabilities are low and individual heterogeneity levels are high. Our methodology can also reduce field effort requirements for marked individual identification, thus, allowing potential investment into additional marking events or resighting surveys.

Methods and application of system identification in shock and vibration.

NASA Technical Reports Server (NTRS)

Collins, J. D.; Young, J. P.; Kiefling, L.

1972-01-01

A logical picture is presented of current useful system identification techniques in the shock and vibration field. A technology tree diagram is developed for the purpose of organizing and categorizing the widely varying approaches according to the fundamental nature of each. Specific examples of accomplished activity for each identification category are noted and discussed. To provide greater insight into the most current trends in the system identification field, a somewhat detailed description is presented of the essential features of a recently developed technique that is based on making the maximum use of all statistically known information about a system.

Direct maldi-tof mass spectrometry assay of blood culture broths for rapid identification of Candida species causing bloodstream infections: an observational study in two large microbiology laboratories.

PubMed

Spanu, Teresa; Posteraro, Brunella; Fiori, Barbara; D'Inzeo, Tiziana; Campoli, Serena; Ruggeri, Alberto; Tumbarello, Mario; Canu, Giulia; Trecarichi, Enrico Maria; Parisi, Gabriella; Tronci, Mirella; Sanguinetti, Maurizio; Fadda, Giovanni

2012-01-01

We evaluated the reliability of the Bruker Daltonik's MALDI Biotyper system in species-level identification of yeasts directly from blood culture bottles. Identification results were concordant with those of the conventional culture-based method for 95.9% of Candida albicans (187/195) and 86.5% of non-albicans Candida species (128/148). Results were available in 30 min (median), suggesting that this approach is a reliable, time-saving tool for routine identification of Candida species causing bloodstream infection.

Proteomic Identification and Quantification of S-glutathionylation in Mouse Macrophages Using Resin-Assisted Enrichment and Isobaric Labeling

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

Su, Dian; Gaffrey, Matthew J.; Guo, Jia

2014-02-11

Protein S-glutathionylation (SSG) is an important regulatory posttranslational modification of protein cysteine (Cys) thiol redox switches, yet the role of specific cysteine residues as targets of modification is poorly understood. We report a novel quantitative mass spectrometry (MS)-based proteomic method for site-specific identification and quantification of S-glutathionylation across different conditions. Briefly, this approach consists of initial blocking of free thiols by alkylation, selective reduction of glutathionylated thiols and enrichment using thiol affinity resins, followed by on-resin tryptic digestion and isobaric labeling with iTRAQ (isobaric tags for relative and absolute quantitation) for MS-based identification and quantification. The overall approach was validatedmore » by application to RAW 264.7 mouse macrophages treated with different doses of diamide to induce glutathionylation. A total of 1071 Cys-sites from 690 proteins were identified in response to diamide treatment, with ~90% of the sites displaying >2-fold increases in SSG-modification compared to controls.. This approach was extended to identify potential SSG modified Cys-sites in response to H2O2, an endogenous oxidant produced by activated macrophages and many pathophysiological stimuli. The results revealed 364 Cys-sites from 265 proteins that were sensitive to S-glutathionylation in response to H2O2 treatment. These proteins covered a range of molecular types and molecular functions with free radical scavenging, and cell death and survival included as the most significantly enriched functional categories. Overall the results demonstrate that our approach is effective for site-specific identification and quantification of S-glutathionylated proteins. The analytical strategy also provides a unique approach to determining the major pathways and cell processes most susceptible to glutathionylation at a proteome-wide scale.« less

Identification of material properties of orthotropic composite plate using experimental frequency response function data

NASA Astrophysics Data System (ADS)

Tam, Jun Hui; Ong, Zhi Chao; Ismail, Zubaidah; Ang, Bee Chin; Khoo, Shin Yee

2018-05-01

The demand for composite materials is increasing due to their great superiority in material properties, e.g., lightweight, high strength and high corrosion resistance. As a result, the invention of composite materials of diverse properties is becoming prevalent, and thus, leading to the development of material identification methods for composite materials. Conventional identification methods are destructive, time-consuming and costly. Therefore, an accurate identification approach is proposed to circumvent these drawbacks, involving the use of Frequency Response Function (FRF) error function defined by the correlation discrepancy between experimental and Finite-Element generated FRFs. A square E-glass epoxy composite plate is investigated under several different configurations of boundary conditions. It is notable that the experimental FRFs are used as the correlation reference, such that, during computation, the predicted FRFs are continuously updated with reference to the experimental FRFs until achieving a solution. The final identified elastic properties, namely in-plane elastic moduli, Ex and Ey, in-plane shear modulus, Gxy, and major Poisson's ratio, vxy of the composite plate are subsequently compared to the benchmark parameters as well as with those obtained using modal-based approach. As compared to the modal-based approach, the proposed method is found to have yielded relatively better results. This can be explained by the direct employment of raw data in the proposed method that avoids errors that might incur during the stage of modal extraction.

GingisKHAN™ protease cleavage allows a high-throughput antibody to Fab conversion enabling direct functional assessment during lead identification of human monoclonal and bispecific IgG1 antibodies

PubMed Central

Moelleken, Jörg; Gassner, Christian; Lingke, Sabine; Tomaschek, Simone; Tyshchuk, Oksana; Lorenz, Stefan; Mølhøj, Michael

2017-01-01

ABSTRACT The determination of the binding strength of immunoglobulins (IgGs) to targets can be influenced by avidity when the targets are soluble di- or multimeric proteins, or associated to cell surfaces, including surfaces introduced from heterogeneous assays. However, for the understanding of the contribution of a second drug-to-target binding site in molecular design, or for ranking of monovalent binders during lead identification, affinity-based assessment of the binding strength is required. Typically, monovalent binders like antigen-binding fragments (Fabs) are generated by proteolytic cleavage with papain, which often results in a combination of under- and over-digestion, and requires specific optimization and chromatographic purification of the desired Fabs. Alternatively, the Fabs are produced by recombinant approaches. Here, we report a lean approach for the functional assessment of human IgG1s during lead identification based on an in-solution digestion with the GingisKHAN™ protease, generating a homogenous pool of intact Fabs and Fcs and enabling direct assaying of the Fab in the digestion mixture. The digest with GingisKHAN™ is highly specific and quantitative, does not require much optimization, and the protease does not interfere with methods typically applied for lead identification, such as surface plasmon resonance or cell-based assays. GingisKHAN™ is highly suited to differentiate between affinity and avidity driven binding of human IgG1 monoclonal and bispecific antibodies during lead identification. PMID:28805498

Metabarcoding of marine nematodes – evaluation of reference datasets used in tree-based taxonomy assignment approach

PubMed Central

2016-01-01

Abstract Background Metabarcoding is becoming a common tool used to assess and compare diversity of organisms in environmental samples. Identification of OTUs is one of the critical steps in the process and several taxonomy assignment methods were proposed to accomplish this task. This publication evaluates the quality of reference datasets, alongside with several alignment and phylogeny inference methods used in one of the taxonomy assignment methods, called tree-based approach. This approach assigns anonymous OTUs to taxonomic categories based on relative placements of OTUs and reference sequences on the cladogram and support that these placements receive. New information In tree-based taxonomy assignment approach, reliable identification of anonymous OTUs is based on their placement in monophyletic and highly supported clades together with identified reference taxa. Therefore, it requires high quality reference dataset to be used. Resolution of phylogenetic trees is strongly affected by the presence of erroneous sequences as well as alignment and phylogeny inference methods used in the process. Two preparation steps are essential for the successful application of tree-based taxonomy assignment approach. Curated collections of genetic information do include erroneous sequences. These sequences have detrimental effect on the resolution of cladograms used in tree-based approach. They must be identified and excluded from the reference dataset beforehand. Various combinations of multiple sequence alignment and phylogeny inference methods provide cladograms with different topology and bootstrap support. These combinations of methods need to be tested in order to determine the one that gives highest resolution for the particular reference dataset. Completing the above mentioned preparation steps is expected to decrease the number of unassigned OTUs and thus improve the results of the tree-based taxonomy assignment approach. PMID:27932919

Metabarcoding of marine nematodes - evaluation of reference datasets used in tree-based taxonomy assignment approach.

PubMed

Holovachov, Oleksandr

2016-01-01

Metabarcoding is becoming a common tool used to assess and compare diversity of organisms in environmental samples. Identification of OTUs is one of the critical steps in the process and several taxonomy assignment methods were proposed to accomplish this task. This publication evaluates the quality of reference datasets, alongside with several alignment and phylogeny inference methods used in one of the taxonomy assignment methods, called tree-based approach. This approach assigns anonymous OTUs to taxonomic categories based on relative placements of OTUs and reference sequences on the cladogram and support that these placements receive. In tree-based taxonomy assignment approach, reliable identification of anonymous OTUs is based on their placement in monophyletic and highly supported clades together with identified reference taxa. Therefore, it requires high quality reference dataset to be used. Resolution of phylogenetic trees is strongly affected by the presence of erroneous sequences as well as alignment and phylogeny inference methods used in the process. Two preparation steps are essential for the successful application of tree-based taxonomy assignment approach. Curated collections of genetic information do include erroneous sequences. These sequences have detrimental effect on the resolution of cladograms used in tree-based approach. They must be identified and excluded from the reference dataset beforehand.Various combinations of multiple sequence alignment and phylogeny inference methods provide cladograms with different topology and bootstrap support. These combinations of methods need to be tested in order to determine the one that gives highest resolution for the particular reference dataset.Completing the above mentioned preparation steps is expected to decrease the number of unassigned OTUs and thus improve the results of the tree-based taxonomy assignment approach.

A Time-Space Domain Information Fusion Method for Specific Emitter Identification Based on Dempster-Shafer Evidence Theory.

PubMed

Jiang, Wen; Cao, Ying; Yang, Lin; He, Zichang

2017-08-28

Specific emitter identification plays an important role in contemporary military affairs. However, most of the existing specific emitter identification methods haven't taken into account the processing of uncertain information. Therefore, this paper proposes a time-space domain information fusion method based on Dempster-Shafer evidence theory, which has the ability to deal with uncertain information in the process of specific emitter identification. In this paper, radars will generate a group of evidence respectively based on the information they obtained, and our main task is to fuse the multiple groups of evidence to get a reasonable result. Within the framework of recursive centralized fusion model, the proposed method incorporates a correlation coefficient, which measures the relevance between evidence and a quantum mechanical approach, which is based on the parameters of radar itself. The simulation results of an illustrative example demonstrate that the proposed method can effectively deal with uncertain information and get a reasonable recognition result.

Drug Discovery for Neglected Diseases: Molecular Target-Based and Phenotypic Approaches

PubMed Central

2013-01-01

Drug discovery for neglected tropical diseases is carried out using both target-based and phenotypic approaches. In this paper, target-based approaches are discussed, with a particular focus on human African trypanosomiasis. Target-based drug discovery can be successful, but careful selection of targets is required. There are still very few fully validated drug targets in neglected diseases, and there is a high attrition rate in target-based drug discovery for these diseases. Phenotypic screening is a powerful method in both neglected and non-neglected diseases and has been very successfully used. Identification of molecular targets from phenotypic approaches can be a way to identify potential new drug targets. PMID:24015767

A unified framework for evaluating the risk of re-identification of text de-identification tools.

PubMed

Scaiano, Martin; Middleton, Grant; Arbuckle, Luk; Kolhatkar, Varada; Peyton, Liam; Dowling, Moira; Gipson, Debbie S; El Emam, Khaled

2016-10-01

It has become regular practice to de-identify unstructured medical text for use in research using automatic methods, the goal of which is to remove patient identifying information to minimize re-identification risk. The metrics commonly used to determine if these systems are performing well do not accurately reflect the risk of a patient being re-identified. We therefore developed a framework for measuring the risk of re-identification associated with textual data releases. We apply the proposed evaluation framework to a data set from the University of Michigan Medical School. Our risk assessment results are then compared with those that would be obtained using a typical contemporary micro-average evaluation of recall in order to illustrate the difference between the proposed evaluation framework and the current baseline method. We demonstrate how this framework compares against common measures of the re-identification risk associated with an automated text de-identification process. For the probability of re-identification using our evaluation framework we obtained a mean value for direct identifiers of 0.0074 and a mean value for quasi-identifiers of 0.0022. The 95% confidence interval for these estimates were below the relevant thresholds. The threshold for direct identifier risk was based on previously used approaches in the literature. The threshold for quasi-identifiers was determined based on the context of the data release following commonly used de-identification criteria for structured data. Our framework attempts to correct for poorly distributed evaluation corpora, accounts for the data release context, and avoids the often optimistic assumptions that are made using the more traditional evaluation approach. It therefore provides a more realistic estimate of the true probability of re-identification. This framework should be used as a basis for computing re-identification risk in order to more realistically evaluate future text de-identification tools. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Identification and induction of human, social, and cultural capitals through an experimental approach to stormwater management

EPA Science Inventory

Decentralized stormwater management is based on the dispersal of stormwater management practices (SWMP) throughout a watershed to manage stormwater runoff volume and potentially restore natural hydrologic processes. This approach to stormwater management is increasingly popular b...

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA.

PubMed

Lemons, Angela R; Lindsley, William G; Green, Brett J

2018-05-02

Traditional methods of identifying fungal exposures in occupational environments, such as culture and microscopy-based approaches, have several limitations that have resulted in the exclusion of many species. Advances in the field over the last two decades have led occupational health researchers to turn to molecular-based approaches for identifying fungal hazards. These methods have resulted in the detection of many species within indoor and occupational environments that have not been detected using traditional methods. This protocol details an approach for determining fungal diversity within air samples through genomic DNA extraction, amplification, sequencing, and taxonomic identification of fungal internal transcribed spacer (ITS) regions. ITS sequencing results in the detection of many fungal species that are either not detected or difficult to identify to species level using culture or microscopy. While these methods do not provide quantitative measures of fungal burden, they offer a new approach to hazard identification and can be used to determine overall species richness and diversity within an occupational environment.

A Risk-Based Approach to Variable Load Configuration Validation in Steam Sterilization: Application of PDA Technical Report 1 Load Equivalence Topic.

PubMed

Pavell, Anthony; Hughes, Keith A

2010-01-01

This article describes a method for achieving the load equivalence model, described in Parenteral Drug Association Technical Report 1, using a mass-based approach. The item and load bracketing approach allows for mixed equipment load size variation for operational flexibility along with decreased time to introduce new items to the operation. The article discusses the utilization of approximately 67 items/components (Table IV) identified for routine sterilization with varying quantities required weekly. The items were assessed for worst-case identification using four temperature-related criteria. The criteria were used to provide a data-based identification of worst-case items, and/or item equivalence, to carry forward into cycle validation using a variable load pattern. The mass approach to maximum load determination was used to bracket routine production use and allows for variable loading patterns. The result of the item mapping and load bracketing data is "a proven acceptable range" of sterilizing conditions including loading configuration and location. The application of these approaches, while initially more time/test-intensive than alternate approaches, provides a method of cycle validation with long-term benefit of ease of ongoing qualification, minimizing time and requirements for new equipment qualification for similar loads/use, and for rapid and rigorous assessment of new items for sterilization.

Machine printed text and handwriting identification in noisy document images.

PubMed

Zheng, Yefeng; Li, Huiping; Doermann, David

2004-03-01

In this paper, we address the problem of the identification of text in noisy document images. We are especially focused on segmenting and identifying between handwriting and machine printed text because: 1) Handwriting in a document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content and 2) the segmentation and recognition techniques requested for machine printed and handwritten text are significantly different. A novel aspect of our approach is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise and we further exploit context to refine the classification. A Markov Random Field-based (MRF) approach is used to model the geometrical structure of the printed text, handwriting, and noise to rectify misclassifications. Experimental results show that our approach is robust and can significantly improve page segmentation in noisy document collections.

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.

PubMed

Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza

2018-03-01

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images.

PubMed

Sidibé, Désiré; Sankar, Shrinivasan; Lemaître, Guillaume; Rastgoo, Mojdeh; Massich, Joan; Cheung, Carol Y; Tan, Gavin S W; Milea, Dan; Lamoureux, Ecosse; Wong, Tien Y; Mériaudeau, Fabrice

2017-02-01

This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

CATSI EDM: recent advances in the development and validation of a ruggedized passive standoff CWA sensor

NASA Astrophysics Data System (ADS)

Lavoie, Hugo; Thériault, Jean-Marc; Bouffard, François; Puckrin, Eldon; Turcotte, Caroline S.; Lacasse, Paul

2008-04-01

Defence Research and Development Canada (DRDC) - Valcartier is currently developing a ruggedized passive standoff sensor for the detection of chemical warfare agents (CWAs) based on differential Fourier-transform infrared (FTIR) radiometry. This system is referred to as the Compact ATmospheric Sounding Interferometer (CATSI) Engineering Development Model (EDM). The CATSI EDM sensor is based on the use of a double-beam FTIR spectrometer that is optimized for optical subtraction. A description of the customized sensor is given along with a discussion on the detection and identification approaches that have been developed. Preliminary results of validation from a number of laboratory measurements and open-air trials are analyzed to establish the capability of detection and identification of various toxic and non-toxic chemical vapor plumes. These results clearly demonstrate the capability of the passive differential radiometric approach for the standoff detection and identification of chemical vapors at distances up to a few kilometers from the sensor.

Detection and identification of alkylating agents by using a bioinspired "chemical nose".

PubMed

Hertzog-Ronen, Carmit; Borzin, Elena; Gerchikov, Yulia; Tessler, Nir; Eichen, Yoav

2009-10-12

Alkylating agents are simple and reactive molecules that are commonly used in many and diverse fields such as organic synthesis, medicine, and agriculture. Some highly reactive alkylating species are also being used as blister chemical-warfare agents. The detection and identification of alkylating agents is not a trivial issue because of their high reactivity and simple structure. Herein, we report on a new multispot luminescence-based approach to the detection and identification of alkylating agents. In order to demonstrate the potential of the approach, seven pi-conjugated oligomers and polymers bearing nucleophilic pyridine groups, 1-7, were adsorbed onto a solid support and exposed to vapors of alkylators 8-15. The alkylation-induced color-shift patterns of the seven-spot array allow clear discrimination of the different alkylators. The spots are sensitive to minute concentrations of alkylators and, because the detection is based on the formation of new covalent bonds, these spots saturate at about 50 ppb.

Spectrometric microbiological analyzer

NASA Astrophysics Data System (ADS)

Schlager, Kenneth J.; Meissner, Ken E.

1996-04-01

Currently, there are four general approaches to microbiological analysis, i.e., the detection, identification and quantification of micro-organisms: (1) Traditional culturing and staining procedures, metabolic fermentations and visual morphological characteristics; (2) Immunological approaches employing microbe-specific antibodies; (3) Biotechnical techniques employing DNA probes and related genetic engineering methods; and (4) Physical measurement techniques based on the biophysical properties of micro-organisms. This paper describes an instrumentation development in the fourth of the above categories, physical measurement, that uses a combination of fluorometric and light scatter spectra to detect and identify micro-organisms at the species level. A major advantage of this approach is the rapid turnaround possible in medical diagnostic or water testing applications. Fluorometric spectra serve to define the biochemical characteristics of the microbe, and light scatter spectra the size and shape morphology. Together, the two spectra define a 'fingerprint' for each species of microbe for detection, identification and quantification purposes. A prototype instrument has been developed and tested under NASA sponsorship based on fluorometric spectra alone. This instrument demonstrated identification and quantification capabilities at the species level. The paper reports on test results using this instrument, and the benefits of employing a combination of fluorometric and light scatter spectra.

Biometric identification based on novel frequency domain facial asymmetry measures

NASA Astrophysics Data System (ADS)

Mitra, Sinjini; Savvides, Marios; Vijaya Kumar, B. V. K.

2005-03-01

In the modern world, the ever-growing need to ensure a system's security has spurred the growth of the newly emerging technology of biometric identification. The present paper introduces a novel set of facial biometrics based on quantified facial asymmetry measures in the frequency domain. In particular, we show that these biometrics work well for face images showing expression variations and have the potential to do so in presence of illumination variations as well. A comparison of the recognition rates with those obtained from spatial domain asymmetry measures based on raw intensity values suggests that the frequency domain representation is more robust to intra-personal distortions and is a novel approach for performing biometric identification. In addition, some feature analysis based on statistical methods comparing the asymmetry measures across different individuals and across different expressions is presented.

Evaluation of trace analyte identification in complex matrices by low-resolution gas chromatography--Mass spectrometry through signal simulation.

PubMed

Bettencourt da Silva, Ricardo J N

2016-04-01

The identification of trace levels of compounds in complex matrices by conventional low-resolution gas chromatography hyphenated with mass spectrometry is based in the comparison of retention times and abundance ratios of characteristic mass spectrum fragments of analyte peaks from calibrators with sample peaks. Statistically sound criteria for the comparison of these parameters were developed based on the normal distribution of retention times and the simulation of possible non-normal distribution of correlated abundances ratios. The confidence level used to set the statistical maximum and minimum limits of parameters defines the true positive rates of identifications. The false positive rate of identification was estimated from worst-case signal noise models. The estimated true and false positive identifications rate from one retention time and two correlated ratios of three fragments abundances were combined using simple Bayes' statistics to estimate the probability of compound identification being correct designated examination uncertainty. Models of the variation of examination uncertainty with analyte quantity allowed the estimation of the Limit of Examination as the lowest quantity that produced "Extremely strong" evidences of compound presence. User friendly MS-Excel files are made available to allow the easy application of developed approach in routine and research laboratories. The developed approach was successfully applied to the identification of chlorpyrifos-methyl and malathion in QuEChERS method extracts of vegetables with high water content for which the estimated Limit of Examination is 0.14 mg kg(-1) and 0.23 mg kg(-1) respectively. Copyright © 2015 Elsevier B.V. All rights reserved.

Stochastic global identification of a bio-inspired self-sensing composite UAV wing via wind tunnel experiments

NASA Astrophysics Data System (ADS)

Kopsaftopoulos, Fotios; Nardari, Raphael; Li, Yu-Hung; Wang, Pengchuan; Chang, Fu-Kuo

2016-04-01

In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bioinspired self-sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self-sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real-time information on the coupled air flow-structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the "global" coupled airflow-structural dynamics of the wing and their correlation with dynamic utter and stall. The obtained results demonstrate the successful system-level integration and effectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of "fly-by-fee" UAVs.

An odor identification approach based on event-related pupil dilation and gaze focus.

PubMed

Aguillon-Hernandez, Nadia; Naudin, Marine; Roché, Laëtitia; Bonnet-Brilhault, Frédérique; Belzung, Catherine; Martineau, Joëlle; Atanasova, Boriana

2015-06-01

Olfactory disorders constitute a potential marker of many diseases and are considered valuable clues to the diagnosis and evaluation of progression for many disorders. The most commonly used test for the evaluation of impairments of olfactory identification requires the active participation of the subject, who must select the correct name of the perceived odor from a list. An alternative method is required because speech may be impaired or not yet learned in many patients. As odor identification is known to be facilitated by searching for visual clues, we aimed to develop an objective, vision-based approach for the evaluation of odor identification. We used an eye tracking method to quantify pupillary and ocular responses during the simultaneous presentation of olfactory and visual stimuli, in 39 healthy participants aged from 19 to 77years. Odor presentation triggered an increase in pupil dilation and gaze focus on the picture corresponding to the odor presented. These results suggest that odorant stimuli increase recruitment of the sympathetic system (as demonstrated by the reactivity of the pupil) and draw attention to the visual clue. These results validate the objectivity of this method. Copyright © 2015 Elsevier B.V. All rights reserved.

Automatic Identification of Character Types from Film Dialogs

PubMed Central

Skowron, Marcin; Trapp, Martin; Payr, Sabine; Trappl, Robert

2016-01-01

ABSTRACT We study the detection of character types from fictional dialog texts such as screenplays. As approaches based on the analysis of utterances’ linguistic properties are not sufficient to identify all fictional character types, we develop an integrative approach that complements linguistic analysis with interactive and communication characteristics, and show that it can improve the identification performance. The interactive characteristics of fictional characters are captured by the descriptive analysis of semantic graphs weighted by linguistic markers of expressivity and social role. For this approach, we introduce a new data set of action movie character types with their corresponding sequences of dialogs. The evaluation results demonstrate that the integrated approach outperforms baseline approaches on the presented data set. Comparative in-depth analysis of a single screenplay leads on to the discussion of possible limitations of this approach and to directions for future research. PMID:29118463

Identifying Critical Issues and Problems in Technology Education Using a Modified-Delphi Technique.

ERIC Educational Resources Information Center

Wicklein, Robert C.

1993-01-01

Critical issues for technology education (TE) identified by a 25-member Delphi panel were identification of the knowledge base, curriculum development approaches, interdisciplinary approaches, and teacher education reform. Problems identified included inadequate marketing/public relations, teacher shortage, lack of content consensus, and…

Environmental Barcoding: A Next-Generation Sequencing Approach for Biomonitoring Applications Using River Benthos

PubMed Central

Hajibabaei, Mehrdad; Shokralla, Shadi; Zhou, Xin; Singer, Gregory A. C.; Baird, Donald J.

2011-01-01

Timely and accurate biodiversity analysis poses an ongoing challenge for the success of biomonitoring programs. Morphology-based identification of bioindicator taxa is time consuming, and rarely supports species-level resolution especially for immature life stages. Much work has been done in the past decade to develop alternative approaches for biodiversity analysis using DNA sequence-based approaches such as molecular phylogenetics and DNA barcoding. On-going assembly of DNA barcode reference libraries will provide the basis for a DNA-based identification system. The use of recently introduced next-generation sequencing (NGS) approaches in biodiversity science has the potential to further extend the application of DNA information for routine biomonitoring applications to an unprecedented scale. Here we demonstrate the feasibility of using 454 massively parallel pyrosequencing for species-level analysis of freshwater benthic macroinvertebrate taxa commonly used for biomonitoring. We designed our experiments in order to directly compare morphology-based, Sanger sequencing DNA barcoding, and next-generation environmental barcoding approaches. Our results show the ability of 454 pyrosequencing of mini-barcodes to accurately identify all species with more than 1% abundance in the pooled mixture. Although the approach failed to identify 6 rare species in the mixture, the presence of sequences from 9 species that were not represented by individuals in the mixture provides evidence that DNA based analysis may yet provide a valuable approach in finding rare species in bulk environmental samples. We further demonstrate the application of the environmental barcoding approach by comparing benthic macroinvertebrates from an urban region to those obtained from a conservation area. Although considerable effort will be required to robustly optimize NGS tools to identify species from bulk environmental samples, our results indicate the potential of an environmental barcoding approach for biomonitoring programs. PMID:21533287

Reliable Detection of Herpes Simplex Virus Sequence Variation by High-Throughput Resequencing.

PubMed

Morse, Alison M; Calabro, Kaitlyn R; Fear, Justin M; Bloom, David C; McIntyre, Lauren M

2017-08-16

High-throughput sequencing (HTS) has resulted in data for a number of herpes simplex virus (HSV) laboratory strains and clinical isolates. The knowledge of these sequences has been critical for investigating viral pathogenicity. However, the assembly of complete herpesviral genomes, including HSV, is complicated due to the existence of large repeat regions and arrays of smaller reiterated sequences that are commonly found in these genomes. In addition, the inherent genetic variation in populations of isolates for viruses and other microorganisms presents an additional challenge to many existing HTS sequence assembly pipelines. Here, we evaluate two approaches for the identification of genetic variants in HSV1 strains using Illumina short read sequencing data. The first, a reference-based approach, identifies variants from reads aligned to a reference sequence and the second, a de novo assembly approach, identifies variants from reads aligned to de novo assembled consensus sequences. Of critical importance for both approaches is the reduction in the number of low complexity regions through the construction of a non-redundant reference genome. We compared variants identified in the two methods. Our results indicate that approximately 85% of variants are identified regardless of the approach. The reference-based approach to variant discovery captures an additional 15% representing variants divergent from the HSV1 reference possibly due to viral passage. Reference-based approaches are significantly less labor-intensive and identify variants across the genome where de novo assembly-based approaches are limited to regions where contigs have been successfully assembled. In addition, regions of poor quality assembly can lead to false variant identification in de novo consensus sequences. For viruses with a well-assembled reference genome, a reference-based approach is recommended.

The best of both worlds: A combined approach for analyzing microalgal diversity via metabarcoding and morphology-based methods

PubMed Central

Kahlert, Maria; Fink, Patrick

2017-01-01

An increasing number of studies use next generation sequencing (NGS) to analyze complex communities, but is the method sensitive enough when it comes to identification and quantification of species? We compared NGS with morphology-based identification methods in an analysis of microalgal (periphyton) communities. We conducted a mesocosm experiment in which we allowed two benthic grazer species to feed upon benthic biofilms, which resulted in altered periphyton communities. Morphology-based identification and 454 (Roche) pyrosequencing of the V4 region in the small ribosomal unit (18S) rDNA gene were used to investigate the community change caused by grazing. Both the NGS-based data and the morphology-based method detected a marked shift in the biofilm composition, though the two methods varied strongly in their abilities to detect and quantify specific taxa, and neither method was able to detect all species in the biofilms. For quantitative analysis, we therefore recommend using both metabarcoding and microscopic identification when assessing the community composition of eukaryotic microorganisms. PMID:28234997

Adaptive Modal Identification for Flutter Suppression Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Drew, Michael; Swei, Sean S.

2016-01-01

In this paper, we will develop an adaptive modal identification method for identifying the frequencies and damping of a flutter mode based on model-reference adaptive control (MRAC) and least-squares methods. The least-squares parameter estimation will achieve parameter convergence in the presence of persistent excitation whereas the MRAC parameter estimation does not guarantee parameter convergence. Two adaptive flutter suppression control approaches are developed: one based on MRAC and the other based on the least-squares method. The MRAC flutter suppression control is designed as an integral part of the parameter estimation where the feedback signal is used to estimate the modal information. On the other hand, the separation principle of control and estimation is applied to the least-squares method. The least-squares modal identification is used to perform parameter estimation.

Integrating DNA-based data into bioassessments improves our understanding of species distributions and species habitat relationships

EPA Science Inventory

The integration of DNA-based identification methods into bioassessments could result in more accurate representations of species distributions and species-habitat relationships. DNA-based approaches may be particularly informative for tracking the distributions of rare and/or inv...

Identification of glia phenotype modulators based on select glial function regulatory signaling pathways.

PubMed

Lee, Sun-Hwa; Suk, Kyoungho

2018-04-20

Despite the considerable social and economic burden on the healthcare system worldwide due to neurodegenerative diseases, there are currently few disease-altering treatment options for many of these conditions. Therefore, new approaches for both prevention and intervention for neurodegenerative diseases are urgently required. Microglia-mediated neurotoxicity is one of the pathologic hallmarks common to Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Current therapeutic approaches to target microglia-mediated neurotoxicity are focused on the identification of glia phenotype modulators (GPMs), which can inhibit the 'classical' pro-inflammatory and neurotoxic phenotypes of microglia. Areas covered: This article reviews selected microglial molecular targets and pathways involved in either neurotoxicity or neuroprotection and how their identification. Expert opinion: Microglial activation and their signaling pathways have important implications in the neurotoxicity and brain disorders. Pharmacological modulation of microglial activation may serve as a potential therapeutic approach for targeting microglia-mediated neurotoxicity. However, given that microglia change their activation states depending on the timing, stage, and severity of disease, and even aging, the appropriate window should be considered for this approach to be clinically effective. In the future, the identification of unknown extracellular signals and intracellular molecular switches that control phenotypic shifts may facilitate the development of novel therapeutics targeting microglia-mediated neurotoxicity.

A Fully Coupled Simulation and Optimization Scheme for the Design of 3D Powder Injection Molding Processes

NASA Astrophysics Data System (ADS)

Ayad, G.; Song, J.; Barriere, T.; Liu, B.; Gelin, J. C.

2007-05-01

The paper is concerned with optimization and parametric identification of Powder Injection Molding process that consists first in injection of powder mixture with polymer binder and then to the sintering of the resulting powders parts by solid state diffusion. In the first part, one describes an original methodology to optimize the injection stage based on the combination of Design Of Experiments and an adaptive Response Surface Modeling. Then the second part of the paper describes the identification strategy that one proposes for the sintering stage, using the identification of sintering parameters from dilatometer curves followed by the optimization of the sintering process. The proposed approaches are applied to the optimization for manufacturing of a ceramic femoral implant. One demonstrates that the proposed approach give satisfactory results.

Identification of species with DNA-based technology: current progress and challenges.

PubMed

Pereira, Filipe; Carneiro, João; Amorim, António

2008-01-01

One of the grand challenges of modern biology is to develop accurate and reliable technologies for a rapid screening of DNA sequence variation. This topic of research is of prime importance for the detection and identification of species in numerous fields of investigation, such as taxonomy, epidemiology, forensics, archaeology or ecology. Molecular identification is also central for the diagnosis, treatment and control of infections caused by different pathogens. In recent years, a variety of DNA-based approaches have been developed for the identification of individuals in a myriad of taxonomic groups. Here, we provide an overview of most commonly used assays, with emphasis on those based on DNA hybridizations, restriction enzymes, random PCR amplifications, species-specific PCR primers and DNA sequencing. A critical evaluation of all methods is presented focusing on their discriminatory power, reproducibility and user-friendliness. Having in mind that the current trend is to develop small-scale devices with a high-throughput capacity, we briefly review recent technological achievements for DNA analysis that offer great potentials for the identification of species.

Identification of the structure parameters using short-time non-stationary stochastic excitation

NASA Astrophysics Data System (ADS)

Jarczewska, Kamila; Koszela, Piotr; Śniady, PaweŁ; Korzec, Aleksandra

2011-07-01

In this paper, we propose an approach to the flexural stiffness or eigenvalue frequency identification of a linear structure using a non-stationary stochastic excitation process. The idea of the proposed approach lies within time domain input-output methods. The proposed method is based on transforming the dynamical problem into a static one by integrating the input and the output signals. The output signal is the structure reaction, i.e. structure displacements due to the short-time, irregular load of random type. The systems with single and multiple degrees of freedom, as well as continuous systems are considered.

Advances in environmental and occupational disorders in 2012.

PubMed

Peden, David B; Bush, Robert K

2013-03-01

The year 2012 produced a number of advances in our understanding of the effect of environmental factors on allergic diseases, identification of new allergens, immune mechanisms in host defense, factors involved in asthma severity, and therapeutic approaches. This review focuses on the articles published in the Journal in 2012 that enhance our knowledge base of environmental and occupational disorders. Identification of novel allergens can improve diagnostics, risk factor analysis can aid preventative approaches, and studies of genetic-environmental interactions and immune mechanisms will lead to better therapeutics. Copyright © 2013 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

GTA: a game theoretic approach to identifying cancer subnetwork markers.

PubMed

Farahmand, S; Goliaei, S; Ansari-Pour, N; Razaghi-Moghadam, Z

2016-03-01

The identification of genetic markers (e.g. genes, pathways and subnetworks) for cancer has been one of the most challenging research areas in recent years. A subset of these studies attempt to analyze genome-wide expression profiles to identify markers with high reliability and reusability across independent whole-transcriptome microarray datasets. Therefore, the functional relationships of genes are integrated with their expression data. However, for a more accurate representation of the functional relationships among genes, utilization of the protein-protein interaction network (PPIN) seems to be necessary. Herein, a novel game theoretic approach (GTA) is proposed for the identification of cancer subnetwork markers by integrating genome-wide expression profiles and PPIN. The GTA method was applied to three distinct whole-transcriptome breast cancer datasets to identify the subnetwork markers associated with metastasis. To evaluate the performance of our approach, the identified subnetwork markers were compared with gene-based, pathway-based and network-based markers. We show that GTA is not only capable of identifying robust metastatic markers, it also provides a higher classification performance. In addition, based on these GTA-based subnetworks, we identified a new bonafide candidate gene for breast cancer susceptibility.

Is Dysfunctional Use of the Mobile Phone a Behavioural Addiction? Confronting Symptom-Based Versus Process-Based Approaches.

PubMed

Billieux, Joël; Philippot, Pierre; Schmid, Cécile; Maurage, Pierre; De Mol, Jan; Van der Linden, Martial

2015-01-01

Dysfunctional use of the mobile phone has often been conceptualized as a 'behavioural addiction' that shares most features with drug addictions. In the current article, we challenge the clinical utility of the addiction model as applied to mobile phone overuse. We describe the case of a woman who overuses her mobile phone from two distinct approaches: (1) a symptom-based categorical approach inspired from the addiction model of dysfunctional mobile phone use and (2) a process-based approach resulting from an idiosyncratic clinical case conceptualization. In the case depicted here, the addiction model was shown to lead to standardized and non-relevant treatment, whereas the clinical case conceptualization allowed identification of specific psychological processes that can be targeted with specific, empirically based psychological interventions. This finding highlights that conceptualizing excessive behaviours (e.g., gambling and sex) within the addiction model can be a simplification of an individual's psychological functioning, offering only limited clinical relevance. The addiction model, applied to excessive behaviours (e.g., gambling, sex and Internet-related activities) may lead to non-relevant standardized treatments. Clinical case conceptualization allowed identification of specific psychological processes that can be targeted with specific empirically based psychological interventions. The biomedical model might lead to the simplification of an individual's psychological functioning with limited clinical relevance. Copyright © 2014 John Wiley & Sons, Ltd.

Hybrid Air Quality Modeling Approach for use in the Hear-road Exposures to Urban air pollutant Study(NEXUS)

EPA Science Inventory

The paper presents a hybrid air quality modeling approach and its application in NEXUS in order to provide spatial and temporally varying exposure estimates and identification of the mobile source contribution to the total pollutant exposure. Model-based exposure metrics, associa...

System identification using Nuclear Norm & Tabu Search optimization

NASA Astrophysics Data System (ADS)

Ahmed, Asif A.; Schoen, Marco P.; Bosworth, Ken W.

2018-01-01

In recent years, subspace System Identification (SI) algorithms have seen increased research, stemming from advanced minimization methods being applied to the Nuclear Norm (NN) approach in system identification. These minimization algorithms are based on hard computing methodologies. To the authors’ knowledge, as of now, there has been no work reported that utilizes soft computing algorithms to address the minimization problem within the nuclear norm SI framework. A linear, time-invariant, discrete time system is used in this work as the basic model for characterizing a dynamical system to be identified. The main objective is to extract a mathematical model from collected experimental input-output data. Hankel matrices are constructed from experimental data, and the extended observability matrix is employed to define an estimated output of the system. This estimated output and the actual - measured - output are utilized to construct a minimization problem. An embedded rank measure assures minimum state realization outcomes. Current NN-SI algorithms employ hard computing algorithms for minimization. In this work, we propose a simple Tabu Search (TS) algorithm for minimization. TS algorithm based SI is compared with the iterative Alternating Direction Method of Multipliers (ADMM) line search optimization based NN-SI. For comparison, several different benchmark system identification problems are solved by both approaches. Results show improved performance of the proposed SI-TS algorithm compared to the NN-SI ADMM algorithm.

Identification of Diatraea spp. (Lepidoptera: Crambidae) based on cytochrome oxidase II.

PubMed

Barrera, Gloria Patricia; Villamizar, Laura Fernanda; Espinel, Carlos; Quintero, Edgar Mauricio; Belaich, Mariano Nicolás; Toloza, Deisy Liseth; Ghiringhelli, Pablo Daniel; Vargas, Germán

2017-01-01

Diatraea spp. (Lepidoptera: Crambidae) are a group of insects that are agriculture pests in many economically relevant crops such as sugarcane, sorghum, corn and rice. Recognized species for this genus respond differentially to natural enemies used in their biological control, emphasizing the importance of species in a regional approach. Currently, identification is based on the male genitalia. However, the availability of specimens collected from field and subjectivity based on the character recognition can seriously hamper species identification, and therefore result in inadequate pest management. To overcome this, individuals of Diatraea spp. preliminarily classified male genitalia and obtained from reared conditions and the field (both derived from natural populations occurring in Colombia) were analyzed using genitalic morphometry and molecular biology specifically using a fragment of the cytochrome oxidase subunit II (CO II) mitochondrial gene. Although morphometric analysis did not show any overriding results regarding genitalia morphology, the bioinformatics analyses of CO II sequences resulted in an adequate classification of the individuals within the recognized species. It also, revealed that the occurrence of clades associated with geographical distribution may be associated with cryptic species. The latter was also confirmed by a Single-Strand Conformation Polymorphism (SSCP) methodology evaluating the same fragment of CO II. This experimental approach allows properly recognizing each species and in consequence is proposed as an effective tool in Diatraea species identification.

DNA-based identification of invasive alien species in relation to Canadian federal policy and law, and the basis of rapid-response management.

PubMed

Thomas, Vernon G; Hanner, Robert H; Borisenko, Alex V

2016-11-01

Managing invasive alien species in Canada requires reliable taxonomic identification as the basis of rapid-response management. This can be challenging, especially when organisms are small and lack morphological diagnostic features. DNA-based techniques, such as DNA barcoding, offer a reliable, rapid, and inexpensive toolkit for taxonomic identification of individual or bulk samples, forensic remains, and even environmental DNA. Well suited for this requirement, they could be more broadly deployed and incorporated into the operating policy and practices of Canadian federal departments and should be authorized under these agencies' articles of law. These include Fisheries and Oceans Canada, Canadian Food Inspection Agency, Transport Canada, Environment Canada, Parks Canada, and Health Canada. These efforts should be harmonized with the appropriate provisions of provincial jurisdictions, for example, the Ontario Invasive Species Act. This approach necessitates that a network of accredited, certified laboratories exists, and that updated DNA reference libraries are readily accessible. Harmonizing this approach is vital among Canadian federal agencies, and between the federal and provincial levels of government. Canadian policy and law must also be harmonized with that of the USA when detecting, and responding to, invasive species in contiguous lands and waters. Creating capacity in legislation for use of DNA-based identifications brings the authority to fund, train, deploy, and certify staff, and to refine further developments in this molecular technology.

Identification of Diatraea spp. (Lepidoptera: Crambidae) based on cytochrome oxidase II

PubMed Central

Villamizar, Laura Fernanda; Espinel, Carlos; Quintero, Edgar Mauricio; Belaich, Mariano Nicolás; Toloza, Deisy Liseth

2017-01-01

Diatraea spp. (Lepidoptera: Crambidae) are a group of insects that are agriculture pests in many economically relevant crops such as sugarcane, sorghum, corn and rice. Recognized species for this genus respond differentially to natural enemies used in their biological control, emphasizing the importance of species in a regional approach. Currently, identification is based on the male genitalia. However, the availability of specimens collected from field and subjectivity based on the character recognition can seriously hamper species identification, and therefore result in inadequate pest management. To overcome this, individuals of Diatraea spp. preliminarily classified male genitalia and obtained from reared conditions and the field (both derived from natural populations occurring in Colombia) were analyzed using genitalic morphometry and molecular biology specifically using a fragment of the cytochrome oxidase subunit II (CO II) mitochondrial gene. Although morphometric analysis did not show any overriding results regarding genitalia morphology, the bioinformatics analyses of CO II sequences resulted in an adequate classification of the individuals within the recognized species. It also, revealed that the occurrence of clades associated with geographical distribution may be associated with cryptic species. The latter was also confirmed by a Single-Strand Conformation Polymorphism (SSCP) methodology evaluating the same fragment of CO II. This experimental approach allows properly recognizing each species and in consequence is proposed as an effective tool in Diatraea species identification. PMID:28873431

matK-QR classifier: a patterns based approach for plant species identification.

