Earthquake Damage Assessment over Port-au-Prince (Haiti) by Fusing Optical and SAR Data

NASA Astrophysics Data System (ADS)

Romaniello, V.; Piscini, A.; Bignami, C.; Anniballe, R.; Pierdicca, N.; Stramondo, S.

2016-08-01

This work proposes methodologies aiming at evaluating the sensitivity of optical and SAR change features obtained from satellite images with respect to the damage grade. The proposed methods are derived from the literature ([1], [2], [3], [4]) and the main novelty concerns the estimation of these change features at object scale.The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010.The analysis of change detection indicators is based on ground truth information collected during a post- earthquake survey. We have generated the damage map of Port-au-Prince by considering a set of polygons extracted from the open source Open Street Map geo- database. The resulting damage map was calculated in terms of collapse ratio [5].We selected some features having a good sensitivity with damage at object scale [6]: the Normalised Difference Index, the Kullback-Libler Divergence, the Mutual Information and the Intensity Correlation Difference.The Naive Bayes and the Support Vector Machine classifiers were used to evaluate the goodness of these features. The classification results demonstrate that the simultaneous use of several change features from EO observations can improve the damage estimation at object scale.

Combined distributed and concentrated transducer network for failure indication

NASA Astrophysics Data System (ADS)

Ostachowicz, Wieslaw; Wandowski, Tomasz; Malinowski, Pawel

2010-03-01

In this paper algorithm for discontinuities localisation in thin panels made of aluminium alloy is presented. Mentioned algorithm uses Lamb wave propagation methods for discontinuities localisation. Elastic waves were generated and received using piezoelectric transducers. They were arranged in concentrated arrays distributed on the specimen surface. In this way almost whole specimen could be monitored using this combined distributed-concentrated transducer network. Excited elastic waves propagate and reflect from panel boundaries and discontinuities existing in the panel. Wave reflection were registered through the piezoelectric transducers and used in signal processing algorithm. Proposed processing algorithm consists of two parts: signal filtering and extraction of obstacles location. The first part was used in order to enhance signals by removing noise from them. Second part allowed to extract features connected with wave reflections from discontinuities. Extracted features damage influence maps were a basis to create damage influence maps. Damage maps indicated intensity of elastic wave reflections which corresponds to obstacles coordinates. Described signal processing algorithms were implemented in the MATLAB environment. It should be underlined that in this work results based only on experimental signals were presented.

New nonlinear features for inspection, robotics, and face recognition

NASA Astrophysics Data System (ADS)

Casasent, David P.; Talukder, Ashit

1999-10-01

Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data. Other applications of these new feature spaces in robotics and face recognition are also noted.

Coefficient of Friction Patterns Can Identify Damage in Native and Engineered Cartilage Subjected to Frictional-Shear Stress

PubMed Central

Whitney, G. A.; Mansour, J. M.; Dennis, J. E.

2015-01-01

The mechanical loading environment encountered by articular cartilage in situ makes frictional-shear testing an invaluable technique for assessing engineered cartilage. Despite the important information that is gained from this testing, it remains under-utilized, especially for determining damage behavior. Currently, extensive visual inspection is required to assess damage; this is cumbersome and subjective. Tools to simplify, automate, and remove subjectivity from the analysis may increase the accessibility and usefulness of frictional-shear testing as an evaluation method. The objective of this study was to determine if the friction signal could be used to detect damage that occurred during the testing. This study proceeded in two phases: first, a simplified model of biphasic lubrication that does not require knowledge of interstitial fluid pressure was developed. In the second phase, frictional-shear tests were performed on 74 cartilage samples, and the simplified model was used to extract characteristic features from the friction signals. Using support vector machine classifiers, the extracted features were able to detect damage with a median accuracy of approximately 90%. The accuracy remained high even in samples with minimal damage. In conclusion, the friction signal acquired during frictional-shear testing can be used to detect resultant damage to a high level of accuracy. PMID:25691395

Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Rajagopal, Ram; Kiremidjian, Anne S.

2012-04-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

NASA Astrophysics Data System (ADS)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

NASA Technical Reports Server (NTRS)

Turso, James A.; Litt, Jonathan S.

2004-01-01

A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

A Multidisciplinary Approach to Health Monitoring and Materials Damage Prognosis for Metallic Aerospace Systems

DTIC Science & Technology

2013-03-01

framework of orientation distribution functions and crack-induced texture o Quantify effects of temperature on damage behavior and damage monitoring...measurement model was obtained from hidden Markov modeling (HMM) of joint time-frequency (TF) features extracted from the PZT sensor signals using the...considered PZT sensor signals recorded from a bolted aluminum plate. About only 20% of the samples of a signal were first randomly selected as

Mode separation in frequency-wavenumber domain through compressed sensing of far-field Lamb waves

NASA Astrophysics Data System (ADS)

Gao, Fei; Zeng, Liang; Lin, Jing; Luo, Zhi

2017-07-01

This method based on Lamb waves shows great potential for long-range damage detection. Mode superposition resulting from multi-modal and dispersive characteristics makes signal interpretation and damage feature extraction difficult. Mode separation in the frequency-wavenumber (f-k) domain using a 1D sparse sensing array is a promising solution. However, due to the lack of prior knowledge about damage location, this method based on 1D linear measurement, for the mode extraction of arbitrary reflections caused by defects that are not in line with the sensor array, is restricted. In this paper, an improved compressed sensing method under the far-field assumption is established, which is beneficial to the reconstruction of reflections in the f-k domain. Hence, multiple components consisting of structure and damage features could be recovered via a limited number of measurements. Subsequently, a mode sweeping process based on theoretical dispersion curves has been designed for mode characterization and direction of arrival estimation. Moreover, 2D f-k filtering and inverse transforms are applied to the reconstructed f-k distribution in order to extract the purified mode of interest. As a result, overlapping waveforms can be separated and the direction of defects can be estimated. A uniform linear sensor array consisting of 16 laser excitations is finally employed for experimental investigations and the results demonstrate the efficiency of the proposed method.

Damage detection of engine bladed-disks using multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Fang, X.; Tang, J.

2006-03-01

The timely detection of damage in aero-engine bladed-disks is an extremely important and challenging research topic. Bladed-disks have high modal density and, particularly, their vibration responses are subject to significant uncertainties due to manufacturing tolerance (blade-to-blade difference or mistuning), operating condition change and sensor noise. In this study, we present a new methodology for the on-line damage detection of engine bladed-disks using their vibratory responses during spin-up or spin-down operations which can be measured by blade-tip-timing sensing technique. We apply a principle component analysis (PCA)-based approach for data compression, feature extraction, and denoising. The non-model based damage detection is achieved by analyzing the change between response features of the healthy structure and of the damaged one. We facilitate such comparison by incorporating the Hotelling's statistic T2 analysis, which yields damage declaration with a given confidence level. The effectiveness of the method is demonstrated by case studies.

Application of outlier analysis for baseline-free damage diagnosis

NASA Astrophysics Data System (ADS)

Kim, Seung Dae; In, Chi Won; Cronin, Kelly E.; Sohn, Hoon; Harries, Kent

2006-03-01

As carbon fiber-reinforced polymer (CFRP) laminates have been widely accepted as valuable materials for retrofitting civil infrastructure systems, an appropriate assessment of bonding conditions between host structures and CFRP laminates becomes a critical issue to guarantee the performance of CFRP strengthened structures. This study attempts to develop a continuous performance monitoring system for CFRP strengthened structures by autonomously inspecting the bonding conditions between the CFRP layers and the host structure. The uniqueness of this study is to develop a new concept and theoretical framework of nondestructive testing (NDT), in which debonding is detected "without using past baseline data." The proposed baseline-free damage diagnosis is achieved in two stages. In the first step, features sensitive to debonding of the CFPR layers but insensitive to loading conditions are extracted based on a concept referred to as a time reversal process. This time reversal process allows extracting damage-sensitive features without direct comparison with past baseline data. Then, a statistical damage classifier will be developed in the second step to make a decision regarding the bonding condition of the CFRP layers. The threshold necessary for decision making will be adaptively determined without predetermined threshold values. Monotonic and fatigue load tests of full-scale CFRP strengthened RC beams are conducted to demonstrate the potential of the proposed reference-free debonding monitoring system.

Dry olive leaf extract counteracts L-thyroxine-induced genotoxicity in human peripheral blood leukocytes in vitro.