PubMed

More, Ravi Prabhakar; Mane, Rupali Chandrashekhar; Purohit, Hemant J

2016-01-01

DNA barcoding is widely used and most efficient approach that facilitates rapid and accurate identification of plant species based on the short standardized segment of the genome. The nucleotide sequences of maturaseK ( matK ) and ribulose-1, 5-bisphosphate carboxylase ( rbcL ) marker loci are commonly used in plant species identification. Here, we present a new and highly efficient approach for identifying a unique set of discriminating nucleotide patterns to generate a signature (i.e. regular expression) for plant species identification. In order to generate molecular signatures, we used matK and rbcL loci datasets, which encompass 125 plant species in 52 genera reported by the CBOL plant working group. Initially, we performed Multiple Sequence Alignment (MSA) of all species followed by Position Specific Scoring Matrix (PSSM) for both loci to achieve a percentage of discrimination among species. Further, we detected Discriminating Patterns (DP) at genus and species level using PSSM for the matK dataset. Combining DP and consecutive pattern distances, we generated molecular signatures for each species. Finally, we performed a comparative assessment of these signatures with the existing methods including BLASTn, Support Vector Machines (SVM), Jrip-RIPPER, J48 (C4.5 algorithm), and the Naïve Bayes (NB) methods against NCBI-GenBank matK dataset. Due to the higher discrimination success obtained with the matK as compared to the rbcL , we selected matK gene for signature generation. We generated signatures for 60 species based on identified discriminating patterns at genus and species level. Our comparative assessment results suggest that a total of 46 out of 60 species could be correctly identified using generated signatures, followed by BLASTn (34 species), SVM (18 species), C4.5 (7 species), NB (4 species) and RIPPER (3 species) methods As a final outcome of this study, we converted signatures into QR codes and developed a software matK -QR Classifier (http://www.neeri.res.in/matk_classifier/index.htm), which search signatures in the query matK gene sequences and predict corresponding plant species. This novel approach of employing pattern-based signatures opens new avenues for the classification of species. In addition to existing methods, we believe that matK -QR Classifier would be a valuable tool for molecular taxonomists enabling precise identification of plant species.

Conventional liquid chromatography/triple quadrupole mass spectrometer-based metabolite identification and semi-quantitative estimation approach in the investigation of dabigatran etexilate in vitro metabolism

PubMed Central

Hu, Zhe-Yi; Parker, Robert B.; Herring, Vanessa L.; Laizure, S. Casey

2012-01-01

Dabigatran etexilate (DABE) is an oral prodrug that is rapidly converted by esterases to dabigatran (DAB), a direct inhibitor of thrombin. To elucidate the esterase-mediated metabolic pathway of DABE, a high-performance liquid chromatography/mass spectrometer (LC-MS/MS)-based metabolite identification and semi-quantitative estimation approach was developed. To overcome the poor full-scan sensitivity of conventional triple quadrupole mass spectrometry, precursor-product ion pairs were predicted, to search for the potential in vitro metabolites. The detected metabolites were confirmed by the product ion scan. A dilution method was introduced to evaluate the matrix effects of tentatively identified metabolites without chemical standards. Quantitative information on detected metabolites was obtained using ‘metabolite standards’ generated from incubation samples that contain a high concentration of metabolite in combination with a correction factor for mass spectrometry response. Two in vitro metabolites of DABE (M1 and M2) were identified, and quantified by the semi-quantitative estimation approach. It is noteworthy that CES1 convert DABE to M1 while CES2 mediates the conversion of DABE to M2. M1 (or M2) was further metabolized to DAB by CES2 (or CES1). The approach presented here provides a solution to a bioanalytical need for fast identification and semi-quantitative estimation of CES metabolites in preclinical samples. PMID:23239178

Leadership competencies in the context of health services.

PubMed

Jahrami, Haitham; Marnoch, Gordon; Gray, Ann Marie

2008-05-01

In a rapidly changing health-care environment, clinicians are increasingly called upon to assume complex leadership responsibilities. The research was undertaken to develop an understanding of the limits to the conceptual and methodological basis of leadership competency modelling in health services context. Data were collected from all of the clinicians in a Psychiatric Hospital, Bahrain using a researcher-developed questionnaire. Data were gathered to critically assess the validity of the competency-based approach to leadership on the basis of subjects' capacity to discriminate in terms of importance and accomplishment between the items featured in a research tool containing a comprehensive list of 124 leadership competencies. The results of the analyses indicate a weak identification with the competencies in the sense of revealing low levels of discriminatory sophistication on the part of subjects. The study design was limited to participants working in single hospital; therefore, the conclusions made cannot yet be regarded categorically as generalizable. Leadership selection, development and education activities may not achieve their ultimate outcomes due to the subject identification problem associated with the competence approach. It might be necessary to reconsider the efficiency of human resource activities that rely solely on the competency approach. The conceptual basis of leadership competence in health services has been previously neglected. This research casts doubt on competency approaches to leadership if based on subject identification with pre-defined items.

Type I Error Inflation in DIF Identification with Mantel-Haenszel: An Explanation and a Solution

ERIC Educational Resources Information Center

Magis, David; De Boeck, Paul

2014-01-01

It is known that sum score-based methods for the identification of differential item functioning (DIF), such as the Mantel-Haenszel (MH) approach, can be affected by Type I error inflation in the absence of any DIF effect. This may happen when the items differ in discrimination and when there is item impact. On the other hand, outlier DIF methods…

Security and matching of partial fingerprint recognition systems

NASA Astrophysics Data System (ADS)

Jea, Tsai-Yang; Chavan, Viraj S.; Govindaraju, Venu; Schneider, John K.

2004-08-01

Despite advances in fingerprint identification techniques, matching incomplete or partial fingerprints still poses a difficult challenge. While the introduction of compact silicon chip-based sensors that capture only a part of the fingerprint area have made this problem important from a commercial perspective, there is also considerable interest on the topic for processing partial and latent fingerprints obtained at crime scenes. Attempts to match partial fingerprints using singular ridge structures-based alignment techniques fail when the partial print does not include such structures (e.g., core or delta). We present a multi-path fingerprint matching approach that utilizes localized secondary features derived using only the relative information of minutiae. Since the minutia-based fingerprint representation, is an ANSI-NIST standard, our approach has the advantage of being directly applicable to already existing databases. We also analyze the vulnerability of partial fingerprint identification systems to brute force attacks. The described matching approach has been tested on one of FVC2002"s DB1 database11. The experimental results show that our approach achieves an equal error rate of 1.25% and a total error rate of 1.8% (with FAR at 0.2% and FRR at 1.6%).

A knowledge-based, concept-oriented view generation system for clinical data.

PubMed

Zeng, Q; Cimino, J J

2001-04-01

Information overload is a well-known problem for clinicians who must review large amounts of data in patient records. Concept-oriented views, which organize patient data around clinical concepts such as diagnostic strategies and therapeutic goals, may offer a solution to the problem of information overload. However, although concept-oriented views are desirable, they are difficult to create and maintain. We have developed a general-purpose, knowledge-based approach to the generation of concept-oriented views and have developed a system to test our approach. The system creates concept-oriented views through automated identification of relevant patient data. The knowledge in the system is represented by both a semantic network and rules. The key relevant data identification function is accomplished by a rule-based traversal of the semantic network. This paper focuses on the design and implementation of the system; an evaluation of the system is reported separately.

Application of MALDI-TOF MS for the Identification of Food Borne Bacteria

PubMed Central

Pavlovic, Melanie; Huber, Ingrid; Konrad, Regina; Busch, Ulrich

2013-01-01

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has recently emerged as a powerful tool for the routine identification of clinical isolates. MALDI-TOF MS based identification of bacteria has been shown to be more rapid, accurate and cost-efficient than conventional phenotypic techniques or molecular methods. Rapid and reliable identification of food-associated bacteria is also of crucial importance for food processing and product quality. This review is concerned with the applicability of MALDI-TOF MS for routine identification of foodborne bacteria taking the specific requirements of food microbiological laboratories and the food industry into account. The current state of knowledge including recent findings and new approaches are discussed. PMID:24358065

An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems: ADAPTIVE GAUSSIAN PROCESS-BASED INVERSION

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

Zhang, Jiangjiang; Li, Weixuan; Zeng, Lingzao

Surrogate models are commonly used in Bayesian approaches such as Markov Chain Monte Carlo (MCMC) to avoid repetitive CPU-demanding model evaluations. However, the approximation error of a surrogate may lead to biased estimations of the posterior distribution. This bias can be corrected by constructing a very accurate surrogate or implementing MCMC in a two-stage manner. Since the two-stage MCMC requires extra original model evaluations, the computational cost is still high. If the information of measurement is incorporated, a locally accurate approximation of the original model can be adaptively constructed with low computational cost. Based on this idea, we propose amore » Gaussian process (GP) surrogate-based Bayesian experimental design and parameter estimation approach for groundwater contaminant source identification problems. A major advantage of the GP surrogate is that it provides a convenient estimation of the approximation error, which can be incorporated in the Bayesian formula to avoid over-confident estimation of the posterior distribution. The proposed approach is tested with a numerical case study. Without sacrificing the estimation accuracy, the new approach achieves about 200 times of speed-up compared to our previous work using two-stage MCMC.« less

Methods for parameter identification in oscillatory networks and application to cortical and thalamic 600 Hz activity.

PubMed

Leistritz, L; Suesse, T; Haueisen, J; Hilgenfeld, B; Witte, H

2006-01-01

Directed information transfer in the human brain occurs presumably by oscillations. As of yet, most approaches for the analysis of these oscillations are based on time-frequency or coherence analysis. The present work concerns the modeling of cortical 600 Hz oscillations, localized within the Brodmann Areas 3b and 1 after stimulation of the nervus medianus, by means of coupled differential equations. This approach leads to the so-called parameter identification problem, where based on a given data set, a set of unknown parameters of a system of ordinary differential equations is determined by special optimization procedures. Some suitable algorithms for this task are presented in this paper. Finally an oscillatory network model is optimally fitted to the data taken from ten volunteers.

Hyper sausage neuron: Recognition of transgenic sugar-beet based on terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Jianjun; Li, Zhi; Hu, Fangrong; Chen, Tao; Du, Yong; Xin, Haitao

2015-01-01

This paper presents a novel approach for identification of terahertz (THz) spectral of genetically modified organisms (GMOs) based on Hyper Sausage Neuron (HSN), and THz transmittance spectra of some typical transgenic sugar-beet samples are investigated to demonstrate its feasibility. Principal component analysis (PCA) is applied to extract features of the spectrum data, and instead of the original spectrum data, the feature signals are fed into the HSN pattern recognition, a new multiple weights neural network (MWNN). The experimental result shows that the HSN model not only can correctly classify different types of transgenic sugar-beets, but also can reject identity non similar samples in the same type. The proposed approach provides a new effective method for detection and identification of GMOs by using THz spectroscopy.

Identification of Transgenic Organisms Based on Terahertz Spectroscopy and Hyper Sausage Neuron

NASA Astrophysics Data System (ADS)

Liu, J.; Li, Zh.; Hu, F.; Chen, T.; Du, Y.; Xin, H.

2015-03-01

This paper presents a novel approach for identifi cation of terahertz (THz) spectra of genetically modifi ed organisms (GMOs) based on hyper sausage neuron (HSN), and THz transmittance spectra of some typical transgenic sugarbeet samples are investigated to demonstrate its feasibility. Principal component analysis (PCA) is applied to extract features of the spectrum data, and instead of the original spectrum data, the feature signals are fed into the HSN pattern recognition, a new multiple weights neural network (MWNN). The experimental result shows that the HSN model not only can correctly classify different types of transgenic sugar-beets, but also can reject nonsimilar samples of the same type. The proposed approach provides a new effective method for detection and identification of genetically modified organisms by using THz spectroscopy.

Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network.

PubMed

Bobin, C; Bichler, O; Lourenço, V; Thiam, C; Thévenin, M

2016-03-01

Portal radiation monitors dedicated to the prevention of illegal traffic of nuclear materials at international borders need to deliver as fast as possible a radionuclide identification of a potential radiological threat. Spectrometry techniques applied to identify the radionuclides contributing to γ-emitter mixtures are usually performed using off-line spectrum analysis. As an alternative to these usual methods, a real-time processing based on an artificial neural network and Bayes' rule is proposed for fast radionuclide identification. The validation of this real-time approach was carried out using γ-emitter spectra ((241)Am, (133)Ba, (207)Bi, (60)Co, (137)Cs) obtained with a high-efficiency well-type NaI(Tl). The first tests showed that the proposed algorithm enables a fast identification of each γ-emitting radionuclide using the information given by the whole spectrum. Based on an iterative process, the on-line analysis only needs low-statistics spectra without energy calibration to identify the nature of a radiological threat. Copyright © 2015 Elsevier Ltd. All rights reserved.

Use of 16S rRNA gene for identification of a broad range of clinically relevant bacterial pathogens

DOE PAGES

Srinivasan, Ramya; Karaoz, Ulas; Volegova, Marina; ...

2015-02-06

According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n =more » 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci.« less

Use of 16S rRNA Gene for Identification of a Broad Range of Clinically Relevant Bacterial Pathogens

PubMed Central

Srinivasan, Ramya; Karaoz, Ulas; Volegova, Marina; MacKichan, Joanna; Kato-Maeda, Midori; Miller, Steve; Nadarajan, Rohan; Brodie, Eoin L.; Lynch, Susan V.

2015-01-01

According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n = 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci. PMID:25658760

Structure Identification Using High Resolution Mass ...

EPA Pesticide Factsheets

The iCSS CompTox Dashboard is a publicly accessible dashboard provided by the National Center for Computation Toxicology at the US-EPA. It serves a number of purposes, including providing a chemistry database underpinning many of our public-facing projects (e.g. ToxCast and ExpoCast). The available data and searches provide a valuable path to structure identification using mass spectrometry as the source data. With an underlying database of over 720,000 chemicals, the dashboard has already been used to assist in identifying chemicals present in house dust. This poster reviews the benefits of the EPA’s platform and underlying algorithms used for the purpose of compound identification using high-resolution mass spectrometry data. Standard approaches for both mass and formula lookup are available but the dashboard delivers a novel approach for hit ranking based on functional use of the chemicals. The focus on high-quality data, novel ranking approaches and integration to other resources of value to mass spectrometrists makes the CompTox Dashboard a valuable resource for the identification of environmental chemicals. This abstract does not reflect U.S. EPA policy poster presented at the Eastern Analytical Symposium (EAS) held in Somerset, NJ

Approach to risk identification in undifferentiated mental disorders

PubMed Central

Silveira, José; Rockman, Patricia; Fulford, Casey; Hunter, Jon

2016-01-01

Abstract Objective To provide primary care physicians with a novel approach to risk identification and related clinical decision making in the management of undifferentiated mental disorders. Sources of information We conducted a review of the literature in PubMed, CINAHL, PsycINFO, and Google Scholar using the search terms diagnostic uncertainty, diagnosis, risk identification, risk assessment/methods, risk, risk factors, risk management/methods, cognitive biases and psychiatry, decision making, mental disorders/diagnosis, clinical competence, evidence-based medicine, interviews as topic, psychiatry/education, psychiatry/methods, documentation/methods, forensic psychiatry/education, forensic psychiatry/methods, mental disorders/classification, mental disorders/psychology, violence/prevention and control, and violence/psychology. Main message Mental disorders are a large component of practice in primary care and often present in an undifferentiated manner, remaining so for prolonged periods. The challenging search for a diagnosis can divert attention from risk identification, as diagnosis is commonly presumed to be necessary before treatment can begin. This might inadvertently contribute to preventable adverse events. Focusing on salient aspects of the patient presentation related to risk should be prioritized. This article presents a novel approach to organizing patient information to assist risk identification and decision making in the management of patients with undifferentiated mental disorders. Conclusion A structured approach can help physicians to manage the clinical uncertainty common to risk identification in patients with mental disorders and cope with the common anxiety and cognitive biases that affect priorities in risk-related decision making. By focusing on risk, functional impairments, and related symptoms using a novel framework, physicians can meet their patients’ immediate needs while continuing the search for diagnostic clarity and long-term treatment. PMID:27965330

Approach to risk identification in undifferentiated mental disorders.

PubMed

Silveira, José; Rockman, Patricia; Fulford, Casey; Hunter, Jon

2016-12-01

To provide primary care physicians with a novel approach to risk identification and related clinical decision making in the management of undifferentiated mental disorders. We conducted a review of the literature in PubMed, CINAHL, PsycINFO, and Google Scholar using the search terms diagnostic uncertainty, diagnosis, risk identification, risk assessment/methods, risk, risk factors, risk management/methods, cognitive biases and psychiatry, decision making, mental disorders/diagnosis, clinical competence, evidence-based medicine, interviews as topic, psychiatry/education, psychiatry/methods, documentation/methods, forensic psychiatry/education, forensic psychiatry/methods, mental disorders/classification, mental disorders/psychology, violence/prevention and control, and violence/psychology. Mental disorders are a large component of practice in primary care and often present in an undifferentiated manner, remaining so for prolonged periods. The challenging search for a diagnosis can divert attention from risk identification, as diagnosis is commonly presumed to be necessary before treatment can begin. This might inadvertently contribute to preventable adverse events. Focusing on salient aspects of the patient presentation related to risk should be prioritized. This article presents a novel approach to organizing patient information to assist risk identification and decision making in the management of patients with undifferentiated mental disorders. A structured approach can help physicians to manage the clinical uncertainty common to risk identification in patients with mental disorders and cope with the common anxiety and cognitive biases that affect priorities in risk-related decision making. By focusing on risk, functional impairments, and related symptoms using a novel framework, physicians can meet their patients' immediate needs while continuing the search for diagnostic clarity and long-term treatment. Copyright© the College of Family Physicians of Canada.

An intercomparison of artificial intelligence approaches for polar scene identification

NASA Technical Reports Server (NTRS)

Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

1993-01-01

The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

Identification and quantitation of semi-crystalline microplastics using image analysis and differential scanning calorimetry.

PubMed

Rodríguez Chialanza, Mauricio; Sierra, Ignacio; Pérez Parada, Andrés; Fornaro, Laura

2018-06-01

There are several techniques used to analyze microplastics. These are often based on a combination of visual and spectroscopic techniques. Here we introduce an alternative workflow for identification and mass quantitation through a combination of optical microscopy with image analysis (IA) and differential scanning calorimetry (DSC). We studied four synthetic polymers with environmental concern: low and high density polyethylene (LDPE and HDPE, respectively), polypropylene (PP), and polyethylene terephthalate (PET). Selected experiments were conducted to investigate (i) particle characterization and counting procedures based on image analysis with open-source software, (ii) chemical identification of microplastics based on DSC signal processing, (iii) dependence of particle size on DSC signal, and (iv) quantitation of microplastics mass based on DSC signal. We describe the potential and limitations of these techniques to increase reliability for microplastic analysis. Particle size demonstrated to have particular incidence in the qualitative and quantitative performance of DSC signals. Both, identification (based on characteristic onset temperature) and mass quantitation (based on heat flow) showed to be affected by particle size. As a result, a proper sample treatment which includes sieving of suspended particles is particularly required for this analytical approach.

Metabolite identification through multiple kernel learning on fragmentation trees.

PubMed

Shen, Huibin; Dührkop, Kai; Böcker, Sebastian; Rousu, Juho

2014-06-15

Metabolite identification from tandem mass spectrometric data is a key task in metabolomics. Various computational methods have been proposed for the identification of metabolites from tandem mass spectra. Fragmentation tree methods explore the space of possible ways in which the metabolite can fragment, and base the metabolite identification on scoring of these fragmentation trees. Machine learning methods have been used to map mass spectra to molecular fingerprints; predicted fingerprints, in turn, can be used to score candidate molecular structures. Here, we combine fragmentation tree computations with kernel-based machine learning to predict molecular fingerprints and identify molecular structures. We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches. Experiments on two large reference datasets show that the new methods significantly improve molecular fingerprint prediction accuracy. These improvements result in better metabolite identification, doubling the number of metabolites ranked at the top position of the candidates list. © The Author 2014. Published by Oxford University Press.

Full-envelope aerodynamic modeling of the Harrier aircraft

NASA Technical Reports Server (NTRS)

Mcnally, B. David

1986-01-01

A project to identify a full-envelope model of the YAV-8B Harrier using flight-test and parameter identification techniques is described. As part of the research in advanced control and display concepts for V/STOL aircraft, a full-envelope aerodynamic model of the Harrier is identified, using mathematical model structures and parameter identification methods. A global-polynomial model structure is also used as a basis for the identification of the YAV-8B aerodynamic model. State estimation methods are used to ensure flight data consistency prior to parameter identification.Equation-error methods are used to identify model parameters. A fixed-base simulator is used extensively to develop flight test procedures and to validate parameter identification software. Using simple flight maneuvers, a simulated data set was created covering the YAV-8B flight envelope from about 0.3 to 0.7 Mach and about -5 to 15 deg angle of attack. A singular value decomposition implementation of the equation-error approach produced good parameter estimates based on this simulated data set.

CASTE (Course Assembly System and Tutorial Environment) and CVI: (Combat Vehicle Identification) A First Application of an Intelligent Tutorial System to Combat Vehicle Identification.

DTIC Science & Technology

1984-09-01

based training systems and hence to realize an embedded trainer that is both intelligent and effective . The o(Continued) DO,; FOAM AM 71 1ឹ...Performance Effectiveness and Simulation Approved for public releate; dlitribution unlimited iii &a3laAfc*ia £&&etaL* ■’—’,£-«.■£./■.,’-f...oriented approaches to computer-based training systems and hence realise an embedded trainer that is both intelli- gent and effective . To this end

DNA barcoding for the identification of sand fly species (Diptera, Psychodidae, Phlebotominae) in Colombia.

PubMed

Contreras Gutiérrez, María Angélica; Vivero, Rafael J; Vélez, Iván D; Porter, Charles H; Uribe, Sandra

2014-01-01

Sand flies include a group of insects that are of medical importance and that vary in geographic distribution, ecology, and pathogen transmission. Approximately 163 species of sand flies have been reported in Colombia. Surveillance of the presence of sand fly species and the actualization of species distribution are important for predicting risks for and monitoring the expansion of diseases which sand flies can transmit. Currently, the identification of phlebotomine sand flies is based on morphological characters. However, morphological identification requires considerable skills and taxonomic expertise. In addition, significant morphological similarity between some species, especially among females, may cause difficulties during the identification process. DNA-based approaches have become increasingly useful and promising tools for estimating sand fly diversity and for ensuring the rapid and accurate identification of species. A partial sequence of the mitochondrial cytochrome oxidase gene subunit I (COI) is currently being used to differentiate species in different animal taxa, including insects, and it is referred as a barcoding sequence. The present study explored the utility of the DNA barcode approach for the identification of phlebotomine sand flies in Colombia. We sequenced 700 bp of the COI gene from 36 species collected from different geographic localities. The COI barcode sequence divergence within a single species was <2% in most cases, whereas this divergence ranged from 9% to 26.6% among different species. These results indicated that the barcoding gene correctly discriminated among the previously morphologically identified species with an efficacy of nearly 100%. Analyses of the generated sequences indicated that the observed species groupings were consistent with the morphological identifications. In conclusion, the barcoding gene was useful for species discrimination in sand flies from Colombia.

DNA Barcoding for the Identification of Sand Fly Species (Diptera, Psychodidae, Phlebotominae) in Colombia

PubMed Central

Contreras Gutiérrez, María Angélica; Vivero, Rafael J.; Vélez, Iván D.; Porter, Charles H.; Uribe, Sandra

2014-01-01

Sand flies include a group of insects that are of medical importance and that vary in geographic distribution, ecology, and pathogen transmission. Approximately 163 species of sand flies have been reported in Colombia. Surveillance of the presence of sand fly species and the actualization of species distribution are important for predicting risks for and monitoring the expansion of diseases which sand flies can transmit. Currently, the identification of phlebotomine sand flies is based on morphological characters. However, morphological identification requires considerable skills and taxonomic expertise. In addition, significant morphological similarity between some species, especially among females, may cause difficulties during the identification process. DNA-based approaches have become increasingly useful and promising tools for estimating sand fly diversity and for ensuring the rapid and accurate identification of species. A partial sequence of the mitochondrial cytochrome oxidase gene subunit I (COI) is currently being used to differentiate species in different animal taxa, including insects, and it is referred as a barcoding sequence. The present study explored the utility of the DNA barcode approach for the identification of phlebotomine sand flies in Colombia. We sequenced 700 bp of the COI gene from 36 species collected from different geographic localities. The COI barcode sequence divergence within a single species was <2% in most cases, whereas this divergence ranged from 9% to 26.6% among different species. These results indicated that the barcoding gene correctly discriminated among the previously morphologically identified species with an efficacy of nearly 100%. Analyses of the generated sequences indicated that the observed species groupings were consistent with the morphological identifications. In conclusion, the barcoding gene was useful for species discrimination in sand flies from Colombia. PMID:24454877

Clinical applications of the functional connectome

PubMed Central

Castellanos, F. Xavier; Di Martino, Adriana; Craddock, R. Cameron; Mehta, Ashesh D.; Milham, Michael P.

2013-01-01

Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the functional connectome that can be attributed to clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. Here, we assess evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). We focus on current R-fMRI-based prediction efforts, and survey R-fMRI used for neurosurgical planning. We identify gaps and needs for R-fMRI-based biomarker identification, highlighting the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them. Additionally, we note the need to expand future efforts beyond identification of biomarkers for disease status alone to include clinical variables related to risk, expected treatment response and prognosis. PMID:23631991

Lead Discovery Strategies for Identification of Chlamydia pneumoniae Inhibitors.

PubMed

Hanski, Leena; Vuorela, Pia

2016-11-28

Throughout its known history, the gram-negative bacterium Chlamydia pneumoniae has remained a challenging target for antibacterial chemotherapy and drug discovery. Owing to its well-known propensity for persistence and recent reports on antimicrobial resistence within closely related species, new approaches for targeting this ubiquitous human pathogen are urgently needed. In this review, we describe the strategies that have been successfully applied for the identification of nonconventional antichlamydial agents, including target-based and ligand-based virtual screening, ethnopharmacological approach and pharmacophore-based design of antimicrobial peptide-mimicking compounds. Among the antichlamydial agents identified via these strategies, most translational work has been carried out with plant phenolics. Thus, currently available data on their properties as antichlamydial agents are described, highlighting their potential mechanisms of action. In this context, the role of mitogen-activated protein kinase activation in the intracellular growth and survival of C . pneumoniae is discussed. Owing to the complex and often complementary pathways applied by C. pneumoniae in the different stages of its life cycle, multitargeted therapy approaches are expected to provide better tools for antichlamydial therapy than agents with a single molecular target.

Lead Discovery Strategies for Identification of Chlamydia pneumoniae Inhibitors

PubMed Central

Hanski, Leena; Vuorela, Pia

2016-01-01

Throughout its known history, the gram-negative bacterium Chlamydia pneumoniae has remained a challenging target for antibacterial chemotherapy and drug discovery. Owing to its well-known propensity for persistence and recent reports on antimicrobial resistence within closely related species, new approaches for targeting this ubiquitous human pathogen are urgently needed. In this review, we describe the strategies that have been successfully applied for the identification of nonconventional antichlamydial agents, including target-based and ligand-based virtual screening, ethnopharmacological approach and pharmacophore-based design of antimicrobial peptide-mimicking compounds. Among the antichlamydial agents identified via these strategies, most translational work has been carried out with plant phenolics. Thus, currently available data on their properties as antichlamydial agents are described, highlighting their potential mechanisms of action. In this context, the role of mitogen-activated protein kinase activation in the intracellular growth and survival of C. pneumoniae is discussed. Owing to the complex and often complementary pathways applied by C. pneumoniae in the different stages of its life cycle, multitargeted therapy approaches are expected to provide better tools for antichlamydial therapy than agents with a single molecular target. PMID:27916800

Reverse engineering a social agent-based hidden markov model--visage.

PubMed

Chen, Hung-Ching Justin; Goldberg, Mark; Magdon-Ismail, Malik; Wallace, William A

2008-12-01

We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.

Online Databases for Taxonomy and Identification of Pathogenic Fungi and Proposal for a Cloud-Based Dynamic Data Network Platform

PubMed Central

Prakash, Peralam Yegneswaran; Irinyi, Laszlo; Halliday, Catriona; Chen, Sharon; Robert, Vincent

2017-01-01

ABSTRACT The increase in public online databases dedicated to fungal identification is noteworthy. This can be attributed to improved access to molecular approaches to characterize fungi, as well as to delineate species within specific fungal groups in the last 2 decades, leading to an ever-increasing complexity of taxonomic assortments and nomenclatural reassignments. Thus, well-curated fungal databases with substantial accurate sequence data play a pivotal role for further research and diagnostics in the field of mycology. This minireview aims to provide an overview of currently available online databases for the taxonomy and identification of human and animal-pathogenic fungi and calls for the establishment of a cloud-based dynamic data network platform. PMID:28179406

Optimal Design of Material and Process Parameters in Powder Injection Molding

NASA Astrophysics Data System (ADS)

Ayad, G.; Barriere, T.; Gelin, J. C.; Song, J.; Liu, B.

2007-04-01

The paper is concerned with optimization and parametric identification for the different stages in Powder Injection Molding process that consists first in injection of powder mixture with polymer binder and then to the sintering of the resulting powders part by solid state diffusion. In the first part, one describes an original methodology to optimize the process and geometry parameters in injection stage based on the combination of design of experiments and an adaptive Response Surface Modeling. Then the second part of the paper describes the identification strategy that one proposes for the sintering stage, using the identification of sintering parameters from dilatometeric curves followed by the optimization of the sintering process. The proposed approaches are applied to the optimization of material and process parameters for manufacturing a ceramic femoral implant. One demonstrates that the proposed approach give satisfactory results.

Automatically identifying health outcome information in MEDLINE records.

PubMed

Demner-Fushman, Dina; Few, Barbara; Hauser, Susan E; Thoma, George

2006-01-01

Understanding the effect of a given intervention on the patient's health outcome is one of the key elements in providing optimal patient care. This study presents a methodology for automatic identification of outcomes-related information in medical text and evaluates its potential in satisfying clinical information needs related to health care outcomes. An annotation scheme based on an evidence-based medicine model for critical appraisal of evidence was developed and used to annotate 633 MEDLINE citations. Textual, structural, and meta-information features essential to outcome identification were learned from the created collection and used to develop an automatic system. Accuracy of automatic outcome identification was assessed in an intrinsic evaluation and in an extrinsic evaluation, in which ranking of MEDLINE search results obtained using PubMed Clinical Queries relied on identified outcome statements. The accuracy and positive predictive value of outcome identification were calculated. Effectiveness of the outcome-based ranking was measured using mean average precision and precision at rank 10. Automatic outcome identification achieved 88% to 93% accuracy. The positive predictive value of individual sentences identified as outcomes ranged from 30% to 37%. Outcome-based ranking improved retrieval accuracy, tripling mean average precision and achieving 389% improvement in precision at rank 10. Preliminary results in outcome-based document ranking show potential validity of the evidence-based medicine-model approach in timely delivery of information critical to clinical decision support at the point of service.

Decoupling Identification for Serial Two-Link Two-Inertia System

NASA Astrophysics Data System (ADS)

Oaki, Junji; Adachi, Shuichi

The purpose of our study is to develop a precise model by applying the technique of system identification for the model-based control of a nonlinear robot arm, under taking joint-elasticity into consideration. We previously proposed a systematic identification method, called “decoupling identification,” for a “SCARA-type” planar two-link robot arm with elastic joints caused by the Harmonic-drive® reduction gears. The proposed method serves as an extension of the conventional rigid-joint-model-based identification. The robot arm is treated as a serial two-link two-inertia system with nonlinearity. The decoupling identification method using link-accelerometer signals enables the serial two-link two-inertia system to be divided into two linear one-link two-inertia systems. The MATLAB®'s commands for state-space model estimation are utilized in the proposed method. Physical parameters such as motor inertias, link inertias, joint-friction coefficients, and joint-spring coefficients are estimated through the identified one-link two-inertia systems using a gray-box approach. This paper describes accuracy evaluations using the two-link arm for the decoupling identification method under introducing closed-loop-controlled elements and varying amplitude-setup of identification-input. Experimental results show that the identification method also works with closed-loop-controlled elements. Therefore, the identification method is applicable to a “PUMA-type” vertical robot arm under gravity.

High-Throughput Effect-Directed Analysis Using Downscaled in Vitro Reporter Gene Assays To Identify Endocrine Disruptors in Surface Water

PubMed Central

2018-01-01

Effect-directed analysis (EDA) is a commonly used approach for effect-based identification of endocrine disruptive chemicals in complex (environmental) mixtures. However, for routine toxicity assessment of, for example, water samples, current EDA approaches are considered time-consuming and laborious. We achieved faster EDA and identification by downscaling of sensitive cell-based hormone reporter gene assays and increasing fractionation resolution to allow testing of smaller fractions with reduced complexity. The high-resolution EDA approach is demonstrated by analysis of four environmental passive sampler extracts. Downscaling of the assays to a 384-well format allowed analysis of 64 fractions in triplicate (or 192 fractions without technical replicates) without affecting sensitivity compared to the standard 96-well format. Through a parallel exposure method, agonistic and antagonistic androgen and estrogen receptor activity could be measured in a single experiment following a single fractionation. From 16 selected candidate compounds, identified through nontargeted analysis, 13 could be confirmed chemically and 10 were found to be biologically active, of which the most potent nonsteroidal estrogens were identified as oxybenzone and piperine. The increased fractionation resolution and the higher throughput that downscaling provides allow for future application in routine high-resolution screening of large numbers of samples in order to accelerate identification of (emerging) endocrine disruptors. PMID:29547277

Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets.

PubMed

Tichy, Diana; Pickl, Julia Maria Anna; Benner, Axel; Sültmann, Holger

2017-03-31

The identification of microRNA (miRNA) target genes is crucial for understanding miRNA function. Many methods for the genome-wide miRNA target identification have been developed in recent years; however, they have several limitations including the dependence on low-confident prediction programs and artificial miRNA manipulations. Ago-RNA immunoprecipitation combined with high-throughput sequencing (Ago-RIP-Seq) is a promising alternative. However, appropriate statistical data analysis algorithms taking into account the experimental design and the inherent noise of such experiments are largely lacking.Here, we investigate the experimental design for Ago-RIP-Seq and examine biostatistical methods to identify de novo miRNA target genes. Statistical approaches considered are either based on a negative binomial model fit to the read count data or applied to transformed data using a normal distribution-based generalized linear model. We compare them by a real data simulation study using plasmode data sets and evaluate the suitability of the approaches to detect true miRNA targets by sensitivity and false discovery rates. Our results suggest that simple approaches like linear regression models on (appropriately) transformed read count data are preferable. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A practical approach to object based requirements analysis

NASA Technical Reports Server (NTRS)

Drew, Daniel W.; Bishop, Michael

1988-01-01

Presented here is an approach developed at the Unisys Houston Operation Division, which supports the early identification of objects. This domain oriented analysis and development concept is based on entity relationship modeling and object data flow diagrams. These modeling techniques, based on the GOOD methodology developed at the Goddard Space Flight Center, support the translation of requirements into objects which represent the real-world problem domain. The goal is to establish a solid foundation of understanding before design begins, thereby giving greater assurance that the system will do what is desired by the customer. The transition from requirements to object oriented design is also promoted by having requirements described in terms of objects. Presented is a five step process by which objects are identified from the requirements to create a problem definition model. This process involves establishing a base line requirements list from which an object data flow diagram can be created. Entity-relationship modeling is used to facilitate the identification of objects from the requirements. An example is given of how semantic modeling may be used to improve the entity-relationship model and a brief discussion on how this approach might be used in a large scale development effort.

Comparison of μ-ATR-FTIR spectroscopy and py-GCMS as identification tools for microplastic particles and fibers isolated from river sediments.

PubMed

Käppler, Andrea; Fischer, Marten; Scholz-Böttcher, Barbara M; Oberbeckmann, Sonja; Labrenz, Matthias; Fischer, Dieter; Eichhorn, Klaus-Jochen; Voit, Brigitte

2018-06-16

In recent years, many studies on the analysis of microplastics (MP) in environmental samples have been published. These studies are hardly comparable due to different sampling, sample preparation, as well as identification and quantification techniques. Here, MP identification is one of the crucial pitfalls. Visual identification approaches using morphological criteria alone often lead to significant errors, being especially true for MP fibers. Reliable, chemical structure-based identification methods are indispensable. In this context, the frequently used vibrational spectroscopic techniques but also thermoanalytical methods are established. However, no critical comparison of these fundamentally different approaches has ever been carried out with regard to analyzing MP in environmental samples. In this blind study, we investigated 27 single MP particles and fibers of unknown material isolated from river sediments. Successively micro-attenuated total reflection Fourier transform infrared spectroscopy (μ-ATR-FTIR) and pyrolysis gas chromatography-mass spectrometry (py-GCMS) in combination with thermochemolysis were applied. Both methods differentiated between plastic vs. non-plastic in the same way in 26 cases, with 19 particles and fibers (22 after re-evaluation) identified as the same polymer type. To illustrate the different approaches and emphasize the complementarity of their information content, we exemplarily provide a detailed comparison of four particles and three fibers and a critical discussion of advantages and disadvantages of both methods.

Toward the identification of causal genes in complex diseases: a gene-centric joint test of significance combining genomic and transcriptomic data.

PubMed

Charlesworth, Jac C; Peralta, Juan M; Drigalenko, Eugene; Göring, Harald Hh; Almasy, Laura; Dyer, Thomas D; Blangero, John

2009-12-15

Gene identification using linkage, association, or genome-wide expression is often underpowered. We propose that formal combination of information from multiple gene-identification approaches may lead to the identification of novel loci that are missed when only one form of information is available. Firstly, we analyze the Genetic Analysis Workshop 16 Framingham Heart Study Problem 2 genome-wide association data for HDL-cholesterol using a "gene-centric" approach. Then we formally combine the association test results with genome-wide transcriptional profiling data for high-density lipoprotein cholesterol (HDL-C), from the San Antonio Family Heart Study, using a Z-transform test (Stouffer's method). We identified 39 genes by the joint test at a conservative 1% false-discovery rate, including 9 from the significant gene-based association test and 23 whose expression was significantly correlated with HDL-C. Seven genes identified as significant in the joint test were not independently identified by either the association or expression tests. This combined approach has increased power and leads to the direct nomination of novel candidate genes likely to be involved in the determination of HDL-C levels. Such information can then be used as justification for a more exhaustive search for functional sequence variation within the nominated genes. We anticipate that this type of analysis will improve our speed of identification of regulatory genes causally involved in disease risk.

Intelligent measurement and compensation of linear motor force ripple: a projection-based learning approach in the presence of noise

NASA Astrophysics Data System (ADS)

Liu, Yang; Song, Fazhi; Yang, Xiaofeng; Dong, Yue; Tan, Jiubin

2018-06-01

Due to their structural simplicity, linear motors are increasingly receiving attention for use in high velocity and high precision applications. The force ripple, as a space-periodic disturbance, however, would deteriorate the achievable dynamic performance. Conventional force ripple measurement approaches are time-consuming and have high requirements on the experimental conditions. In this paper, a novel learning identification algorithm is proposed for force ripple intelligent measurement and compensation. Existing identification schemes always use all the error signals to update the parameters in the force ripple. However, the error induced by noise is non-effective for force ripple identification, and even deteriorates the identification process. In this paper only the most pertinent information in the error signal is utilized for force ripple identification. Firstly, the effective error signals caused by the reference trajectory and the force ripple are extracted by projecting the overall error signals onto a subspace spanned by the physical model of the linear motor as well as the sinusoidal model of the force ripple. The time delay in the linear motor is compensated in the basis functions. Then, a data-driven approach is proposed to design the learning gain. It balances the trade-off between convergence speed and robustness against noise. Simulation and experimental results validate the proposed method and confirm its effectiveness and superiority.

CRISPR Approaches to Small Molecule Target Identification. | Office of Cancer Genomics

Cancer.gov

A long-standing challenge in drug development is the identification of the mechanisms of action of small molecules with therapeutic potential. A number of methods have been developed to address this challenge, each with inherent strengths and limitations. We here provide a brief review of these methods with a focus on chemical-genetic methods that are based on systematically profiling the effects of genetic perturbations on drug sensitivity.

Acquisition Management for Systems-of-Systems: Analysis of Alternatives via Computational Exploratory Model

DTIC Science & Technology

2012-02-03

node to the analysis of eigenmodes (connected trees /networks) of disruption sequences. The identification of disruption eigenmodes is particularly...investment portfolio approach enables the identification of optimal SoS network topologies and provides a tool for acquisition professionals to...a program based on its ability to provide a new capability for a given cost, and not on its ability to meet specific performance requirements ( Spacy

Age Identification in the Framework of Successful Aging: A Study of Older Finnish People

ERIC Educational Resources Information Center

Uotinen, Virpi; Suutama, Timo; Ruoppila, Isto

2003-01-01

A person-oriented approach was used in a study of age identification among community-dwelling older people. The study was based on 8-year follow-up data; 843 persons aged 65-84 were involved in the first phase of the study, and 426 persons aged 73-92, in the second phase. Loosely, on the basis of the distinction between successful, usual, and…

Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification.

PubMed

Xu, Xinxing; Li, Wen; Xu, Dong

2015-12-01

In this paper, we propose a new approach to improve face verification and person re-identification in the RGB images by leveraging a set of RGB-D data, in which we have additional depth images in the training data captured using depth cameras such as Kinect. In particular, we extract visual features and depth features from the RGB images and depth images, respectively. As the depth features are available only in the training data, we treat the depth features as privileged information, and we formulate this task as a distance metric learning with privileged information problem. Unlike the traditional face verification and person re-identification tasks that only use visual features, we further employ the extra depth features in the training data to improve the learning of distance metric in the training process. Based on the information-theoretic metric learning (ITML) method, we propose a new formulation called ITML with privileged information (ITML+) for this task. We also present an efficient algorithm based on the cyclic projection method for solving the proposed ITML+ formulation. Extensive experiments on the challenging faces data sets EUROCOM and CurtinFaces for face verification as well as the BIWI RGBD-ID data set for person re-identification demonstrate the effectiveness of our proposed approach.