PubMed

Topalović, Dijana Žukovec; Živković, Lada; Čabarkapa, Andrea; Djelić, Ninoslav; Bajić, Vladan; Dekanski, Dragana; Spremo-Potparević, Biljana

2015-01-01

The thyroid hormones change the rate of basal metabolism, modulating the consumption of oxygen and causing production of reactive oxygen species, which leads to the development of oxidative stress and DNA strand breaks. Olive (Olea europaea L.) leaf contains many potentially bioactive compounds, making it one of the most potent natural antioxidants. The objective of this study was to evaluate the genotoxicity of L-thyroxine and to investigate antioxidative and antigenotoxic potential of the standardized oleuropein-rich dry olive leaf extract (DOLE) against hydrogen peroxide and L-thyroxine-induced DNA damage in human peripheral blood leukocytes by using the comet assay. Various concentrations of the extract were tested with both DNA damage inducers, under two different experimental conditions, pretreatment and posttreatment. Results indicate that L-thyroxine exhibited genotoxic effect and that DOLE displayed protective effect against thyroxine-induced genotoxicity. The number of cells with DNA damage, was significantly reduced, in both pretreated and posttreated samples (P < 0.05). Comparing the beneficial effect of all tested concentrations of DOLE, in both experimental protocols, it appears that extract was more effective in reducing DNA damage in the pretreatment, exhibiting protective role against L-thyroxine effect. This feature of DOLE can be explained by its capacity to act as potent free radical scavenger.

Dry Olive Leaf Extract Counteracts L-Thyroxine-Induced Genotoxicity in Human Peripheral Blood Leukocytes In Vitro

PubMed Central

Žukovec Topalović, Dijana; Živković, Lada; Čabarkapa, Andrea; Djelić, Ninoslav; Bajić, Vladan; Spremo-Potparević, Biljana

2015-01-01

The thyroid hormones change the rate of basal metabolism, modulating the consumption of oxygen and causing production of reactive oxygen species, which leads to the development of oxidative stress and DNA strand breaks. Olive (Olea europaea L.) leaf contains many potentially bioactive compounds, making it one of the most potent natural antioxidants. The objective of this study was to evaluate the genotoxicity of L-thyroxine and to investigate antioxidative and antigenotoxic potential of the standardized oleuropein-rich dry olive leaf extract (DOLE) against hydrogen peroxide and L-thyroxine-induced DNA damage in human peripheral blood leukocytes by using the comet assay. Various concentrations of the extract were tested with both DNA damage inducers, under two different experimental conditions, pretreatment and posttreatment. Results indicate that L-thyroxine exhibited genotoxic effect and that DOLE displayed protective effect against thyroxine-induced genotoxicity. The number of cells with DNA damage, was significantly reduced, in both pretreated and posttreated samples (P < 0.05). Comparing the beneficial effect of all tested concentrations of DOLE, in both experimental protocols, it appears that extract was more effective in reducing DNA damage in the pretreatment, exhibiting protective role against L-thyroxine effect. This feature of DOLE can be explained by its capacity to act as potent free radical scavenger. PMID:25789081

A multi-approach feature extractions for iris recognition

NASA Astrophysics Data System (ADS)

Sanpachai, H.; Settapong, M.

2014-04-01

Biometrics is a promising technique that is used to identify individual traits and characteristics. Iris recognition is one of the most reliable biometric methods. As iris texture and color is fully developed within a year of birth, it remains unchanged throughout a person's life. Contrary to fingerprint, which can be altered due to several aspects including accidental damage, dry or oily skin and dust. Although iris recognition has been studied for more than a decade, there are limited commercial products available due to its arduous requirement such as camera resolution, hardware size, expensive equipment and computational complexity. However, at the present time, technology has overcome these obstacles. Iris recognition can be done through several sequential steps which include pre-processing, features extractions, post-processing, and matching stage. In this paper, we adopted the directional high-low pass filter for feature extraction. A box-counting fractal dimension and Iris code have been proposed as feature representations. Our approach has been tested on CASIA Iris Image database and the results are considered successful.

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

NASA Astrophysics Data System (ADS)

Liu, Chao; Gong, Yongqiang; Laflamme, Simon; Phares, Brent; Sarkar, Soumik

2017-01-01

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

A rapid extraction of landslide disaster information research based on GF-1 image

NASA Astrophysics Data System (ADS)

Wang, Sai; Xu, Suning; Peng, Ling; Wang, Zhiyi; Wang, Na

2015-08-01

In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 .Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.