Training Teachers to Conduct Trial-Based Functional Analyses

ERIC Educational Resources Information Center

Kunnavatana, S. Shanun; Bloom, Sarah E.; Samaha, Andrew L.; Dayton, Elizabeth

2013-01-01

The trial-based functional analysis (FA) is a promising approach to identification of behavioral function and is especially suited for use in educational settings. Not all studies on trial-based FA have included teachers as therapists, and those studies that have, included minimal information on teacher training. The purpose of this study was to…

White blood cells identification system based on convolutional deep neural learning networks.

PubMed

Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

2017-11-16

White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

A combined qualitative-quantitative approach for the identification of highly co-creative technology-driven firms

NASA Astrophysics Data System (ADS)

Milyakov, Hristo; Tanev, Stoyan; Ruskov, Petko

2011-03-01

Value co-creation, is an emerging business and innovation paradigm, however, there is not enough clarity on the distinctive characteristics of value co-creation as compared to more traditional value creation approaches. The present paper summarizes the results from an empirically-derived research study focusing on the development of a systematic procedure for the identification of firms that are active in value co-creation. The study is based on a sample 273 firms that were selected for being representative of the breadth of their value co-creation activities. The results include: i) the identification of the key components of value co-creation based on a research methodology using web search and Principal Component Analysis techniques, and ii) the comparison of two different classification techniques identifying the firms with the highest degree of involvement in value co-creation practices. To the best of our knowledge this is the first study using sophisticated data collection techniques to provide a classification of firms according to the degree of their involvement in value co-creation.

Harnessing mtDNA variation to resolve ambiguity in ‘Redfish’ sold in Europe

PubMed Central

Moore, Lauren; Pampoulie, Christophe; Di Muri, Cristina; Vandamme, Sara; Mariani, Stefano

2017-01-01

Morphology-based identification of North Atlantic Sebastes has long been controversial and misidentification may produce misleading data, with cascading consequences that negatively affect fisheries management and seafood labelling. North Atlantic Sebastes comprises of four species, commonly known as ‘redfish’, but little is known about the number, identity and labelling accuracy of redfish species sold across Europe. We used a molecular approach to identify redfish species from ‘blind’ specimens to evaluate the performance of the Barcode of Life (BOLD) and Genbank databases, as well as carrying out a market product accuracy survey from retailers across Europe. The conventional BOLD approach proved ambiguous, and phylogenetic analysis based on mtDNA control region sequences provided a higher resolution for species identification. By sampling market products from four countries, we found the presence of two species of redfish (S. norvegicus and S. mentella) and one unidentified Pacific rockfish marketed in Europe. Furthermore, public databases revealed the existence of inaccurate reference sequences, likely stemming from species misidentification from previous studies, which currently hinders the efficacy of DNA methods for the identification of Sebastes market samples. PMID:29018597

Identification of Poly(ethylene glycol) and Poly(ethylene glycol)-Based Detergents Using Peptide Search Engines.

PubMed

Ahmadi, Shiva; Winter, Dominic

2018-06-05

Poly(ethylene glycol) (PEG) is one of the most common polymer contaminations in mass spectrometry (MS) samples. At present, the detection of PEG and other polymers relies largely on manual inspection of raw data, which is laborious and frequently difficult due to sample complexity and retention characteristics of polymer species in reversed-phase chromatography. We developed a new strategy for the automated identification of PEG molecules from tandem mass spectrometry (MS/MS) data using protein identification algorithms in combination with a database containing "PEG-proteins". Through definition of variable modifications, we extend the approach for the identification of commonly used PEG-based detergents. We exemplify the identification of different types of polymers by static nanoelectrospray tandem mass spectrometry (nanoESI-MS/MS) analysis of pure detergent solutions and data analysis using Mascot. Analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS) runs of a PEG-contaminated sample by Mascot identified 806 PEG spectra originating from four PEG species using a defined set of modifications covering PEG and common PEG-based detergents. Further characterization of the sample for unidentified PEG species using error-tolerant and mass-tolerant searches resulted in identification of 3409 and 3187 PEG-related MS/MS spectra, respectively. We further demonstrate the applicability of the strategy for Protein Pilot and MaxQuant.

Basics of identification measurement technology

NASA Astrophysics Data System (ADS)

Klikushin, Yu N.; Kobenko, V. Yu; Stepanov, P. P.

2018-01-01

All available algorithms and suitable for pattern recognition do not give 100% guarantee, therefore there is a field of scientific night activity in this direction, studies are relevant. It is proposed to develop existing technologies for pattern recognition in the form of application of identification measurements. The purpose of the study is to identify the possibility of recognizing images using identification measurement technologies. In solving problems of pattern recognition, neural networks and hidden Markov models are mainly used. A fundamentally new approach to the solution of problems of pattern recognition based on the technology of identification signal measurements (IIS) is proposed. The essence of IIS technology is the quantitative evaluation of the shape of images using special tools and algorithms.

Nonlinear dynamic macromodeling techniques for audio systems

NASA Astrophysics Data System (ADS)

Ogrodzki, Jan; Bieńkowski, Piotr

2015-09-01

This paper develops a modelling method and a models identification technique for the nonlinear dynamic audio systems. Identification is performed by means of a behavioral approach based on a polynomial approximation. This approach makes use of Discrete Fourier Transform and Harmonic Balance Method. A model of an audio system is first created and identified and then it is simulated in real time using an algorithm of low computational complexity. The algorithm consists in real time emulation of the system response rather than in simulation of the system itself. The proposed software is written in Python language using object oriented programming techniques. The code is optimized for a multithreads environment.

A Tale of Two Methods: Chart and Interview Methods for Identifying Delirium

PubMed Central

Saczynski, Jane S.; Kosar, Cyrus M.; Xu, Guoquan; Puelle, Margaret R.; Schmitt, Eva; Jones, Richard N.; Marcantonio, Edward R.; Wong, Bonnie; Isaza, Ilean; Inouye, Sharon K.

2014-01-01

Background Interview and chart-based methods for identifying delirium have been validated. However, relative strengths and limitations of each method have not been described, nor has a combined approach (using both interviews and chart), been systematically examined. Objectives To compare chart and interview-based methods for identification of delirium. Design, Setting and Participants Participants were 300 patients aged 70+ undergoing major elective surgery (majority were orthopedic surgery) interviewed daily during hospitalization for delirium using the Confusion Assessment Method (CAM; interview-based method) and whose medical charts were reviewed for delirium using a validated chart-review method (chart-based method). We examined rate of agreement on the two methods and patient characteristics of those identified using each approach. Predictive validity for clinical outcomes (length of stay, postoperative complications, discharge disposition) was compared. In the absence of a gold-standard, predictive value could not be calculated. Results The cumulative incidence of delirium was 23% (n= 68) by the interview-based method, 12% (n=35) by the chart-based method and 27% (n=82) by the combined approach. Overall agreement was 80%; kappa was 0.30. The methods differed in detection of psychomotor features and time of onset. The chart-based method missed delirium in CAM-identified patients laacking features of psychomotor agitation or inappropriate behavior. The CAM-based method missed chart-identified cases occurring during the night shift. The combined method had high predictive validity for all clinical outcomes. Conclusions Interview and chart-based methods have specific strengths for identification of delirium. A combined approach captures the largest number and the broadest range of delirium cases. PMID:24512042

Single cell adhesion force measurement for cell viability identification using an AFM cantilever-based micro putter

NASA Astrophysics Data System (ADS)

Shen, Yajing; Nakajima, Masahiro; Kojima, Seiji; Homma, Michio; Kojima, Masaru; Fukuda, Toshio

2011-11-01

Fast and sensitive cell viability identification is a key point for single cell analysis. To address this issue, this paper reports a novel single cell viability identification method based on the measurement of single cell shear adhesion force using an atomic force microscopy (AFM) cantilever-based micro putter. Viable and nonviable yeast cells are prepared and put onto three kinds of substrate surfaces, i.e. tungsten probe, gold and ITO substrate surfaces. A micro putter is fabricated from the AFM cantilever by focused ion beam etching technique. The spring constant of the micro putter is calibrated using the nanomanipulation approach. The shear adhesion force between the single viable or nonviable cell and each substrate is measured using the micro putter based on the nanorobotic manipulation system inside an environmental scanning electron microscope. The adhesion force is calculated based on the deflection of the micro putter beam. The results show that the adhesion force of the viable cell to the substrate is much larger than that of the nonviable cell. This identification method is label free, fast, sensitive and can give quantitative results at the single cell level.

[Neurophysiological identification of the cranial nerves in endoscopic endonasal surgery of skull base tumors].

PubMed

Shkarubo, A N; Ogurtsova, A A; Moshchev, D A; Lubnin, A Yu; Andreev, D N; Koval', K V; Chernov, I V

2016-01-01

Intraoperative identification of the cranial nerves is a useful technique in removal of skull base tumors through the endoscopic endonasal approach. Searching through the scientific literature found one pilot study on the use of triggered electromyography (t-EMG) for identification of the VIth nerve in endonasal endoscopic surgery of skull base tumors (D. San-Juan, et al, 2014). The study objective was to prevent iatrogenic injuries to the cranial nerves without reducing the completeness of tumor tissue resection. In 2014, 5 patients were operated on using the endoscopic endonasal approach. Surgeries were performed for large skull base chordomas (2 cases) and trigeminal nerve neurinomas located in the cavernous sinus (3). Intraoperatively, identification of the cranial nerves was performed by triggered electromyography using a bipolar electrode (except 1 case of chordoma where a monopolar electrode was used). Evaluation of the functional activity of the cranial nerves was carried out both preoperatively and postoperatively. Tumor resection was total in 4 out of 5 cases and subtotal (chordoma) in 1 case. Intraoperatively, the IIIrd (2 patients), Vth (2), and VIth (4) cranial nerves were identified. No deterioration in the function of the intraoperatively identified nerves was observed in the postoperative period. In one case, no responses from the VIth nerve on the right (in the cavernous sinus region) were intraoperatively obtained, and deep paresis (up to plegia) of the nerve-innervated muscles developed in the postoperative period. The nerve function was not impaired before surgery. The t-EMG technique is promising and requires further research.

Post-acquisition data mining techniques for LC-MS/MS-acquired data in drug metabolite identification.

PubMed

Dhurjad, Pooja Sukhdev; Marothu, Vamsi Krishna; Rathod, Rajeshwari

2017-08-01

Metabolite identification is a crucial part of the drug discovery process. LC-MS/MS-based metabolite identification has gained widespread use, but the data acquired by the LC-MS/MS instrument is complex, and thus the interpretation of data becomes troublesome. Fortunately, advancements in data mining techniques have simplified the process of data interpretation with improved mass accuracy and provide a potentially selective, sensitive, accurate and comprehensive way for metabolite identification. In this review, we have discussed the targeted (extracted ion chromatogram, mass defect filter, product ion filter, neutral loss filter and isotope pattern filter) and untargeted (control sample comparison, background subtraction and metabolomic approaches) post-acquisition data mining techniques, which facilitate the drug metabolite identification. We have also discussed the importance of integrated data mining strategy.

Identification of Putative Cardiovascular System Developmental Toxicants using a Classification Model based on Signaling Pathway-Adverse Outcome Pathways

EPA Science Inventory

An important challenge for an integrative approach to developmental systems toxicology is associating putative molecular initiating events (MIEs), cell signaling pathways, cell function and modeled fetal exposure kinetics. We have developed a chemical classification model based o...

Bifocal Stereo for Multipath Person Re-Identification

NASA Astrophysics Data System (ADS)

Blott, G.; Heipke, C.

2017-11-01

This work presents an approach for the task of person re-identification by exploiting bifocal stereo cameras. Present monocular person re-identification approaches show a decreasing working distance, when increasing the image resolution to obtain a higher reidentification performance. We propose a novel 3D multipath bifocal approach, containing a rectilinear lens with larger focal length for long range distances and a fish eye lens of a smaller focal length for the near range. The person re-identification performance is at least on par with 2D re-identification approaches but the working distance of the approach is increased and on average 10% more re-identification performance can be achieved in the overlapping field of view compared to a single camera. In addition, the 3D information is exploited from the overlapping field of view to solve potential 2D ambiguities.

Comparison of System Identification Techniques for the Hydraulic Manipulator Test Bed (HMTB)

NASA Technical Reports Server (NTRS)

Morris, A. Terry

1996-01-01

In this thesis linear, dynamic, multivariable state-space models for three joints of the ground-based Hydraulic Manipulator Test Bed (HMTB) are identified. HMTB, housed at the NASA Langley Research Center, is a ground-based version of the Dexterous Orbital Servicing System (DOSS), a representative space station manipulator. The dynamic models of the HMTB manipulator will first be estimated by applying nonparametric identification methods to determine each joint's response characteristics using various input excitations. These excitations include sum of sinusoids, pseudorandom binary sequences (PRBS), bipolar ramping pulses, and chirp input signals. Next, two different parametric system identification techniques will be applied to identify the best dynamical description of the joints. The manipulator is localized about a representative space station orbital replacement unit (ORU) task allowing the use of linear system identification methods. Comparisons, observations, and results of both parametric system identification techniques are discussed. The thesis concludes by proposing a model reference control system to aid in astronaut ground tests. This approach would allow the identified models to mimic on-orbit dynamic characteristics of the actual flight manipulator thus providing astronauts with realistic on-orbit responses to perform space station tasks in a ground-based environment.

Critical Assessment of Small Molecule Identification 2016: automated methods.

PubMed

Schymanski, Emma L; Ruttkies, Christoph; Krauss, Martin; Brouard, Céline; Kind, Tobias; Dührkop, Kai; Allen, Felicity; Vaniya, Arpana; Verdegem, Dries; Böcker, Sebastian; Rousu, Juho; Shen, Huibin; Tsugawa, Hiroshi; Sajed, Tanvir; Fiehn, Oliver; Ghesquière, Bart; Neumann, Steffen

2017-03-27

The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification-In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification-Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract .

Can physicians recognize their own patients in de-identified notes?

PubMed

Meystre, Stéphane; Shen, Shuying; Hofmann, Deborah; Gundlapalli, Adi

2014-01-01

The adoption of Electronic Health Records is growing at a fast pace, and this growth results in very large quantities of patient clinical information becoming available in electronic format, with tremendous potentials, but also equally growing concern for patient confidentiality breaches. De-identification of patient information has been proposed as a solution to both facilitate secondary uses of clinical information, and protect patient information confidentiality. Automated approaches based on Natural Language Processing have been implemented and evaluated, allowing for much faster text de-identification than manual approaches. A U.S. Veterans Affairs clinical text de-identification project focused on investigating the current state of the art of automatic clinical text de-identification, on developing a best-of-breed de-identification application for clinical documents, and on evaluating its impact on subsequent text uses and the risk for re-identification. To evaluate this risk, we de-identified discharge summaries from 86 patients using our 'best-of-breed' text de-identification application with resynthesis of the identifiers detected. We then asked physicians working in the ward the patients were hospitalized in if they could recognize these patients when reading the de-identified documents. Each document was examined by at least one resident and one attending physician, and with 4.65% of the documents, physicians thought they recognized the patient because of specific clinical information, but after verification, none was correctly re-identified.

Identification of maca (Lepidium meyenii Walp.) and its adulterants by a DNA-barcoding approach based on the ITS sequence.

PubMed

Chen, Jin-Jin; Zhao, Qing-Sheng; Liu, Yi-Lan; Zha, Sheng-Hua; Zhao, Bing

2015-09-01

Maca (Lepidium meyenii) is an herbaceous plant that grows in high plateaus and has been used as both food and folk medicine for centuries because of its benefits to human health. In the present study, ITS (internal transcribed spacer) sequences of forty-three maca samples, collected from different regions or vendors, were amplified and analyzed. The ITS sequences of nineteen potential adulterants of maca were also collected and analyzed. The results indicated that the ITS sequence of maca was consistent in all samples and unique when compared with its adulterants. Therefore, this DNA-barcoding approach based on the ITS sequence can be used for the molecular identification of maca and its adulterants. Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

Texture-based approach to palmprint retrieval for personal identification

NASA Astrophysics Data System (ADS)

Li, Wenxin; Zhang, David; Xu, Z.; You, J.

2000-12-01

This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered - feature selection, similarity measures and dynamic search for the best matching of the sample in the image database. We propose a texture-based method for palmprint feature representation. The concept of texture energy is introduced to define a palm print's global and local features, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The search is carried out in a layered fashion: first global features are used to guide the fast selection of a small set of similar candidates from the database from the database and then local features are used to decide the final output within the candidate set. The experimental results demonstrate the effectiveness and accuracy of the proposed method.

Texture-based approach to palmprint retrieval for personal identification

NASA Astrophysics Data System (ADS)

Li, Wenxin; Zhang, David; Xu, Z.; You, J.

2001-01-01

This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered - feature selection, similarity measures and dynamic search for the best matching of the sample in the image database. We propose a texture-based method for palmprint feature representation. The concept of texture energy is introduced to define a palm print's global and local features, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The search is carried out in a layered fashion: first global features are used to guide the fast selection of a small set of similar candidates from the database from the database and then local features are used to decide the final output within the candidate set. The experimental results demonstrate the effectiveness and accuracy of the proposed method.

Inferring Time-Varying Network Topologies from Gene Expression Data

PubMed Central

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence. PMID:18309363

Inferring time-varying network topologies from gene expression data.

PubMed

Rao, Arvind; Hero, Alfred O; States, David J; Engel, James Douglas

2007-01-01

Most current methods for gene regulatory network identification lead to the inference of steady-state networks, that is, networks prevalent over all times, a hypothesis which has been challenged. There has been a need to infer and represent networks in a dynamic, that is, time-varying fashion, in order to account for different cellular states affecting the interactions amongst genes. In this work, we present an approach, regime-SSM, to understand gene regulatory networks within such a dynamic setting. The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster--to infer a network adjacency matrix. We finally indicate our results on the mouse embryonic kidney dataset as well as the T-cell activation-based expression dataset and demonstrate conformity with reported experimental evidence.

Blind identification of the kinetic parameters in three-compartment models

NASA Astrophysics Data System (ADS)

Riabkov, Dmitri Y.; Di Bella, Edward V. R.

2004-03-01

Quantified knowledge of tissue kinetic parameters in the regions of the brain and other organs can offer information useful in clinical and research applications. Dynamic medical imaging with injection of radioactive or paramagnetic tracer can be used for this measurement. The kinetics of some widely used tracers such as [18F]2-fluoro-2-deoxy-D-glucose can be described by a three-compartment physiological model. The kinetic parameters of the tissue can be estimated from dynamically acquired images. Feasibility of estimation by blind identification, which does not require knowledge of the blood input, is considered analytically and numerically in this work for the three-compartment type of tissue response. The non-uniqueness of the two-region case for blind identification of kinetic parameters in three-compartment model is shown; at least three regions are needed for the blind identification to be unique. Numerical results for the accuracy of these blind identification methods in different conditions were considered. Both a separable variables least-squares (SLS) approach and an eigenvector-based algorithm for multichannel blind deconvolution approach were used. The latter showed poor accuracy. Modifications for non-uniform time sampling were also developed. Also, another method which uses a model for the blood input was compared. Results for the macroparameter K, which reflects the metabolic rate of glucose usage, using three regions with noise showed comparable accuracy for the separable variables least squares method and for the input model-based method, and slightly worse accuracy for SLS with the non-uniform sampling modification.

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting.

PubMed

Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

2017-09-01

Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics.

Remote sensing based on hyperspectral data analysis

NASA Astrophysics Data System (ADS)

Sharifahmadian, Ershad

In remote sensing, accurate identification of far objects, especially concealed objects is difficult. In this study, to improve object detection from a distance, the hyperspecral imaging and wideband technology are employed with the emphasis on wideband radar. As the wideband data includes a broad range of frequencies, it can reveal information about both the surface of the object and its content. Two main contributions are made in this study: 1) Developing concept of return loss for target detection: Unlike typical radar detection methods which uses radar cross section to detect an object, it is possible to enhance the process of detection and identification of concealed targets using the wideband radar based on the electromagnetic characteristics --conductivity, permeability, permittivity, and return loss-- of materials. During the identification process, collected wideband data is evaluated with information from wideband signature library which has already been built. In fact, several classes (e.g. metal, wood, etc.) and subclasses (ex. metals with high conductivity) have been defined based on their electromagnetic characteristics. Materials in a scene are then classified based on these classes. As an example, materials with high electrical conductivity can be conveniently detected. In fact, increasing relative conductivity leads to a reduction in the return loss. Therefore, metals with high conductivity (ex. copper) shows stronger radar reflections compared with metals with low conductivity (ex. stainless steel). Thus, it is possible to appropriately discriminate copper from stainless steel. 2) Target recognition techniques: To detect and identify targets, several techniques have been proposed, in particular the Multi-Spectral Wideband Radar Image (MSWRI) which is able to localize and identify concealed targets. The MSWRI is based on the theory of robust capon beamformer. During identification process, information from wideband signature library is utilized. The WB signature library includes such parameters as conductivity, permeability, permittivity, and return loss at different frequencies for possible materials related to a target. In the MSWRI approach, identification procedure is performed by calculating the RLs at different selected frequencies. Based on similarity of the calculated RLs and RL from WB signature library, targets are detected and identified. Based on the simulation and experimental results, it is concluded that the MSWRI technique is a promising approach for standoff target detection.

The use of roving discs and orthogonal natural frequencies for crack identification and location in rotors

NASA Astrophysics Data System (ADS)

Haji, Zyad N.; Olutunde Oyadiji, S.

2014-11-01

A variety of approaches that have been developed for the identification and localisation of cracks in a rotor system, which exploit natural frequencies, require a finite element model to obtain the natural frequencies of the intact rotor as baseline data. In fact, such approaches can give erroneous results about the location and depth of a crack if an inaccurate finite element model is used to represent an uncracked model. A new approach for the identification and localisation of cracks in rotor systems, which does not require the use of the natural frequencies of an intact rotor as a baseline data, is presented in this paper. The approach, named orthogonal natural frequencies (ONFs), is based only on the natural frequencies of the non-rotating cracked rotor in the two lateral bending vibration x-z and y-z planes. The approach uses the cracked natural frequencies in the horizontal x-z plane as the reference data instead of the intact natural frequencies. Also, a roving disc is traversed along the rotor in order to enhance the dynamics of the rotor at the cracked locations. At each spatial location of the roving disc, the two ONFs of the rotor-disc system are determined from which the corresponding ONF ratio is computed. The ONF ratios are normalised by the maximum ONF ratio to obtain normalised orthogonal natural frequency curves (NONFCs). The non-rotating cracked rotor is simulated by the finite element method using the Bernoulli-Euler beam theory. The unique characteristics of the proposed approach are the sharp, notched peaks at the crack locations but rounded peaks at non-cracked locations. These features facilitate the unambiguous identification and locations of cracks in rotors. The effects of crack depth, crack location, and mass of a roving disc are investigated. The results show that the proposed method has a great potential in the identification and localisation of cracks in a non-rotating cracked rotor.

Do Something, Even if It's Wrong! An Approach to Better Management and Planning in Public Schools through a Systematic Approach to Improvement of Basic Skills.

ERIC Educational Resources Information Center

Noggle, Vernon R.

Maintaining that lack of action is one of the biggest errors school managers make, this author describes development of competency based education (CBE) programs as one example of how action can be taken by identifying a problem area and systematically approaching it. He defines CBE programs as those involving identification of basic skills, a…

Metabolomics as a tool in the identification of dietary biomarkers.

PubMed

Gibbons, Helena; Brennan, Lorraine

2017-02-01

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.

Time frequency analysis for automated sleep stage identification in fullterm and preterm neonates.

PubMed

Fraiwan, Luay; Lweesy, Khaldon; Khasawneh, Natheer; Fraiwan, Mohammad; Wenz, Heinrich; Dickhaus, Hartmut

2011-08-01

This work presents a new methodology for automated sleep stage identification in neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Wigner-Ville distribution (WVD), Hilbert-Hough spectrum (HHS) and continuous wavelet transform (CWT) time frequency distributions were used to represent the EEG signal from which features were extracted using time frequency entropy. The classification of features was done using feed forward back-propagation ANN. The system was trained and tested using data taken from neonates of post-conceptual age of 40 weeks for both preterm (14 recordings) and fullterm (15 recordings). The identification of sleep stages was successfully implemented and the classification based on the WVD outperformed the approaches based on CWT and HHS. The accuracy and kappa coefficient were found to be 0.84 and 0.65 respectively for the fullterm neonates' recordings and 0.74 and 0.50 respectively for preterm neonates' recordings.

Unsupervised real-time speaker identification for daily movies

NASA Astrophysics Data System (ADS)

Li, Ying; Kuo, C.-C. Jay

2002-07-01

The problem of identifying speakers for movie content analysis is addressed in this paper. While most previous work on speaker identification was carried out in a supervised mode using pure audio data, more robust results can be obtained in real-time by integrating knowledge from multiple media sources in an unsupervised mode. In this work, both audio and visual cues will be employed and subsequently combined in a probabilistic framework to identify speakers. Particularly, audio information is used to identify speakers with a maximum likelihood (ML)-based approach while visual information is adopted to distinguish speakers by detecting and recognizing their talking faces based on face detection/recognition and mouth tracking techniques. Moreover, to accommodate for speakers' acoustic variations along time, we update their models on the fly by adapting to their newly contributed speech data. Encouraging results have been achieved through extensive experiments, which shows a promising future of the proposed audiovisual-based unsupervised speaker identification system.

From the patient to the clinical mycology laboratory: how can we optimise microscopy and culture methods for mould identification?

PubMed

Vyzantiadis, Timoleon-Achilleas A; Johnson, Elizabeth M; Kibbler, Christopher C

2012-06-01

The identification of fungi relies mainly on morphological criteria. However, there is a need for robust and definitive phenotypic identification procedures in order to evaluate continuously evolving molecular methods. For the future, there is an emerging consensus that a combined (phenotypic and molecular) approach is more powerful for fungal identification, especially for moulds. Most of the procedures used for phenotypic identification are based on experience rather than comparative studies of effectiveness or performance and there is a need for standardisation among mycology laboratories. This review summarises and evaluates the evidence for the major existing phenotypic identification procedures for the predominant causes of opportunistic mould infection. We have concentrated mainly on Aspergillus, Fusarium and mucoraceous mould species, as these are the most important clinically and the ones for which there are the most molecular taxonomic data.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

PubMed

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Efficient Bayesian experimental design for contaminant source identification

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.

2013-12-01

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameter identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from indirect concentration measurements in identifying unknown source parameters such as the release time, strength and location. In this approach, the sampling location that gives the maximum relative entropy is selected as the optimal one. Once the sampling location is determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown source parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. Compared with the traditional optimal design, which is based on the Gaussian linear assumption, the method developed in this study can cope with arbitrary nonlinearity. It can be used to assist in groundwater monitor network design and identification of unknown contaminant sources. Contours of the expected information gain. The optimal observing location corresponds to the maximum value. Posterior marginal probability densities of unknown parameters, the thick solid black lines are for the designed location. For comparison, other 7 lines are for randomly chosen locations. The true values are denoted by vertical lines. It is obvious that the unknown parameters are estimated better with the desinged location.

EVALUATION OF HOST SPECIFIC PCR-BASED METHODS FOR THE IDENTIFICATION OF FECAL POLLUTION

EPA Science Inventory

Microbial Source Tracking (MST) is an approach to determine the origin of fecal pollution impacting a body of water. MST is based on the assumption that, given the appropriate method and indicator, the source of microbial pollution can be identified. One of the key elements of...

Incorporating Physical Activity Into the Schools Using a 3-Tiered Approach

ERIC Educational Resources Information Center

Fedewa, Alicia L.; Candelaria, Ashley; Erwin, Heather E.; Clark, Teresa P.

2013-01-01

Background: Public health models have been used to address a number of school-based concerns, notably in the identification and treatment of students at-risk for academic or behavioral deficits. Significant benefits are associated with this model as, compared to a traditional approach, the focus is shifted from remediation to prevention, and from…

Integrative Approaches for the Identification and Localization of Specialized Metabolites in Tripterygium Roots.

PubMed

Lange, B Markus; Fischedick, Justin T; Lange, Malte F; Srividya, Narayanan; Šamec, Dunja; Poirier, Brenton C

2017-01-01

Members of the genus Tripterygium are known to contain an astonishing diversity of specialized metabolites. The lack of authentic standards has been an impediment to the rapid identification of such metabolites in extracts. We employed an approach that involves the searching of multiple, complementary chromatographic and spectroscopic data sets against the Spektraris database to speed up the metabolite identification process. Mass spectrometry-based imaging indicated a differential localization of triterpenoids to the periderm and sesquiterpene alkaloids to the cortex layer of Tripterygium roots. We further provide evidence that triterpenoids are accumulated to high levels in cells that contain suberized cell walls, which might indicate a mechanism for storage. To our knowledge, our data provide first insights into the cell type specificity of metabolite accumulation in Tripterygium and set the stage for furthering our understanding of the biological implications of specialized metabolites in this genus. © 2017 American Society of Plant Biologists. All Rights Reserved.

Integrative Approaches for the Identification and Localization of Specialized Metabolites in Tripterygium Roots1[OPEN

PubMed Central

Fischedick, Justin T.; Lange, Malte F.; Poirier, Brenton C.

2017-01-01

Members of the genus Tripterygium are known to contain an astonishing diversity of specialized metabolites. The lack of authentic standards has been an impediment to the rapid identification of such metabolites in extracts. We employed an approach that involves the searching of multiple, complementary chromatographic and spectroscopic data sets against the Spektraris database to speed up the metabolite identification process. Mass spectrometry-based imaging indicated a differential localization of triterpenoids to the periderm and sesquiterpene alkaloids to the cortex layer of Tripterygium roots. We further provide evidence that triterpenoids are accumulated to high levels in cells that contain suberized cell walls, which might indicate a mechanism for storage. To our knowledge, our data provide first insights into the cell type specificity of metabolite accumulation in Tripterygium and set the stage for furthering our understanding of the biological implications of specialized metabolites in this genus. PMID:27864443

Online Databases for Taxonomy and Identification of Pathogenic Fungi and Proposal for a Cloud-Based Dynamic Data Network Platform.

PubMed

Prakash, Peralam Yegneswaran; Irinyi, Laszlo; Halliday, Catriona; Chen, Sharon; Robert, Vincent; Meyer, Wieland

2017-04-01

The increase in public online databases dedicated to fungal identification is noteworthy. This can be attributed to improved access to molecular approaches to characterize fungi, as well as to delineate species within specific fungal groups in the last 2 decades, leading to an ever-increasing complexity of taxonomic assortments and nomenclatural reassignments. Thus, well-curated fungal databases with substantial accurate sequence data play a pivotal role for further research and diagnostics in the field of mycology. This minireview aims to provide an overview of currently available online databases for the taxonomy and identification of human and animal-pathogenic fungi and calls for the establishment of a cloud-based dynamic data network platform. Copyright © 2017 American Society for Microbiology.

A Markov random field based approach to the identification of meat and bone meal in feed by near-infrared spectroscopic imaging.

PubMed

Jiang, Xunpeng; Yang, Zengling; Han, Lujia

2014-07-01

Contaminated meat and bone meal (MBM) in animal feedstuff has been the source of bovine spongiform encephalopathy (BSE) disease in cattle, leading to a ban in its use, so methods for its detection are essential. In this study, five pure feed and five pure MBM samples were used to prepare two sets of sample arrangements: set A for investigating the discrimination of individual feed/MBM particles and set B for larger numbers of overlapping particles. The two sets were used to test a Markov random field (MRF)-based approach. A Fourier transform infrared (FT-IR) imaging system was used for data acquisition. The spatial resolution of the near-infrared (NIR) spectroscopic image was 25 μm × 25 μm. Each spectrum was the average of 16 scans across the wavenumber range 7,000-4,000 cm(-1), at intervals of 8 cm(-1). This study introduces an innovative approach to analyzing NIR spectroscopic images: an MRF-based approach has been developed using the iterated conditional mode (ICM) algorithm, integrating initial labeling-derived results from support vector machine discriminant analysis (SVMDA) and observation data derived from the results of principal component analysis (PCA). The results showed that MBM covered by feed could be successfully recognized with an overall accuracy of 86.59% and a Kappa coefficient of 0.68. Compared with conventional methods, the MRF-based approach is capable of extracting spectral information combined with spatial information from NIR spectroscopic images. This new approach enhances the identification of MBM using NIR spectroscopic imaging.

Consistency of the Performance and Nonperformance Methods in Gifted Identification

ERIC Educational Resources Information Center

Acar, Selcuk; Sen, Sedat; Cayirdag, Nur

2016-01-01

Current approaches to gifted identification suggest collecting multiple sources of evidence. Some gifted identification guidelines allow for the interchangeable use of "performance" and "nonperformance" identification methods. This multiple criteria approach lacks a strong overlap between the assessment tools; however,…

Acoustic surface perception from naturally occurring step sounds of a dexterous hexapod robot

NASA Astrophysics Data System (ADS)

Cuneyitoglu Ozkul, Mine; Saranli, Afsar; Yazicioglu, Yigit

2013-10-01

Legged robots that exhibit dynamic dexterity naturally interact with the surface to generate complex acoustic signals carrying rich information on the surface as well as the robot platform itself. However, the nature of a legged robot, which is a complex, hybrid dynamic system, renders the more common approach of model-based system identification impractical. The present paper focuses on acoustic surface identification and proposes a non-model-based analysis and classification approach adopted from the speech processing literature. A novel feature set composed of spectral band energies augmented by their vector time derivatives and time-domain averaged zero crossing rate is proposed. Using a multi-dimensional vector classifier, these features carry enough information to accurately classify a range of commonly occurring indoor and outdoor surfaces without using of any mechanical system model. A comparative experimental study is carried out and classification performance and computational complexity are characterized. Different feature combinations, classifiers and changes in critical design parameters are investigated. A realistic and representative acoustic data set is collected with the robot moving at different speeds on a number of surfaces. The study demonstrates promising performance of this non-model-based approach, even in an acoustically uncontrolled environment. The approach also has good chance of performing in real-time.

Aircraft applications of fault detection and isolation techniques

NASA Astrophysics Data System (ADS)

Marcos Esteban, Andres

In this thesis the problems of fault detection & isolation and fault tolerant systems are studied from the perspective of LTI frequency-domain, model-based techniques. Emphasis is placed on the applicability of these LTI techniques to nonlinear models, especially to aerospace systems. Two applications of Hinfinity LTI fault diagnosis are given using an open-loop (no controller) design approach: one for the longitudinal motion of a Boeing 747-100/200 aircraft, the other for a turbofan jet engine. An algorithm formalizing a robust identification approach based on model validation ideas is also given and applied to the previous jet engine. A general linear fractional transformation formulation is given in terms of the Youla and Dual Youla parameterizations for the integrated (control and diagnosis filter) approach. This formulation provides better insight into the trade-off between the control and the diagnosis objectives. It also provides the basic groundwork towards the development of nested schemes for the integrated approach. These nested structures allow iterative improvements on the control/filter Youla parameters based on successive identification of the system uncertainty (as given by the Dual Youla parameter). The thesis concludes with an application of Hinfinity LTI techniques to the integrated design for the longitudinal motion of the previous Boeing 747-100/200 model.

Gyro-based Maximum-Likelihood Thruster Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Wilson, Edward; Lages, Chris; Mah, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

When building smaller, less expensive spacecraft, there is a need for intelligent fault tolerance vs. increased hardware redundancy. If fault tolerance can be achieved using existing navigation sensors, cost and vehicle complexity can be reduced. A maximum likelihood-based approach to thruster fault detection and identification (FDI) for spacecraft is developed here and applied in simulation to the X-38 space vehicle. The system uses only gyro signals to detect and identify hard, abrupt, single and multiple jet on- and off-failures. Faults are detected within one second and identified within one to five accords,

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

Propensity Score-Based Methods versus MTE-Based Methods in Causal Inference: Identification, Estimation, and Application

ERIC Educational Resources Information Center

Zhou, Xiang; Xie, Yu

2016-01-01

Since the seminal introduction of the propensity score (PS) by Rosenbaum and Rubin, PS-based methods have been widely used for drawing causal inferences in the behavioral and social sciences. However, the PS approach depends on the ignorability assumption: there are no unobserved confounders once observed covariates are taken into account. For…

The Extent to Which Collaborative Teams of Educators Link the Results of Functional Assessment to Function-Based Interventions

ERIC Educational Resources Information Center

de Courcy-Bower, Laurie

2010-01-01

A promising approach to addressing challenging behavior in schools is to develop and implement "function-based interventions" (Dunlap et al., 2006; Hanley, Iwata, & McCord, 2003). Function-based interventions are individualized interventions in which five key outcomes of functional assessment (i.e., identification of challenging behavior,…

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

PubMed

Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

2017-10-01

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

A novel and efficient technique for identification and classification of GPCRs.

PubMed

Gupta, Ravi; Mittal, Ankush; Singh, Kuldip

2008-07-01

G-protein coupled receptors (GPCRs) play a vital role in different biological processes, such as regulation of growth, death, and metabolism of cells. GPCRs are the focus of significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs. The dipeptide-based support vector machine (SVM) approach is the most accurate technique to identify and classify the GPCRs. However, this approach has two major disadvantages. First, the dimension of dipeptide-based feature vector is equal to 400. The large dimension makes the classification task computationally and memory wise inefficient. Second, it does not consider the biological properties of protein sequence for identification and classification of GPCRs. In this paper, we present a novel-feature-based SVM classification technique. The novel features are derived by applying wavelet-based time series analysis approach on protein sequences. The proposed feature space summarizes the variance information of seven important biological properties of amino acids in a protein sequence. In addition, the dimension of the feature vector for proposed technique is equal to 35. Experiments were performed on GPCRs protein sequences available at GPCRs Database. Our approach achieves an accuracy of 99.9%, 98.06%, 97.78%, and 94.08% for GPCR superfamily, families, subfamilies, and subsubfamilies (amine group), respectively, when evaluated using fivefold cross-validation. Further, an accuracy of 99.8%, 97.26%, and 97.84% was obtained when evaluated on unseen or recall datasets of GPCR superfamily, families, and subfamilies, respectively. Comparison with dipeptide-based SVM technique shows the effectiveness of our approach.

HFIP Extraction Followed by 2D CTAB/SDS-PAGE Separation: A New Methodology for Protein Identification from Tissue Sections after MALDI Mass Spectrometry Profiling for Personalized Medicine Research

PubMed Central

Longuespée, Rémi; Tastet, Christophe; Desmons, Annie; Kerdraon, Olivier; Day, Robert

2014-01-01

Abstract Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and profiling technology have become the easiest methods for quickly accessing the protein composition of a tissue area. Unfortunately, the demand for the identification of these proteins remains unmet. To overcome this bottleneck, we combined several strategies to identify the proteins detected via MALDI profiling including on-tissue protein extraction using hexafluoroIsopropanol (1,1,1,3,3,3-hexafluoro-2-propanol, HFIP) coupled with two-dimensional cetyl trimethylammonium bromide/sodium dodecyl sulfate–polyacrylamide gel electrophoresis (2D CTAB/SDS-PAGE) for separation followed by trypsin digestion and MALDI-MS analyses for identification. This strategy was compared with an on-tissue bottom-up strategy that we previously developed. The data reflect the complementarity of the approaches. An increase in the number of specific proteins identified has been established. This approach demonstrates the potential of adapted extraction procedures and the combination of parallel identification approaches for personalized medicine applications. The anatomical context provides important insight into identifying biomarkers and may be considered a first step for tissue-based biomarker research, as well as the extemporaneous examination of biopsies during surgery. PMID:24841221

In silico mining and PCR-based approaches to transcription factor discovery in non-model plants: gene discovery of the WRKY transcription factors in conifers.

PubMed

Liu, Jun-Jun; Xiang, Yu

2011-01-01

WRKY transcription factors are key regulators of numerous biological processes in plant growth and development, as well as plant responses to abiotic and biotic stresses. Research on biological functions of plant WRKY genes has focused in the past on model plant species or species with largely characterized transcriptomes. However, a variety of non-model plants, such as forest conifers, are essential as feed, biofuel, and wood or for sustainable ecosystems. Identification of WRKY genes in these non-model plants is equally important for understanding the evolutionary and function-adaptive processes of this transcription factor family. Because of limited genomic information, the rarity of regulatory gene mRNAs in transcriptomes, and the sequence divergence to model organism genes, identification of transcription factors in non-model plants using methods similar to those generally used for model plants is difficult. This chapter describes a gene family discovery strategy for identification of WRKY transcription factors in conifers by a combination of in silico-based prediction and PCR-based experimental approaches. Compared to traditional cDNA library screening or EST sequencing at transcriptome scales, this integrated gene discovery strategy provides fast, simple, reliable, and specific methods to unveil the WRKY gene family at both genome and transcriptome levels in non-model plants.

Towards the automated identification of Chrysomya blow flies from wing images.

PubMed

Macleod, N; Hall, M J R; Wardhana, A H

2018-04-15

The Old World screwworm fly (OWSF), Chrysomya bezziana (Diptera: Calliphoridae), is an important agent of traumatic myiasis and, as such, a major human and animal health problem. In the implementation of OWSF control operations, it is important to determine the geographical origins of such disease-causing species in order to establish whether they derive from endemic or invading populations. Gross morphological and molecular studies have demonstrated the existence of two distinct lineages of this species, one African and the other Asian. Wing morphometry is known to be of substantial assistance in identifying the geographical origin of individuals because it provides diagnostic markers that complement molecular diagnostics. However, placement of the landmarks used in traditional geometric morphometric analysis can be time-consuming and subject to error caused by operator subjectivity. Here we report results of an image-based approach to geometric morphometric analysis for delivering wing-based identifications. Our results indicate that this approach can produce identifications that are practically indistinguishable from more traditional landmark-based results. In addition, we demonstrate that the direct analysis of digital wing images can be used to discriminate between three Chrysomya species of veterinary and forensic importance and between C. bezziana genders. © 2018 The Trustees of the Natural History Museum, London. Medical and Veterinary Entomology © 2018 Royal Entomological Society.