Improving the performance of univariate control charts for abnormal detection and classification

NASA Astrophysics Data System (ADS)

Yiakopoulos, Christos; Koutsoudaki, Maria; Gryllias, Konstantinos; Antoniadis, Ioannis

2017-03-01

Bearing failures in rotating machinery can cause machine breakdown and economical loss, if no effective actions are taken on time. Therefore, it is of prime importance to detect accurately the presence of faults, especially at their early stage, to prevent sequent damage and reduce costly downtime. The machinery fault diagnosis follows a roadmap of data acquisition, feature extraction and diagnostic decision making, in which mechanical vibration fault feature extraction is the foundation and the key to obtain an accurate diagnostic result. A challenge in this area is the selection of the most sensitive features for various types of fault, especially when the characteristics of failures are difficult to be extracted. Thus, a plethora of complex data-driven fault diagnosis methods are fed by prominent features, which are extracted and reduced through traditional or modern algorithms. Since most of the available datasets are captured during normal operating conditions, the last decade a number of novelty detection methods, able to work when only normal data are available, have been developed. In this study, a hybrid method combining univariate control charts and a feature extraction scheme is introduced focusing towards an abnormal change detection and classification, under the assumption that measurements under normal operating conditions of the machinery are available. The feature extraction method integrates the morphological operators and the Morlet wavelets. The effectiveness of the proposed methodology is validated on two different experimental cases with bearing faults, demonstrating that the proposed approach can improve the fault detection and classification performance of conventional control charts.

Method for detecting damage in carbon-fibre reinforced plastic-steel structures based on eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Li, Xuan; Liu, Zhiping; Jiang, Xiaoli; Lodewijks, Gabrol

2018-01-01

Eddy current pulsed thermography (ECPT) is well established for non-destructive testing of electrical conductive materials, featuring the advantages of contactless, intuitive detecting and efficient heating. The concept of divergence characterization of the damage rate of carbon fibre-reinforced plastic (CFRP)-steel structures can be extended to ECPT thermal pattern characterization. It was found in this study that the use of ECPT technology on CFRP-steel structures generated a sizeable amount of valuable information for comprehensive material diagnostics. The relationship between divergence and transient thermal patterns can be identified and analysed by deploying mathematical models to analyse the information about fibre texture-like orientations, gaps and undulations in these multi-layered materials. The developed algorithm enabled the removal of information about fibre texture and the extraction of damage features. The model of the CFRP-glue-steel structures with damage was established using COMSOL Multiphysics® software, and quantitative non-destructive damage evaluation from the ECPT image areas was derived. The results of this proposed method illustrate that damaged areas are highly affected by available information about fibre texture. This proposed work can be applied for detection of impact induced damage and quantitative evaluation of CFRP structures.

CMO YAG laser damage test facility

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

Hue, J.; Dijon, J.; Lyan, P.

1996-12-31

The CMO YAG laser damage test facility, which is equipped with a 30Hz laser, is presented in this paper. The main points are described below: (1) The characteristics of the laser beam and the in situ damage detection technique (a scattered light measurement system) are perfectly suited to work up to the frequency of the laser. They are monitored in real time, and work at three wavelengths: 1064 nm, 532 nm, 355 nm. (2) With this same shutter, it is possible to automatically stop the laser on the pulse which induces the first damages. These automatic capabilities enable the samplesmore » to be tested quickly. (3) A Nomarski microscope supplied with a 16-bit CCD camera enables the test sites to be photographed before and after the laser interaction. Image processing enables the authors to extract the first damages. before and after the laser interaction. Image processing enables them to extract the first damages. (4) Six pulse widths are available (between 3ns and 13ns). Therefore, with all these characterization tools, many kinds of laser tests may be considered. These different features are illustrated by experimental results (1-on-1 test or R-on-1 test).« less

Design and analysis of compound flexible skin based on deformable honeycomb

NASA Astrophysics Data System (ADS)

Zou, Tingting; Zhou, Li

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

NASA Astrophysics Data System (ADS)

Mustapha, S.; Braytee, A.; Ye, L.

2017-04-01

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.

Mild-Vectolysis: A nondestructive DNA extraction method for vouchering sand flies and mosquitoes

USDA-ARS?s Scientific Manuscript database

Nondestructive techniques allow the isolation of genomic DNA, without damaging the morphological features of the specimens. Though such techniques are available for numerous insect groups, they have not been applied to any member of the medically important families of mosquitoes (Diptera: Culicidae)...