Automatic Identification of Alpine Mass Movements by a Combination of Seismic and Infrasound Sensors

PubMed Central

Hübl, Johannes; McArdell, Brian W.; Walter, Fabian

2018-01-01

The automatic detection and identification of alpine mass movements such as debris flows, debris floods, or landslides have been of increasing importance for devising mitigation measures in densely populated and intensively used alpine regions. Since these mass movements emit characteristic seismic and acoustic waves in the low-frequency range (<30 Hz), several approaches have already been developed for detection and warning systems based on these signals. However, a combination of the two methods, for improving detection probability and reducing false alarms, is still applied rarely. This paper presents an update and extension of a previously published approach for a detection and identification system based on a combination of seismic and infrasound sensors. Furthermore, this work evaluates the possible early warning times at several test sites and aims to analyze the seismic and infrasound spectral signature produced by different sediment-related mass movements to identify the process type and estimate the magnitude of the event. Thus, this study presents an initial method for estimating the peak discharge and total volume of debris flows based on infrasound data. Tests on several catchments show that this system can detect and identify mass movements in real time directly at the sensor site with high accuracy and a low false alarm ratio. PMID:29789449

Strategies for the Identification and Tracking of Cronobacter Species: An Opportunistic Pathogen of Concern to Neonatal Health

PubMed Central

Yan, Qiongqiong; Fanning, Séamus

2015-01-01

Cronobacter species are emerging opportunistic food-borne pathogens, which consists of seven species, including C. sakazakii, C. malonaticus, C. muytjensii, C. turicensis, C. dublinensis, C. universalis, and C. condimenti. The organism can cause severe clinical infections, including necrotizing enterocolitis, septicemia, and meningitis, predominately among neonates <4 weeks of age. Cronobacter species can be isolated from various foods and their surrounding environments; however, powdered infant formula (PIF) is the most frequently implicated food source linked with Cronobacter infection. This review aims to provide a summary of laboratory-based strategies that can be used to identify and trace Cronobacter species. The identification of Cronobacter species using conventional culture method and immuno-based detection protocols were first presented. The molecular detection and identification at genus-, and species-level along with molecular-based serogroup approaches are also described, followed by the molecular sub-typing methods, in particular pulsed-field gel electrophoresis and multi-locus sequence typing. Next generation sequence approaches, including whole genome sequencing, DNA microarray, and high-throughput whole-transcriptome sequencing, are also highlighted. Appropriate application of these strategies would contribute to reduce the risk of Cronobacter contamination in PIF and production environments, thereby improving food safety and protecting public health. PMID:26000266

KIRMES: kernel-based identification of regulatory modules in euchromatic sequences.

PubMed

Schultheiss, Sebastian J; Busch, Wolfgang; Lohmann, Jan U; Kohlbacher, Oliver; Rätsch, Gunnar

2009-08-15

Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules. We propose a new algorithm that combines the benefits of existing motif finding with the ones of support vector machines (SVMs) to find degenerate motifs in order to improve the modeling of regulatory modules. In experiments on microarray data from Arabidopsis thaliana, we were able to show that the newly developed strategy significantly improves the recognition of TF targets. The python source code (open source-licensed under GPL), the data for the experiments and a Galaxy-based web service are available at http://www.fml.mpg.de/raetsch/suppl/kirmes/.

Rapid Identification of Mycobacterial Whole Cells in Solid and Liquid Culture Media by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry ▿

PubMed Central

Lotz, Aurélie; Ferroni, Agnès; Beretti, Jean-Luc; Dauphin, Brunhilde; Carbonnelle, Etienne; Guet-Revillet, Hélène; Veziris, Nicolas; Heym, Béate; Jarlier, Vincent; Gaillard, Jean-Louis; Pierre-Audigier, Catherine; Frapy, Eric; Berche, Patrick; Nassif, Xavier; Bille, Emmanuelle

2010-01-01

Mycobacterial identification is based on several methods: conventional biochemical tests that require several weeks for accurate identification, and molecular tools that are now routinely used. However, these techniques are expensive and time-consuming. In this study, an alternative method was developed using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). This approach allows a characteristic mass spectral fingerprint to be obtained from whole inactivated mycobacterial cells. We engineered a strategy based on specific profiles in order to identify the most clinically relevant species of mycobacteria. To validate the mycobacterial database, a total of 311 strains belonging to 31 distinct species and 4 species complexes grown in Löwenstein-Jensen (LJ) and liquid (mycobacterium growth indicator tube [MGIT]) media were analyzed. No extraction step was required. Correct identifications were obtained for 97% of strains from LJ and 77% from MGIT media. No misidentification was noted. Our results, based on a very simple protocol, suggest that this system may represent a serious alternative for clinical laboratories to identify mycobacterial species. PMID:20943874

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

PubMed

Lu, Yingjie

2013-01-01

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

Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens

PubMed Central

Lo, Yu-Chen; Senese, Silvia; Li, Chien-Ming; Hu, Qiyang; Huang, Yong; Damoiseaux, Robert; Torres, Jorge Z.

2015-01-01

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/). PMID:25826798

DNA barcoding for identifying synanthropic flesh flies (Diptera, Sarcophagidae) of Colombia.

PubMed

Buenaventura, Eliana; Valverde-Castro, César; Wolff, Marta; Triana-Chavez, Omar; Gómez-Palacio, Andrés

2018-06-01

The first step for a successful use of any insect as indicator in forensic sciences is providing a precise taxonomic identification at species level. Due to morphology-based identification of Sarcophaginae flies (Diptera, Sarcophagidae) is often difficult and requires strong taxonomic expertise, their use as forensic indicators has been limited. Consequently, molecular-based approaches have been accepted as alternative means of identification. Thus, we aimed testing the efficiency of the barcode region of the mitochondrial cytochrome oxidase subunit I (COI) gene for identification of synanthropic flesh flies of several species of the genera Peckia, Oxysarcodexia, Ravinia, and Tricharaea collected in Colombia. The 645-bp fragment of COI was amplified and aligned (215 parsimoniously informative variable sites). We calculated Kimura two-parameter genetic distances and reconstruct a Neighbor-Joining phylogenetic tree. Our Neighbor-Joining tree recovered all species as monophyletic, and confirmed a new species of the genus Ravinia as also indicated by the interspecific genetic divergences and morphological observations. We obtained a 100% of identification success. Thus, the COI barcodes showed efficiency as an alternative mean of identification of species of flesh flies collected on decaying organic matter in Colombia. Copyright © 2018 Elsevier B.V. All rights reserved.

StakeMeter: value-based stakeholder identification and quantification framework for value-based software systems.

PubMed

Babar, Muhammad Imran; Ghazali, Masitah; Jawawi, Dayang N A; Bin Zaheer, Kashif

2015-01-01

Value-based requirements engineering plays a vital role in the development of value-based software (VBS). Stakeholders are the key players in the requirements engineering process, and the selection of critical stakeholders for the VBS systems is highly desirable. Based on the stakeholder requirements, the innovative or value-based idea is realized. The quality of the VBS system is associated with the concrete set of valuable requirements, and the valuable requirements can only be obtained if all the relevant valuable stakeholders participate in the requirements elicitation phase. The existing value-based approaches focus on the design of the VBS systems. However, the focus on the valuable stakeholders and requirements is inadequate. The current stakeholder identification and quantification (SIQ) approaches are neither state-of-the-art nor systematic for the VBS systems. The existing approaches are time-consuming, complex and inconsistent which makes the initiation process difficult. Moreover, the main motivation of this research is that the existing SIQ approaches do not provide the low level implementation details for SIQ initiation and stakeholder metrics for quantification. Hence, keeping in view the existing SIQ problems, this research contributes in the form of a new SIQ framework called 'StakeMeter'. The StakeMeter framework is verified and validated through case studies. The proposed framework provides low-level implementation guidelines, attributes, metrics, quantification criteria and application procedure as compared to the other methods. The proposed framework solves the issues of stakeholder quantification or prioritization, higher time consumption, complexity, and process initiation. The framework helps in the selection of highly critical stakeholders for the VBS systems with less judgmental error.

Design of character-based DNA barcode motif for species identification: A computational approach and its validation in fishes.

PubMed

Chakraborty, Mohua; Dhar, Bishal; Ghosh, Sankar Kumar

2017-11-01

The DNA barcodes are generally interpreted using distance-based and character-based methods. The former uses clustering of comparable groups, based on the relative genetic distance, while the latter is based on the presence or absence of discrete nucleotide substitutions. The distance-based approach has a limitation in defining a universal species boundary across the taxa as the rate of mtDNA evolution is not constant throughout the taxa. However, character-based approach more accurately defines this using a unique set of nucleotide characters. The character-based analysis of full-length barcode has some inherent limitations, like sequencing of the full-length barcode, use of a sparse-data matrix and lack of a uniform diagnostic position for each group. A short continuous stretch of a fragment can be used to resolve the limitations. Here, we observe that a 154-bp fragment, from the transversion-rich domain of 1367 COI barcode sequences can successfully delimit species in the three most diverse orders of freshwater fishes. This fragment is used to design species-specific barcode motifs for 109 species by the character-based method, which successfully identifies the correct species using a pattern-matching program. The motifs also correctly identify geographically isolated population of the Cypriniformes species. Further, this region is validated as a species-specific mini-barcode for freshwater fishes by successful PCR amplification and sequencing of the motif (154 bp) using the designed primers. We anticipate that use of such motifs will enhance the diagnostic power of DNA barcode, and the mini-barcode approach will greatly benefit the field-based system of rapid species identification. © 2017 John Wiley & Sons Ltd.

Computational approaches to protein inference in shotgun proteomics

PubMed Central

2012-01-01

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area. PMID:23176300

An adaptive learning control system for large flexible structures

NASA Technical Reports Server (NTRS)

Thau, F. E.

1985-01-01

The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

Communicating with children and parents: recommendations for a child-parent-centred approach for paediatric dentistry.

PubMed

Freeman, R

2008-02-01

The European Academy of Paediatric Dentistry has called for a series of evidence-based statements to inform their guidelines on the behavioural management of the child patient. Communication between dentist, parent and child based upon scientifically robust research evidence was felt to be central to this request in order to provide empathetic and child-centred care for children and their parents attending for dental health care. Shekelle and colleagues [1999] devised a series of steps to develop an evidence-based clinical guideline. This framework allows first, the identification and refinement of the subject area and secondly, the identification and assessment of the evidence-base. Four areas of communication were identified as being of central importance. These were identification of the mother-child dyad; affective communication skills; problem solving and negotiation skills. It was recommended that paediatric dentists should become knowledgeable and competent in these skills in order to provide patient-centred care for the children and parents attending their clinics for dental treatment.

Use of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Identification of Molds of the Fusarium Genus

PubMed Central

Stubbe, Dirk; De Cremer, Koen; Piérard, Denis; Normand, Anne-Cécile; Piarroux, Renaud; Detandt, Monique; Hendrickx, Marijke

2014-01-01

The rates of infection with Fusarium molds are increasing, and a diverse number of Fusarium spp. belonging to different species complexes can cause infection. Conventional species identification in the clinical laboratory is time-consuming and prone to errors. We therefore evaluated whether matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) is a useful alternative. The 289 Fusarium strains from the Belgian Coordinated Collections of Microorganisms (BCCM)/Institute of Hygiene and Epidemiology Mycology (IHEM) culture collection with validated sequence-based identities and comprising 40 species were used in this study. An identification strategy was developed, applying a standardized MALDI-TOF MS assay and an in-house reference spectrum database. In vitro antifungal testing was performed to assess important differences in susceptibility between clinically relevant species/species complexes. We observed that no incorrect species complex identifications were made by MALDI-TOF MS, and 82.8% of the identifications were correct to the species level. This success rate was increased to 91% by lowering the cutoff for identification. Although the identification of the correct species complex member was not always guaranteed, antifungal susceptibility testing showed that discriminating between Fusarium species complexes can be important for treatment but is not necessarily required between members of a species complex. With this perspective, some Fusarium species complexes with closely related members can be considered as a whole, increasing the success rate of correct identifications to 97%. The application of our user-friendly MALDI-TOF MS identification approach resulted in a dramatic improvement in both time and accuracy compared to identification with the conventional method. A proof of principle of our MALDI-TOF MS approach in the clinical setting using recently isolated Fusarium strains demonstrated its validity. PMID:25411180

Parameters Identification of Interface Friction Model for Ceramic Matrix Composites Based on Stress-Strain Response

NASA Astrophysics Data System (ADS)

Han, Xiao; Gao, Xiguang; Song, Yingdong

2017-10-01

An approach to identify parameters of interface friction model for Ceramic Matrix composites based on stress-strain response was developed. The stress distribution of fibers in the interface slip region and intact region of the damaged composite was determined by adopting the interface friction model. The relation between maximum strain, secant moduli of hysteresis loop and interface shear stress, interface de-bonding stress was established respectively with the method of symbolic-graphic combination. By comparing the experimental strain, secant moduli of hysteresis loop with computation values, the interface shear stress and interface de-bonding stress corresponding to first cycle were identified. Substituting the identification of parameters into interface friction model, the stress-strain curves were predicted and the predicted results fit experiments well. Besides, the influence of number of data points on identifying the value of interface parameters was discussed. And the approach was compared with the method based on the area of hysteresis loop.

Structure-based multiscale approach for identification of interaction partners of PDZ domains.

PubMed

Tiwari, Garima; Mohanty, Debasisa

2014-04-28

PDZ domains are peptide recognition modules which mediate specific protein-protein interactions and are known to have a complex specificity landscape. We have developed a novel structure-based multiscale approach which identifies crucial specificity determining residues (SDRs) of PDZ domains from explicit solvent molecular dynamics (MD) simulations on PDZ-peptide complexes and uses these SDRs in combination with knowledge-based scoring functions for proteomewide identification of their interaction partners. Multiple explicit solvent simulations ranging from 5 to 50 ns duration have been carried out on 28 PDZ-peptide complexes with known binding affinities. MM/PBSA binding energy values calculated from these simulations show a correlation coefficient of 0.755 with the experimental binding affinities. On the basis of the SDRs of PDZ domains identified by MD simulations, we have developed a simple scoring scheme for evaluating binding energies for PDZ-peptide complexes using residue based statistical pair potentials. This multiscale approach has been benchmarked on a mouse PDZ proteome array data set by calculating the binding energies for 217 different substrate peptides in binding pockets of 64 different mouse PDZ domains. Receiver operating characteristic (ROC) curve analysis indicates that, the area under curve (AUC) values for binder vs nonbinder classification by our structure based method is 0.780. Our structure based method does not require experimental PDZ-peptide binding data for training.

Soft Biometrics; Human Identification Using Comparative Descriptions.

PubMed

Reid, Daniel A; Nixon, Mark S; Stevenage, Sarah V

2014-06-01

Soft biometrics are a new form of biometric identification which use physical or behavioral traits that can be naturally described by humans. Unlike other biometric approaches, this allows identification based solely on verbal descriptions, bridging the semantic gap between biometrics and human description. To permit soft biometric identification the description must be accurate, yet conventional human descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe differences between subjects. This innovative approach has been shown to address many problems associated with absolute categorical labels-most critically, the descriptions contain more objective information and have increased discriminatory capabilities. Relative measurements of the subjects' traits can be inferred from comparative human descriptions using the Elo rating system. The resulting soft biometric signatures have been demonstrated to be robust and allow accurate recognition of subjects. Relative measurements can also be obtained from other forms of human representation. This is demonstrated using a support vector machine to determine relative measurements from gait biometric signatures-allowing retrieval of subjects from video footage by using human comparisons, bridging the semantic gap.

Refined identification of Vibrio bacterial flora from Acanthasther planci based on biochemical profiling and analysis of housekeeping genes.

PubMed

Rivera-Posada, J A; Pratchett, M; Cano-Gomez, A; Arango-Gomez, J D; Owens, L

2011-09-09

We used a polyphasic approach for precise identification of bacterial flora (Vibrionaceae) isolated from crown-of-thorns starfish (COTS) from Lizard Island (Great Barrier Reef, Australia) and Guam (U.S.A., Western Pacific Ocean). Previous 16S rRNA gene phylogenetic analysis was useful to allocate and identify isolates within the Photobacterium, Splendidus and Harveyi clades but failed in the identification of Vibrio harveyi-like isolates. Species of the V harveyi group have almost indistinguishable phenotypes and genotypes, and thus, identification by standard biochemical tests and 16S rRNA gene analysis is commonly inaccurate. Biochemical profiling and sequence analysis of additional topA and mreB housekeeping genes were carried out for definitive identification of 19 bacterial isolates recovered from sick and wild COTS. For 8 isolates, biochemical profiles and topA and mreB gene sequence alignments with the closest relatives (GenBank) confirmed previous 16S rRNA-based identification: V. fortis and Photobacterium eurosenbergii species (from wild COTS), and V natriegens (from diseased COTS). Further phylogenetic analysis based on topA and mreB concatenated sequences served to identify the remaining 11 V harveyi-like isolates: V. owensii and V. rotiferianus (from wild COTS), and V. owensii, V. rotiferianus, and V. harveyi (from diseased COTS). This study further confirms the reliability of topA-mreB gene sequence analysis for identification of these close species, and it reveals a wider distribution range of the potentially pathogenic V. harveyi group.

Fragment-Linking Approach Using (19)F NMR Spectroscopy To Obtain Highly Potent and Selective Inhibitors of β-Secretase.

PubMed

Jordan, John B; Whittington, Douglas A; Bartberger, Michael D; Sickmier, E Allen; Chen, Kui; Cheng, Yuan; Judd, Ted

2016-04-28

Fragment-based drug discovery (FBDD) has become a widely used tool in small-molecule drug discovery efforts. One of the most commonly used biophysical methods in detecting weak binding of fragments is nuclear magnetic resonance (NMR) spectroscopy. In particular, FBDD performed with (19)F NMR-based methods has been shown to provide several advantages over (1)H NMR using traditional magnetization-transfer and/or two-dimensional methods. Here, we demonstrate the utility and power of (19)F-based fragment screening by detailing the identification of a second-site fragment through (19)F NMR screening that binds to a specific pocket of the aspartic acid protease, β-secretase (BACE-1). The identification of this second-site fragment allowed the undertaking of a fragment-linking approach, which ultimately yielded a molecule exhibiting a more than 360-fold increase in potency while maintaining reasonable ligand efficiency and gaining much improved selectivity over cathepsin-D (CatD). X-ray crystallographic studies of the molecules demonstrated that the linked fragments exhibited binding modes consistent with those predicted from the targeted screening approach, through-space NMR data, and molecular modeling.

Identifying Recent HIV Infections: From Serological Assays to Genomics.

PubMed

Moyo, Sikhulile; Wilkinson, Eduan; Novitsky, Vladimir; Vandormael, Alain; Gaseitsiwe, Simani; Essex, Max; Engelbrecht, Susan; de Oliveira, Tulio

2015-10-23

In this paper, we review serological and molecular based methods to identify HIV infection recency. The accurate identification of recent HIV infection continues to be an important research area and has implications for HIV prevention and treatment interventions. Longitudinal cohorts that follow HIV negative individuals over time are the current gold standard approach, but they are logistically challenging, time consuming and an expensive enterprise. Methods that utilize cross-sectional testing and biomarker information have become an affordable alternative to the longitudinal approach. These methods use well-characterized biological makers to differentiate between recent and established HIV infections. However, recent results have identified a number of limitations in serological based assays that are sensitive to the variability in immune responses modulated by HIV subtypes, viral load and antiretroviral therapy. Molecular methods that explore the dynamics between the timing of infection and viral evolution are now emerging as a promising approach. The combination of serological and molecular methods may provide a good solution to identify recent HIV infection in cross-sectional data. As part of this review, we present the advantages and limitations of serological and molecular based methods and their potential complementary role for the identification of HIV infection recency.

IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion.

PubMed

Dehzangi, Omid; Taherisadr, Mojtaba; ChangalVala, Raghvendar

2017-11-27

The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF) expansion of human gait cycles in order to capture joint 2 dimensional (2D) spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN) learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level) and late (decision score level) multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF) method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class identification task. Based on our experimental results, 91% subject identification accuracy was achieved using the best individual IMU and 2DTF-DCNN. We then investigated our proposed early and late sensor fusion approaches, which improved the gait identification accuracy of the system to 93.36% and 97.06%, respectively.

Biochemical component identification by plasmonic improved whispering gallery mode optical resonance based sensor

NASA Astrophysics Data System (ADS)

Saetchnikov, Vladimir A.; Tcherniavskaia, Elina A.; Saetchnikov, Anton V.; Schweiger, Gustav; Ostendorf, Andreas

2014-05-01

Experimental data on detection and identification of variety of biochemical agents, such as proteins, microelements, antibiotic of different generation etc. in both single and multi component solutions under varied in wide range concentration analyzed on the light scattering parameters of whispering gallery mode optical resonance based sensor are represented. Multiplexing on parameters and components has been realized using developed fluidic sensor cell with fixed in adhesive layer dielectric microspheres and data processing. Biochemical component identification has been performed by developed network analysis techniques. Developed approach is demonstrated to be applicable both for single agent and for multi component biochemical analysis. Novel technique based on optical resonance on microring structures, plasmon resonance and identification tools has been developed. To improve a sensitivity of microring structures microspheres fixed by adhesive had been treated previously by gold nanoparticle solution. Another technique used thin film gold layers deposited on the substrate below adhesive. Both biomolecule and nanoparticle injections caused considerable changes of optical resonance spectra. Plasmonic gold layers under optimized thickness also improve parameters of optical resonance spectra. Biochemical component identification has been also performed by developed network analysis techniques both for single and for multi component solution. So advantages of plasmon enhancing optical microcavity resonance with multiparameter identification tools is used for development of a new platform for ultra sensitive label-free biomedical sensor.

Toward the characterization of biological toxins using field-based FT-IR spectroscopic instrumentation

NASA Astrophysics Data System (ADS)

Schiering, David W.; Walton, Robert B.; Brown, Christopher W.; Norman, Mark L.; Brewer, Joseph; Scott, James

2004-12-01

IR spectroscopy is a broadly applicable technique for the identification of covalent materials. Recent advances in instrumentation have made Fourier Transform infrared (FT-IR) spectroscopy available for field characterization of suspect materials. Presently, this instrumentation is broadly deployed and used for the identification of potential chemical hazards. This discussion concerns work towards expanding the analytical utility of field-based FT-IR spectrometry in the characterization of biological threats. Two classes of materials were studied: biologically produced chemical toxins which were non-peptide in nature and peptide toxin. The IR spectroscopic identification of aflatoxin-B1, trichothecene T2 mycotoxin, and strychnine was evaluated using the approach of spectral searching against large libraries of materials. For pure components, the IR method discriminated the above toxins at better than the 99% confidence level. The ability to identify non-peptide toxins in mixtures was also evaluated using a "spectral stripping" search approach. For the mixtures evaluated, this method was able to identify the mixture components from ca. 32K spectral library entries. Castor bean extract containing ricin was used as a representative peptide toxin. Due to similarity in protein spectra, a SIMCA pattern recognition methodology was evaluated for classifying peptide toxins. In addition to castor bean extract the method was validated using bovine serum albumin and myoglobin as simulants. The SIMCA approach was successful in correctly classifying these samples at the 95% confidence level.

Semi-automatic forensic approach using mandibular midline lingual structures as fingerprint: a pilot study.

PubMed

Shaheen, E; Mowafy, B; Politis, C; Jacobs, R

2017-12-01

Previous research proposed the use of the mandibular midline neurovascular canal structures as a forensic finger print. In their observer study, an average correct identification of 95% was reached which triggered this study. To present a semi-automatic computer recognition approach to replace the observers and to validate the accuracy of this newly proposed method. Imaging data from Computer Tomography (CT) and Cone Beam Computer Tomography (CBCT) of mandibles scanned at two different moments were collected to simulate an AM and PM situation where the first scan presented AM and the second scan was used to simulate PM. Ten cases with 20 scans were used to build a classifier which relies on voxel based matching and results with classification into one of two groups: "Unmatched" and "Matched". This protocol was then tested using five other scans out of the database. Unpaired t-testing was applied and accuracy of the computerized approach was determined. A significant difference was found between the "Unmatched" and "Matched" classes with means of 0.41 and 0.86 respectively. Furthermore, the testing phase showed an accuracy of 100%. The validation of this method pushes this protocol further to a fully automatic identification procedure for victim identification based on the mandibular midline canals structures only in cases with available AM and PM CBCT/CT data.

Automatic detection of protected health information from clinic narratives.

PubMed

Yang, Hui; Garibaldi, Jonathan M

2015-12-01

This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories and 25 associated sub-categories. A hybrid model was proposed which combines machine learning techniques with keyword-based and rule-based approaches to deal with the complexity inherent in PHI categories. Our proposed approaches exploit a rich set of linguistic features, both syntactic and word surface-oriented, which are further enriched by task-specific features and regular expression template patterns to characterize the semantics of various PHI categories. Our system achieved promising accuracy on the challenge test data with an overall micro-averaged F-measure of 93.6%, which was the winner of this de-identification challenge. Copyright © 2015 Elsevier Inc. All rights reserved.

A knowledge-based approach to identification and adaptation in dynamical systems control

NASA Technical Reports Server (NTRS)

Glass, B. J.; Wong, C. M.

1988-01-01

Artificial intelligence techniques are applied to the problems of model form and parameter identification of large-scale dynamic systems. The object-oriented knowledge representation is discussed in the context of causal modeling and qualitative reasoning. Structured sets of rules are used for implementing qualitative component simulations, for catching qualitative discrepancies and quantitative bound violations, and for making reconfiguration and control decisions that affect the physical system. These decisions are executed by backward-chaining through a knowledge base of control action tasks. This approach was implemented for two examples: a triple quadrupole mass spectrometer and a two-phase thermal testbed. Results of tests with both of these systems demonstrate that the software replicates some or most of the functionality of a human operator, thereby reducing the need for a human-in-the-loop in the lower levels of control of these complex systems.

An ensemble-based approach for breast mass classification in mammography images

NASA Astrophysics Data System (ADS)

Ribeiro, Patricia B.; Papa, João. P.; Romero, Roseli A. F.

2017-03-01

Mammography analysis is an important tool that helps detecting breast cancer at the very early stages of the disease, thus increasing the quality of life of hundreds of thousands of patients worldwide. In Computer-Aided Detection systems, the identification of mammograms with and without masses (without clinical findings) is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest that may contain some suspicious content. In this work, the introduce a variant of the Optimum-Path Forest (OPF) classifier for breast mass identification, as well as we employed an ensemble-based approach that can enhance the effectiveness of individual classifiers aiming at dealing with the aforementioned purpose. The experimental results also comprise the naïve OPF and a traditional neural network, being the most accurate results obtained through the ensemble of classifiers, with an accuracy nearly to 86%.

Functional clustering of time series gene expression data by Granger causality

PubMed Central

2012-01-01

Background A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them. PMID:23107425

FBRDLR: Fast blind reconstruction approach with dictionary learning regularization for infrared microscopy spectra

NASA Astrophysics Data System (ADS)

Liu, Tingting; Liu, Hai; Chen, Zengzhao; Chen, Yingying; Wang, Shengming; Liu, Zhi; Zhang, Hao

2018-05-01

Infrared (IR) spectra are the fingerprints of the molecules, and the spectral band location closely relates to the structure of a molecule. Thus, specimen identification can be performed based on IR spectroscopy. However, spectrally overlapping components prevent the specific identification of hyperfine molecular information of different substances. In this paper, we propose a fast blind reconstruction approach for IR spectra, which is based on sparse and redundant representations over a dictionary. The proposed method recovers the spectrum with the discrete wavelet transform dictionary on its content. The experimental results demonstrate that the proposed method is superior because of the better performance when compared with other state-of-the-art methods. The method the authors used remove the instrument aging issue to a large extent, thus leading the reconstruction IR spectra a more convenient tool for extracting features of an unknown material and interpreting it.

Further Validation of Simulated Dynamic Interface Testing Techniques as a Tool in the Forecasting of Air Vehicle Deck Limits

DTIC Science & Technology

2010-01-01

UAV Autonomy program which includes intelligent reasoning for autonomy, technologies to enhance see and avoid capabilities, object identification ...along the ship’s base recovery course (BRC). The pilot then flies toward the stern of the ship, aligning his approach path with the ship’s lineup line...quiescent point identification . CONCLUSIONS The primary goal for conducting dynamic interface analysis is to expand existing operating envelopes and

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting

PubMed Central

Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

2017-01-01

Purpose Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Methods Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. Results There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). Conclusions MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics. PMID:28873173

Study on the Spatial Resolution of Single and Multiple Coincidences Compton Camera

NASA Astrophysics Data System (ADS)

Andreyev, Andriy; Sitek, Arkadiusz; Celler, Anna

2012-10-01

In this paper we study the image resolution that can be obtained from the Multiple Coincidences Compton Camera (MCCC). The principle of MCCC is based on a simultaneous acquisition of several gamma-rays emitted in cascade from a single nucleus. Contrary to a standard Compton camera, MCCC can theoretically provide the exact location of a radioactive source (based only on the identification of the intersection point of three cones created by a single decay), without complicated tomographic reconstruction. However, practical implementation of the MCCC approach encounters several problems, such as low detection sensitivities result in very low probability of coincident triple gamma-ray detection, which is necessary for the source localization. It is also important to evaluate how the detection uncertainties (finite energy and spatial resolution) influence identification of the intersection of three cones, thus the resulting image quality. In this study we investigate how the spatial resolution of the reconstructed images using the triple-cone reconstruction (TCR) approach compares to images reconstructed from the same data using standard iterative method based on single-cone. Results show, that FWHM for the point source reconstructed with TCR was 20-30% higher than the one obtained from the standard iterative reconstruction based on expectation maximization (EM) algorithm and conventional single-cone Compton imaging. Finite energy and spatial resolutions of the MCCC detectors lead to errors in conical surfaces definitions (“thick” conical surfaces) which only amplify in image reconstruction when intersection of three cones is being sought. Our investigations show that, in spite of being conceptually appealing, the identification of triple cone intersection constitutes yet another restriction of the multiple coincidence approach which limits the image resolution that can be obtained with MCCC and TCR algorithm.

Visual perception-based criminal identification: a query-based approach

NASA Astrophysics Data System (ADS)

Singh, Avinash Kumar; Nandi, G. C.

2017-01-01

The visual perception of eyewitness plays a vital role in criminal identification scenario. It helps law enforcement authorities in searching particular criminal from their previous record. It has been reported that searching a criminal record manually requires too much time to get the accurate result. We have proposed a query-based approach which minimises the computational cost along with the reduction of search space. A symbolic database has been created to perform a stringent analysis on 150 public (Bollywood celebrities and Indian cricketers) and 90 local faces (our data-set). An expert knowledge has been captured to encapsulate every criminal's anatomical and facial attributes in the form of symbolic representation. A fast query-based searching strategy has been implemented using dynamic decision tree data structure which allows four levels of decomposition to fetch respective criminal records. Two types of case studies - viewed and forensic sketches have been considered to evaluate the strength of our proposed approach. We have derived 1200 views of the entire population by taking into consideration 80 participants as eyewitness. The system demonstrates an accuracy level of 98.6% for test case I and 97.8% for test case II. It has also been reported that experimental results reduce the search space up to 30 most relevant records.

OFFGEL electrophoresis and tandem mass spectrometry approach compared with DNA-based PCR method for authentication of meat species from raw and cooked ground meat mixtures containing cattle meat, water buffalo meat and sheep meat.

PubMed

Naveena, Basappa M; Jagadeesh, Deepak S; Jagadeesh Babu, A; Madhava Rao, T; Kamuni, Veeranna; Vaithiyanathan, S; Kulkarni, Vinayak V; Rapole, Srikanth

2017-10-15

The present study compared the accuracy of an OFFGEL electrophoresis and tandem mass spectrometry-based proteomic approach with a DNA-based method for meat species identification from raw and cooked ground meat mixes containing cattle, water buffalo and sheep meat. The proteomic approach involved the separation of myofibrillar proteins using OFFGEL electrophoresis, SDS-PAGE and protein identification by MALDI-TOF MS. Species-specific peptides derived from myosin light chain-1 and 2 were identified for authenticating buffalo meat spiked at a minimum 0.5% level in sheep meat with high confidence. Relative quantification of buffalo meat mixed with sheep meat was done by quantitative label-free mass spectrometry using UPLC-QTOF and PLGS search engine to substantiate the confidence level of the data. In the DNA-based method, PCR amplification of mitochondrial D loop gene using species specific primers found 226bp and 126bp product amplicons for buffalo and cattle meat, respectively. The method was efficient in detecting a minimum of 0.5% and 1.0% when buffalo meat was spiked with cattle meat in raw and cooked meat mixes. Copyright © 2017 Elsevier Ltd. All rights reserved.

The specific diagnosis of gastrointestinal nematode infections in livestock: larval culture technique, its limitations and alternative DNA-based approaches.

PubMed

Roeber, Florian; Kahn, Lewis

2014-10-15

The specific diagnosis of gastrointestinal nematode infections in ruminants is routinely based on larval culture technique and on the morphological identification of developed third-stage larvae. However, research on the ecology and developmental requirements of different species suggests that environmental conditions (e.g., temperature and humidity) for optimal development to occur vary between the different species. Thus, employing a common culture protocol for all species will favour the development of certain species over others and can cause a biased result in particular when species proportions in a mixed infection are to be determined. Furthermore, the morphological identification of L3 larvae is complicated by a lack of distinctive, obvious features that would allow the identification of all key species. In the present paper we review in detail the potential limitations of larval culture technique and morphological identification and provide account to some modern molecular alternatives to the specific diagnosis of gastrointestinal nematode infection in ruminants. Copyright © 2014 Elsevier B.V. All rights reserved.

Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

PubMed Central

Sun, Jimeng; Hu, Jianying; Luo, Dijun; Markatou, Marianthi; Wang, Fei; Edabollahi, Shahram; Steinhubl, Steven E.; Daar, Zahra; Stewart, Walter F.

2012-01-01

Background: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. Methods: We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. Results: The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003–2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. Conclusion: We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors. PMID:23304365

Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

NASA Technical Reports Server (NTRS)

Innocenti, M.; Napolitano, M.

2003-01-01

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets.

PubMed

Vasaikar, Suhas; Bhatia, Pooja; Bhatia, Partap G; Chu Yaiw, Koon

2016-11-21

In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.

LC-MS-Based Lipidomics and Automated Identification of Lipids Using the LipidBlast In-Silico MS/MS Library.

PubMed

Cajka, Tomas; Fiehn, Oliver

2017-01-01

This protocol describes the analysis, specifically the identification, of blood plasma lipids. Plasma lipids are extracted using methyl tert-butyl ether (MTBE), methanol, and water followed by separation and data acquisition of isolated lipids using reversed-phase liquid chromatography coupled to quadrupole/time-of-flight mass spectrometry (RPLC-QTOFMS) operated in MS/MS mode. For lipid identification, acquired MS/MS spectra are converted to the mascot generic format (MGF) followed by library search using the in-silico MS/MS library LipidBlast. Using this approach, lipid classes, carbon-chain lengths, and degree of unsaturation of fatty-acid components are annotated.

Training Strategies to Mitigate Expectancy-Induced Response Bias in Combat Identification: A Research Agenda

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

Greitzer, Frank L.; Andrews, Dee H.

Historical assessments of combat fratricide reveal principal contributing factors in the effects of stress, continuous operations or sleep deprivation, poor situational awareness, emotions, and lack of training. This paper discusses what and how improvements in combat identification (CID) may be achieved through training. In addition to skill-based training, CID training must focus on countering the negative effects of expectancy in the face of heightened anxiety and stressors of continuous operations that lead to combat errors or fratricide. The paper examines possible approaches to training for overcoming erroneous expectancies and emotional factors that may distort or limit accurate "blue force" identification.

Training Strategies to Mitigate Expectancy-Induced Response Bias in Combat Identification: A Research Agenda

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

Greitzer, Frank L.; Andrews, Dee H.

2008-04-15

Historical assessments of combat fratricide reveal principal contributing factors in the effects of stress, continuous operations or sleep deprivation, poor situational awareness, emotions, and lack of training. This paper discusses what and how improvements in combat identification (CID) may be achieved through training. In addition to skill-based training, CID training must focus on countering the negative effects of expectancy in the face of heightened anxiety and stressors of continuous operations that lead to combat errors or fratricide. The paper examines possible approaches to training for overcoming erroneous expectancies and emotional factors that may distort or limit accurate "blue force" identification.

Large-scale identification of target proteins of a glycosyltransferase isozyme by Lectin-IGOT-LC/MS, an LC/MS-based glycoproteomic approach

PubMed Central

Sugahara, Daisuke; Kaji, Hiroyuki; Sugihara, Kazushi; Asano, Masahide; Narimatsu, Hisashi

2012-01-01

Model organisms containing deletion or mutation in a glycosyltransferase-gene exhibit various physiological abnormalities, suggesting that specific glycan motifs on certain proteins play important roles in vivo. Identification of the target proteins of glycosyltransferase isozymes is the key to understand the roles of glycans. Here, we demonstrated the proteome-scale identification of the target proteins specific for a glycosyltransferase isozyme, β1,4-galactosyltransferase-I (β4GalT-I). Although β4GalT-I is the most characterized glycosyltransferase, its distinctive contribution to β1,4-galactosylation has been hardly described so far. We identified a large number of candidates for the target proteins specific to β4GalT-I by comparative analysis of β4GalT-I-deleted and wild-type mice using the LC/MS-based technique with the isotope-coded glycosylation site-specific tagging (IGOT) of lectin-captured N-glycopeptides. Our approach to identify the target proteins in a proteome-scale offers common features and trends in the target proteins, which facilitate understanding of the mechanism that controls assembly of a particular glycan motif on specific proteins. PMID:23002422

Intraspecific ITS Variability in the Kingdom Fungi as Expressed in the International Sequence Databases and Its Implications for Molecular Species Identification

PubMed Central

Nilsson, R. Henrik; Kristiansson, Erik; Ryberg, Martin; Hallenberg, Nils; Larsson, Karl-Henrik

2008-01-01

The internal transcribed spacer (ITS) region of the nuclear ribosomal repeat unit is the most popular locus for species identification and subgeneric phylogenetic inference in sequence-based mycological research. The region is known to show certain variability even within species, although its intraspecific variability is often held to be limited and clearly separated from interspecific variability. The existence of such a divide between intra- and interspecific variability is implicitly assumed by automated approaches to species identification, but whether intraspecific variability indeed is negligible within the fungal kingdom remains contentious. The present study estimates the intraspecific ITS variability in all fungi presently available to the mycological community through the international sequence databases. Substantial differences were found within the kingdom, and the results are not easily correlated to the taxonomic affiliation or nutritional mode of the taxa considered. No single unifying yet stringent upper limit for intraspecific variability, such as the canonical 3% threshold, appears to be applicable with the desired outcome throughout the fungi. Our results caution against simplified approaches to automated ITS-based species delimitation and reiterate the need for taxonomic expertise in the translation of sequence data into species names. PMID:19204817

Polluter identification with spaceborne radar imagery, AIS and forward drift modeling.

PubMed

Longépé, N; Mouche, A A; Goacolou, M; Granier, N; Carrere, L; Lebras, J Y; Lozach, P; Besnard, S

2015-12-30

This study defines and assesses a new operational concept to identify the origin of pollution at sea, based on Synthetic Aperture Radar, Automatic Identification System, and a forward drift model. As opposed to traditional methodologies where the SAR detected pollution is backtracked in the past, our approach assumes that all the vessels pollute all along their way. Based on all the AIS data flows, the forward-tracked simulated pollutions are then compared to the detected pollution, and the potential polluter can be finally identified. Case studies are presented to showcase its usefulness in a variety of maritime situations with a focus on orphan pollutions in a dense traffic area. Out of the identification of the suspected polluters, the age and eventually the type of the pollution can be retrieved. Copyright © 2015 Elsevier Ltd. All rights reserved.

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil

2010-01-01

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

Gene prioritization and clustering by multi-view text mining

PubMed Central

2010-01-01

Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification. PMID:20074336

Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation

NASA Astrophysics Data System (ADS)

Ebrahimian, Hamed; Astroza, Rodrigo; Conte, Joel P.; de Callafon, Raymond A.

2017-02-01

This paper presents a framework for structural health monitoring (SHM) and damage identification of civil structures. This framework integrates advanced mechanics-based nonlinear finite element (FE) modeling and analysis techniques with a batch Bayesian estimation approach to estimate time-invariant model parameters used in the FE model of the structure of interest. The framework uses input excitation and dynamic response of the structure and updates a nonlinear FE model of the structure to minimize the discrepancies between predicted and measured response time histories. The updated FE model can then be interrogated to detect, localize, classify, and quantify the state of damage and predict the remaining useful life of the structure. As opposed to recursive estimation methods, in the batch Bayesian estimation approach, the entire time history of the input excitation and output response of the structure are used as a batch of data to estimate the FE model parameters through a number of iterations. In the case of non-informative prior, the batch Bayesian method leads to an extended maximum likelihood (ML) estimation method to estimate jointly time-invariant model parameters and the measurement noise amplitude. The extended ML estimation problem is solved efficiently using a gradient-based interior-point optimization algorithm. Gradient-based optimization algorithms require the FE response sensitivities with respect to the model parameters to be identified. The FE response sensitivities are computed accurately and efficiently using the direct differentiation method (DDM). The estimation uncertainties are evaluated based on the Cramer-Rao lower bound (CRLB) theorem by computing the exact Fisher Information matrix using the FE response sensitivities with respect to the model parameters. The accuracy of the proposed uncertainty quantification approach is verified using a sampling approach based on the unscented transformation. Two validation studies, based on realistic structural FE models of a bridge pier and a moment resisting steel frame, are performed to validate the performance and accuracy of the presented nonlinear FE model updating approach and demonstrate its application to SHM. These validation studies show the excellent performance of the proposed framework for SHM and damage identification even in the presence of high measurement noise and/or way-out initial estimates of the model parameters. Furthermore, the detrimental effects of the input measurement noise on the performance of the proposed framework are illustrated and quantified through one of the validation studies.

On parameters identification of computational models of vibrations during quiet standing of humans

NASA Astrophysics Data System (ADS)

Barauskas, R.; Krušinskienė, R.

2007-12-01

Vibration of the center of pressure (COP) of human body on the base of support during quiet standing is a very popular clinical research, which provides useful information about the physical and health condition of an individual. In this work, vibrations of COP of a human body in forward-backward direction during still standing are generated using controlled inverted pendulum (CIP) model with a single degree of freedom (dof) supplied with proportional, integral and differential (PID) controller, which represents the behavior of the central neural system of a human body and excited by cumulative disturbance vibration, generated within the body due to breathing or any other physical condition. The identification of the model and disturbance parameters is an important stage while creating a close-to-reality computational model able to evaluate features of disturbance. The aim of this study is to present the CIP model parameters identification approach based on the information captured by time series of the COP signal. The identification procedure is based on an error function minimization. Error function is formulated in terms of time laws of computed and experimentally measured COP vibrations. As an alternative, error function is formulated in terms of the stabilogram diffusion function (SDF). The minimization of error functions is carried out by employing methods based on sensitivity functions of the error with respect to model and excitation parameters. The sensitivity functions are obtained by using the variational techniques. The inverse dynamic problem approach has been employed in order to establish the properties of the disturbance time laws ensuring the satisfactory coincidence of measured and computed COP vibration laws. The main difficulty of the investigated problem is encountered during the model validation stage. Generally, neither the PID controller parameter set nor the disturbance time law are known in advance. In this work, an error function formulated in terms of time derivative of disturbance torque has been proposed in order to obtain PID controller parameters, as well as the reference time law of the disturbance. The disturbance torque is calculated from experimental data using the inverse dynamic approach. Experiments presented in this study revealed that vibrations of disturbance torque and PID controller parameters identified by the method may be qualified as feasible in humans. Presented approach may be easily extended to structural models with any number of dof or higher structural complexity.

Peptide Array X-Linking (PAX): A New Peptide-Protein Identification Approach

PubMed Central

Okada, Hirokazu; Uezu, Akiyoshi; Soderblom, Erik J.; Moseley, M. Arthur; Gertler, Frank B.; Soderling, Scott H.

2012-01-01

Many protein interaction domains bind short peptides based on canonical sequence consensus motifs. Here we report the development of a peptide array-based proteomics tool to identify proteins directly interacting with ligand peptides from cell lysates. Array-formatted bait peptides containing an amino acid-derived cross-linker are photo-induced to crosslink with interacting proteins from lysates of interest. Indirect associations are removed by high stringency washes under denaturing conditions. Covalently trapped proteins are subsequently identified by LC-MS/MS and screened by cluster analysis and domain scanning. We apply this methodology to peptides with different proline-containing consensus sequences and show successful identifications from brain lysates of known and novel proteins containing polyproline motif-binding domains such as EH, EVH1, SH3, WW domains. These results suggest the capacity of arrayed peptide ligands to capture and subsequently identify proteins by mass spectrometry is relatively broad and robust. Additionally, the approach is rapid and applicable to cell or tissue fractions from any source, making the approach a flexible tool for initial protein-protein interaction discovery. PMID:22606326

Real-time energy-saving metro train rescheduling with primary delay identification

PubMed Central

Li, Keping; Schonfeld, Paul

2018-01-01

This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph and system analysis, the primary and flow-on delays are specifically analyzed and identified with a critical path algorithm. For the second stage a hybrid genetic algorithm is designed to optimize the schedule, with the delay identification results as input. Then, based on the infrastructure data of Beijing Subway Line 4 of China, case studies are presented to demonstrate the effectiveness and efficiency of the solution approach. The results show that the algorithm can quickly and accurately identify primary delays among different types of delays. The economic cost of energy consumption and total delay is considerably reduced (by more than 10% in each case). The computation time of the Hybrid-GA is low enough for rescheduling online. Sensitivity analyses further demonstrate that the proposed approach can be used as a decision-making support tool for operators. PMID:29474471

A machine learning approach for ranking clusters of docked protein‐protein complexes by pairwise cluster comparison

PubMed Central

Pfeiffenberger, Erik; Chaleil, Raphael A.G.; Moal, Iain H.

2017-01-01

ABSTRACT Reliable identification of near‐native poses of docked protein–protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein–protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near‐native from incorrect clusters. The results show that our approach is able to identify clusters containing near‐native protein–protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528–543. © 2016 Wiley Periodicals, Inc. PMID:27935158

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

PubMed Central

Zhang, Shihua; Zhang, Liang; Tai, Yuling; Wang, Xuewen; Ho, Chi-Tang; Wan, Xiaochun

2018-01-01

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant. PMID:29915604

Final Progress Report: Isotope Identification Algorithm for Rapid and Accurate Determination of Radioisotopes Feasibility Study

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

Rawool-Sullivan, Mohini; Bounds, John Alan; Brumby, Steven P.

2012-04-30

This is the final report of the project titled, 'Isotope Identification Algorithm for Rapid and Accurate Determination of Radioisotopes,' PMIS project number LA10-HUMANID-PD03. The goal of the work was to demonstrate principles of emulating a human analysis approach towards the data collected using radiation isotope identification devices (RIIDs). It summarizes work performed over the FY10 time period. The goal of the work was to demonstrate principles of emulating a human analysis approach towards the data collected using radiation isotope identification devices (RIIDs). Human analysts begin analyzing a spectrum based on features in the spectrum - lines and shapes that aremore » present in a given spectrum. The proposed work was to carry out a feasibility study that will pick out all gamma ray peaks and other features such as Compton edges, bremsstrahlung, presence/absence of shielding and presence of neutrons and escape peaks. Ultimately success of this feasibility study will allow us to collectively explain identified features and form a realistic scenario that produced a given spectrum in the future. We wanted to develop and demonstrate machine learning algorithms that will qualitatively enhance the automated identification capabilities of portable radiological sensors that are currently being used in the field.« less

Strategies for target identification of antimicrobial natural products.

PubMed

Farha, Maya A; Brown, Eric D

2016-05-04

Covering: 2000 to 2015Despite a pervasive decline in natural product research at many pharmaceutical companies over the last two decades, natural products have undeniably been a prolific and unsurpassed source for new lead antibacterial compounds. Due to their inherent complexity, natural extracts face several hurdles in high-throughout discovery programs, including target identification. Target identification and validation is a crucial process for advancing hits through the discovery pipeline, but has remained a major bottleneck. In the case of natural products, extremely low yields and limited compound supply further impede the process. Here, we review the wealth of target identification strategies that have been proposed and implemented for the characterization of novel antibacterials. Traditionally, these have included genomic and biochemical-based approaches, which, in recent years, have been improved with modern-day technology and better honed for natural product discovery. Further, we discuss the more recent innovative approaches for uncovering the target of new antibacterial natural products, which have resulted from modern advances in chemical biology tools. Finally, we present unique screening platforms implemented to streamline the process of target identification. The different innovative methods to respond to the challenge of characterizing the mode of action for antibacterial natural products have cumulatively built useful frameworks that may advocate a renovated interest in natural product drug discovery programs.

Optics and materials research for controlled radiant energy transfer in buildings

NASA Astrophysics Data System (ADS)

Goldner, R. B.

1983-11-01

The overall objective of the Tufts research program was to identify and attempt to solve some of the key materials problems associated with practical approaches for achieving controlled radiant energy transfer (CRET) through building windows and envelopes, so as to decrease heating and cooling loads in buildings. Major accomplishments included: the identification of electrochromic (EC)-based structures as the preferred structures for achieving CRET; the identification of modulated reflectivity as the preferred mode of operation for EC-based structures; demonstration of the feasibility of operating EC-materials in a modulated R(lambda) mode; and demonstration of the applicability of free electron model to colored polycrystalline WO3 films.

Minimum constitutive relation error based static identification of beams using force method

NASA Astrophysics Data System (ADS)

Guo, Jia; Takewaki, Izuru

2017-05-01

A new static identification approach based on the minimum constitutive relation error (CRE) principle for beam structures is introduced. The exact stiffness and the exact bending moment are shown to make the CRE minimal for given displacements to beam damages. A two-step substitution algorithm—a force-method step for the bending moment and a constitutive-relation step for the stiffness—is developed and its convergence is rigorously derived. Identifiability is further discussed and the stiffness in the undeformed region is found to be unidentifiable. An extra set of static measurements is complemented to remedy the drawback. Convergence and robustness are finally verified through numerical examples.

Road sign recognition using Viapix module and correlation

NASA Astrophysics Data System (ADS)

Ouerhani, Y.; Desthieux, M.; Alfalou, A.

2015-03-01

In this paper, we propose and validate a new system used to explore road assets. In this work we are interested on the vertical road signs. To do this, we are based on the combination of road signs detection, recognition and identification using data provides by sensors. The proposed approach consists on using panoramic views provided by the innovative device, VIAPIX®1, developed by our company ACTRIS2. We are based also on the optimized correlation technique for road signs recognition and identification on pictures. Obtained results shows the interest on using panoramic views compared to results obtained using images provided using only one camera.

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction.

PubMed

Dhanasekaran, A Ranjitha; Pearson, Jon L; Ganesan, Balasubramanian; Weimer, Bart C

2015-02-25

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism's genome as a database restricts metabolite identification to only those compounds that the organism can produce. To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment's MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis. Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

Numerical studies of identification in nonlinear distributed parameter systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Lo, C. K.; Reich, Simeon; Rosen, I. G.

1989-01-01

An abstract approximation framework and convergence theory for the identification of first and second order nonlinear distributed parameter systems developed previously by the authors and reported on in detail elsewhere are summarized and discussed. The theory is based upon results for systems whose dynamics can be described by monotone operators in Hilbert space and an abstract approximation theorem for the resulting nonlinear evolution system. The application of the theory together with numerical evidence demonstrating the feasibility of the general approach are discussed in the context of the identification of a first order quasi-linear parabolic model for one dimensional heat conduction/mass transport and the identification of a nonlinear dissipation mechanism (i.e., damping) in a second order one dimensional wave equation. Computational and implementational considerations, in particular, with regard to supercomputing, are addressed.

Bacterial Identification Using Light Scattering Measurements: a Preliminary Report

NASA Technical Reports Server (NTRS)

Wilkins, J. R.

1971-01-01

The light scattering properties of single bacterial cells were examined as a possible means of identification. Three species were studied with streptococcus faecalis exhibiting a unique pattern; the light-scattering traces for staphylococcus aureus and escherichia coli were quite similar although differences existed. Based on preliminary investigations, the light scattering approach appeared promising with additional research needed to include a wide variety of bacterial species, computer capability to handle and analyze data, and expansion of light scattering theory to include bacterial cells.

Fragment-based approaches to TB drugs.

PubMed

Marchetti, Chiara; Chan, Daniel S H; Coyne, Anthony G; Abell, Chris

2018-02-01

Tuberculosis is an infectious disease associated with significant mortality and morbidity worldwide, particularly in developing countries. The rise of antibiotic resistance in Mycobacterium tuberculosis (Mtb) urgently demands the development of new drug leads to tackle resistant strains. Fragment-based methods have recently emerged at the forefront of pharmaceutical development as a means to generate more effective lead structures, via the identification of fragment molecules that form weak but high quality interactions with the target biomolecule and subsequent fragment optimization. This review highlights a number of novel inhibitors of Mtb targets that have been developed through fragment-based approaches in recent years.

StakeMeter: Value-Based Stakeholder Identification and Quantification Framework for Value-Based Software Systems

PubMed Central

Babar, Muhammad Imran; Ghazali, Masitah; Jawawi, Dayang N. A.; Zaheer, Kashif Bin

2015-01-01

Value-based requirements engineering plays a vital role in the development of value-based software (VBS). Stakeholders are the key players in the requirements engineering process, and the selection of critical stakeholders for the VBS systems is highly desirable. Based on the stakeholder requirements, the innovative or value-based idea is realized. The quality of the VBS system is associated with the concrete set of valuable requirements, and the valuable requirements can only be obtained if all the relevant valuable stakeholders participate in the requirements elicitation phase. The existing value-based approaches focus on the design of the VBS systems. However, the focus on the valuable stakeholders and requirements is inadequate. The current stakeholder identification and quantification (SIQ) approaches are neither state-of-the-art nor systematic for the VBS systems. The existing approaches are time-consuming, complex and inconsistent which makes the initiation process difficult. Moreover, the main motivation of this research is that the existing SIQ approaches do not provide the low level implementation details for SIQ initiation and stakeholder metrics for quantification. Hence, keeping in view the existing SIQ problems, this research contributes in the form of a new SIQ framework called ‘StakeMeter’. The StakeMeter framework is verified and validated through case studies. The proposed framework provides low-level implementation guidelines, attributes, metrics, quantification criteria and application procedure as compared to the other methods. The proposed framework solves the issues of stakeholder quantification or prioritization, higher time consumption, complexity, and process initiation. The framework helps in the selection of highly critical stakeholders for the VBS systems with less judgmental error. PMID:25799490

Development and testing of an optimized method for DNA-based identification of jaguar (Panthera onca) and puma (Puma concolor) faecal samples for use in ecological and genetic studies.

PubMed

Haag, Taiana; Santos, Anelisie S; De Angelo, Carlos; Srbek-Araujo, Ana Carolina; Sana, Dênis A; Morato, Ronaldo G; Salzano, Francisco M; Eizirik, Eduardo

2009-07-01

The elusive nature and endangered status of most carnivore species imply that efficient approaches for their non-invasive sampling are required to allow for genetic and ecological studies. Faecal samples are a major potential source of information, and reliable approaches are needed to foster their application in this field, particularly in areas where few studies have been conducted. A major obstacle to the reliable use of faecal samples is their uncertain species-level identification in the field, an issue that can be addressed with DNA-based assays. In this study we describe a sequence-based approach that efficiently distinguishes jaguar versus puma scats, and that presents several desirable properties: (1) considerably high amplification and sequencing rates; (2) multiple diagnostic sites reliably differentiating the two focal species; (3) high information content that allows for future application in other carnivores; (4) no evidence of amplification of prey DNA; and (5) no evidence of amplification of a nuclear mitochondrial DNA insertion known to occur in the jaguar. We demonstrate the reliability and usefulness of this approach by evaluating 55 field-collected samples from four locations in the highly fragmented Atlantic Forest biome of Brazil and Argentina, and document the presence of one or both of these endangered felids in each of these areas.

Identification of Trypanosome Proteins in Plasma from African Sleeping Sickness Patients Infected with T. b. rhodesiense

PubMed Central

Enyaru, John C.; Carr, Steven A.; Pearson, Terry W.

2013-01-01

Control of human African sleeping sickness, caused by subspecies of the protozoan parasite Trypanosoma brucei, is based on preventing transmission by elimination of the tsetse vector and by active diagnostic screening and treatment of infected patients. To identify trypanosome proteins that have potential as biomarkers for detection and monitoring of African sleeping sickness, we have used a ‘deep-mining” proteomics approach to identify trypanosome proteins in human plasma. Abundant human plasma proteins were removed by immunodepletion. Depleted plasma samples were then digested to peptides with trypsin, fractionated by basic reversed phase and each fraction analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). This sample processing and analysis method enabled identification of low levels of trypanosome proteins in pooled plasma from late stage sleeping sickness patients infected with Trypanosoma brucei rhodesiense. A total of 254 trypanosome proteins were confidently identified. Many of the parasite proteins identified were of unknown function, although metabolic enzymes, chaperones, proteases and ubiquitin-related/acting proteins were found. This approach to the identification of conserved, soluble trypanosome proteins in human plasma offers a possible route to improved disease diagnosis and monitoring, since these molecules are potential biomarkers for the development of a new generation of antigen-detection assays. The combined immuno-depletion/mass spectrometric approach can be applied to a variety of infectious diseases for unbiased biomarker identification. PMID:23951171

A targeted mass spectrometry-based approach for the identification and characterization of proteins containing α-aminoadipic and γ-glutamic semialdehyde residues

PubMed Central

Chavez, Juan D.; Bisson, William H.

2011-01-01

The site-specific identification of α-aminoadipic semialdehyde (AAS) and γ-glutamic semialdehyde (GGS) residues in proteins is reported. Semialdehydic protein modifications result from the metal-catalyzed oxidation of Lys or Arg and Pro residues, respectively. Most of the analytical methods for the analysis of protein carbonylation measure change to the global level of carbonylation and fail to provide details regarding protein identity, site, and chemical nature of the carbonylation. In this work, we used a targeted approach, which combines chemical labeling, enrichment, and tandem mass spectrometric analysis, for the site-specific identification of AAS and GGS sites in proteins. The approach is applied to in vitro oxidized glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and an untreated biological sample, namely cardiac mitochondrial proteins. The analysis of GAPDH resulted in the site-specific identification of two AAA and four GGS residues. Computational evaluation of the identified AAS and GGS sites in GAPDH indicated that these sites are located in flexible regions, show high solvent accessibility values, and are in proximity with possible metal ion binding sites. The targeted proteomic analysis of semialdehydic modifications in cardiac mitochondria yielded nine AAS modification sites which were unambiguously assigned to distinct lysine residues in the following proteins: ATP/ATP translocase isoforms 1 and 2, ubiquinol cytochrome-c reductase core protein 2, and ATP synthase α-subunit. PMID:20957471

Identification of Trypanosome proteins in plasma from African sleeping sickness patients infected with T. b. rhodesiense.

PubMed

Eyford, Brett A; Ahmad, Rushdy; Enyaru, John C; Carr, Steven A; Pearson, Terry W

2013-01-01

Control of human African sleeping sickness, caused by subspecies of the protozoan parasite Trypanosoma brucei, is based on preventing transmission by elimination of the tsetse vector and by active diagnostic screening and treatment of infected patients. To identify trypanosome proteins that have potential as biomarkers for detection and monitoring of African sleeping sickness, we have used a 'deep-mining" proteomics approach to identify trypanosome proteins in human plasma. Abundant human plasma proteins were removed by immunodepletion. Depleted plasma samples were then digested to peptides with trypsin, fractionated by basic reversed phase and each fraction analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). This sample processing and analysis method enabled identification of low levels of trypanosome proteins in pooled plasma from late stage sleeping sickness patients infected with Trypanosoma brucei rhodesiense. A total of 254 trypanosome proteins were confidently identified. Many of the parasite proteins identified were of unknown function, although metabolic enzymes, chaperones, proteases and ubiquitin-related/acting proteins were found. This approach to the identification of conserved, soluble trypanosome proteins in human plasma offers a possible route to improved disease diagnosis and monitoring, since these molecules are potential biomarkers for the development of a new generation of antigen-detection assays. The combined immuno-depletion/mass spectrometric approach can be applied to a variety of infectious diseases for unbiased biomarker identification.

Cross-Battery Assessment Pattern of Strengths and Weaknesses Approach to the Identification of Specific Learning Disorders: Evidence-Based Practice or Pseudoscience?

ERIC Educational Resources Information Center

Kranzler, John H.; Floyd, Randy G.; Benson, Nicholas; Zaboski, Brian; Thibodaux, Lia

2016-01-01

In this rejoinder, the authors describe the aim of the original study as an effort to conduct a critical test of an important postulate underlying the Cross-Battery Assessment PSW approach (XBA PSW; Kranzler, Floyd, Benson, Zaboski, & Thibodaux, this issue). The authors used classification agreement analysis to examine the concordance between…

From laptop to benchtop to bedside: Structure-based Drug Design on Protein Targets

PubMed Central

Chen, Lu; Morrow, John K.; Tran, Hoang T.; Phatak, Sharangdhar S.; Du-Cuny, Lei; Zhang, Shuxing

2013-01-01

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. PMID:22316152

Current genetic methodologies in the identification of disaster victims and in forensic analysis.

PubMed

Ziętkiewicz, Ewa; Witt, Magdalena; Daca, Patrycja; Zebracka-Gala, Jadwiga; Goniewicz, Mariusz; Jarząb, Barbara; Witt, Michał

2012-02-01

This review presents the basic problems and currently available molecular techniques used for genetic profiling in disaster victim identification (DVI). The environmental conditions of a mass disaster often result in severe fragmentation, decomposition and intermixing of the remains of victims. In such cases, traditional identification based on the anthropological and physical characteristics of the victims is frequently inconclusive. This is the reason why DNA profiling became the gold standard for victim identification in mass-casualty incidents (MCIs) or any forensic cases where human remains are highly fragmented and/or degraded beyond recognition. The review provides general information about the sources of genetic material for DNA profiling, the genetic markers routinely used during genetic profiling (STR markers, mtDNA and single-nucleotide polymorphisms [SNP]) and the basic statistical approaches used in DNA-based disaster victim identification. Automated technological platforms that allow the simultaneous analysis of a multitude of genetic markers used in genetic identification (oligonucleotide microarray techniques and next-generation sequencing) are also presented. Forensic and population databases containing information on human variability, routinely used for statistical analyses, are discussed. The final part of this review is focused on recent developments, which offer particularly promising tools for forensic applications (mRNA analysis, transcriptome variation in individuals/populations and genetic profiling of specific cells separated from mixtures).

DeepSig: deep learning improves signal peptide detection in proteins.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Casadio, Rita

2018-05-15

The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. pierluigi.martelli@unibo.it. Supplementary data are available at Bioinformatics online.

Adaptive identification of vessel's added moments of inertia with program motion

NASA Astrophysics Data System (ADS)

Alyshev, A. S.; Melnikov, V. G.

2018-05-01

In this paper, we propose a new experimental method for determining the moments of inertia of the ship model. The paper gives a brief review of existing methods, a description of the proposed method and experimental stand, test procedures and calculation formulas and experimental results. The proposed method is based on the energy approach with special program motions. The ship model is fixed in a special rack consisting of a torsion element and a set of additional servo drives with flywheels (reactive wheels), which correct the motion. The servo drives with an adaptive controller provide the symmetry of the motion, which is necessary for the proposed identification procedure. The effectiveness of the proposed approach is confirmed by experimental results.

Inquiry-Based Approach to a Carbohydrate Analysis Experiment

NASA Astrophysics Data System (ADS)

Senkbeil, Edward G.

1999-01-01

The analysis of an unknown carbohydrate in an inquiry-based learning format has proven to be a valuable and interesting undergraduate biochemistry laboratory experiment. Students are given a list of carbohydrates and a list of references for carbohydrate analysis. The references contain a variety of well-characterized wet chemistry and instrumental techniques for carbohydrate identification, but the students must develop an appropriate sequential protocol for unknown identification. The students are required to provide a list of chemicals and procedures and a flow chart for identification before the lab. During the 3-hour laboratory period, they utilize their accumulated information and knowledge to classify and identify their unknown. Advantages of the inquiry-based format are (i) students must be well prepared in advance to be successful in the laboratory, (ii) students feel a sense of accomplishment in both designing and carrying out a successful experiment, and (iii) the carbohydrate background information digested by the students significantly decreases the amount of lecture time required for this topic.

Theoretical and Empirical Base for Implementation Components of Health-Promoting Schools

ERIC Educational Resources Information Center

Samdal, Oddrun; Rowling, Louise

2011-01-01

Purpose: Efforts to create a scientific base for the health-promoting school approach have so far not articulated a clear "Science of Delivery". There is thus a need for systematic identification of clearly operationalised implementation components. To address a next step in the refinement of the health-promoting schools' work, this paper sets out…

The Value of Web Log Data in Use-based Design and Testing.

ERIC Educational Resources Information Center

Burton, Mary C.; Walther, Joseph B.

2001-01-01

Suggests Web-based logs contain useful empirical data with which World Wide Web designers and design theorists can assess usability and effectiveness of design choices. Enumerates identification of types of Web server logs, client logs, types and uses of log data, and issues associated with the validity of these data. Presents an approach to…

Hitting the right target: taxonomic challenges for, and of, plant invasions

PubMed Central

Pyšek, Petr; Hulme, Philip E.; Meyerson, Laura A.; Smith, Gideon F.; Boatwright, James S.; Crouch, Neil R.; Figueiredo, Estrela; Foxcroft, Llewellyn C.; Jarošík, Vojtěch; Richardson, David M.; Suda, Jan; Wilson, John R. U.

2013-01-01

This paper explores how a lack of taxonomic expertise, and by implication a dearth of taxonomic products such as identification tools, has hindered progress in understanding and managing biological invasions. It also explores how the taxonomic endeavour could benefit from studies of invasive species. We review the literature on the current situation in taxonomy with a focus on the challenges of identifying alien plant species and explore how this has affected the study of biological invasions. Biosecurity strategies, legislation dealing with invasive species, quarantine, weed surveillance and monitoring all depend on accurate and rapid identification of non-native taxa. However, such identification can be challenging because the taxonomic skill base in most countries is diffuse and lacks critical mass. Taxonomic resources are essential for the effective management of invasive plants and incorrect identifications can impede ecological studies. On the other hand, biological invasions have provided important tests of basic theories about species concepts. Better integration of classical alpha taxonomy and modern genetic taxonomic approaches will improve the accuracy of species identification and further refine taxonomic classification at the level of populations and genotypes in the field and laboratory. Modern taxonomy therefore needs to integrate both classical and new concepts and approaches. In particular, differing points of view between the proponents of morphological and molecular approaches should be negotiated because a narrow taxonomic perspective is harmful; the rigour of taxonomic decision-making clearly increases if insights from a variety of different complementary disciplines are combined and confronted. Taxonomy plays a critical role in the study of plant invasions and in turn benefits from the insights gained from these studies.

Insights from quantitative metaproteomics and protein-stable isotope probing into microbial ecology.

PubMed

von Bergen, Martin; Jehmlich, Nico; Taubert, Martin; Vogt, Carsten; Bastida, Felipe; Herbst, Florian-Alexander; Schmidt, Frank; Richnow, Hans-Hermann; Seifert, Jana

2013-10-01

The recent development of metaproteomics has enabled the direct identification and quantification of expressed proteins from microbial communities in situ, without the need for microbial enrichment. This became possible by (1) significant increases in quality and quantity of metagenome data and by improvements of (2) accuracy and (3) sensitivity of modern mass spectrometers (MS). The identification of physiologically relevant enzymes can help to understand the role of specific species within a community or an ecological niche. Beside identification, relative and absolute quantitation is also crucial. We will review label-free and label-based methods of quantitation in MS-based proteome analysis and the contribution of quantitative proteome data to microbial ecology. Additionally, approaches of protein-based stable isotope probing (protein-SIP) for deciphering community structures are reviewed. Information on the species-specific metabolic activity can be obtained when substrates or nutrients are labeled with stable isotopes in a protein-SIP approach. The stable isotopes ((13)C, (15)N, (36)S) are incorporated into proteins and the rate of incorporation can be used for assessing the metabolic activity of the corresponding species. We will focus on the relevance of the metabolic and phylogenetic information retrieved with protein-SIP studies and for detecting and quantifying the carbon flux within microbial consortia. Furthermore, the combination of protein-SIP with established tools in microbial ecology such as other stable isotope probing techniques are discussed.

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

PubMed

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species identification strategy.

Generalized approach for identification and evaluation of technology-insertion options for military avionics systems

NASA Astrophysics Data System (ADS)

Harkness, Linda L.; Sjoberg, Eric S.

1996-06-01

The Georgia Tech Research Institute, sponsored by the Warner Robins Air Logistics Center, has developed an approach for efficiently postulating and evaluating methods for extending the life of radars and other avionics systems. The technique identified specific assemblies for potential replacement and evaluates the system level impact, including performance, reliability and life-cycle cost of each action. The initial impetus for this research was the increasing obsolescence of integrated circuits contained in the AN/APG-63 system. The operational life of military electronics is typically in excess of twenty years, which encompasses several generations of IC technology. GTRI has developed a systems approach to inserting modern technology components into older systems based upon identification of those functions which limit the system's performance or reliability and which are cost drivers. The presentation will discuss the above methodology and a technique for evaluating and ranking the different potential system upgrade options.

New approaches to the treatment of orphan genetic disorders: Mitigating molecular pathologies using chemicals.

PubMed

Velho, Renata V; Sperb-Ludwig, Fernanda; Schwartz, Ida V D

2015-08-01

With the advance and popularization of molecular techniques, the identification of genetic mutations that cause diseases has increased dramatically. Thus, the number of laboratories available to investigate a given disorder and the number of subsequent diagnosis have increased over time. Although it is necessary to identify mutations and provide diagnosis, it is also critical to develop specific therapeutic approaches based on this information. This review aims to highlight recent advances in mutation-targeted therapies with chemicals that mitigate mutational pathology at the molecular level, for disorders that, for the most part, have no effective treatment. Currently, there are several strategies being used to correct different types of mutations, including the following: the identification and characterization of translational readthrough compounds; antisense oligonucleotide-mediated splicing redirection; mismatch repair; and exon skipping. These therapies and other approaches are reviewed in this paper.

Towards Open-World Person Re-Identification by One-Shot Group-Based Verification.

PubMed

Zheng, Wei-Shi; Gong, Shaogang; Xiang, Tao

2016-03-01

Solving the problem of matching people across non-overlapping multi-camera views, known as person re-identification (re-id), has received increasing interests in computer vision. In a real-world application scenario, a watch-list (gallery set) of a handful of known target people are provided with very few (in many cases only a single) image(s) (shots) per target. Existing re-id methods are largely unsuitable to address this open-world re-id challenge because they are designed for (1) a closed-world scenario where the gallery and probe sets are assumed to contain exactly the same people, (2) person-wise identification whereby the model attempts to verify exhaustively against each individual in the gallery set, and (3) learning a matching model using multi-shots. In this paper, a novel transfer local relative distance comparison (t-LRDC) model is formulated to address the open-world person re-identification problem by one-shot group-based verification. The model is designed to mine and transfer useful information from a labelled open-world non-target dataset. Extensive experiments demonstrate that the proposed approach outperforms both non-transfer learning and existing transfer learning based re-id methods.

Docking and scoring in virtual screening for drug discovery: methods and applications.

PubMed

Kitchen, Douglas B; Decornez, Hélène; Furr, John R; Bajorath, Jürgen

2004-11-01

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.

PubMed

Nam, Seungyoon

2017-04-01

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems.

PubMed

Kazemi, Mahdi; Arefi, Mohammad Mehdi

2017-03-01

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Writers Identification Based on Multiple Windows Features Mining

NASA Astrophysics Data System (ADS)

Fadhil, Murad Saadi; Alkawaz, Mohammed Hazim; Rehman, Amjad; Saba, Tanzila

2016-03-01

Now a days, writer identification is at high demand to identify the original writer of the script at high accuracy. The one of the main challenge in writer identification is how to extract the discriminative features of different authors' scripts to classify precisely. In this paper, the adaptive division method on the offline Latin script has been implemented using several variant window sizes. Fragments of binarized text a set of features are extracted and classified into clusters in the form of groups or classes. Finally, the proposed approach in this paper has been tested on various parameters in terms of text division and window sizes. It is observed that selection of the right window size yields a well positioned window division. The proposed approach is tested on IAM standard dataset (IAM, Institut für Informatik und angewandte Mathematik, University of Bern, Bern, Switzerland) that is a constraint free script database. Finally, achieved results are compared with several techniques reported in the literature.

Estimating False Positive Contamination in Crater Annotations from Citizen Science Data

NASA Astrophysics Data System (ADS)

Tar, P. D.; Bugiolacchi, R.; Thacker, N. A.; Gilmour, J. D.

2017-01-01

Web-based citizen science often involves the classification of image features by large numbers of minimally trained volunteers, such as the identification of lunar impact craters under the Moon Zoo project. Whilst such approaches facilitate the analysis of large image data sets, the inexperience of users and ambiguity in image content can lead to contamination from false positive identifications. We give an approach, using Linear Poisson Models and image template matching, that can quantify levels of false positive contamination in citizen science Moon Zoo crater annotations. Linear Poisson Models are a form of machine learning which supports predictive error modelling and goodness-of-fits, unlike most alternative machine learning methods. The proposed supervised learning system can reduce the variability in crater counts whilst providing predictive error assessments of estimated quantities of remaining true verses false annotations. In an area of research influenced by human subjectivity, the proposed method provides a level of objectivity through the utilisation of image evidence, guided by candidate crater identifications.

Integration of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry in blood culture diagnostics: a fast and effective approach.

PubMed

Klein, Sabrina; Zimmermann, Stefan; Köhler, Christine; Mischnik, Alexander; Alle, Werner; Bode, Konrad A

2012-03-01

Sepsis is a major cause of mortality in hospitalized patients worldwide, with lethality rates ranging from 30 to 70 %. Sepsis is caused by a variety of different pathogens, and rapid diagnosis is of outstanding importance, as early and adequate antimicrobial therapy correlates with positive clinical outcome. In recent years, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) fingerprinting has become a powerful tool in microbiological diagnostics. The direct identification of micro-organisms in a positive blood culture by MALDI-TOF MS can shorten the diagnostic procedure significantly. Therefore, the aim of the present study was to evaluate whether identification rates could be improved by using the new Sepsityper kit from Bruker Daltonics for direct isolation and identification of bacteria from positive blood cultures by MALDI-TOF MS compared with the use of conventional separator gel columns, and to integrate the MALDI-TOF MS-based identification method into the routine course of blood culture diagnostics in the setting of a microbiological laboratory at a university hospital in Germany. The identification of Gram-negative bacteria by MALDI-TOF MS was significantly better using the Sepsityper kit compared with a separator gel tube-based method (99 and 68 % correct identification, respectively). For Gram-positive bacteria, only 73 % were correctly identified by MALDI-TOF with the Sepsityper kit and 59 % with the separator gel tube assay. A major problem of both methods was the poor identification of Gram-positive grape-like clustered cocci. As differentiation of Staphylococcus aureus from coagulase-negative staphylococci is of clinical importance, a PCR was additionally established that was capable of identifying S. aureus directly from positive blood cultures, thus closing this diagnostic gap. Another benefit of the PCR approach is the possibility of directly detecting the genes responsible for meticillin resistance in staphylococci and for vancomycin resistance in enterococci, which is of high importance for early adequate treatment. Both of the described methods were finally integrated into a protocol for fast and effective identification of bacteria from positive blood cultures.

Demonstration of a single-wavelength spectral-imaging-based Thai jasmine rice identification

NASA Astrophysics Data System (ADS)

Suwansukho, Kajpanya; Sumriddetchkajorn, Sarun; Buranasiri, Prathan

2011-07-01

A single-wavelength spectral-imaging-based Thai jasmine rice breed identification is demonstrated. Our nondestructive identification approach relies on a combination of fluorescent imaging and simple image processing techniques. Especially, we apply simple image thresholding, blob filtering, and image subtracting processes to either a 545 or a 575nm image in order to identify our desired Thai jasmine rice breed from others. Other key advantages include no waste product and fast identification time. In our demonstration, UVC light is used as our exciting light, a liquid crystal tunable optical filter is used as our wavelength seclector, and a digital camera with 640activepixels×480activepixels is used to capture the desired spectral image. Eight Thai rice breeds having similar size and shape are tested. Our experimental proof of concept shows that by suitably applying image thresholding, blob filtering, and image subtracting processes to the selected fluorescent image, the Thai jasmine rice breed can be identified with measured false acceptance rates of <22.9% and <25.7% for spectral images at 545 and 575nm wavelengths, respectively. A measured fast identification time is 25ms, showing high potential for real-time applications.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

PubMed Central

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6–7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification. PMID:24086666

A new analytical platform based on field-flow fractionation and olfactory sensor to improve the detection of viable and non-viable bacteria in food.

PubMed

Roda, Barbara; Mirasoli, Mara; Zattoni, Andrea; Casale, Monica; Oliveri, Paolo; Bigi, Alessandro; Reschiglian, Pierluigi; Simoni, Patrizia; Roda, Aldo

2016-10-01

An integrated sensing system is presented for the first time, where a metal oxide semiconductor sensor-based electronic olfactory system (MOS array), employed for pathogen bacteria identification based on their volatile organic compound (VOC) characterisation, is assisted by a preliminary separative technique based on gravitational field-flow fractionation (GrFFF). In the integrated system, a preliminary step using GrFFF fractionation of a complex sample provided bacteria-enriched fractions readily available for subsequent MOS array analysis. The MOS array signals were then analysed employing a chemometric approach using principal components analysis (PCA) for a first-data exploration, followed by linear discriminant analysis (LDA) as a classification tool, using the PCA scores as input variables. The ability of the GrFFF-MOS system to distinguish between viable and non-viable cells of the same strain was demonstrated for the first time, yielding 100 % ability of correct prediction. The integrated system was also applied as a proof of concept for multianalyte purposes, for the detection of two bacterial strains (Escherichia coli O157:H7 and Yersinia enterocolitica) simultaneously present in artificially contaminated milk samples, obtaining a 100 % ability of correct prediction. Acquired results show that GrFFF band slicing before MOS array analysis can significantly increase reliability and reproducibility of pathogen bacteria identification based on their VOC production, simplifying the analytical procedure and largely eliminating sample matrix effects. The developed GrFFF-MOS integrated system can be considered a simple straightforward approach for pathogen bacteria identification directly from their food matrix. Graphical abstract An integrated sensing system is presented for pathogen bacteria identification in food, in which field-flow fractionation is exploited to prepare enriched cell fractions prior to their analysis by electronic olfactory system analysis.

Enhancing Membrane Protein Identification Using a Simplified Centrifugation and Detergent-Based Membrane Extraction Approach.

PubMed

Zhou, Yanting; Gao, Jing; Zhu, Hongwen; Xu, Jingjing; He, Han; Gu, Lei; Wang, Hui; Chen, Jie; Ma, Danjun; Zhou, Hu; Zheng, Jing

2018-02-20

Membrane proteins may act as transporters, receptors, enzymes, and adhesion-anchors, accounting for nearly 70% of pharmaceutical drug targets. Difficulties in efficient enrichment, extraction, and solubilization still exist because of their relatively low abundance and poor solubility. A simplified membrane protein extraction approach with advantages of user-friendly sample processing procedures, good repeatability and significant effectiveness was developed in the current research for enhancing enrichment and identification of membrane proteins. This approach combining centrifugation and detergent along with LC-MS/MS successfully identified higher proportion of membrane proteins, integral proteins and transmembrane proteins in membrane fraction (76.6%, 48.1%, and 40.6%) than in total cell lysate (41.6%, 16.4%, and 13.5%), respectively. Moreover, our method tended to capture membrane proteins with high degree of hydrophobicity and number of transmembrane domains as 486 out of 2106 (23.0%) had GRAVY > 0 in membrane fraction, 488 out of 2106 (23.1%) had TMs ≥ 2. It also provided for improved identification of membrane proteins as more than 60.6% of the commonly identified membrane proteins in two cell samples were better identified in membrane fraction with higher sequence coverage. Data are available via ProteomeXchange with identifier PXD008456.

CNES reliability approach for the qualification of MEMS for space

NASA Astrophysics Data System (ADS)

Pressecq, Francis; Lafontan, Xavier; Perez, Guy; Fortea, Jean-Pierre

2001-10-01

This paper describes the reliability approach performs at CNES to evaluate MEMS for space application. After an introduction and a detailed state of the art on the space requirements and on the use of MEMS for space, different approaches for taking into account MEMS in the qualification phases are presented. CNES proposes improvement to theses approaches in term of failure mechanisms identification. Our approach is based on a design and test phase deeply linked with a technology study. This workflow is illustrated with an example: the case of a variable capacitance processed with MUMPS process is presented.

Young siblings of children with cancer deserve care and a personalized approach.

PubMed

Massimo, Luisa M; Wiley, Thomas J

2008-03-01

The youngest siblings may be both emotionally vulnerable and often neglected members of the family of a childhood cancer patient. The prompt identification of signs of distress in these subjects allows trained caregivers to intervene with personalized, age-appropriate, attention, and care. A narrative approach, based on personalized listening, writings, and spontaneous drawings, can provide the means to elicit markers of psychological maladjustment in even the youngest of siblings. Two exemplary cases are reported to illustrate this approach. (c) 2007 Wiley-Liss, Inc.

A subsystem identification method based on the path concept with coupling strength estimation

NASA Astrophysics Data System (ADS)

Magrans, Francesc Xavier; Poblet-Puig, Jordi; Rodríguez-Ferran, Antonio

2018-02-01

For complex geometries, the definition of the subsystems is not a straightforward task. We present here a subsystem identification method based on the direct transfer matrix, which represents the first-order paths. The key ingredient is a cluster analysis of the rows of the powers of the transfer matrix. These powers represent high-order paths in the system and are more affected than low-order paths by damping. Once subsystems are identified, the proposed approach also provides a quantification of the degree of coupling between subsystems. This information is relevant to decide whether a subsystem may be analysed in a computer model or measured in the laboratory independently of the rest or subsystems or not. The two features (subsystem identification and quantification of the degree of coupling) are illustrated by means of numerical examples: plates coupled by means of springs and rooms connected by means of a cavity.

Full-Field Strain Measurement On Titanium Welds And Local Elasto-Plastic Identification With The Virtual Fields Method

NASA Astrophysics Data System (ADS)

Tattoli, F.; Pierron, F.; Rotinat, R.; Casavola, C.; Pappalettere, C.

2011-01-01

One of the main problems in welding is the microstructural transformation within the area affected by the thermal history. The resulting heterogeneous microstructure within the weld nugget and the heat affected zones is often associated with changes in local material properties. The present work deals with the identification of material parameters governing the elasto—plastic behaviour of the fused and heat affected zones as well as the base material for titanium hybrid welded joints (Ti6Al4V alloy). The material parameters are identified from heterogeneous strain fields with the Virtual Fields Method. This method is based on a relevant use of the principle of virtual work and it has been shown to be useful and much less time consuming than classical finite element model updating approaches applied to similar problems. The paper will present results and discuss the problem of selection of the weld zones for the identification.

Identification of a broad-spectrum inhibitor of virus RNA synthesis: validation of a prototype virus-based approach

PubMed Central

Filone, Claire Marie; Hodges, Erin N.; Honeyman, Brian; Bushkin, G. Guy; Boyd, Karla; Platt, Andrew; Ni, Feng; Strom, Kyle; Hensley, Lisa; Snyder, John K.; Connor, John H.

2013-01-01

There are no approved therapeutics for the most deadly nonsegmented negative-strand (NNS) RNA viruses, including Ebola (EBOV). To identify new chemical scaffolds for development of broad-spectrum antivirals, we undertook a prototype-based lead identification screen. Using the prototype NNS virus, vesicular stomatitis virus (VSV), multiple inhibitory compounds were identified. Three compounds were investigated for broad-spectrum activity, and inhibited EBOV infection. The most potent, CMLDBU3402, was selected for further study. CMLDBU3402 did not show significant activity against segmented negative-strand RNA viruses suggesting proscribed broad-spectrum activity. Mechanistic analysis indicated that CMLDBU3402 blocked VSV viral RNA synthesis and inhibited EBOV RNA transcription, demonstrating a consistent mechanism of action against genetically distinct viruses. The identification of this chemical backbone as a broad-spectrum inhibitor of viral RNA synthesis offers significant potential for the development of new therapies for highly pathogenic viruses. PMID:23521799

An approximation theory for the identification of linear thermoelastic systems

NASA Technical Reports Server (NTRS)

Rosen, I. G.; Su, Chien-Hua Frank

1990-01-01

An abstract approximation framework and convergence theory for the identification of thermoelastic systems is developed. Starting from an abstract operator formulation consisting of a coupled second order hyperbolic equation of elasticity and first order parabolic equation for heat conduction, well-posedness is established using linear semigroup theory in Hilbert space, and a class of parameter estimation problems is then defined involving mild solutions. The approximation framework is based upon generic Galerkin approximation of the mild solutions, and convergence of solutions of the resulting sequence of approximating finite dimensional parameter identification problems to a solution of the original infinite dimensional inverse problem is established using approximation results for operator semigroups. An example involving the basic equations of one dimensional linear thermoelasticity and a linear spline based scheme are discussed. Numerical results indicate how the approach might be used in a study of damping mechanisms in flexible structures.

Analytical minimization of synchronicity errors in stochastic identification

NASA Astrophysics Data System (ADS)

Bernal, D.

2018-01-01

An approach to minimize error due to synchronicity faults in stochastic system identification is presented. The scheme is based on shifting the time domain signals so the phases of the fundamental eigenvector estimated from the spectral density are zero. A threshold on the mean of the amplitude-weighted absolute value of these phases, above which signal shifting is deemed justified, is derived and found to be proportional to the first mode damping ratio. It is shown that synchronicity faults do not map precisely to phasor multiplications in subspace identification and that the accuracy of spectral density estimated eigenvectors, for inputs with arbitrary spectral density, decrease with increasing mode number. Selection of a corrective strategy based on signal alignment, instead of eigenvector adjustment using phasors, is shown to be the product of the foregoing observations. Simulations that include noise and non-classical damping suggest that the scheme can provide sufficient accuracy to be of practical value.