Characterizing Hypervelocity Impact (HVI)-Induced Pitting Damage Using Active Guided Ultrasonic Waves: From Linear to Nonlinear

PubMed Central

Liu, Menglong; Wang, Kai; Lissenden, Cliff J.; Wang, Qiang; Zhang, Qingming; Long, Renrong; Su, Zhongqing; Cui, Fangsen

2017-01-01

Hypervelocity impact (HVI), ubiquitous in low Earth orbit with an impacting velocity in excess of 1 km/s, poses an immense threat to the safety of orbiting spacecraft. Upon penetration of the outer shielding layer of a typical two-layer shielding system, the shattered projectile, together with the jetted materials of the outer shielding material, subsequently impinge the inner shielding layer, to which pitting damage is introduced. The pitting damage includes numerous craters and cracks disorderedly scattered over a wide region. Targeting the quantitative evaluation of this sort of damage (multitudinous damage within a singular inspection region), a characterization strategy, associating linear with nonlinear features of guided ultrasonic waves, is developed. Linear-wise, changes in the signal features in the time domain (e.g., time-of-flight and energy dissipation) are extracted, for detecting gross damage whose characteristic dimensions are comparable to the wavelength of the probing wave; nonlinear-wise, changes in the signal features in the frequency domain (e.g., second harmonic generation), which are proven to be more sensitive than their linear counterparts to small-scale damage, are explored to characterize HVI-induced pitting damage scattered in the inner layer. A numerical simulation, supplemented with experimental validation, quantitatively reveals the accumulation of nonlinearity of the guided waves when the waves traverse the pitting damage, based on which linear and nonlinear damage indices are proposed. A path-based rapid imaging algorithm, in conjunction with the use of the developed linear and nonlinear indices, is developed, whereby the HVI-induced pitting damage is characterized in images in terms of the probability of occurrence. PMID:28772908

Structural damage identification using damping: a compendium of uses and features

NASA Astrophysics Data System (ADS)

Cao, M. S.; Sha, G. G.; Gao, Y. F.; Ostachowicz, W.

2017-04-01

The vibration responses of structures under controlled or ambient excitation can be used to detect structural damage by correlating changes in structural dynamic properties extracted from responses with damage. Typical dynamic properties refer to modal parameters: natural frequencies, mode shapes, and damping. Among these parameters, natural frequencies and mode shapes have been investigated extensively for their use in damage characterization by associating damage with reduction in local stiffness of structures. In contrast, the use of damping as a dynamic property to represent structural damage has not been comprehensively elucidated, primarily due to the complexities of damping measurement and analysis. With advances in measurement technologies and analysis tools, the use of damping to identify damage is becoming a focus of increasing attention in the damage detection community. Recently, a number of studies have demonstrated that damping has greater sensitivity for characterizing damage than natural frequencies and mode shapes in various applications, but damping-based damage identification is still a research direction ‘in progress’ and is not yet well resolved. This situation calls for an overall survey of the state-of-the-art and the state-of-the-practice of using damping to detect structural damage. To this end, this study aims to provide a comprehensive survey of uses and features of applying damping in structural damage detection. First, we present various methods for damping estimation in different domains including the time domain, the frequency domain, and the time-frequency domain. Second, we investigate the features and applications of damping-based damage detection methods on the basis of two predominant infrastructure elements, reinforced concrete structures and fiber-reinforced composites. Third, we clarify the influential factors that can impair the capability of damping to characterize damage. Finally, we recommend future research directions for advancing damping-based damage detection. This work holds the promise of (a) helping researchers identify crucial components in damping-based damage detection theories, methods, and technologies, and (b) leading practitioners to better implement damping-based structural damage identification.

Towards a More Efficient Detection of Earthquake Induced FAÇADE Damages Using Oblique Uav Imagery

NASA Astrophysics Data System (ADS)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2017-08-01

Urban search and rescue (USaR) teams require a fast and thorough building damage assessment, to focus their rescue efforts accordingly. Unmanned aerial vehicles (UAV) are able to capture relevant data in a short time frame and survey otherwise inaccessible areas after a disaster, and have thus been identified as useful when coupled with RGB cameras for façade damage detection. Existing literature focuses on the extraction of 3D and/or image features as cues for damage. However, little attention has been given to the efficiency of the proposed methods which hinders its use in an urban search and rescue context. The framework proposed in this paper aims at a more efficient façade damage detection using UAV multi-view imagery. This was achieved directing all damage classification computations only to the image regions containing the façades, hence discarding the irrelevant areas of the acquired images and consequently reducing the time needed for such task. To accomplish this, a three-step approach is proposed: i) building extraction from the sparse point cloud computed from the nadir images collected in an initial flight; ii) use of the latter as proxy for façade location in the oblique images captured in subsequent flights, and iii) selection of the façade image regions to be fed to a damage classification routine. The results show that the proposed framework successfully reduces the extracted façade image regions to be assessed for damage 6 fold, hence increasing the efficiency of subsequent damage detection routines. The framework was tested on a set of UAV multi-view images over a neighborhood of the city of L'Aquila, Italy, affected in 2009 by an earthquake.