The SOLUTIONS project: challenges and responses for present and future emerging pollutants in land and water resources management.

PubMed

Brack, Werner; Altenburger, Rolf; Schüürmann, Gerrit; Krauss, Martin; López Herráez, David; van Gils, Jos; Slobodnik, Jaroslav; Munthe, John; Gawlik, Bernd Manfred; van Wezel, Annemarie; Schriks, Merijn; Hollender, Juliane; Tollefsen, Knut Erik; Mekenyan, Ovanes; Dimitrov, Saby; Bunke, Dirk; Cousins, Ian; Posthuma, Leo; van den Brink, Paul J; López de Alda, Miren; Barceló, Damià; Faust, Michael; Kortenkamp, Andreas; Scrimshaw, Mark; Ignatova, Svetlana; Engelen, Guy; Massmann, Gudrun; Lemkine, Gregory; Teodorovic, Ivana; Walz, Karl-Heinz; Dulio, Valeria; Jonker, Michiel T O; Jäger, Felix; Chipman, Kevin; Falciani, Francesco; Liska, Igor; Rooke, David; Zhang, Xiaowei; Hollert, Henner; Vrana, Branislav; Hilscherova, Klara; Kramer, Kees; Neumann, Steffen; Hammerbacher, Ruth; Backhaus, Thomas; Mack, Juliane; Segner, Helmut; Escher, Beate; de Aragão Umbuzeiro, Gisela

2015-01-15

SOLUTIONS (2013 to 2018) is a European Union Seventh Framework Programme Project (EU-FP7). The project aims to deliver a conceptual framework to support the evidence-based development of environmental policies with regard to water quality. SOLUTIONS will develop the tools for the identification, prioritisation and assessment of those water contaminants that may pose a risk to ecosystems and human health. To this end, a new generation of chemical and effect-based monitoring tools is developed and integrated with a full set of exposure, effect and risk assessment models. SOLUTIONS attempts to address legacy, present and future contamination by integrating monitoring and modelling based approaches with scenarios on future developments in society, economy and technology and thus in contamination. The project follows a solutions-oriented approach by addressing major problems of water and chemicals management and by assessing abatement options. SOLUTIONS takes advantage of the access to the infrastructure necessary to investigate the large basins of the Danube and Rhine as well as relevant Mediterranean basins as case studies, and puts major efforts on stakeholder dialogue and support. Particularly, the EU Water Framework Directive (WFD) Common Implementation Strategy (CIS) working groups, International River Commissions, and water works associations are directly supported with consistent guidance for the early detection, identification, prioritisation, and abatement of chemicals in the water cycle. SOLUTIONS will give a specific emphasis on concepts and tools for the impact and risk assessment of complex mixtures of emerging pollutants, their metabolites and transformation products. Analytical and effect-based screening tools will be applied together with ecological assessment tools for the identification of toxicants and their impacts. The SOLUTIONS approach is expected to provide transparent and evidence-based candidates or River Basin Specific Pollutants in the case study basins and to assist future review of priority pollutants under the WFD as well as potential abatement options. Copyright © 2014 Elsevier B.V. All rights reserved.

A simple molecular technique for distinguishing species reveals frequent misidentification of Hawaiian corals in the genus Pocillopora

PubMed Central

Forsman, Zac H.; Toonen, Robert J.

2018-01-01

Species within the scleractinian genus Pocillopora Lamarck 1816 exhibit extreme phenotypic plasticity, making identification based on morphology difficult. However, the mitochondrial open reading frame (mtORF) marker provides a useful genetic tool for identification of most species in this genus, with a notable exception of P. eydouxi and P. meandrina. Based on recent genomic work, we present a quick and simple, gel-based restriction fragment length polymorphism (RFLP) method for the identification of all six Pocillopora species occurring in Hawai‘i by amplifying either the mtORF region, a newly discovered histone region, or both, and then using the restriction enzymes targeting diagnostic sequences we unambiguously identify each species. Using this approach, we documented frequent misidentification of Pocillopora species based on colony morphology. We found that P. acuta colonies are frequently mistakenly identified as P. damicornis in Kāne‘ohe Bay, O‘ahu. We also found that P. meandrina likely has a northern range limit in the Northwest Hawaiian Islands, above which P. ligulata was regularly mistaken for P. meandrina. PMID:29441239

Advances in molecular identification, taxonomy, genetic variation and diagnosis of Toxocara spp.

PubMed

Chen, Jia; Zhou, Dong-Hui; Nisbet, Alasdair J; Xu, Min-Jun; Huang, Si-Yang; Li, Ming-Wei; Wang, Chun-Ren; Zhu, Xing-Quan

2012-10-01

The genus Toxocara contains parasitic nematodes of human and animal health significance, such as Toxocara canis, Toxocara cati and Toxocara vitulorum. T. canis and T. cati are among the most prevalent parasites of dogs and cats with a worldwide distribution. Human infection with T. canis and T. cati, which can cause a number of clinical manifestations such as visceral larva migrans (VLMs), ocular larva migrans (OLMs), eosinophilic meningoencephalitis (EME), covert toxocariasis (CT) and neurotoxocariasis, is considered the most prevalent neglected helminthiasis in industrialized countries. The accurate identification Toxocara spp. and their unequivocal differentiation from each other and from other ascaridoid nematodes causing VLMs and OLMs has important implications for studying their taxonomy, epidemiology, population genetics, diagnosis and control. Due to the limitations of traditional (morphological) approaches for identification and diagnosis of Toxocara spp., PCR-based techniques utilizing a range of genetic markers in the nuclear and mitochondrial genomes have been developed as useful alternative approaches because of their high sensitivity, specificity, rapidity and utility. In this article, we summarize the current state of knowledge and advances in molecular identification, taxonomy, genetic variation and diagnosis of Toxocara spp. with prospects for further studies. Copyright © 2012 Elsevier B.V. All rights reserved.

Genetic Mapping Identifies Novel Highly Protective Antigens for an Apicomplexan Parasite

PubMed Central

Blake, Damer P.; Billington, Karen J.; Copestake, Susan L.; Oakes, Richard D.; Quail, Michael A.; Wan, Kiew-Lian; Shirley, Martin W.; Smith, Adrian L.

2011-01-01

Apicomplexan parasites are responsible for a myriad of diseases in humans and livestock; yet despite intensive effort, development of effective sub-unit vaccines remains a long-term goal. Antigenic complexity and our inability to identify protective antigens from the pool that induce response are serious challenges in the development of new vaccines. Using a combination of parasite genetics and selective barriers with population-based genetic fingerprinting, we have identified that immunity against the most important apicomplexan parasite of livestock (Eimeria spp.) was targeted against a few discrete regions of the genome. Herein we report the identification of six genomic regions and, within two of those loci, the identification of true protective antigens that confer immunity as sub-unit vaccines. The first of these is an Eimeria maxima homologue of apical membrane antigen-1 (AMA-1) and the second is a previously uncharacterised gene that we have termed ‘immune mapped protein-1’ (IMP-1). Significantly, homologues of the AMA-1 antigen are protective with a range of apicomplexan parasites including Plasmodium spp., which suggest that there may be some characteristic(s) of protective antigens shared across this diverse group of parasites. Interestingly, homologues of the IMP-1 antigen, which is protective against E. maxima infection, can be identified in Toxoplasma gondii and Neospora caninum. Overall, this study documents the discovery of novel protective antigens using a population-based genetic mapping approach allied with a protection-based screen of candidate genes. The identification of AMA-1 and IMP-1 represents a substantial step towards development of an effective anti-eimerian sub-unit vaccine and raises the possibility of identification of novel antigens for other apicomplexan parasites. Moreover, validation of the parasite genetics approach to identify effective antigens supports its adoption in other parasite systems where legitimate protective antigen identification is difficult. PMID:21347348

The UAB Informatics Institute and 2016 CEGS N-GRID de-identification shared task challenge.

PubMed

Bui, Duy Duc An; Wyatt, Mathew; Cimino, James J

2017-11-01

Clinical narratives (the text notes found in patients' medical records) are important information sources for secondary use in research. However, in order to protect patient privacy, they must be de-identified prior to use. Manual de-identification is considered to be the gold standard approach but is tedious, expensive, slow, and impractical for use with large-scale clinical data. Automated or semi-automated de-identification using computer algorithms is a potentially promising alternative. The Informatics Institute of the University of Alabama at Birmingham is applying de-identification to clinical data drawn from the UAB hospital's electronic medical records system before releasing them for research. We participated in a shared task challenge by the Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDoC Individualized Domains (N-GRID) at the de-identification regular track to gain experience developing our own automatic de-identification tool. We focused on the popular and successful methods from previous challenges: rule-based, dictionary-matching, and machine-learning approaches. We also explored new techniques such as disambiguation rules, term ambiguity measurement, and used multi-pass sieve framework at a micro level. For the challenge's primary measure (strict entity), our submissions achieved competitive results (f-measures: 87.3%, 87.1%, and 86.7%). For our preferred measure (binary token HIPAA), our submissions achieved superior results (f-measures: 93.7%, 93.6%, and 93%). With those encouraging results, we gain the confidence to improve and use the tool for the real de-identification task at the UAB Informatics Institute. Copyright © 2017 Elsevier Inc. All rights reserved.

Bio-inspired digital signal processing for fast radionuclide mixture identification

NASA Astrophysics Data System (ADS)

Thevenin, M.; Bichler, O.; Thiam, C.; Bobin, C.; Lourenço, V.

2015-05-01

Countries are trying to equip their public transportation infrastructure with fixed radiation portals and detectors to detect radiological threat. Current works usually focus on neutron detection, which could be useless in the case of dirty bomb that would not use fissile material. Another approach, such as gamma dose rate variation monitoring is a good indication of the presence of radionuclide. However, some legitimate products emit large quantities of natural gamma rays; environment also emits gamma rays naturally. They can lead to false detections. Moreover, such radio-activity could be used to hide a threat such as material to make a dirty bomb. Consequently, radionuclide identification is a requirement and is traditionally performed by gamma spectrometry using unique spectral signature of each radionuclide. These approaches require high-resolution detectors, sufficient integration time to get enough statistics and large computing capacities for data analysis. High-resolution detectors are fragile and costly, making them bad candidates for large scale homeland security applications. Plastic scintillator and NaI detectors fit with such applications but their resolution makes identification difficult, especially radionuclides mixes. This paper proposes an original signal processing strategy based on artificial spiking neural networks to enable fast radionuclide identification at low count rate and for mixture. It presents results obtained for different challenging mixtures of radionuclides using a NaI scintillator. Results show that a correct identification is performed with less than hundred counts and no false identification is reported, enabling quick identification of a moving threat in a public transportation. Further work will focus on using plastic scintillators.

Taxonomic Identification of Mediterranean Pines and Their Hybrids Based on the High Resolution Melting (HRM) and trnL Approaches: From Cytoplasmic Inheritance to Timber Tracing

PubMed Central

Ganopoulos, Ioannis; Aravanopoulos, Filippos; Madesis, Panagiotis; Pasentsis, Konstantinos; Bosmali, Irene; Ouzounis, Christos; Tsaftaris, Athanasios

2013-01-01

Fast and accurate detection of plant species and their hybrids using molecular tools will facilitate the assessment and monitoring of local biodiversity in an era of climate and environmental change. Herein, we evaluate the utility of the plastid trnL marker for species identification applied to Mediterranean pines (Pinus spp.). Our results indicate that trnL is a very sensitive marker for delimiting species biodiversity. Furthermore, High Resolution Melting (HRM) analysis was exploited as a molecular fingerprint for fast and accurate discrimination of Pinus spp. DNA sequence variants. The trnL approach and the HRM analyses were extended to wood samples of two species (Pinus nigra and Pinus sylvestris) with excellent results, congruent to those obtained using leaf tissue. Both analyses demonstrate that hybrids from the P. brutia (maternal parent) × P. halepensis (paternal parent) cross, exhibit the P. halepensis profile, confirming paternal plastid inheritance in Group Halepensis pines. Our study indicates that a single one-step reaction method and DNA marker are sufficient for the identification of Mediterranean pines, their hybrids and the origin of pine wood. Furthermore, our results underline the potential for certain DNA regions to be used as novel biological information markers combined with existing morphological characters and suggest a relatively reliable and open taxonomic system that can link DNA variation to phenotype-based species or hybrid assignment status and direct taxa identification from recalcitrant tissues such as wood samples. PMID:23577179

Taxonomic identification of mediterranean pines and their hybrids based on the high resolution melting (HRM) and trnL approaches: from cytoplasmic inheritance to timber tracing.

PubMed

Ganopoulos, Ioannis; Aravanopoulos, Filippos; Madesis, Panagiotis; Pasentsis, Konstantinos; Bosmali, Irene; Ouzounis, Christos; Tsaftaris, Athanasios

2013-01-01

Fast and accurate detection of plant species and their hybrids using molecular tools will facilitate the assessment and monitoring of local biodiversity in an era of climate and environmental change. Herein, we evaluate the utility of the plastid trnL marker for species identification applied to Mediterranean pines (Pinus spp.). Our results indicate that trnL is a very sensitive marker for delimiting species biodiversity. Furthermore, High Resolution Melting (HRM) analysis was exploited as a molecular fingerprint for fast and accurate discrimination of Pinus spp. DNA sequence variants. The trnL approach and the HRM analyses were extended to wood samples of two species (Pinus nigra and Pinus sylvestris) with excellent results, congruent to those obtained using leaf tissue. Both analyses demonstrate that hybrids from the P. brutia (maternal parent) × P. halepensis (paternal parent) cross, exhibit the P. halepensis profile, confirming paternal plastid inheritance in Group Halepensis pines. Our study indicates that a single one-step reaction method and DNA marker are sufficient for the identification of Mediterranean pines, their hybrids and the origin of pine wood. Furthermore, our results underline the potential for certain DNA regions to be used as novel biological information markers combined with existing morphological characters and suggest a relatively reliable and open taxonomic system that can link DNA variation to phenotype-based species or hybrid assignment status and direct taxa identification from recalcitrant tissues such as wood samples.

Integrated quantification and identification of aldehydes and ketones in biological samples.

PubMed

Siegel, David; Meinema, Anne C; Permentier, Hjalmar; Hopfgartner, Gérard; Bischoff, Rainer

2014-05-20

The identification of unknown compounds remains to be a bottleneck of mass spectrometry (MS)-based metabolomics screening experiments. Here, we present a novel approach which facilitates the identification and quantification of analytes containing aldehyde and ketone groups in biological samples by adding chemical information to MS data. Our strategy is based on rapid autosampler-in-needle-derivatization with p-toluenesulfonylhydrazine (TSH). The resulting TSH-hydrazones are separated by ultrahigh-performance liquid chromatography (UHPLC) and detected by electrospray ionization-quadrupole-time-of-flight (ESI-QqTOF) mass spectrometry using a SWATH (Sequential Window Acquisition of all Theoretical Fragment-Ion Spectra) data-independent high-resolution mass spectrometry (HR-MS) approach. Derivatization makes small, poorly ionizable or retained analytes amenable to reversed phase chromatography and electrospray ionization in both polarities. Negatively charged TSH-hydrazone ions furthermore show a simple and predictable fragmentation pattern upon collision induced dissociation, which enables the chemo-selective screening for unknown aldehydes and ketones via a signature fragment ion (m/z 155.0172). By means of SWATH, targeted and nontargeted application scenarios of the suggested derivatization route are enabled in the frame of a single UHPLC-ESI-QqTOF-HR-MS workflow. The method's ability to simultaneously quantify and identify molecules containing aldehyde and ketone groups is demonstrated using 61 target analytes from various compound classes and a (13)C labeled yeast matrix. The identification of unknowns in biological samples is detailed using the example of indole-3-acetaldehyde.

Evolutions in fragment-based drug design: the deconstruction–reconstruction approach

PubMed Central

Chen, Haijun; Zhou, Xiaobin; Wang, Ailan; Zheng, Yunquan; Gao, Yu; Zhou, Jia

2014-01-01

Recent advances in the understanding of molecular recognition and protein–ligand interactions have facilitated rapid development of potent and selective ligands for therapeutically relevant targets. Over the past two decades, a variety of useful approaches and emerging techniques have been developed to promote the identification and optimization of leads that have high potential for generating new therapeutic agents. Intriguingly, the innovation of a fragment-based drug design (FBDD) approach has enabled rapid and efficient progress in drug discovery. In this critical review, we focus on the construction of fragment libraries and the advantages and disadvantages of various fragment-based screening (FBS) for constructing such libraries. We also highlight the deconstruction–reconstruction strategy by utilizing privileged fragments of reported ligands. PMID:25263697

Learning Disabilities: From Identification to Intervention

ERIC Educational Resources Information Center

Fletcher, Jack M.; Lyon, G. Reid; Fuchs, Lynn S.; Barnes, Marcia A.

2006-01-01

Evidence based and comprehensive, this important work offers a new approach to understanding and intervening with students with learning disabilities. The authors--leading experts in neuropsychology and special education--present a unique model of learning disabilities that integrates the cognitive, neural, genetic, and contextual factors…

The Torrance Phenomenon.

ERIC Educational Resources Information Center

Magyari-Beck, Istvan

1996-01-01

Addresses issues concerned with the investigation of creativity across various human cultures, including data collection from a limited cultural base, a three-level approach to cross-cultural studies of creativity, identification of basic cultural paradigms characteristic of a specific culture, and barriers to mature cross-cultural relativism. (DB)

IDENTIFICATION OF SEDIMENT SOURCE AREAS WITHIN A WATERSHED

EPA Science Inventory

Two methods, one using a travel time approach and the other based on optimization techniques, were developed to identify sediment generating areas within a watershed. Both methods rely on hydrograph and sedimentograph data collected at the mouth of the watershed. Data from severa...

Development of Cross-Assembly Phage PCR-Based Methods for Human Fecal Source Identification

EPA Science Inventory

Technologies that can characterize human fecal pollution in environmental waters offer many advantages over traditional general indicator approaches. However, many human-associated methods cross-react with non-human animal sources and lack suitable sensitivity for fecal source id...

Distorted Representations of the "Capability Approach" in Australian School Education

ERIC Educational Resources Information Center

Skourdoumbis, Andrew

2015-01-01

Recently, curriculum developments in Australia have seen the incorporation of functionalist "general capabilities" as essential markers of schooling, meaning that any pedagogical expression of classroom-based practice, including subsequent instruction, should entail the identification and development of operational general capabilities.…

An Evaluation of Alternative Screening Procedures.

ERIC Educational Resources Information Center

Reid, Carol; Romanoff, Brenda; Algozzine, Bob; Udall, Ann

2000-01-01

This study compared the Problem Solving Assessment (PSA) procedure, an application based on Gardner's theory of multiple intelligences, with more traditional criteria for the identification of minority students for gifted education programs. Although positive correlations among approaches and intelligences were observed, different groups of…

"In this together": Social identification predicts health outcomes (via self-efficacy) in a chronic disease self-management program.

PubMed

Cameron, James E; Voth, Jennifer; Jaglal, Susan B; Guilcher, Sara J T; Hawker, Gillian; Salbach, Nancy M

2018-03-05

Self-management programs are an established approach to helping people cope with the challenges of chronic disease, but the psychological mechanisms underlying their effectiveness are not fully understood. A key assumption of self-management interventions is that enhancing people's self-efficacy (e.g., via the development of relevant skills and behaviours) encourages adaptive health-related behaviors and improved health outcomes. However, the group-based nature of the programs allows for the possibility that identification with other program members is itself a social psychological platform for positive changes in illness-related confidence (i.e., group-derived efficacy) and physical and mental health. The researchers evaluated this hypothesis in a telehealth version of a chronic disease self-management program delivered in 13 rural and remote communities in northern Ontario, Canada (September 2007 to June 2008). Participants were 213 individuals with a self-reported physician diagnosis of chronic lung disease, heart disease, stroke, or arthritis. Measures of social identification, group-derived efficacy, and individual efficacy were administered seven weeks after baseline, and mental and physical health outcomes (health distress, psychological well-being, depression, vitality, pain, role limits, and disability) were assessed at four months. Structural equation modeling indicated that social identification was a positive predictor of group-derived efficacy and (in turn) individual self-efficacy (controlling for baseline), which was significantly associated with better physical and mental health outcomes. The results are consistent with growing evidence of the value of a social identity-based approach in various health and clinical settings. The success of chronic disease self-management programs could be enhanced by attending to and augmenting group identification during and after the program. Copyright © 2018 Elsevier Ltd. All rights reserved.

Anthropometric correlations between parts of the upper and lower limb: models for personal identification in a Sudanese population.

PubMed

Ahmed, Altayeb Abdalla

2016-09-01

Identification of a deceased individual is an essential component of medicolegal practice. However, personal identification based on commingled limbs or parts of limbs, necessary in investigations of mass disasters or some crimes, is a difficult task. Limb measurements have been utilized in the development of biological parameters for personal identification, but the possibility to estimate the dimensions of parts of limbs other than hands and feet has not been assessed. The present study proposes an approach to estimate the dimensions of various parts of limbs based on other limb measurements. The study included 320 Sudanese adults, with equal representation of men and women. Nine limb dimensions were measured (five based on the upper limb, four based on the lower limb), and extensive statistical analysis of the distribution of values was performed. The results showed that all of the measured dimensions were sexually dimorphic and that there was a significant positive correlation between the dimensions of various parts of limbs. Regression models (direct and stepwise) were developed to estimate the dimensions of parts of limbs based on measurements pertaining to one or more other parts of limbs. The study revealed that the dimensions of parts of the upper and lower limb can be estimated from one another. These findings can be used in medicolegal practice and extended to constructive surgery, orthopedics, and prosthesis design for lost limbs.

FVID: Fishing Vessel Type Identification Based on VMS Trajectories

NASA Astrophysics Data System (ADS)

Huang, Haiguang; Hong, Feng; Liu, Jing; Liu, Chao; Feng, Yuan; Guo, Zhongwen

2018-05-01

Vessel Monitoring System (VMS) provides a new opportunity for quantified fishing research. Many approaches have been proposed to recognize fishing activities with VMS trajectories based on the types of fishing vessels. However, one research problem is still calling for solutions, how to identify the fishing vessel type based on only VMS trajectories. This problem is important because it requires the fishing vessel type as a preliminary to recognize fishing activities from VMS trajectories. This paper proposes fishing vessel type identification scheme (FVID) based only on VMS trajectories. FVID exploits feature engineering and machine learning schemes of XGBoost as its two key blocks and classifies fishing vessels into nine types. The dataset contains all the fishing vessel trajectories in the East China Sea in March 2017, including 10031 pre-registered fishing vessels and 1350 unregistered vessels of unknown types. In order to verify type identification accuracy, we first conduct a 4-fold cross-validation on the trajectories of registered fishing vessels. The classification accuracy is 95.42%. We then apply FVID to the unregistered fishing vessels to identify their types. After classifying the unregistered fishing vessel types, their fishing activities are further recognized based upon their types. At last, we calculate and compare the fishing density distribution in the East China Sea before and after applying the unregistered fishing vessels, confirming the importance of type identification of unregistered fishing vessels.

A Comprehensive Approach in Dissemination of Evidence-Based Care for PTSD

DTIC Science & Technology

2011-09-01

facilitate practice evaluation and identification of potential gaps in care. APIRE staff have met with key clinical staff from select behavioral health...provide evidence-based care and identify potential gaps in care. Finally, strategies to implement the PCL-C, PHQ-9, and AUDIT-C for routine screening and...systems-level, facilitate detection of potential gaps in evidence-based care, and speed the adoption of evidence-based care into clinical practice

Towards a model-based cognitive neuroscience of stopping - a neuroimaging perspective.

PubMed

Sebastian, Alexandra; Forstmann, Birte U; Matzke, Dora

2018-07-01

Our understanding of the neural correlates of response inhibition has greatly advanced over the last decade. Nevertheless the specific function of regions within this stopping network remains controversial. The traditional neuroimaging approach cannot capture many processes affecting stopping performance. Despite the shortcomings of the traditional neuroimaging approach and a great progress in mathematical and computational models of stopping, model-based cognitive neuroscience approaches in human neuroimaging studies are largely lacking. To foster model-based approaches to ultimately gain a deeper understanding of the neural signature of stopping, we outline the most prominent models of response inhibition and recent advances in the field. We highlight how a model-based approach in clinical samples has improved our understanding of altered cognitive functions in these disorders. Moreover, we show how linking evidence-accumulation models and neuroimaging data improves the identification of neural pathways involved in the stopping process and helps to delineate these from neural networks of related but distinct functions. In conclusion, adopting a model-based approach is indispensable to identifying the actual neural processes underlying stopping. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

DNA metabarcoding and microscopic analyses of sea turtles biofilms: Complementary to understand turtle behavior

PubMed Central

Vasselon, Valentin; Ballorain, Katia; Carpentier, Alice; Wetzel, Carlos E.; Ector, Luc; Bouchez, Agnès; Rimet, Frédéric

2018-01-01

Sea turtles are distributed in tropical and subtropical seas worldwide. They play several ecological roles and are considered important indicators of the health of marine ecosystems. Studying epibiotic diatoms living on turtle shells suggestively has great potential in the study of turtle behavior because diatoms are always there. However, diatom identification at the species level is time consuming, requires well-trained specialists, and there is a high probability of finding new taxa growing on turtle shells, which makes identification tricky. An alternative approach based on DNA barcoding and high throughput sequencing (HTS), metabarcoding, has been developed in recent years to identify species at the community level by using a DNA reference library. The suitabilities of morphological and molecular approaches were compared. Diatom assemblages were sampled from seven juvenile green turtles (Chelonia mydas) from Mayotte Island, France. The structures of the epibiotic diatom assemblages differed between both approaches. This resulted in different clustering of the turtles based on their diatom communities. Metabarcoding allowed better discrimination between turtles based on their epibiotic diatom assemblages and put into evidence the presence of a cryptic diatom diversity. Microscopy, for its part, provided more ecological information of sea turtles based on historical bibliographical data and the abundances of ecological guilds of the diatom species present in the samples. This study shows the complementary nature of these two methods for studying turtle behavior. PMID:29659610

DNA metabarcoding and microscopic analyses of sea turtles biofilms: Complementary to understand turtle behavior.

PubMed

Rivera, Sinziana F; Vasselon, Valentin; Ballorain, Katia; Carpentier, Alice; Wetzel, Carlos E; Ector, Luc; Bouchez, Agnès; Rimet, Frédéric

2018-01-01

Sea turtles are distributed in tropical and subtropical seas worldwide. They play several ecological roles and are considered important indicators of the health of marine ecosystems. Studying epibiotic diatoms living on turtle shells suggestively has great potential in the study of turtle behavior because diatoms are always there. However, diatom identification at the species level is time consuming, requires well-trained specialists, and there is a high probability of finding new taxa growing on turtle shells, which makes identification tricky. An alternative approach based on DNA barcoding and high throughput sequencing (HTS), metabarcoding, has been developed in recent years to identify species at the community level by using a DNA reference library. The suitabilities of morphological and molecular approaches were compared. Diatom assemblages were sampled from seven juvenile green turtles (Chelonia mydas) from Mayotte Island, France. The structures of the epibiotic diatom assemblages differed between both approaches. This resulted in different clustering of the turtles based on their diatom communities. Metabarcoding allowed better discrimination between turtles based on their epibiotic diatom assemblages and put into evidence the presence of a cryptic diatom diversity. Microscopy, for its part, provided more ecological information of sea turtles based on historical bibliographical data and the abundances of ecological guilds of the diatom species present in the samples. This study shows the complementary nature of these two methods for studying turtle behavior.

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

PubMed Central

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-01

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier. PMID:28124985

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

PubMed

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-23

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

Two-dimensional PCA-based human gait identification

NASA Astrophysics Data System (ADS)

Chen, Jinyan; Wu, Rongteng

2012-11-01

It is very necessary to recognize person through visual surveillance automatically for public security reason. Human gait based identification focus on recognizing human by his walking video automatically using computer vision and image processing approaches. As a potential biometric measure, human gait identification has attracted more and more researchers. Current human gait identification methods can be divided into two categories: model-based methods and motion-based methods. In this paper a two-Dimensional Principal Component Analysis and temporal-space analysis based human gait identification method is proposed. Using background estimation and image subtraction we can get a binary images sequence from the surveillance video. By comparing the difference of two adjacent images in the gait images sequence, we can get a difference binary images sequence. Every binary difference image indicates the body moving mode during a person walking. We use the following steps to extract the temporal-space features from the difference binary images sequence: Projecting one difference image to Y axis or X axis we can get two vectors. Project every difference image in the difference binary images sequence to Y axis or X axis difference binary images sequence we can get two matrixes. These two matrixes indicate the styles of one walking. Then Two-Dimensional Principal Component Analysis(2DPCA) is used to transform these two matrixes to two vectors while at the same time keep the maximum separability. Finally the similarity of two human gait images is calculated by the Euclidean distance of the two vectors. The performance of our methods is illustrated using the CASIA Gait Database.

Damage Identification of Piles Based on Vibration Characteristics

PubMed Central

Zhang, Xiaozhong; Yao, Wenjuan; Chen, Bo; Liu, Dewen

2014-01-01

A method of damage identification of piles was established by using vibration characteristics. The approach focused on the application of the element strain energy and sensitive modals. A damage identification equation of piles was deduced using the structural vibration equation. The equation contained three major factors: change rate of element modal strain energy, damage factor of pile, and sensitivity factor of modal damage. The sensitive modals of damage identification were selected by using sensitivity factor of modal damage firstly. Subsequently, the indexes for early-warning of pile damage were established by applying the change rate of strain energy. Then the technology of computational analysis of wavelet transform was used to damage identification for pile. The identification of small damage of pile was completely achieved, including the location of damage and the extent of damage. In the process of identifying the extent of damage of pile, the equation of damage identification was used in many times. Finally, a stadium project was used as an example to demonstrate the effectiveness of the proposed method of damage identification for piles. The correctness and practicability of the proposed method were verified by comparing the results of damage identification with that of low strain test. The research provided a new way for damage identification of piles. PMID:25506062

Flight test planning and parameter extraction for rotorcraft system identification

NASA Technical Reports Server (NTRS)

Wang, J. C.; Demiroz, M. Y.; Talbot, P. D.

1986-01-01

The present study is concerned with the mathematical modelling of aircraft dynamics on the basis of an investigation conducted with the aid of the Rotor System Research Aircraft (RSRA). The particular characteristics of RSRA make it possible to investigate aircraft properties which cannot be readily studied elsewhere, for example in the wind tunnel. The considered experiment had mainly the objective to develop an improved understanding of the physics of rotor flapping dynamics and rotor loads in maneuvers. The employed approach is based on a utilization of parameter identification methodology (PID) with application to helicopters. A better understanding of the contribution of the main rotor to the overall aircraft forces and moments is also to be obtained. Attention is given to the mathematical model of a rotorcraft system, an integrated identification method, flight data processing, and the identification of RSRA mathematical models.

Mid-course multi-target tracking using continuous representation

NASA Technical Reports Server (NTRS)

Zak, Michail; Toomarian, Nikzad

1991-01-01

The thrust of this paper is to present a new approach to multi-target tracking for the mid-course stage of the Strategic Defense Initiative (SDI). This approach is based upon a continuum representation of a cluster of flying objects. We assume that the velocities of the flying objects can be embedded into a smooth velocity field. This assumption is based upon the impossibility of encounters in a high density cluster between the flying objects. Therefore, the problem is reduced to an identification of a moving continuum based upon consecutive time frame observations. In contradistinction to the previous approaches, here each target is considered as a center of a small continuous neighborhood subjected to a local-affine transformation, and therefore, the target trajectories do not mix. Obviously, their mixture in plane of sensor view is apparent. The approach is illustrated by an example.

Gaussian mixture model based identification of arterial wall movement for computation of distension waveform.

PubMed

Patil, Ravindra B; Krishnamoorthy, P; Sethuraman, Shriram

2015-01-01

This work proposes a novel Gaussian Mixture Model (GMM) based approach for accurate tracking of the arterial wall and subsequent computation of the distension waveform using Radio Frequency (RF) ultrasound signal. The approach was evaluated on ultrasound RF data acquired using a prototype ultrasound system from an artery mimicking flow phantom. The effectiveness of the proposed algorithm is demonstrated by comparing with existing wall tracking algorithms. The experimental results show that the proposed method provides 20% reduction in the error margin compared to the existing approaches in tracking the arterial wall movement. This approach coupled with ultrasound system can be used to estimate the arterial compliance parameters required for screening of cardiovascular related disorders.

Use of matrix-assisted laser desorption ionization-time of flight mass spectrometry for identification of molds of the Fusarium genus.

PubMed

Triest, David; Stubbe, Dirk; De Cremer, Koen; Piérard, Denis; Normand, Anne-Cécile; Piarroux, Renaud; Detandt, Monique; Hendrickx, Marijke

2015-02-01

The rates of infection with Fusarium molds are increasing, and a diverse number of Fusarium spp. belonging to different species complexes can cause infection. Conventional species identification in the clinical laboratory is time-consuming and prone to errors. We therefore evaluated whether matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is a useful alternative. The 289 Fusarium strains from the Belgian Coordinated Collections of Microorganisms (BCCM)/Institute of Hygiene and Epidemiology Mycology (IHEM) culture collection with validated sequence-based identities and comprising 40 species were used in this study. An identification strategy was developed, applying a standardized MALDI-TOF MS assay and an in-house reference spectrum database. In vitro antifungal testing was performed to assess important differences in susceptibility between clinically relevant species/species complexes. We observed that no incorrect species complex identifications were made by MALDI-TOF MS, and 82.8% of the identifications were correct to the species level. This success rate was increased to 91% by lowering the cutoff for identification. Although the identification of the correct species complex member was not always guaranteed, antifungal susceptibility testing showed that discriminating between Fusarium species complexes can be important for treatment but is not necessarily required between members of a species complex. With this perspective, some Fusarium species complexes with closely related members can be considered as a whole, increasing the success rate of correct identifications to 97%. The application of our user-friendly MALDI-TOF MS identification approach resulted in a dramatic improvement in both time and accuracy compared to identification with the conventional method. A proof of principle of our MALDI-TOF MS approach in the clinical setting using recently isolated Fusarium strains demonstrated its validity. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Ultrabroadband phased-array radio frequency (RF) receivers based on optical techniques

NASA Astrophysics Data System (ADS)

Overmiller, Brock M.; Schuetz, Christopher A.; Schneider, Garrett; Murakowski, Janusz; Prather, Dennis W.

2014-03-01

Military operations require the ability to locate and identify electronic emissions in the battlefield environment. However, recent developments in radio detection and ranging (RADAR) and communications technology are making it harder to effectively identify such emissions. Phased array systems aid in discriminating emitters in the scene by virtue of their relatively high-gain beam steering and nulling capabilities. For the purpose of locating emitters, we present an approach realize a broadband receiver based on optical processing techniques applied to the response of detectors in conformal antenna arrays. This approach utilizes photonic techniques that enable us to capture, route, and process the incoming signals. Optical modulators convert the incoming signals up to and exceeding 110 GHz with appreciable conversion efficiency and route these signals via fiber optics to a central processing location. This central processor consists of a closed loop phase control system which compensates for phase fluctuations induced on the fibers due to thermal or acoustic vibrations as well as an optical heterodyne approach for signal conversion down to baseband. Our optical heterodyne approach uses injection-locked paired optical sources to perform heterodyne downconversion/frequency identification of the detected emission. Preliminary geolocation and frequency identification testing of electronic emissions has been performed demonstrating the capabilities of our RF receiver.

Grade 8 students' capability of analytical thinking and attitude toward science through teaching and learning about soil and its' pollution based on science technology and society (STS) approach

NASA Astrophysics Data System (ADS)

Boonprasert, Lapisarin; Tupsai, Jiraporn; Yuenyong, Chokchai

2018-01-01

This study reported Grade 8 students' analytical thinking and attitude toward science in teaching and learning about soil and its' pollution through science technology and society (STS) approach. The participants were 36 Grade 8 students in Naklang, Nongbualumphu, Thailand. The teaching and learning about soil and its' pollution through STS approach had carried out for 6 weeks. The soil and its' pollution unit through STS approach was developed based on framework of Yuenyong (2006) that consisted of five stages including (1) identification of social issues, (2) identification of potential solutions, (3) need for knowledge, (4) decision-making, and (5) socialization stage. Students' analytical thinking and attitude toward science was collected during their learning by participant observation, analytical thinking test, students' tasks, and journal writing. The findings revealed that students could gain their capability of analytical thinking. They could give ideas or behave the characteristics of analytical thinking such as thinking for classifying, compare and contrast, reasoning, interpreting, collecting data and decision making. Students' journal writing reflected that the STS class of soil and its' pollution motivated students. The paper will discuss implications of these for science teaching and learning through STS in Thailand.

A unified framework for unraveling the functional interaction structure of a biomolecular network based on stimulus-response experimental data.

PubMed

Cho, Kwang-Hyun; Choo, Sang-Mok; Wellstead, Peter; Wolkenhauer, Olaf

2005-08-15

We propose a unified framework for the identification of functional interaction structures of biomolecular networks in a way that leads to a new experimental design procedure. In developing our approach, we have built upon previous work. Thus we begin by pointing out some of the restrictions associated with existing structure identification methods and point out how these restrictions may be eased. In particular, existing methods use specific forms of experimental algebraic equations with which to identify the functional interaction structure of a biomolecular network. In our work, we employ an extended form of these experimental algebraic equations which, while retaining their merits, also overcome some of their disadvantages. Experimental data are required in order to estimate the coefficients of the experimental algebraic equation set associated with the structure identification task. However, experimentalists are rarely provided with guidance on which parameters to perturb, and to what extent, to perturb them. When a model of network dynamics is required then there is also the vexed question of sample rate and sample time selection to be resolved. Supplying some answers to these questions is the main motivation of this paper. The approach is based on stationary and/or temporal data obtained from parameter perturbations, and unifies the previous approaches of Kholodenko et al. (PNAS 99 (2002) 12841-12846) and Sontag et al. (Bioinformatics 20 (2004) 1877-1886). By way of demonstration, we apply our unified approach to a network model which cannot be properly identified by existing methods. Finally, we propose an experiment design methodology, which is not limited by the amount of parameter perturbations, and illustrate its use with an in numero example.

Novel therapeutic strategy in the management of COPD: a systems medicine approach.

PubMed

Lococo, Filippo; Cesario, Alfredo; Del Bufalo, Alessandra; Ciarrocchi, Alessia; Prinzi, Giulia; Mina, Marco; Bonassi, Stefano; Russo, Patrizia

2015-01-01

Respiratory diseases including chronic-obstructive-pulmonary-disease (COPD) are globally increasing, with COPD predicted to become the third leading cause of global mortality by 2020. COPD is a heterogeneous disease with COPD-patients displaying different phenotypes as a result of a complex interaction between various genetic, environmental and life-style factors. In recent years, several investigations have been performed to better define such interactions, but the identification of the resulting phenotypes is still somewhat difficult, and may lead to inadequate assessment and management of COPD (usually based solely on the severity of airflow limitation parameter FEV1). In this new scenario, the management of COPD has been driven towards an integrative and holistic approach. The degree of complexity requires analyses based on large datasets (also including advanced functional genomic assays) and novel computational biology approaches (essential to extract information relevant for the clinical decision process and for the development of new drugs). Therefore, according to the emerging "systems/network medicine", COPD should be re.-evaluated considering multiple network(s) perturbations such as genetic and environmental changes. Systems Medicine (SM) platforms, in which patients are extensively characterized, offer a basis for a more targeted clinical approach, which is predictive, preventive, personalized and participatory ("P4-medicine"). It clearly emerges that in the next future, new opportunities will become available for clinical research on rare COPD patterns and for the identification of new biomarkers of comorbidity, severity, and progression. Herein, we overview the literature discussing the opportunity coming from the adoption of SMapproaches in COPD management, focusing on proteomics and metabolomics, and emphasizing the identification of disease sub-clusters, to improve the development of more effective therapies.

Rapid gene identification in sugar beet using deep sequencing of DNA from phenotypic pools selected from breeding panels.

PubMed

Ries, David; Holtgräwe, Daniela; Viehöver, Prisca; Weisshaar, Bernd

2016-03-15

The combination of bulk segregant analysis (BSA) and next generation sequencing (NGS), also known as mapping by sequencing (MBS), has been shown to significantly accelerate the identification of causal mutations for species with a reference genome sequence. The usual approach is to cross homozygous parents that differ for the monogenic trait to address, to perform deep sequencing of DNA from F2 plants pooled according to their phenotype, and subsequently to analyze the allele frequency distribution based on a marker table for the parents studied. The method has been successfully applied for EMS induced mutations as well as natural variation. Here, we show that pooling genetically diverse breeding lines according to a contrasting phenotype also allows high resolution mapping of the causal gene in a crop species. The test case was the monogenic locus causing red vs. green hypocotyl color in Beta vulgaris (R locus). We determined the allele frequencies of polymorphic sequences using sequence data from two diverging phenotypic pools of 180 B. vulgaris accessions each. A single interval of about 31 kbp among the nine chromosomes was identified which indeed contained the causative mutation. By applying a variation of the mapping by sequencing approach, we demonstrated that phenotype-based pooling of diverse accessions from breeding panels and subsequent direct determination of the allele frequency distribution can be successfully applied for gene identification in a crop species. Our approach made it possible to identify a small interval around the causative gene. Sequencing of parents or individual lines was not necessary. Whenever the appropriate plant material is available, the approach described saves time compared to the generation of an F2 population. In addition, we provide clues for planning similar experiments with regard to pool size and the sequencing depth required.

A Comprehensive Approach in Dissemination of Evidence-Based Care for PTSD

DTIC Science & Technology

2012-09-01

been published, to provide evidence-based resources to facilitate practice evaluation and identification of potential gaps in care. In order to...practice’s capacity to provide evidence-based care and identify potential gaps in care as targets for improvement. Finally, strategies to implement...existing patients, the PIP tools can inform improvement efforts at the clinician-, practice-or systems-level, facilitate detection of potential gaps in

Assessing macroinvertebrate biodiversity in freshwater ecosystems: Advances and challenges in dna-based approaches

USGS Publications Warehouse

Pfrender, M.E.; Ferrington, L.C.; Hawkins, C.P.; Hartzell, P.L.; Bagley, M.; Jackson, S.; Courtney, G.W.; Larsen, D.P.; Creutzburg, B.R.; Levesque, C.A.; Epler, J.H.; Morse, J.C.; Fend, S.; Petersen, M.J.; Ruiter, D.; Schindel, D.; Whiting, M.

2010-01-01

Assessing the biodiversity of macroinvertebrate fauna in freshwater ecosystems is an essential component of both basic ecological inquiry and applied ecological assessments. Aspects of taxonomic diversity and composition in freshwater communities are widely used to quantify water quality and measure the efficacy of remediation and restoration efforts. The accuracy and precision of biodiversity assessments based on standard morphological identifications are often limited by taxonomic resolution and sample size. Morphologically based identifications are laborious and costly, significantly constraining the sample sizes that can be processed. We suggest that the development of an assay platform based on DNA signatures will increase the precision and ease of quantifying biodiversity in freshwater ecosystems. Advances in this area will be particularly relevant for benthic and planktonic invertebrates, which are often monitored by regulatory agencies. Adopting a genetic assessment platform will alleviate some of the current limitations to biodiversity assessment strategies. We discuss the benefits and challenges associated with DNA-based assessments and the methods that are currently available. As recent advances in microarray and next-generation sequencing technologies will facilitate a transition to DNA-based assessment approaches, future research efforts should focus on methods for data collection, assay platform development, establishing linkages between DNA signatures and well-resolved taxonomies, and bioinformatics. ?? 2010 by The University of Chicago Press.

Generic framework for mining cellular automata models on protein-folding simulations.

PubMed

Diaz, N; Tischer, I

2016-05-13

Cellular automata model identification is an important way of building simplified simulation models. In this study, we describe a generic architectural framework to ease the development process of new metaheuristic-based algorithms for cellular automata model identification in protein-folding trajectories. Our framework was developed by a methodology based on design patterns that allow an improved experience for new algorithms development. The usefulness of the proposed framework is demonstrated by the implementation of four algorithms, able to obtain extremely precise cellular automata models of the protein-folding process with a protein contact map representation. Dynamic rules obtained by the proposed approach are discussed, and future use for the new tool is outlined.

Codestream-Based Identification of JPEG 2000 Images with Different Coding Parameters

NASA Astrophysics Data System (ADS)

Watanabe, Osamu; Fukuhara, Takahiro; Kiya, Hitoshi

A method of identifying JPEG 2000 images with different coding parameters, such as code-block sizes, quantization-step sizes, and resolution levels, is presented. It does not produce false-negative matches regardless of different coding parameters (compression rate, code-block size, and discrete wavelet transform (DWT) resolutions levels) or quantization step sizes. This feature is not provided by conventional methods. Moreover, the proposed approach is fast because it uses the number of zero-bit-planes that can be extracted from the JPEG 2000 codestream by only parsing the header information without embedded block coding with optimized truncation (EBCOT) decoding. The experimental results revealed the effectiveness of image identification based on the new method.

Optics and materials research for controlled radiant energy transfer in buildings

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

Goldner, R.B.

1983-11-01

The overall objective of the Tufts research program was to identify and attempt to solve some of the key materials problems associated with practical approaches for achieving controlled radiant energy transfer (CRET) through building windows and envelopes, so as to decrease heating and cooling loads in buildings. Major accomplishments included: the identification of electrochromic (EC)-based structures as the preferred structures for achieving CRET the identification of modulated reflectivity as the preferred mode of operation for EC-based structures demonstration of the feasibility of operating EC-materials in a modulated R(lambda) mode and demonstration of the applicability of free electron model to coloredmore » polycrystalline WO3 films.« less

Biomimetics: determining engineering opportunities from nature

NASA Astrophysics Data System (ADS)

Fish, Frank E.

2009-08-01

The biomimetic approach seeks to incorporate designs based on biological organisms into engineered technologies. Biomimetics can be used to engineer machines that emulate the performance of organisms, particularly in instances where the organism's performance exceeds current mechanical technology or provides new directions to solve existing problems. For biologists, an adaptationist program has allowed for the identification of novel features of organisms based on engineering principles; whereas for engineers, identification of such novel features is necessary to exploit them for biomimetic development. Adaptations (leading edge tubercles to passively modify flow and high efficiency oscillatory propulsive systems) from marine animals demonstrate potential utility in the development of biomimetic products. Nature retains a store of untouched knowledge, which would be beneficial in advancing technology.

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

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

Kou, Qiang; Wu, Si; Tolić, Nikola

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a “bird’s eye view” of intact proteoforms. The combinatorial explosion of various alterations on a protein may result inmore » billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets showed that TopMG outperformed existing methods in identifying complex proteoforms.« less

SSME fault monitoring and diagnosis expert system

NASA Technical Reports Server (NTRS)

Ali, Moonis; Norman, Arnold M.; Gupta, U. K.

1989-01-01

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

A program to form a multidisciplinary data base and analysis for dynamic systems

NASA Technical Reports Server (NTRS)

Taylor, L. W.; Suit, W. T.; Mayo, M. H.

1984-01-01

Diverse sets of experimental data and analysis programs have been assembled for the purpose of facilitating research in systems identification, parameter estimation and state estimation techniques. The data base analysis programs are organized to make it easy to compare alternative approaches. Additional data and alternative forms of analysis will be included as they become available.

Development of an Adolescent Alcohol Misuse Intervention Based on the Prototype Willingness Model: A Delphi Study

ERIC Educational Resources Information Center

Davies, Emma; Martin, Jilly; Foxcroft, David

2016-01-01

Purpose: The purpose of this paper is to report on the use of the Delphi method to gain expert feedback on the identification of behaviour change techniques (BCTs) and development of a novel intervention to reduce adolescent alcohol misuse, based on the Prototype Willingness Model (PWM) of health risk behaviour. Design/methodology/approach: Four…

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

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

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

DOE PAGES

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; ...

2015-07-09

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

PubMed Central

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-01-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required. PMID:26156000

Bioinformatics-Based Identification of Candidate Genes from QTLs Associated with Cell Wall Traits in Populus

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

Ranjan, Priya; Yin, Tongming; Zhang, Xinye

2009-11-01

Quantitative trait locus (QTL) studies are an integral part of plant research and are used to characterize the genetic basis of phenotypic variation observed in structured populations and inform marker-assisted breeding efforts. These QTL intervals can span large physical regions on a chromosome comprising hundreds of genes, thereby hampering candidate gene identification. Genome history, evolution, and expression evidence can be used to narrow the genes in the interval to a smaller list that is manageable for detailed downstream functional genomics characterization. Our primary motivation for the present study was to address the need for a research methodology that identifies candidatemore » genes within a broad QTL interval. Here we present a bioinformatics-based approach for subdividing candidate genes within QTL intervals into alternate groups of high probability candidates. Application of this approach in the context of studying cell wall traits, specifically lignin content and S/G ratios of stem and root in Populus plants, resulted in manageable sets of genes of both known and putative cell wall biosynthetic function. These results provide a roadmap for future experimental work leading to identification of new genes controlling cell wall recalcitrance and, ultimately, in the utility of plant biomass as an energy feedstock.« less

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

NASA Astrophysics Data System (ADS)

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-07-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.

Application of CE-ICP-MS and CE-ESI-MS/MS for identification of Zn-binding ligands in Goji berries extracts.

PubMed

Ruzik, Lena; Kwiatkowski, Piotr

2018-06-01

The identification of groups of ligands binding metals is a crucial issue for the better understanding of their bioaccessibility. In the current study, we have intended an approach for identification of Zn-binding ligands based on using capillary electrophoresis combined with inductively coupled plasma mass spectrometry (CE-ICP-MS) and tandem electrospray ionization mass spectrometry (CE-ESI-MS/MS). The approach, which featured the use of the coupling of capillary electrophoresis with inductively coupled plasma mass spectrometry allows to separate and observe zinc ions present in complexes with respect to their size and charge and to identify nine compounds with zinc isotopic profile. CE-ICP-MS provides us with information about presence of zinc species and elemental information about zinc distribution. CE-ESI-MS/MS provide us with information about the most favorable Zn binding ligands: amino acids, flavonols, stilbenoids, fenolic acids and carotenoids. The presented work is the continuation of previous studies based on using LC-ESI-MS/MS, though, now we presented a new solutions with the possibility of changing detectors without changing the separation techniques, what is important without re-optimizing the method. The new presented method allows to identify the zinc-binding ligands in shorter time. Copyright © 2018 Elsevier B.V. All rights reserved.

Mechanism-based testing strategy using in vitro approaches for identification of thyroid hormone disrupting chemicals

EPA Science Inventory

The thyroid hormone (TH) system is involved in several important physiological processes, including regulation of energy metabolism, growth and differentiation, development and maintenance of brain function, thermo-regulation, osmo-regulation, and axis of regulation of other endo...

Application of the wavelet transform for speech processing

NASA Technical Reports Server (NTRS)

Maes, Stephane

1994-01-01

Speaker identification and word spotting will shortly play a key role in space applications. An approach based on the wavelet transform is presented that, in the context of the 'modulation model,' enables extraction of speech features which are used as input for the classification process.

Identification of propulsion systems

NASA Technical Reports Server (NTRS)

Merrill, Walter; Guo, Ten-Huei; Duyar, Ahmet

1991-01-01

This paper presents a tutorial on the use of model identification techniques for the identification of propulsion system models. These models are important for control design, simulation, parameter estimation, and fault detection. Propulsion system identification is defined in the context of the classical description of identification as a four step process that is unique because of special considerations of data and error sources. Propulsion system models are described along with the dependence of system operation on the environment. Propulsion system simulation approaches are discussed as well as approaches to propulsion system identification with examples for both air breathing and rocket systems.

The potential of a fluorescent-based approach for bioassay of antifungal agents against chili anthracnose disease in Thailand.

PubMed

Chutrakul, Chanikul; Khaokhajorn, Pratoomporn; Auncharoen, Patchanee; Boonruengprapa, Tanapong; Mongkolporn, Orarat

2013-01-01

Severe chili anthracnose disease in Thailand is caused by Colletotrichum gloeosporioides and C. capsici. To discover anti-anthracnose substances we developed an efficient dual-fluorescent labeling bioassay based on a microdilution approach. Indicator strains used in the assay were constructed by integrating synthetic green fluorescent protein (sGFP) and Discosoma sp. red fluorescent protein (DsRedExp) genes into the genomes of C. gloeosporioides or C. capsici respectively. Survival of co-spore cultures in the presence of inhibitors was determined by the expression levels of these fluorescent proteins. This developed assay has high potential for utilization in the investigation of selective inhibition activity to either one of the pathogens as well as the broad-range inhibitory effect against both pathogens. The value of using the dual-fluorescent assay is rapid, reliable, and consistent identification of anti-anthracnose agents. Most of all, the assay enables the identification of specific inhibitors under the co-cultivation condition.

Rapid and accurate identification of in vivo-induced haploid seeds based on oil content in maize

PubMed Central

Melchinger, Albrecht E.; Schipprack, Wolfgang; Würschum, Tobias; Chen, Shaojiang; Technow, Frank

2013-01-01

The needs of a growing human population require rapid and efficient development of improved cultivars by plant breeders. The doubled haploid (DH) technology enables generating completely homozygous lines in a single step and, thus, is central to modern genetics and breeding approaches. Rapid and reliable identification of seeds with a haploid embryo after in vivo haploid induction is elementary in the method utilized in maize but current systems have severe shortcomings preventing their use in many germplasm types. Here, we describe an alternative method for discrimination of haploid from diploid seeds based on differences in their oil content stemming from pollination with high oil inducers. After presenting some fundamental theory, we provide a proof-of-concept with experimental results, demonstrating acceptable error rates across different germplasm. Our approach represents a breakthrough in DH technology in maize, because it is amenable to automated high-throughput screening and applicable to any maize germplasm worldwide. PMID:23820577

Rocket Engine Health Management: Early Definition of Critical Flight Measurements

NASA Technical Reports Server (NTRS)

Christenson, Rick L.; Nelson, Michael A.; Butas, John P.

2003-01-01

The NASA led Space Launch Initiative (SLI) program has established key requirements related to safety, reliability, launch availability and operations cost to be met by the next generation of reusable launch vehicles. Key to meeting these requirements will be an integrated vehicle health management ( M) system that includes sensors, harnesses, software, memory, and processors. Such a system must be integrated across all the vehicle subsystems and meet component, subsystem, and system requirements relative to fault detection, fault isolation, and false alarm rate. The purpose of this activity is to evolve techniques for defining critical flight engine system measurements-early within the definition of an engine health management system (EHMS). Two approaches, performance-based and failure mode-based, are integrated to provide a proposed set of measurements to be collected. This integrated approach is applied to MSFC s MC-1 engine. Early identification of measurements supports early identification of candidate sensor systems whose design and impacts to the engine components must be considered in engine design.

A reliable Raman-spectroscopy-based approach for diagnosis, classification and follow-up of B-cell acute lymphoblastic leukemia

NASA Astrophysics Data System (ADS)

Managò, Stefano; Valente, Carmen; Mirabelli, Peppino; Circolo, Diego; Basile, Filomena; Corda, Daniela; de Luca, Anna Chiara

2016-04-01

Acute lymphoblastic leukemia type B (B-ALL) is a neoplastic disorder that shows high mortality rates due to immature lymphocyte B-cell proliferation. B-ALL diagnosis requires identification and classification of the leukemia cells. Here, we demonstrate the use of Raman spectroscopy to discriminate normal lymphocytic B-cells from three different B-leukemia transformed cell lines (i.e., RS4;11, REH, MN60 cells) based on their biochemical features. In combination with immunofluorescence and Western blotting, we show that these Raman markers reflect the relative changes in the potential biological markers from cell surface antigens, cytoplasmic proteins, and DNA content and correlate with the lymphoblastic B-cell maturation/differentiation stages. Our study demonstrates the potential of this technique for classification of B-leukemia cells into the different differentiation/maturation stages, as well as for the identification of key biochemical changes under chemotherapeutic treatments. Finally, preliminary results from clinical samples indicate high consistency of, and potential applications for, this Raman spectroscopy approach.

Identification of Milk Component in Ancient Food Residue by Proteomics

PubMed Central

Hong, Chuan; Jiang, Hongen; Lü, Enguo; Wu, Yunfei; Guo, Lihai; Xie, Yongming; Wang, Changsui; Yang, Yimin

2012-01-01

Background Proteomic approaches based on mass spectrometry have been recently used in archaeological and art researches, generating promising results for protein identification. Little information is known about eastward spread and eastern limits of prehistoric milking in eastern Eurasia. Methodology/Principal Finding In this paper, an ancient visible food remain from Subeixi Cemeteries (cal. 500 to 300 years BC) of the Turpan Basin in Xinjiang, China, preliminarily determined containing 0.432 mg/kg cattle casein with ELISA, was analyzed by using an improved method based on liquid chromatography (LC) coupled with MALDI-TOF/TOF-MS to further identify protein origin. The specific sequence of bovine casein and the homology sequence of goat/sheep casein were identified. Conclusions/Significance The existence of milk component in ancient food implies goat/sheep and cattle milking in ancient Subeixi region, the furthest eastern location of prehistoric milking in the Old World up to date. It is envisioned that this work provides a new approach for ancient residue analysis and other archaeometry field. PMID:22615887

A Bayesian Approach for Sensor Optimisation in Impact Identification

PubMed Central

Mallardo, Vincenzo; Sharif Khodaei, Zahra; Aliabadi, Ferri M. H.

2016-01-01

This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM) system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence. PMID:28774064

A Multiple Identity Approach to Gender: Identification with Women, Identification with Feminists, and Their Interaction

PubMed Central

van Breen, Jolien A.; Spears, Russell; Kuppens, Toon; de Lemus, Soledad

2017-01-01

Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that identification with women reflects attitudes toward the content society gives to group membership: what does it mean to be a woman in terms of group characteristics, interests and values? Identification with feminists, on the other hand, is a politicized identity dimension reflecting attitudes toward the social position of the group: what does it mean to be a woman in terms of disadvantage, inequality, and relative status? We examine the utility of this multiple identity approach in four studies. Study 1 showed that identification with women reflects attitudes toward group characteristics, such as femininity and self-stereotyping, while identification with feminists reflects attitudes toward the group's social position, such as perceived sexism. The two dimensions are shown to be largely independent, and as such provide support for the multiple identity approach. In Studies 2–4, we examine the utility of this multiple identity approach in predicting qualitative differences in gender attitudes. Results show that specific combinations of identification with women and feminists predicted attitudes toward collective action and gender stereotypes. Higher identification with feminists led to endorsement of radical collective action (Study 2) and critical attitudes toward gender stereotypes (Studies 3–4), especially at lower levels of identification with women. The different combinations of high vs. low identification with women and feminists can be thought of as reflecting four theoretical identity “types.” A woman can be (1) strongly identified with neither women nor feminists (“low identifier”), (2) strongly identified with women but less so with feminists (“traditional identifier”), (3) strongly identified with both women and feminists (“dual identifier”), or (4) strongly identified with feminists but less so with women (“distinctive feminist”). In sum, by considering identification with women and identification with feminists as multiple identities we aim to show how the multiple identity approach predicts distinct attitudes to gender issues and offer a new perspective on gender identity. PMID:28713297

A Multiple Identity Approach to Gender: Identification with Women, Identification with Feminists, and Their Interaction.

PubMed

van Breen, Jolien A; Spears, Russell; Kuppens, Toon; de Lemus, Soledad

2017-01-01

Across four studies, we examine multiple identities in the context of gender and propose that women's attitudes toward gender group membership are governed by two largely orthogonal dimensions of gender identity: identification with women and identification with feminists. We argue that identification with women reflects attitudes toward the content society gives to group membership: what does it mean to be a woman in terms of group characteristics, interests and values? Identification with feminists, on the other hand, is a politicized identity dimension reflecting attitudes toward the social position of the group: what does it mean to be a woman in terms of disadvantage, inequality, and relative status? We examine the utility of this multiple identity approach in four studies. Study 1 showed that identification with women reflects attitudes toward group characteristics, such as femininity and self-stereotyping, while identification with feminists reflects attitudes toward the group's social position, such as perceived sexism. The two dimensions are shown to be largely independent, and as such provide support for the multiple identity approach. In Studies 2-4, we examine the utility of this multiple identity approach in predicting qualitative differences in gender attitudes. Results show that specific combinations of identification with women and feminists predicted attitudes toward collective action and gender stereotypes. Higher identification with feminists led to endorsement of radical collective action (Study 2) and critical attitudes toward gender stereotypes (Studies 3-4), especially at lower levels of identification with women. The different combinations of high vs. low identification with women and feminists can be thought of as reflecting four theoretical identity "types." A woman can be (1) strongly identified with neither women nor feminists ("low identifier"), (2) strongly identified with women but less so with feminists ("traditional identifier"), (3) strongly identified with both women and feminists ("dual identifier"), or (4) strongly identified with feminists but less so with women ("distinctive feminist"). In sum, by considering identification with women and identification with feminists as multiple identities we aim to show how the multiple identity approach predicts distinct attitudes to gender issues and offer a new perspective on gender identity.

Identification of "insoluble" red dyewoods by high performance liquid chromatography-photodiode array detection (HPLC-PDA) fingerprinting.

PubMed

Surowiec, Izabella; Nowik, Witold; Trojanowicz, Marek

2004-02-01

The paper describes a high performance liquid chromatography-UV/Vis spectrometry detection analytical approach to the identification of some redwood species of historical importance in textile dyeing. The group of extracted dyestuffs considered as "insoluble" because of their non-aqueous or alkaline extraction conditions is present in the wood of the Pterocarpus family and Baphia nitida species. First, the crude extracts of tinctorial and related species and their chromatographic fingerprints were studied. This part of work shows that some species not yet mentioned in the literature have potential dyeing properties. Subsequent experiments performed on the redwood cargo of a 200-year-old archaeological shipwreck allowed identification of the water-logged wood species. Furthermore, the different methods of dyestuff extraction used for dyeing according to traditional recipes and their impact on analytical results were studied. They show that standard recovery obtained by acid hydrolysis of dyestuff from dyed yarns is inadequate. Hence, alternative solvent-based procedures were proposed. The identification of species in textile threads then becomes possible. The applied approach was validated by analysis of dyed reference yarns with some indications of crude material extraction mode. The employed method of analysis seems to be useful for "insoluble" wood species identification in cultural heritage artifacts as well as for phytochemical purposes, despite the fact that very few detected color compounds were chemically identified.

Identification of beer-spoilage bacteria using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

PubMed

Wieme, Anneleen D; Spitaels, Freek; Aerts, Maarten; De Bruyne, Katrien; Van Landschoot, Anita; Vandamme, Peter

2014-08-18

Applicability of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for identification of beer-spoilage bacteria was examined. To achieve this, an extensive identification database was constructed comprising more than 4200 mass spectra, including biological and technical replicates derived from 273 acetic acid bacteria (AAB) and lactic acid bacteria (LAB), covering a total of 52 species, grown on at least three growth media. Sequence analysis of protein coding genes was used to verify aberrant MALDI-TOF MS identification results and confirmed the earlier misidentification of 34 AAB and LAB strains. In total, 348 isolates were collected from culture media inoculated with 14 spoiled beer and brewery samples. Peak-based numerical analysis of MALDI-TOF MS spectra allowed a straightforward species identification of 327 (94.0%) isolates. The remaining isolates clustered separately and were assigned through sequence analysis of protein coding genes either to species not known as beer-spoilage bacteria, and thus not present in the database, or to novel AAB species. An alternative, classifier-based approach for the identification of spoilage bacteria was evaluated by combining the identification results obtained through peak-based cluster analysis and sequence analysis of protein coding genes as a standard. In total, 263 out of 348 isolates (75.6%) were correctly identified at species level and 24 isolates (6.9%) were misidentified. In addition, the identification results of 50 isolates (14.4%) were considered unreliable, and 11 isolates (3.2%) could not be identified. The present study demonstrated that MALDI-TOF MS is well-suited for the rapid, high-throughput and accurate identification of bacteria isolated from spoiled beer and brewery samples, which makes the technique appropriate for routine microbial quality control in the brewing industry. Copyright © 2014 Elsevier B.V. All rights reserved.

A Cooperative Approach To Teaching Mineral Identification.

ERIC Educational Resources Information Center

Constantopoulos, Terri Lynn

1994-01-01

Describes Jigsaw Teaching, a cooperative learning approach, in relation to mineral identification. This technique may also be applied to rock identification. Students work in groups of four and learn to identify 20 minerals, becoming an "expert" on five of them. Helping to teach other students reinforces what each student has learned.…

Remote monitoring of soldier safety through body posture identification using wearable sensor networks

NASA Astrophysics Data System (ADS)

Biswas, Subir; Quwaider, Muhannad

2008-04-01

The physical safety and well being of the soldiers in a battlefield is the highest priority of Incident Commanders. Currently, the ability to track and monitor soldiers rely on visual and verbal communication which can be somewhat limited in scenarios where the soldiers are deployed inside buildings and enclosed areas that are out of visual range of the commanders. Also, the need for being stealth can often prevent a battling soldier to send verbal clues to a commander about his or her physical well being. Sensor technologies can remotely provide various data about the soldiers including physiological monitoring and personal alert safety system functionality. This paper presents a networked sensing solution in which a body area wireless network of multi-modal sensors can monitor the body movement and other physiological parameters for statistical identification of a soldier's body posture, which can then be indicative of the physical conditions and safety alerts of the soldier in question. The specific concept is to leverage on-body proximity sensing and a Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of human body postures using a wearable sensor network. The key idea is to collect relative proximity information between wireless sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and differentiation between low activity postures such as sitting and standing. We develop a wearable sensor network that monitors relative proximity using Radio Signal Strength indication (RSSI), and then construct a HMM system for posture identification in the presence of sensing errors. Controlled experiments using human subjects were carried out for evaluating the accuracy of the HMM identified postures compared to a naÃve threshold based mechanism, and its variations over different human subjects. A large spectrum of target human postures, including lie down, sit (straight and reclined), stand, walk, run, sprint and stair climbing, are used for validating the proposed system.

Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.

PubMed

Leifeld, Thomas; Zhang, Zhihua; Zhang, Ping

2018-01-01

Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.

DNA-barcoding of forensically important blow flies (Diptera: Calliphoridae) in the Caribbean Region

PubMed Central

Agnarsson, Ingi

2017-01-01

Correct identification of forensically important insects, such as flies in the family Calliphoridae, is a crucial step for them to be used as evidence in legal investigations. Traditional identification based on morphology has been effective, but has some limitations when it comes to identifying immature stages of certain species. DNA-barcoding, using COI, has demonstrated potential for rapid and accurate identification of Calliphoridae, however, this gene does not reliably distinguish among some recently diverged species, raising questions about its use for delimitation of species of forensic importance. To facilitate DNA based identification of Calliphoridae in the Caribbean we developed a vouchered reference collection from across the region, and a DNA sequence database, and further added the nuclear ITS2 as a second marker to increase accuracy of identification through barcoding. We morphologically identified freshly collected specimens, did phylogenetic analyses and employed several species delimitation methods for a total of 468 individuals representing 19 described species. Our results show that combination of COI + ITS2 genes yields more accurate identification and diagnoses, and better agreement with morphological data, than the mitochondrial barcodes alone. All of our results from independent and concatenated trees and most of the species delimitation methods yield considerably higher diversity estimates than the distance based approach and morphology. Molecular data support at least 24 distinct clades within Calliphoridae in this study, recovering substantial geographic variation for Lucilia eximia, Lucilia retroversa, Lucilia rica and Chloroprocta idioidea, probably indicating several cryptic species. In sum, our study demonstrates the importance of employing a second nuclear marker for barcoding analyses and species delimitation of calliphorids, and the power of molecular data in combination with a complete reference database to enable identification of taxonomically and geographically diverse insects of forensic importance. PMID:28761780

An animal tracking system for behavior analysis using radio frequency identification.

PubMed

Catarinucci, Luca; Colella, Riccardo; Mainetti, Luca; Patrono, Luigi; Pieretti, Stefano; Secco, Andrea; Sergi, Ilaria

2014-09-01

Evaluating the behavior of mice and rats has substantially contributed to the progress of research in many scientific fields. Researchers commonly observe recorded video of animal behavior and manually record their observations for later analysis, but this approach has several limitations. The authors developed an automated system for tracking and analyzing the behavior of rodents that is based on radio frequency identification (RFID) in an ultra-high-frequency bandwidth. They provide an overview of the system's hardware and software components as well as describe their technique for surgically implanting passive RFID tags in mice. Finally, the authors present the findings of two validation studies to compare the accuracy of the RFID system versus commonly used approaches for evaluating the locomotor activity and object exploration of mice.

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.

PubMed

Lee, Alexandra J; Chang, Ivan; Burel, Julie G; Lindestam Arlehamn, Cecilia S; Mandava, Aishwarya; Weiskopf, Daniela; Peters, Bjoern; Sette, Alessandro; Scheuermann, Richard H; Qian, Yu

2018-04-17

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

Determining Plant – Leaf Miner – Parasitoid Interactions: A DNA Barcoding Approach

PubMed Central

Derocles, Stéphane A. P.; Evans, Darren M.; Nichols, Paul C.; Evans, S. Aifionn; Lunt, David H.

2015-01-01

A major challenge in network ecology is to describe the full-range of species interactions in a community to create highly-resolved food-webs. We developed a molecular approach based on DNA full barcoding and mini-barcoding to describe difficult to observe plant – leaf miner – parasitoid interactions, consisting of animals commonly regarded as agricultural pests and their natural enemies. We tested the ability of universal primers to amplify the remaining DNA inside leaf miner mines after the emergence of the insect. We compared the results of a) morphological identification of adult specimens; b) identification based on the shape of the mines; c) the COI Mini-barcode (130 bp) and d) the COI full barcode (658 bp) fragments to accurately identify the leaf-miner species. We used the molecular approach to build and analyse a tri-partite ecological network of plant – leaf miner – parasitoid interactions. We were able to detect the DNA of leaf-mining insects within their feeding mines on a range of host plants using mini-barcoding primers: 6% for the leaves collected empty and 33% success after we observed the emergence of the leaf miner. We suggest that the low amplification success of leaf mines collected empty was mainly due to the time since the adult emerged and discuss methodological improvements. Nevertheless our approach provided new species-interaction data for the ecological network. We found that the 130 bp fragment is variable enough to identify all the species included in this study. Both COI fragments reveal that some leaf miner species could be composed of cryptic species. The network built using the molecular approach was more accurate in describing tri-partite interactions compared with traditional approaches based on morphological criteria. PMID:25710377

Using complementary approaches to identify trans-domain nuclear gene transfers in the extremophile Galdieria sulphuraria (Rhodophyta).

PubMed

Pandey, Ravi S; Saxena, Garima; Bhattacharya, Debashish; Qiu, Huan; Azad, Rajeev K

2017-02-01

Identification of horizontal gene transfers (HGTs) has primarily relied on phylogenetic tree based methods, which require a rich sampling of sequenced genomes to ensure a reliable inference. Because the success of phylogenetic approaches depends on the breadth and depth of the database, researchers usually apply stringent filters to detect only the most likely gene transfers in the genomes of interest. One such study focused on a highly conservative estimate of trans-domain gene transfers in the extremophile eukaryote, Galdieria sulphuraria (Galdieri) Merola (Rhodophyta), by applying multiple filters in their phylogenetic pipeline. This led to the identification of 75 inter-domain acquisitions from Bacteria or Archaea. Because of the evolutionary, ecological, and potential biotechnological significance of foreign genes in algae, alternative approaches and pipelines complementing phylogenetics are needed for a more comprehensive assessment of HGT. We present here a novel pipeline that uncovered 17 novel foreign genes of prokaryotic origin in G. sulphuraria, results that are supported by multiple lines of evidence including composition-based, comparative data, and phylogenetics. These genes encode a variety of potentially adaptive functions, from metabolite transport to DNA repair. © 2016 Phycological Society of America.

Cutting the Wires: Modularization of Cellular Networks for Experimental Design

PubMed Central

Lang, Moritz; Summers, Sean; Stelling, Jörg

2014-01-01

Understanding naturally evolved cellular networks requires the consecutive identification and revision of the interactions between relevant molecular species. In this process, initially often simplified and incomplete networks are extended by integrating new reactions or whole subnetworks to increase consistency between model predictions and new measurement data. However, increased consistency with experimental data alone is not sufficient to show the existence of biomolecular interactions, because the interplay of different potential extensions might lead to overall similar dynamics. Here, we present a graph-based modularization approach to facilitate the design of experiments targeted at independently validating the existence of several potential network extensions. Our method is based on selecting the outputs to measure during an experiment, such that each potential network extension becomes virtually insulated from all others during data analysis. Each output defines a module that only depends on one hypothetical network extension, and all other outputs act as virtual inputs to achieve insulation. Given appropriate experimental time-series measurements of the outputs, our modules can be analyzed, simulated, and compared to the experimental data separately. Our approach exemplifies the close relationship between structural systems identification and modularization, an interplay that promises development of related approaches in the future. PMID:24411264

Micro transflection on a metallic stick: an innovative approach of reflection infrared spectroscopy for minimally invasive investigation of painting varnishes.

PubMed

Rosi, Francesca; Legan, Lea; Miliani, Costanza; Ropret, Polonca

2017-05-01

A new analytical approach, based on micro-transflection measurements from a diamond-coated metal sampling stick, is presented for the analysis of painting varnishes. Minimally invasive sampling is performed from the varnished surface using the stick, which is directly used as a transflection substrate for micro Fourier transform infrared (FTIR) measurements. With use of a series of varnished model paints, the micro-transflection method has been proved to be a valuable tool for the identification of surface components thanks to the selectivity of the sampling, the enhancement of the absorbance signal, and the easier spectral interpretation because the profiles are similar to transmission mode ones. Driven by these positive outcomes, the method was then tested as tool supporting noninvasive reflection FTIR spectroscopy during the assessment of varnish removal by solvent cleaning on paint models. Finally, the integrated analytical approach based on the two reflection methods was successfully applied for the monitoring of the cleaning of the sixteenth century painting Presentation in the Temple by Vittore Carpaccio. Graphical Abstract Micro-transflection FTIR on a metallic stick for the identification of varnishes during painting cleanings.

Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations.

PubMed

Singh, Pankaj Kumar; Negi, Arvind; Gupta, Pawan Kumar; Chauhan, Monika; Kumar, Raj

2016-08-01

Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.

Dynamic Stability Analysis of Linear Time-varying Systems via an Extended Modal Identification Approach

NASA Astrophysics Data System (ADS)

Ma, Zhisai; Liu, Li; Zhou, Sida; Naets, Frank; Heylen, Ward; Desmet, Wim

2017-03-01

The problem of linear time-varying(LTV) system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the "frozen-time" assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stability-preserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system's stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam experimental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides a new way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

HTSFinder: Powerful Pipeline of DNA Signature Discovery by Parallel and Distributed Computing

PubMed Central

Karimi, Ramin; Hajdu, Andras

2016-01-01

Comprehensive effort for low-cost sequencing in the past few years has led to the growth of complete genome databases. In parallel with this effort, a strong need, fast and cost-effective methods and applications have been developed to accelerate sequence analysis. Identification is the very first step of this task. Due to the difficulties, high costs, and computational challenges of alignment-based approaches, an alternative universal identification method is highly required. Like an alignment-free approach, DNA signatures have provided new opportunities for the rapid identification of species. In this paper, we present an effective pipeline HTSFinder (high-throughput signature finder) with a corresponding k-mer generator GkmerG (genome k-mers generator). Using this pipeline, we determine the frequency of k-mers from the available complete genome databases for the detection of extensive DNA signatures in a reasonably short time. Our application can detect both unique and common signatures in the arbitrarily selected target and nontarget databases. Hadoop and MapReduce as parallel and distributed computing tools with commodity hardware are used in this pipeline. This approach brings the power of high-performance computing into the ordinary desktop personal computers for discovering DNA signatures in large databases such as bacterial genome. A considerable number of detected unique and common DNA signatures of the target database bring the opportunities to improve the identification process not only for polymerase chain reaction and microarray assays but also for more complex scenarios such as metagenomics and next-generation sequencing analysis. PMID:26884678

HTSFinder: Powerful Pipeline of DNA Signature Discovery by Parallel and Distributed Computing.

PubMed

Karimi, Ramin; Hajdu, Andras

2016-01-01

Comprehensive effort for low-cost sequencing in the past few years has led to the growth of complete genome databases. In parallel with this effort, a strong need, fast and cost-effective methods and applications have been developed to accelerate sequence analysis. Identification is the very first step of this task. Due to the difficulties, high costs, and computational challenges of alignment-based approaches, an alternative universal identification method is highly required. Like an alignment-free approach, DNA signatures have provided new opportunities for the rapid identification of species. In this paper, we present an effective pipeline HTSFinder (high-throughput signature finder) with a corresponding k-mer generator GkmerG (genome k-mers generator). Using this pipeline, we determine the frequency of k-mers from the available complete genome databases for the detection of extensive DNA signatures in a reasonably short time. Our application can detect both unique and common signatures in the arbitrarily selected target and nontarget databases. Hadoop and MapReduce as parallel and distributed computing tools with commodity hardware are used in this pipeline. This approach brings the power of high-performance computing into the ordinary desktop personal computers for discovering DNA signatures in large databases such as bacterial genome. A considerable number of detected unique and common DNA signatures of the target database bring the opportunities to improve the identification process not only for polymerase chain reaction and microarray assays but also for more complex scenarios such as metagenomics and next-generation sequencing analysis.

Duplex DNA-Invading γ-Modified Peptide Nucleic Acids Enable Rapid Identification of Bloodstream Infections in Whole Blood.

PubMed

Nölling, Jörk; Rapireddy, Srinivas; Amburg, Joel I; Crawford, Elizabeth M; Prakash, Ranjit A; Rabson, Arthur R; Tang, Yi-Wei; Singer, Alon

2016-04-19

Bloodstream infections are a leading cause of morbidity and mortality. Early and targeted antimicrobial intervention is lifesaving, yet current diagnostic approaches fail to provide actionable information within a clinically viable time frame due to their reliance on blood culturing. Here, we present a novel pathogen identification (PID) platform that features the use of duplex DNA-invading γ-modified peptide nucleic acids (γPNAs) for the rapid identification of bacterial and fungal pathogens directly from blood, without culturing. The PID platform provides species-level information in under 2.5 hours while reaching single-CFU-per-milliliter sensitivity across the entire 21-pathogen panel. The clinical utility of the PID platform was demonstrated through assessment of 61 clinical specimens, which showed >95% sensitivity and >90% overall correlation to blood culture findings. This rapid γPNA-based platform promises to improve patient care by enabling the administration of a targeted first-line antimicrobial intervention. Bloodstream infections continue to be a major cause of death for hospitalized patients, despite significant improvements in both the availability of treatment options as well their application. Since early and targeted antimicrobial intervention is one of the prime determinants of patient outcome, the rapid identification of the pathogen can be lifesaving. Unfortunately, current diagnostic approaches for identifying these infections all rely on time-consuming blood culture, which precludes immediate intervention with a targeted antimicrobial. To address this, we have developed and characterized a new and comprehensive methodology, from patient specimen to result, for the rapid identification of both bacterial and fungal pathogens without the need for culturing. We anticipate broad interest in our work, given the novelty of our technical approach combined with an immense unmet need. Copyright © 2016 Nölling et al.

A survey on the utility of the USEPA CADDIS stressor identification procedure.

PubMed

Harwood, John J; Stroud, Robert Adam

2012-06-01

The Environmental Protection Agency (EPA) has made available on the worldwide web a systematic stream stressor identification procedure, the "Causal Analysis/Diagnosis Decision Information System" or CADDIS. We report here the results of a survey of regulators and scientists in 11 states who use CADDIS or another stressor identification procedure in their work. The 13 survey questions address guidelines as to what impairment scenarios to approach with stressor identification, what information is needed to perform stressor identification, and what the stakeholder role is in performing stressor identification. At the time of this survey (the summer of 2009), the EPA CADDIS website was less commonly used among the state regulators surveyed than the published EPA stressor identification document on which it is based. The respondents generally find the EPA stressor identification procedure useful and capable of being adapted to their individual needs. Survey respondents all use stressor identification in their Total Maximum Daily Load work, but also in a wide variety of other applications. All the "types of evidence" included in the CADDIS stressor identification procedure are used by the practitioners surveyed with the exception of the results of ecological simulation models. While the CADDIS documentation encourages the involvement of stakeholders in stressor identification, most respondents do not assemble stakeholder teams of local officials and citizens to participate in stressor analyses.

Rapid identification of single microbes by various Raman spectroscopic techniques

NASA Astrophysics Data System (ADS)

Rösch, Petra; Harz, Michaela; Schmitt, Michael; Peschke, Klaus-Dieter; Ronneberger, Olaf; Burkhardt, Hans; Motzkus, Hans-Walter; Lankers, Markus; Hofer, Stefan; Thiele, Hans; Popp, Jürgen

2006-02-01

A fast and unambiguous identification of microorganisms is necessary not only for medical purposes but also in technical processes such as the production of pharmaceuticals. Conventional microbiological identification methods are based on the morphology and the ability of microbes to grow under different conditions on various cultivation media depending on their biochemical properties. These methods require pure cultures which need cultivation of at least 6 h but normally much longer. Recently also additional methods to identify bacteria are established e.g. mass spectroscopy, polymerase chain reaction (PCR), flow cytometry or fluorescence spectroscopy. Alternative approaches for the identification of microorganisms are vibrational spectroscopic techniques. With Raman spectroscopy a spectroscopic fingerprint of the microorganisms can be achieved. Using UV-resonance Raman spectroscopy (UVRR) macromolecules like DNA/RNA and proteins are resonantly enhanced. With an excitation wavelength of e.g. 244 nm it is possible to determine the ratio of guanine/cytosine to all DNA bases which allows a genotypic identification of microorganisms. The application of UVRR requires a large amount of microorganisms (> 10 6 cells) e.g. at least a micro colony. For the analysis of single cells micro-Raman spectroscopy with an excitation wavelength of 532 nm can be used. Here, the obtained information is from all type of molecules inside the cells which lead to a chemotaxonomic identification. In this contribution we show how wavelength dependent Raman spectroscopy yields significant molecular information applicable for the identification of microorganisms on a single cell level.

Comprehensive viral enrichment enables sensitive respiratory virus genomic identification and analysis by next generation sequencing.

PubMed

O'Flaherty, Brigid M; Li, Yan; Tao, Ying; Paden, Clinton R; Queen, Krista; Zhang, Jing; Dinwiddie, Darrell L; Gross, Stephen M; Schroth, Gary P; Tong, Suxiang

2018-06-01

Next generation sequencing (NGS) technologies have revolutionized the genomics field and are becoming more commonplace for identification of human infectious diseases. However, due to the low abundance of viral nucleic acids (NAs) in relation to host, viral identification using direct NGS technologies often lacks sufficient sensitivity. Here, we describe an approach based on two complementary enrichment strategies that significantly improves the sensitivity of NGS-based virus identification. To start, we developed two sets of DNA probes to enrich virus NAs associated with respiratory diseases. The first set of probes spans the genomes, allowing for identification of known viruses and full genome sequencing, while the second set targets regions conserved among viral families or genera, providing the ability to detect both known and potentially novel members of those virus groups. Efficiency of enrichment was assessed by NGS testing reference virus and clinical samples with known infection. We show significant improvement in viral identification using enriched NGS compared to unenriched NGS. Without enrichment, we observed an average of 0.3% targeted viral reads per sample. However, after enrichment, 50%-99% of the reads per sample were the targeted viral reads for both the reference isolates and clinical specimens using both probe sets. Importantly, dramatic improvements on genome coverage were also observed following virus-specific probe enrichment. The methods described here provide improved sensitivity for virus identification by NGS, allowing for a more comprehensive analysis of disease etiology. © 2018 O'Flaherty et al.; Published by Cold Spring Harbor Laboratory Press.

An informatics-based approach to reducing heart failure all-cause readmissions: the Stanford heart failure dashboard.