Using different classification models in wheat grading utilizing visual features

NASA Astrophysics Data System (ADS)

Basati, Zahra; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-04-01

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively

Sequential projection pursuit for optimised vibration-based damage detection in an experimental wind turbine blade

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2018-02-01

To advance the concept of smart structures in large systems, such as wind turbines (WTs), it is desirable to be able to detect structural damage early while using minimal instrumentation. Data-driven vibration-based damage detection methods can be competitive in that respect because global vibrational responses encompass the entire structure. Multivariate damage sensitive features (DSFs) extracted from acceleration responses enable to detect changes in a structure via statistical methods. However, even though such DSFs contain information about the structural state, they may not be optimised for the damage detection task. This paper addresses the shortcoming by exploring a DSF projection technique specialised for statistical structural damage detection. High dimensional initial DSFs are projected onto a low-dimensional space for improved damage detection performance and simultaneous computational burden reduction. The technique is based on sequential projection pursuit where the projection vectors are optimised one by one using an advanced evolutionary strategy. The approach is applied to laboratory experiments with a small-scale WT blade under wind-like excitations. Autocorrelation function coefficients calculated from acceleration signals are employed as DSFs. The optimal numbers of projection vectors are identified with the help of a fast forward selection procedure. To benchmark the proposed method, selections of original DSFs as well as principal component analysis scores from these features are additionally investigated. The optimised DSFs are tested for damage detection on previously unseen data from the healthy state and a wide range of damage scenarios. It is demonstrated that using selected subsets of the initial and transformed DSFs improves damage detectability compared to the full set of features. Furthermore, superior results can be achieved by projecting autocorrelation coefficients onto just a single optimised projection vector.

Cointegration as a data normalization tool for structural health monitoring applications

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2012-04-01

The structural health monitoring literature has shown an abundance of features sensitive to various types of damage in laboratory tests. However, robust feature extraction in the presence of varying operational and environmental conditions has proven to be one of the largest obstacles in the development of practical structural health monitoring systems. Cointegration, a technique adapted from the field of econometrics, has recently been introduced to the SHM field as one solution to the data normalization problem. Response measurements and feature histories often show long-run nonstationarity due to fluctuating temperature, load conditions, or other factors that leads to the occurrence of false positives. Cointegration theory allows nonstationary trends common to two or more time series to be modeled and subsequently removed. Thus, the residual retains sensitivity to damage with dependence on operational and environmental variability removed. This study further explores the use of cointegration as a data normalization tool for structural health monitoring applications.

An illustration of new methods in machine condition monitoring, Part I: stochastic resonance

NASA Astrophysics Data System (ADS)

Worden, K.; Antoniadou, I.; Marchesiello, S.; Mba, C.; Garibaldi, L.

2017-05-01

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach.

A Tensor-Based Structural Damage Identification and Severity Assessment

PubMed Central

Anaissi, Ali; Makki Alamdari, Mehrisadat; Rakotoarivelo, Thierry; Khoa, Nguyen Lu Dang

2018-01-01

Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monitoring (SHM) challenges. This paper presents a novel algorithm to detect and assess damage in structures such as bridges. This method applies tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies, i.e., structural damage. To evaluate this approach, we collected acceleration data from a sensor-based SHM system, which we deployed on a real bridge and on a laboratory specimen. The results show that our tensor method outperforms a state-of-the-art approach using the wavelet energy spectrum of the measured data. In the specimen case, our approach succeeded in detecting 92.5% of induced damage cases, as opposed to 61.1% for the wavelet-based approach. While our method was applied to bridges, its algorithm and computation can be used on other structures or sensor-data analysis problems, which involve large series of correlated data from multiple sensors. PMID:29301314

Dynamic fiber Bragg gratings based health monitoring system of composite aerospace structures

NASA Astrophysics Data System (ADS)

Panopoulou, A.; Loutas, T.; Roulias, D.; Fransen, S.; Kostopoulos, V.