PubMed

Banerjee, Dipanjan; Thompson, Christine; Kell, Charlene; Shetty, Rajesh; Vetteth, Yohan; Grossman, Helene; DiBiase, Aria; Fowler, Michael

2017-05-01

Reduction of 30-day all-cause readmissions for heart failure (HF) has become an important quality-of-care metric for health care systems. Many hospitals have implemented quality improvement programs designed to reduce 30-day all-cause readmissions for HF. Electronic medical record (EMR)-based measures have been employed to aid in these efforts, but their use has been largely adjunctive to, rather than integrated with, the overall effort. We hypothesized that a comprehensive EMR-based approach utilizing an HF dashboard in addition to an established HF readmission reduction program would further reduce 30-day all-cause index hospital readmission rates for HF. After establishing a quality improvement program to reduce 30-day HF readmission rates, we instituted EMR-based measures designed to improve cohort identification, intervention tracking, and readmission analysis, the latter 2 supported by an electronic HF dashboard. Our primary outcome measure was the 30-day index hospital readmission rate for HF, with secondary measures including the accuracy of identification of patients with HF and the percentage of patients receiving interventions designed to reduce all-cause readmissions for HF. The HF dashboard facilitated improved penetration of our interventions and reduced readmission rates by allowing the clinical team to easily identify cohorts with high readmission rates and/or low intervention rates. We significantly reduced 30-day index hospital all-cause HF readmission rates from 18.2% at baseline to 14% after implementation of our quality improvement program ( P  = .045). Implementation of our EMR-based approach further significantly reduced 30-day index hospital readmission rates for HF to 10.1% ( P for trend = .0001). Daily time to screen patients decreased from 1 hour to 15 minutes, accuracy of cohort identification improved from 83% to 94.6% ( P  = .0001), and the percentage of patients receiving our interventions, such as patient education, also improved significantly from 22% to 100% over time ( P  < .0001). In an institution with a quality improvement program already in place to reduce 30-day readmission rates for HF, an EMR-based approach further significantly reduced 30-day index hospital readmission rates. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Automated Analysis of siRNA Screens of Virus Infected Cells Based on Immunofluorescence Microscopy

NASA Astrophysics Data System (ADS)

Matula, Petr; Kumar, Anil; Wörz, Ilka; Harder, Nathalie; Erfle, Holger; Bartenschlager, Ralf; Eils, Roland; Rohr, Karl

We present an image analysis approach as part of a high-throughput microscopy screening system based on cell arrays for the identification of genes involved in Hepatitis C and Dengue virus replication. Our approach comprises: cell nucleus segmentation, quantification of virus replication level in cells, localization of regions with transfected cells, cell classification by infection status, and quality assessment of an experiment. The approach is fully automatic and has been successfully applied to a large number of cell array images from screening experiments. The experimental results show a good agreement with the expected behavior of positive as well as negative controls and encourage the application to screens from further high-throughput experiments.

Proteomic strategy for the identification of critical actors in reorganization of the post-meiotic male genome.

PubMed

Govin, Jerome; Gaucher, Jonathan; Ferro, Myriam; Debernardi, Alexandra; Garin, Jerome; Khochbin, Saadi; Rousseaux, Sophie

2012-01-01

After meiosis, during the final stages of spermatogenesis, the haploid male genome undergoes major structural changes, resulting in a shift from a nucleosome-based genome organization to the sperm-specific, highly compacted nucleoprotamine structure. Recent data support the idea that region-specific programming of the haploid male genome is of high importance for the post-fertilization events and for successful embryo development. Although these events constitute a unique and essential step in reproduction, the mechanisms by which they occur have remained completely obscure and the factors involved have mostly remained uncharacterized. Here, we sought a strategy to significantly increase our understanding of proteins controlling the haploid male genome reprogramming, based on the identification of proteins in two specific pools: those with the potential to bind nucleic acids (basic proteins) and proteins capable of binding basic proteins (acidic proteins). For the identification of acidic proteins, we developed an approach involving a transition-protein (TP)-based chromatography, which has the advantage of retaining not only acidic proteins due to the charge interactions, but also potential TP-interacting factors. A second strategy, based on an in-depth bioinformatic analysis of the identified proteins, was then applied to pinpoint within the lists obtained, male germ cells expressed factors relevant to the post-meiotic genome organization. This approach reveals a functional network of DNA-packaging proteins and their putative chaperones and sheds a new light on the way the critical transitions in genome organizations could take place. This work also points to a new area of research in male infertility and sperm quality assessments.

Forensic interlaboratory evaluation of the ForFLUID kit for vaginal fluids identification.

PubMed

Giampaoli, Saverio; Alessandrini, Federica; Berti, Andrea; Ripani, Luigi; Choi, Ajin; Crab, Roselien; De Vittori, Elisabetta; Egyed, Balazs; Haas, Cordula; Lee, Hwan Young; Korabecná, Marie; Noel, Fabrice; Podini, Daniele; Tagliabracci, Adriano; Valentini, Alessio; Romano Spica, Vincenzo

2014-01-01

Identification of vaginal fluids is an important step in the process of sexual assaults confirmation. Advances in both microbiology and molecular biology defined technical approaches allowing the discrimination of body fluids. These protocols are based on the identification of specific bacterial communities by microfloraDNA (mfDNA) amplification. A multiplex real time-PCR assay (ForFLUID kit) has been developed for identifying biological fluids and for discrimination among vaginal, oral and fecal samples. In order to test its efficacy and reliability of the assay in the identification of vaginal fluids, an interlaboratory evaluation has been performed on homogeneous vaginal swabs. All the involved laboratories were able to correctly recognize all the vaginal swabs, and no false positives were identified when the assay was applied on non-vaginal samples. The assay represents an useful molecular tool that can be easily adopted by forensic geneticists involved in vaginal fluid identification. Copyright © 2013 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Automated colour identification in melanocytic lesions.

PubMed

Sabbaghi, S; Aldeen, M; Garnavi, R; Varigos, G; Doliantis, C; Nicolopoulos, J

2015-08-01

Colour information plays an important role in classifying skin lesion. However, colour identification by dermatologists can be very subjective, leading to cases of misdiagnosis. Therefore, a computer-assisted system for quantitative colour identification is highly desirable for dermatologists to use. Although numerous colour detection systems have been developed, few studies have focused on imitating the human visual perception of colours in melanoma application. In this paper we propose a new methodology based on QuadTree decomposition technique for automatic colour identification in dermoscopy images. Our approach mimics the human perception of lesion colours. The proposed method is trained on a set of 47 images from NIH dataset and applied to a test set of 190 skin lesions obtained from PH2 dataset. The results of our proposed method are compared with a recently reported colour identification method using the same dataset. The effectiveness of our method in detecting colours in dermoscopy images is vindicated by obtaining approximately 93% accuracy when the CIELab1 colour space is used.

Specific identification of Bacillus anthracis strains

NASA Astrophysics Data System (ADS)

Krishnamurthy, Thaiya; Deshpande, Samir; Hewel, Johannes; Liu, Hongbin; Wick, Charles H.; Yates, John R., III

2007-01-01

Accurate identification of human pathogens is the initial vital step in treating the civilian terrorism victims and military personnel afflicted in biological threat situations. We have applied a powerful multi-dimensional protein identification technology (MudPIT) along with newly generated software termed Profiler to identify the sequences of specific proteins observed for few strains of Bacillus anthracis, a human pathogen. Software termed Profiler was created to initially screen the MudPIT data of B. anthracis strains and establish the observed proteins specific for its strains. A database was also generated using Profiler containing marker proteins of B. anthracis and its strains, which in turn could be used for detecting the organism and its corresponding strains in samples. Analysis of the unknowns by our methodology, combining MudPIT and Profiler, led to the accurate identification of the anthracis strains present in samples. Thus, a new approach for the identification of B. anthracis strains in unknown samples, based on the molecular mass and sequences of marker proteins, has been ascertained.

DNA-based taxonomic identification of basidiospores in hallucinogenic mushrooms cultivated in "grow-kits" seized by the police: LC-UV quali-quantitative determination of psilocybin and psilocin.

PubMed

Gambaro, Veniero; Roda, Gabriella; Visconti, Giacomo Luca; Arnoldi, Sebastiano; Casagni, Eleonora; Dell'Acqua, Lucia; Farè, Fiorenza; Paladino, Eleonora; Rusconi, Chiara; Arioli, Stefania; Mora, Diego

2016-06-05

The taxonomic identification of the biological material contained in the hallucinogenic mushrooms culture media, was carried out using a DNA-based approach, thus highlighting the usefulness of this approach in the forensic identification of illegal samples also when they are present as basidiospores mixed in culture media and spore-bearing fruiting body are not present. This approach is very useful as it allows the unequivocal identification of potentially illicit material before the cultivation and it enables to stop the material to the Customs and to destroy it due to its dangerousness without cultivating the "grow-kits" and without instructing a criminal case. In fact, even if psilocin and psilocybin and the whole mushrooms are illegal in many countries, there is no specific indication in the law about the so called "grow-kits", containing the spores. To confirm the data obtained by the taxonomic identification, a simple, reliable, efficient LC-UV method, using tryptamine as internal standard, suitable for the forensic quali-quantitative determination of psilocin and psilocybin in hallucinogenic mushroom was optimized, validated and applied to the mushrooms grown after the cultivation of the grow-kits seized by the judicial authority, with the authorization of the Ministry of Health. A cation exchange column was used in a gradient elution mode (Phase A: 50mMK2HPO4; 100mM NaCl pH=3 Phase B: methanol). The developed method was linear over the calibration range with a R(2)>0.9992 for both the analytes. The detection and quantification limits were respectively 0.01 and 0.1μg/mL for psilocybin and 0.05μg/mL and 0.1μg/mL for psilocin and the intra- and inter-day precision was satisfactory (coefficients of variation <2.0% for both the analytes). The content of psilocybin in the mushrooms grown from the seized "grow-kits" ranged from 1.02 to 7.60mg/g of dry vegetable material, while the content of psilocin from 0.415 to 8.36mg/g. Copyright © 2016 Elsevier B.V. All rights reserved.

A machine learning based approach to identify protected health information in Chinese clinical text.

PubMed

Du, Liting; Xia, Chenxi; Deng, Zhaohua; Lu, Gary; Xia, Shuxu; Ma, Jingdong

2018-08-01

With the increasing application of electronic health records (EHRs) in the world, protecting private information in clinical text has drawn extensive attention from healthcare providers to researchers. De-identification, the process of identifying and removing protected health information (PHI) from clinical text, has been central to the discourse on medical privacy since 2006. While de-identification is becoming the global norm for handling medical records, there is a paucity of studies on its application on Chinese clinical text. Without efficient and effective privacy protection algorithms in place, the use of indispensable clinical information would be confined. We aimed to (i) describe the current process for PHI in China, (ii) propose a machine learning based approach to identify PHI in Chinese clinical text, and (iii) validate the effectiveness of the machine learning algorithm for de-identification in Chinese clinical text. Based on 14,719 discharge summaries from regional health centers in Ya'an City, Sichuan province, China, we built a conditional random fields (CRF) model to identify PHI in clinical text, and then used the regular expressions to optimize the recognition results of the PHI categories with fewer samples. We constructed a Chinese clinical text corpus with PHI tags through substantial manual annotation, wherein the descriptive statistics of PHI manifested its wide range and diverse categories. The evaluation showed with a high F-measure of 0.9878 that our CRF-based model had a good performance for identifying PHI in Chinese clinical text. The rapid adoption of EHR in the health sector has created an urgent need for tools that can parse patient specific information from Chinese clinical text. Our application of CRF algorithms for de-identification has shown the potential to meet this need by offering a highly accurate and flexible solution to analyzing Chinese clinical text. Copyright © 2018 Elsevier B.V. All rights reserved.

Drawings as a Component of Triangulated Assessment

ERIC Educational Resources Information Center

Otto, Charlotte A.; Everett, Susan A.; Luera, Gail R.; Burke, Christopher F. J.

2013-01-01

Action research (AR) in an educational setting as described by Tillotson (2000), is an approach to "classroom-based problems" or "specific school issues". This process involves identification of the issue or problem, development and implementation of an action plan, gathering and interpreting data, sharing the results within…

77 FR 36999 - Marine Mammals; File No. 16160

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-20

... targeted for research are listed as threatened or endangered: Killer whales (Orcinus orca) from the.... acutorostrata), and killer whales. The research involves harassment by vessel approach for photo-identification... permit be amended to increase Southern Resident killer whale takes from 50 to 200 per year based on...

Fast Bayesian approach for modal identification using free vibration data, Part I - Most probable value

NASA Astrophysics Data System (ADS)

Zhang, Feng-Liang; Ni, Yan-Chun; Au, Siu-Kui; Lam, Heung-Fai

2016-03-01

The identification of modal properties from field testing of civil engineering structures is becoming economically viable, thanks to the advent of modern sensor and data acquisition technology. Its demand is driven by innovative structural designs and increased performance requirements of dynamic-prone structures that call for a close cross-checking or monitoring of their dynamic properties and responses. Existing instrumentation capabilities and modal identification techniques allow structures to be tested under free vibration, forced vibration (known input) or ambient vibration (unknown broadband loading). These tests can be considered complementary rather than competing as they are based on different modeling assumptions in the identification model and have different implications on costs and benefits. Uncertainty arises naturally in the dynamic testing of structures due to measurement noise, sensor alignment error, modeling error, etc. This is especially relevant in field vibration tests because the test condition in the field environment can hardly be controlled. In this work, a Bayesian statistical approach is developed for modal identification using the free vibration response of structures. A frequency domain formulation is proposed that makes statistical inference based on the Fast Fourier Transform (FFT) of the data in a selected frequency band. This significantly simplifies the identification model because only the modes dominating the frequency band need to be included. It also legitimately ignores the information in the excluded frequency bands that are either irrelevant or difficult to model, thereby significantly reducing modeling error risk. The posterior probability density function (PDF) of the modal parameters is derived rigorously from modeling assumptions and Bayesian probability logic. Computational difficulties associated with calculating the posterior statistics, including the most probable value (MPV) and the posterior covariance matrix, are addressed. Fast computational algorithms for determining the MPV are proposed so that the method can be practically implemented. In the companion paper (Part II), analytical formulae are derived for the posterior covariance matrix so that it can be evaluated without resorting to finite difference method. The proposed method is verified using synthetic data. It is also applied to modal identification of full-scale field structures.

Knowledge-rich temporal relation identification and classification in clinical notes

PubMed Central

D’Souza, Jennifer; Ng, Vincent

2014-01-01

Motivation: We examine the task of temporal relation classification for the clinical domain. Our approach to this task departs from existing ones in that it is (i) ‘knowledge-rich’, employing sophisticated knowledge derived from discourse relations as well as both domain-independent and domain-dependent semantic relations, and (ii) ‘hybrid’, combining the strengths of rule-based and learning-based approaches. Evaluation results on the i2b2 Clinical Temporal Relations Challenge corpus show that our approach yields a 17–24% and 8–14% relative reduction in error over a state-of-the-art learning-based baseline system when gold-standard and automatically identified temporal relations are used, respectively. Database URL: http://www.hlt.utdallas.edu/~jld082000/temporal-relations/ PMID:25414383

Use of the MicroSeq 500 16S rRNA Gene-Based Sequencing for Identification of Bacterial Isolates That Commercial Automated Systems Failed To Identify Correctly

PubMed Central

Fontana, Carla; Favaro, Marco; Pelliccioni, Marco; Pistoia, Enrico Salvatore; Favalli, Cartesio

2005-01-01

Reliable automated identification and susceptibility testing of clinically relevant bacteria is an essential routine for microbiology laboratories, thus improving patient care. Examples of automated identification systems include the Phoenix (Becton Dickinson) and the VITEK 2 (bioMérieux). However, more and more frequently, microbiologists must isolate “difficult” strains that automated systems often fail to identify. An alternative approach could be the genetic identification of isolates; this is based on 16S rRNA gene sequencing and analysis. The aim of the present study was to evaluate the possible use of MicroSeq 500 (Applera) for sequencing the 16S rRNA gene to identify isolates whose identification is unobtainable by conventional systems. We analyzed 83 “difficult” clinical isolates: 25 gram-positive and 58 gram-negative strains that were contemporaneously identified by both systems—VITEK 2 and Phoenix—while genetic identification was performed by using the MicroSeq 500 system. The results showed that phenotypic identifications by VITEK 2 and Phoenix were remarkably similar: 74% for gram-negative strains (43 of 58) and 80% for gram-positive strains were concordant by both systems and also concordant with genetic characterization. The exceptions were the 15 gram-negative and 9 gram-positive isolates whose phenotypic identifications were contrasting or inconclusive. For these, the use of MicroSeq 500 was fundamental to achieving species identification. In clinical microbiology the use of MicroSeq 500, particularly for strains with ambiguous biochemical profiles (including slow-growing strains), identifies strains more easily than do conventional systems. Moreover, MicroSeq 500 is easy to use and cost-effective, making it applicable also in the clinical laboratory. PMID:15695654

Structural system identification based on variational mode decomposition

NASA Astrophysics Data System (ADS)

Bagheri, Abdollah; Ozbulut, Osman E.; Harris, Devin K.

2018-03-01

In this paper, a new structural identification method is proposed to identify the modal properties of engineering structures based on dynamic response decomposition using the variational mode decomposition (VMD). The VMD approach is a decomposition algorithm that has been developed as a means to overcome some of the drawbacks and limitations of the empirical mode decomposition method. The VMD-based modal identification algorithm decomposes the acceleration signal into a series of distinct modal responses and their respective center frequencies, such that when combined their cumulative modal responses reproduce the original acceleration response. The decaying amplitude of the extracted modal responses is then used to identify the modal damping ratios using a linear fitting function on modal response data. Finally, after extracting modal responses from available sensors, the mode shape vector for each of the decomposed modes in the system is identified from all obtained modal response data. To demonstrate the efficiency of the algorithm, a series of numerical, laboratory, and field case studies were evaluated. The laboratory case study utilized the vibration response of a three-story shear frame, whereas the field study leveraged the ambient vibration response of a pedestrian bridge to characterize the modal properties of the structure. The modal properties of the shear frame were computed using analytical approach for a comparison with the experimental modal frequencies. Results from these case studies demonstrated that the proposed method is efficient and accurate in identifying modal data of the structures.

Enhanced object-based tracking algorithm for convective rain storms and cells

NASA Astrophysics Data System (ADS)

Muñoz, Carlos; Wang, Li-Pen; Willems, Patrick

2018-03-01

This paper proposes a new object-based storm tracking algorithm, based upon TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting). TITAN is a widely-used convective storm tracking algorithm but has limitations in handling small-scale yet high-intensity storm entities due to its single-threshold identification approach. It also has difficulties to effectively track fast-moving storms because of the employed matching approach that largely relies on the overlapping areas between successive storm entities. To address these deficiencies, a number of modifications are proposed and tested in this paper. These include a two-stage multi-threshold storm identification, a new formulation for characterizing storm's physical features, and an enhanced matching technique in synergy with an optical-flow storm field tracker, as well as, according to these modifications, a more complex merging and splitting scheme. High-resolution (5-min and 529-m) radar reflectivity data for 18 storm events over Belgium are used to calibrate and evaluate the algorithm. The performance of the proposed algorithm is compared with that of the original TITAN. The results suggest that the proposed algorithm can better isolate and match convective rainfall entities, as well as to provide more reliable and detailed motion estimates. Furthermore, the improvement is found to be more significant for higher rainfall intensities. The new algorithm has the potential to serve as a basis for further applications, such as storm nowcasting and long-term stochastic spatial and temporal rainfall generation.

Comparative Evaluation of Small Molecular Additives and Their Effects on Peptide/Protein Identification.

PubMed

Gao, Jing; Zhong, Shaoyun; Zhou, Yanting; He, Han; Peng, Shuying; Zhu, Zhenyun; Liu, Xing; Zheng, Jing; Xu, Bin; Zhou, Hu

2017-06-06

Detergents and salts are widely used in lysis buffers to enhance protein extraction from biological samples, facilitating in-depth proteomic analysis. However, these detergents and salt additives must be efficiently removed from the digested samples prior to LC-MS/MS analysis to obtain high-quality mass spectra. Although filter-aided sample preparation (FASP), acetone precipitation (AP), followed by in-solution digestion, and strong cation exchange-based centrifugal proteomic reactors (CPRs) are commonly used for proteomic sample processing, little is known about their efficiencies at removing detergents and salt additives. In this study, we (i) developed an integrative workflow for the quantification of small molecular additives in proteomic samples, developing a multiple reaction monitoring (MRM)-based LC-MS approach for the quantification of six additives (i.e., Tris, urea, CHAPS, SDS, SDC, and Triton X-100) and (ii) systematically evaluated the relationships between the level of additive remaining in samples following sample processing and the number of peptides/proteins identified by mass spectrometry. Although FASP outperformed the other two methods, the results were complementary in terms of peptide/protein identification, as well as the GRAVY index and amino acid distributions. This is the first systematic and quantitative study of the effect of detergents and salt additives on protein identification. This MRM-based approach can be used for an unbiased evaluation of the performance of new sample preparation methods. Data are available via ProteomeXchange under identifier PXD005405.

Methodology for creating dedicated machine and algorithm on sunflower counting

NASA Astrophysics Data System (ADS)

Muracciole, Vincent; Plainchault, Patrick; Mannino, Maria-Rosaria; Bertrand, Dominique; Vigouroux, Bertrand

2007-09-01

In order to sell grain lots in European countries, seed industries need a government certification. This certification requests purity testing, seed counting in order to quantify specified seed species and other impurities in lots, and germination testing. These analyses are carried out within the framework of international trade according to the methods of the International Seed Testing Association. Presently these different analyses are still achieved manually by skilled operators. Previous works have already shown that seeds can be characterized by around 110 visual features (morphology, colour, texture), and thus have presented several identification algorithms. Until now, most of the works in this domain are computer based. The approach presented in this article is based on the design of dedicated electronic vision machine aimed to identify and sort seeds. This machine is composed of a FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor) and a PC bearing the GUI (Human Machine Interface) of the system. Its operation relies on the stroboscopic image acquisition of a seed falling in front of a camera. A first machine was designed according to this approach, in order to simulate all the vision chain (image acquisition, feature extraction, identification) under the Matlab environment. In order to perform this task into dedicated hardware, all these algorithms were developed without the use of the Matlab toolbox. The objective of this article is to present a design methodology for a special purpose identification algorithm based on distance between groups into dedicated hardware machine for seed counting.

Modal parameter identification based on combining transmissibility functions and blind source separation techniques

NASA Astrophysics Data System (ADS)

Araújo, Iván Gómez; Sánchez, Jesús Antonio García; Andersen, Palle

2018-05-01

Transmissibility-based operational modal analysis is a recent and alternative approach used to identify the modal parameters of structures under operational conditions. This approach is advantageous compared with traditional operational modal analysis because it does not make any assumptions about the excitation spectrum (i.e., white noise with a flat spectrum). However, common methodologies do not include a procedure to extract closely spaced modes with low signal-to-noise ratios. This issue is relevant when considering that engineering structures generally have closely spaced modes and that their measured responses present high levels of noise. Therefore, to overcome these problems, a new combined method for modal parameter identification is proposed in this work. The proposed method combines blind source separation (BSS) techniques and transmissibility-based methods. Here, BSS techniques were used to recover source signals, and transmissibility-based methods were applied to estimate modal information from the recovered source signals. To achieve this combination, a new method to define a transmissibility function was proposed. The suggested transmissibility function is based on the relationship between the power spectral density (PSD) of mixed signals and the PSD of signals from a single source. The numerical responses of a truss structure with high levels of added noise and very closely spaced modes were processed using the proposed combined method to evaluate its ability to identify modal parameters in these conditions. Colored and white noise excitations were used for the numerical example. The proposed combined method was also used to evaluate the modal parameters of an experimental test on a structure containing closely spaced modes. The results showed that the proposed combined method is capable of identifying very closely spaced modes in the presence of noise and, thus, may be potentially applied to improve the identification of damping ratios.

Vision-Based Finger Detection, Tracking, and Event Identification Techniques for Multi-Touch Sensing and Display Systems

PubMed Central

Chen, Yen-Lin; Liang, Wen-Yew; Chiang, Chuan-Yen; Hsieh, Tung-Ju; Lee, Da-Cheng; Yuan, Shyan-Ming; Chang, Yang-Lang

2011-01-01

This study presents efficient vision-based finger detection, tracking, and event identification techniques and a low-cost hardware framework for multi-touch sensing and display applications. The proposed approach uses a fast bright-blob segmentation process based on automatic multilevel histogram thresholding to extract the pixels of touch blobs obtained from scattered infrared lights captured by a video camera. The advantage of this automatic multilevel thresholding approach is its robustness and adaptability when dealing with various ambient lighting conditions and spurious infrared noises. To extract the connected components of these touch blobs, a connected-component analysis procedure is applied to the bright pixels acquired by the previous stage. After extracting the touch blobs from each of the captured image frames, a blob tracking and event recognition process analyzes the spatial and temporal information of these touch blobs from consecutive frames to determine the possible touch events and actions performed by users. This process also refines the detection results and corrects for errors and occlusions caused by noise and errors during the blob extraction process. The proposed blob tracking and touch event recognition process includes two phases. First, the phase of blob tracking associates the motion correspondence of blobs in succeeding frames by analyzing their spatial and temporal features. The touch event recognition process can identify meaningful touch events based on the motion information of touch blobs, such as finger moving, rotating, pressing, hovering, and clicking actions. Experimental results demonstrate that the proposed vision-based finger detection, tracking, and event identification system is feasible and effective for multi-touch sensing applications in various operational environments and conditions. PMID:22163990

Social Identification in Sports Teams: The Role of Personal, Social, and Collective Identity Motives.

PubMed

Thomas, William E; Brown, Rupert; Easterbrook, Matthew J; Vignoles, Vivian L; Manzi, Claudia; D'Angelo, Chiara; Holt, Jeremy J

2017-04-01

Based on motivated identity construction theory (MICT; Vignoles, 2011), we offer an integrative approach examining the combined roles of six identity motives (self-esteem, distinctiveness, belonging, meaning, continuity, and efficacy) instantiated at three different motivational levels (personal, social, and collective identity) as predictors of group identification. These identity processes were investigated among 369 members of 45 sports teams from England and Italy in a longitudinal study over 6 months with four time points. Multilevel change modeling and cross-lagged analyses showed that satisfaction of four personal identity motives (individuals' personal feelings of self-esteem, distinctiveness, meaning, and efficacy derived from team membership), three social identity motives (individuals' feelings that the team identity carries a sense of belonging, meaning, and continuity), and one collective identity motive (a shared belief in group distinctiveness) significantly predicted group identification. Motivational processes underlying group identification are complex, multilayered, and not reducible to personal needs.

Comparative Performance Analysis of Different Fingerprint Biometric Scanners for Patient Matching.

PubMed

Kasiiti, Noah; Wawira, Judy; Purkayastha, Saptarshi; Were, Martin C

2017-01-01

Unique patient identification within health services is an operational challenge in healthcare settings. Use of key identifiers, such as patient names, hospital identification numbers, national ID, and birth date are often inadequate for ensuring unique patient identification. In addition approximate string comparator algorithms, such as distance-based algorithms, have proven suboptimal for improving patient matching, especially in low-resource settings. Biometric approaches may improve unique patient identification. However, before implementing the technology in a given setting, such as health care, the right scanners should be rigorously tested to identify an optimal package for the implementation. This study aimed to investigate the effects of factors such as resolution, template size, and scan capture area on the matching performance of different fingerprint scanners for use within health care settings. Performance analysis of eight different scanners was tested using the demo application distributed as part of the Neurotech Verifinger SDK 6.0.

Monitoring scale scores over time via quality control charts, model-based approaches, and time series techniques.

PubMed

Lee, Yi-Hsuan; von Davier, Alina A

2013-07-01

Maintaining a stable score scale over time is critical for all standardized educational assessments. Traditional quality control tools and approaches for assessing scale drift either require special equating designs, or may be too time-consuming to be considered on a regular basis with an operational test that has a short time window between an administration and its score reporting. Thus, the traditional methods are not sufficient to catch unusual testing outcomes in a timely manner. This paper presents a new approach for score monitoring and assessment of scale drift. It involves quality control charts, model-based approaches, and time series techniques to accommodate the following needs of monitoring scale scores: continuous monitoring, adjustment of customary variations, identification of abrupt shifts, and assessment of autocorrelation. Performance of the methodologies is evaluated using manipulated data based on real responses from 71 administrations of a large-scale high-stakes language assessment.

[The establishment, development and application of classification approach of freshwater phytoplankton based on the functional group: a review].

PubMed

Yang, Wen; Zhu, Jin-Yong; Lu, Kai-Hong; Wan, Li; Mao, Xiao-Hua

2014-06-01

Appropriate schemes for classification of freshwater phytoplankton are prerequisites and important tools for revealing phytoplanktonic succession and studying freshwater ecosystems. An alternative approach, functional group of freshwater phytoplankton, has been proposed and developed due to the deficiencies of Linnaean and molecular identification in ecological applications. The functional group of phytoplankton is a classification scheme based on autoecology. In this study, the theoretical basis and classification criterion of functional group (FG), morpho-functional group (MFG) and morphology-based functional group (MBFG) were summarized, as well as their merits and demerits. FG was considered as the optimal classification approach for the aquatic ecology research and aquatic environment evaluation. The application status of FG was introduced, with the evaluation standards and problems of two approaches to assess water quality on the basis of FG, index methods of Q and QR, being briefly discussed.

Theory-based evaluation of a comprehensive Latino education initiative: an interactive evaluation approach.

PubMed

Nesman, Teresa M; Batsche, Catherine; Hernandez, Mario

2007-08-01

Latino student access to higher education has received significant national attention in recent years. This article describes a theory-based evaluation approach used with ENLACE of Hillsborough, a 5-year project funded by the W.K. Kellogg Foundation for the purpose of increasing Latino student graduation from high school and college. Theory-based evaluation guided planning, implementation as well as evaluation through the process of developing consensus on the Latino population of focus, adoption of culturally appropriate principles and values to guide the project, and identification of strategies to reach, engage, and impact outcomes for Latino students and their families. The approach included interactive development of logic models that focused the scope of interventions and guided evaluation designs for addressing three stages of the initiative. Challenges and opportunities created by the approach are discussed, as well as ways in which the initiative impacted Latino students and collaborating educational institutions.

Target Abundance-Based Fitness Screening (TAFiS) Facilitates Rapid Identification of Target-Specific and Physiologically Active Chemical Probes

PubMed Central

Butts, Arielle; DeJarnette, Christian; Peters, Tracy L.; Parker, Josie E.; Kerns, Morgan E.; Eberle, Karen E.; Kelly, Steve L.

2017-01-01

ABSTRACT Traditional approaches to drug discovery are frustratingly inefficient and have several key limitations that severely constrain our capacity to rapidly identify and develop novel experimental therapeutics. To address this, we have devised a second-generation target-based whole-cell screening assay based on the principles of competitive fitness, which can rapidly identify target-specific and physiologically active compounds. Briefly, strains expressing high, intermediate, and low levels of a preselected target protein are constructed, tagged with spectrally distinct fluorescent proteins (FPs), and pooled. The pooled strains are then grown in the presence of various small molecules, and the relative growth of each strain within the mixed culture is compared by measuring the intensity of the corresponding FP tags. Chemical-induced population shifts indicate that the bioactivity of a small molecule is dependent upon the target protein’s abundance and thus establish a specific functional interaction. Here, we describe the molecular tools required to apply this technique in the prevalent human fungal pathogen Candida albicans and validate the approach using two well-characterized drug targets—lanosterol demethylase and dihydrofolate reductase. However, our approach, which we have termed target abundance-based fitness screening (TAFiS), should be applicable to a wide array of molecular targets and in essentially any genetically tractable microbe. IMPORTANCE Conventional drug screening typically employs either target-based or cell-based approaches. The first group relies on biochemical assays to detect modulators of a purified target. However, hits frequently lack drug-like characteristics such as membrane permeability and target specificity. Cell-based screens identify compounds that induce a desired phenotype, but the target is unknown, which severely restricts further development and optimization. To address these issues, we have developed a second-generation target-based whole-cell screening approach that incorporates the principles of both chemical genetics and competitive fitness, which enables the identification of target-specific and physiologically active compounds from a single screen. We have chosen to validate this approach using the important human fungal pathogen Candida albicans with the intention of pursuing novel antifungal targets. However, this approach is broadly applicable and is expected to dramatically reduce the time and resources required to progress from screening hit to lead compound. PMID:28989971

DNA barcoding reveal patterns of species diversity among northwestern Pacific molluscs

PubMed Central

Sun, Shao’e; Li, Qi; Kong, Lingfeng; Yu, Hong; Zheng, Xiaodong; Yu, Ruihai; Dai, Lina; Sun, Yan; Chen, Jun; Liu, Jun; Ni, Lehai; Feng, Yanwei; Yu, Zhenzhen; Zou, Shanmei; Lin, Jiping

2016-01-01

This study represents the first comprehensive molecular assessment of northwestern Pacific molluscs. In total, 2801 DNA barcodes belonging to 569 species from China, Japan and Korea were analyzed. An overlap between intra- and interspecific genetic distances was present in 71 species. We tested the efficacy of this library by simulating a sequence-based specimen identification scenario using Best Match (BM), Best Close Match (BCM) and All Species Barcode (ASB) criteria with three threshold values. BM approach returned 89.15% true identifications (95.27% when excluding singletons). The highest success rate of congruent identifications was obtained with BCM at 0.053 threshold. The analysis of our barcode library together with public data resulted in 582 Barcode Index Numbers (BINs), 72.2% of which was found to be concordantly with morphology-based identifications. The discrepancies were divided in two groups: sequences from different species clustered in a single BIN and conspecific sequences divided in one more BINs. In Neighbour-Joining phenogram, 2,320 (83.0%) queries fromed 355 (62.4%) species-specific barcode clusters allowing their successful identification. 33 species showed paraphyletic and haplotype sharing. 62 cases are represented by deeply diverged lineages. This study suggest an increased species diversity in this region, highlighting taxonomic revision and conservation strategy for the cryptic complexes. PMID:27640675

Exploring the Leaf Beetle Fauna (Coleoptera: Chrysomelidae) of an Ecuadorian Mountain Forest Using DNA Barcoding.

PubMed

Thormann, Birthe; Ahrens, Dirk; Marín Armijos, Diego; Peters, Marcell K; Wagner, Thomas; Wägele, Johann W

2016-01-01

Tropical mountain forests are hotspots of biodiversity hosting a huge but little known diversity of insects that is endangered by habitat destruction and climate change. Therefore, rapid assessment approaches of insect diversity are urgently needed to complement slower traditional taxonomic approaches. We empirically compare different DNA-based species delimitation approaches for a rapid biodiversity assessment of hyperdiverse leaf beetle assemblages along an elevational gradient in southern Ecuador and explore their effect on species richness estimates. Based on a COI barcode data set of 674 leaf beetle specimens (Coleoptera: Chrysomelidae) of 266 morphospecies from three sample sites in the Podocarpus National Park, we employed statistical parsimony analysis, distance-based clustering, GMYC- and PTP-modelling to delimit species-like units and compared them to morphology-based (parataxonomic) species identifications. The four different approaches for DNA-based species delimitation revealed highly similar numbers of molecular operational taxonomic units (MOTUs) (n = 284-289). Estimated total species richness was considerably higher than the sampled amount, 414 for morphospecies (Chao2) and 469-481 for the different MOTU types. Assemblages at different elevational levels (1000 vs. 2000 m) had similar species numbers but a very distinct species composition for all delimitation methods. Most species were found only at one elevation while this turnover pattern was even more pronounced for DNA-based delimitation. Given the high congruence of DNA-based delimitation results, probably due to the sampling structure, our study suggests that when applied to species communities on a regionally limited level with high amount of rare species (i.e. ~50% singletons), the choice of species delimitation method can be of minor relevance for assessing species numbers and turnover in tropical insect communities. Therefore, DNA-based species delimitation is confirmed as a valuable tool for evaluating biodiversity of hyperdiverse insect communities, especially when exact taxonomic identifications are missing.

Exploring the Leaf Beetle Fauna (Coleoptera: Chrysomelidae) of an Ecuadorian Mountain Forest Using DNA Barcoding

PubMed Central

Thormann, Birthe; Ahrens, Dirk; Marín Armijos, Diego; Peters, Marcell K.; Wagner, Thomas; Wägele, Johann W.

2016-01-01

Background Tropical mountain forests are hotspots of biodiversity hosting a huge but little known diversity of insects that is endangered by habitat destruction and climate change. Therefore, rapid assessment approaches of insect diversity are urgently needed to complement slower traditional taxonomic approaches. We empirically compare different DNA-based species delimitation approaches for a rapid biodiversity assessment of hyperdiverse leaf beetle assemblages along an elevational gradient in southern Ecuador and explore their effect on species richness estimates. Methodology/Principal Findings Based on a COI barcode data set of 674 leaf beetle specimens (Coleoptera: Chrysomelidae) of 266 morphospecies from three sample sites in the Podocarpus National Park, we employed statistical parsimony analysis, distance-based clustering, GMYC- and PTP-modelling to delimit species-like units and compared them to morphology-based (parataxonomic) species identifications. The four different approaches for DNA-based species delimitation revealed highly similar numbers of molecular operational taxonomic units (MOTUs) (n = 284–289). Estimated total species richness was considerably higher than the sampled amount, 414 for morphospecies (Chao2) and 469–481 for the different MOTU types. Assemblages at different elevational levels (1000 vs. 2000 m) had similar species numbers but a very distinct species composition for all delimitation methods. Most species were found only at one elevation while this turnover pattern was even more pronounced for DNA-based delimitation. Conclusions/Significance Given the high congruence of DNA-based delimitation results, probably due to the sampling structure, our study suggests that when applied to species communities on a regionally limited level with high amount of rare species (i.e. ~50% singletons), the choice of species delimitation method can be of minor relevance for assessing species numbers and turnover in tropical insect communities. Therefore, DNA-based species delimitation is confirmed as a valuable tool for evaluating biodiversity of hyperdiverse insect communities, especially when exact taxonomic identifications are missing. PMID:26849826

Advances in the Study of Aptamer-Protein Target Identification Using the Chromatographic Approach.

PubMed

Drabik, Anna; Ner-Kluza, Joanna; Mielczarek, Przemyslaw; Civit, Laia; Mayer, Günter; Silberring, Jerzy

2018-06-01

Ever since the development of the process known as the systematic evolution of ligands by exponential enrichment (SELEX), aptamers have been widely used in a variety of studies, including the exploration of new diagnostic tools and the discovery of new treatment methods. Aptamers' ability to bind to proteins with high affinity and specificity, often compared to that of antibodies, enables the search for potential cancer biomarkers and helps us understand the mechanisms of carcinogenesis. The blind spot of those investigations is usually the difficulty in the selective extraction of targets attached to the aptamer. There are many studies describing the cell SELEX for the prime choice of aptamers toward living cancer cells or even whole tumors in the animal models. However, a dilemma arises when a large number of proteins are being identified as potential targets, which is often the case. In this article, we present a new analytical approach designed to selectively target proteins bound to aptamers. During studies, we have focused on the unambiguous identification of the molecular targets of aptamers characterized by high specificity to the prostate cancer cells. We have compared four assay approaches using electrophoretic and chromatographic methods for "fishing out" aptamer protein targets followed by mass spectrometry identification. We have established a new methodology, based on the fluorescent-tagged oligonucleotides commonly used for flow-cytometry experiments or as optic aptasensors, that allowed the detection of specific aptamer-protein interactions by mass spectrometry. The use of atto488-labeled aptamers for the tracking of the formation of specific aptamer-target complexes provides the possibility of studying putative protein counterparts without needing to apply enrichment techniques. Significantly, changes in the hydrophobic properties of atto488-labeled aptamer-protein complexes facilitate their separation by reverse-phase chromatography combined with fluorescence detection followed by mass-spectrometry-based protein identification. These comparative results of several methodological approaches confirmed the universal applicability of this method to studying aptamer-protein interactions with high sensitivity, showing superior properties compared with pull-down techniques.

Effectiveness of a publicly-funded demonstration program to promote management of dryland salinity.

PubMed

Robertson, M J; Measham, T G; Batchelor, G; George, R; Kingwell, R; Hosking, K

2009-07-01

Community and catchment-based approaches to salinity management continue to attract interest in Australia. In one such approach, Catchment Demonstration Initiative (CDI) projects were established by the Western Australian (WA) Government in 2000 for targeted investment in large-scale catchment-based demonstrations of integrated salinity management practices. The aim was to promote a process for technically-informed salinity management by landholders. This paper offers an evaluation of the effectiveness of one CDI project in the central wheatbelt of WA, covering issues including: its role in fostering adoption of salinity management options, the role of research and the technical requirements for design and implementation of on-ground works, the role of monitoring and evaluation, the identification and measurement of public and private benefits, comparison and identification of the place and value of plant-based and engineering-based options, reliance on social processes and impacts of constraints on capacity, management of governance and administration requirements and an appreciation of the value of group-based approaches. A number of factors may reduce the effectiveness of CDI-type approaches in facilitating landholder action to address salinity, many of these are socially-based. Such approaches can create considerable demands on landholders, can be expensive (because of the planning and accountability required) on the basis of dollars per hectare impacted, and can be difficult to garner ownership from all involved. An additional problem could be that few community groups would have the capacity to run such programs and disseminate the new knowledge so that the CDI-type projects can impact outside the focus catchment. In common with many publicly-funded approaches to salinity, we found that direct benefits on public assets are smaller than planned and that results from science-based requirements of monitoring and evaluation have long lead times, causing farmers to either wait for the information or act sooner and take risks based on initial results. We also found that often it is a clear outline of the process that is of most importance in decision making as opposed to the actual results. We identified limitations in regulatory processes and the capacity for local government to engage in the CDI. The opportunities that CDI-type approaches provide centre around the value of its group-based approach. We conclude that they can overcome knowledge constraints in managing salinity by fostering group-based learning, offer a structured process of trialling options so that the costs and benefits can be clearly and transparently quantified, and avoid the costly mistakes and "learning failures" of the past.

A new surface fractal dimension for displacement mode shape-based damage identification of plate-type structures

NASA Astrophysics Data System (ADS)

Shi, Binkai; Qiao, Pizhong

2018-03-01

Vibration-based nondestructive testing is an area of growing interest and worthy of exploring new and innovative approaches. The displacement mode shape is often chosen to identify damage due to its local detailed characteristic and less sensitivity to surrounding noise. Requirement for baseline mode shape in most vibration-based damage identification limits application of such a strategy. In this study, a new surface fractal dimension called edge perimeter dimension (EPD) is formulated, from which an EPD-based window dimension locus (EPD-WDL) algorithm for irregularity or damage identification of plate-type structures is established. An analytical notch-type damage model of simply-supported plates is proposed to evaluate notch effect on plate vibration performance; while a sub-domain of notch cases with less effect is selected to investigate robustness of the proposed damage identification algorithm. Then, fundamental aspects of EPD-WDL algorithm in term of notch localization, notch quantification, and noise immunity are assessed. A mathematical solution called isomorphism is implemented to remove false peaks caused by inflexions of mode shapes when applying the EPD-WDL algorithm to higher mode shapes. The effectiveness and practicability of the EPD-WDL algorithm are demonstrated by an experimental procedure on damage identification of an artificially-induced notched aluminum cantilever plate using a measurement system of piezoelectric lead-zirconate (PZT) actuator and scanning laser Doppler vibrometer (SLDV). As demonstrated in both the analytical and experimental evaluations, the new surface fractal dimension technique developed is capable of effectively identifying damage in plate-type structures.

RFID Student Educational Experiences at the UNT College of Engineering: A Sequential Approach to Creating a Project-Based RFID Course

ERIC Educational Resources Information Center

Vaidyanathan, V. V.; Varanasi, M. R.; Kougianos, E.; Wang, Shuping; Raman, H.

2009-01-01

This paper describes radio frequency identification (RFID) projects, designed and implemented by students in the College of Engineering at the University of North Texas, as part of their senior-design project requirement. The paper also describes an RFID-based project implemented at Rice Middle School in Plano, TX, which went on to win multiple…