2011-09-01

The main purpose of the current work is to develop a new system for structural health monitoring of composite aerospace structures based on real-time dynamic measurements, in order to identify the structural state condition. Long-gauge Fibre Bragg Grating (FBG) optical sensors were used for monitoring the dynamic response of the composite structure. The algorithm that was developed for structural damage detection utilizes the collected dynamic response data, analyzes them in various ways and through an artificial neural network identifies the damage state and its location. Damage was simulated by slightly varying locally the mass of the structure (by adding a known mass) at different zones of the structure. Lumped masses in different locations upon the structure alter the eigen-frequencies in a way similar to actual damage. The structural dynamic behaviour has been numerically simulated and experimentally verified by means of modal testing on two different composite aerospace structures. Advanced digital signal processing techniques, e.g. the wavelet transform (WT), were used for the analysis of the dynamic response for feature extraction. WT's capability of separating the different frequency components in the time domain without loosing frequency information makes it a versatile tool for demanding signal processing applications. The use of WT is also suggested by the no-stationary nature of dynamic response signals and the opportunity of evaluating the temporal evolution of their frequency contents. Feature extraction is the first step of the procedure. The extracted features are effective indices of damage size and location. The classification step comprises of a feed-forward back propagation network, whose output determines the simulated damage location. Finally, dedicated training and validation activities were carried out by means of numerical simulations and experimental procedures. Experimental validation was performed initially on a flat stiffened panel, representing a section of a typical aeronautical structure, manufactured and tested in the lab and, as a second step, on a scaled up space oriented structure, which is a composite honeycomb plate, used as a deployment base for antenna arrays. An integrated FBG sensor network, based on the advantage of multiplexing, was mounted on both structures and different excitation positions and boundary conditions were used. The analysis of operational dynamic responses was employed to identify both the damage and its position. The system that was designed and tested initially on the thin composite panel, was successfully validated on the larger honeycomb structure. Numerical simulation of both structures was used as a support tool at all the steps of the work providing among others the location of the optical sensors used. The proposed work will be the base for the whole system qualification and validation on an antenna reflector in future work.

ANN based Performance Evaluation of BDI for Condition Monitoring of Induction Motor Bearings

NASA Astrophysics Data System (ADS)

Patel, Raj Kumar; Giri, V. K.

2017-06-01

One of the critical parts in rotating machines is bearings and most of the failure arises from the defective bearings. Bearing failure leads to failure of a machine and the unpredicted productivity loss in the performance. Therefore, bearing fault detection and prognosis is an integral part of the preventive maintenance procedures. In this paper vibration signal for four conditions of a deep groove ball bearing; normal (N), inner race defect (IRD), ball defect (BD) and outer race defect (ORD) were acquired from a customized bearing test rig, under four different conditions and three different fault sizes. Two approaches have been opted for statistical feature extraction from the vibration signal. In the first approach, raw signal is used for statistical feature extraction and in the second approach statistical features extracted are based on bearing damage index (BDI). The proposed BDI technique uses wavelet packet node energy coefficients analysis method. Both the features are used as inputs to an ANN classifier to evaluate its performance. A comparison of ANN performance is made based on raw vibration data and data chosen by using BDI. The ANN performance has been found to be fairly higher when BDI based signals were used as inputs to the classifier.

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants

NASA Astrophysics Data System (ADS)

Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

2013-02-01

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants.

PubMed

Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

2013-02-01

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

TH-E-BRF-04: Characterizing the Response of Texture-Based CT Image Features for Quantification of Radiation-Induced Normal Lung Damage

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

Krafft, S; Court, L; Briere, T

2014-06-15

Purpose: Radiation induced lung damage (RILD) is an important dose-limiting toxicity for patients treated with radiation therapy. Scoring systems for RILD are subjective and limit our ability to find robust predictors of toxicity. We investigate the dose and time-related response for texture-based lung CT image features that serve as potential quantitative measures of RILD. Methods: Pre- and post-RT diagnostic imaging studies were collected for retrospective analysis of 21 patients treated with photon or proton radiotherapy for NSCLC. Total lung and selected isodose contours (0–5, 5–15, 15–25Gy, etc.) were deformably registered from the treatment planning scan to the pre-RT and availablemore » follow-up CT studies for each patient. A CT image analysis framework was utilized to extract 3698 unique texture-based features (including co-occurrence and run length matrices) for each region of interest defined by the isodose contours and the total lung volume. Linear mixed models were fit to determine the relationship between feature change (relative to pre-RT), planned dose and time post-RT. Results: Seventy-three follow-up CT scans from 21 patients (median: 3 scans/patient) were analyzed to describe CT image feature change. At the p=0.05 level, dose affected feature change in 2706 (73.1%) of the available features. Similarly, time affected feature change in 408 (11.0%) of the available features. Both dose and time were significant predictors of feature change in a total of 231 (6.2%) of the extracted image features. Conclusion: Characterizing the dose and time-related response of a large number of texture-based CT image features is the first step toward identifying objective measures of lung toxicity necessary for assessment and prediction of RILD. There is evidence that numerous features are sensitive to both the radiation dose and time after RT. Beyond characterizing feature response, further investigation is warranted to determine the utility of these features as surrogates of clinically significant lung injury.« less

Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Acharya, U Rajendra

2017-05-01

Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and optical coherence tomography. These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an automated diagnosis of glaucoma using digital fundus images based on empirical wavelet transform (EWT). The EWT is used to decompose the image, and correntropy features are obtained from decomposed EWT components. These extracted features are ranked based on t value feature selection algorithm. Then, these features are used for the classification of normal and glaucoma images using least-squares support vector machine (LS-SVM) classifier. The LS-SVM is employed for classification with radial basis function, Morlet wavelet, and Mexican-hat wavelet kernels. The classification accuracy of the proposed method is 98.33% and 96.67% using threefold and tenfold cross validation, respectively.

Wisdom tooth extraction causing lingual nerve and styloglossus muscle damage: a mimic of multiple cranial nerve palsies.

PubMed

Carr, Aisling S; Evans, Matthew; Shah, Sachit; Catania, Santi; Warren, Jason D; Gleeson, Michael J; Reilly, Mary M

2017-06-01

The combination of tongue hemianaesthesia, dysgeusia, dysarthria and dysphagia suggests the involvement of multiple cranial nerves. We present a case with sudden onset of these symptoms immediately following wisdom tooth extraction and highlight the clinical features that allowed localisation of the lesion to a focal, iatrogenic injury of the lingual nerve and adjacent styloglossus muscle. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Laser-based structural sensing and surface damage detection

NASA Astrophysics Data System (ADS)

Guldur, Burcu

Damage due to age or accumulated damage from hazards on existing structures poses a worldwide problem. In order to evaluate the current status of aging, deteriorating and damaged structures, it is vital to accurately assess the present conditions. It is possible to capture the in situ condition of structures by using laser scanners that create dense three-dimensional point clouds. This research investigates the use of high resolution three-dimensional terrestrial laser scanners with image capturing abilities as tools to capture geometric range data of complex scenes for structural engineering applications. Laser scanning technology is continuously improving, with commonly available scanners now capturing over 1,000,000 texture-mapped points per second with an accuracy of ~2 mm. However, automatically extracting meaningful information from point clouds remains a challenge, and the current state-of-the-art requires significant user interaction. The first objective of this research is to use widely accepted point cloud processing steps such as registration, feature extraction, segmentation, surface fitting and object detection to divide laser scanner data into meaningful object clusters and then apply several damage detection methods to these clusters. This required establishing a process for extracting important information from raw laser-scanned data sets such as the location, orientation and size of objects in a scanned region, and location of damaged regions on a structure. For this purpose, first a methodology for processing range data to identify objects in a scene is presented and then, once the objects from model library are correctly detected and fitted into the captured point cloud, these fitted objects are compared with the as-is point cloud of the investigated object to locate defects on the structure. The algorithms are demonstrated on synthetic scenes and validated on range data collected from test specimens and test-bed bridges. The second objective of this research is to combine useful information extracted from laser scanner data with color information, which provides information in the fourth dimension that enables detection of damage types such as cracks, corrosion, and related surface defects that are generally difficult to detect using only laser scanner data; moreover, the color information also helps to track volumetric changes on structures such as spalling. Although using images with varying resolution to detect cracks is an extensively researched topic, damage detection using laser scanners with and without color images is a new research area that holds many opportunities for enhancing the current practice of visual inspections. The aim is to combine the best features of laser scans and images to create an automatic and effective surface damage detection method, which will reduce the need for skilled labor during visual inspections and allow automatic documentation of related information. This work enables developing surface damage detection strategies that integrate existing condition rating criteria for a wide range damage types that are collected under three main categories: small deformations already existing on the structure (cracks); damage types that induce larger deformations, but where the initial topology of the structure has not changed appreciably (e.g., bent members); and large deformations where localized changes in the topology of the structure have occurred (e.g., rupture, discontinuities and spalling). The effectiveness of the developed damage detection algorithms are validated by comparing the detection results with the measurements taken from test specimens and test-bed bridges.

Identification of ground motion features for high-tech facility under far field seismic waves using wavelet packet transform

NASA Astrophysics Data System (ADS)

Huang, Shieh-Kung; Loh, Chin-Hsiung; Chen, Chin-Tsun

2016-04-01

Seismic records collected from earthquake with large magnitude and far distance may contain long period seismic waves which have small amplitude but with dominant period up to 10 sec. For a general situation, the long period seismic waves will not endanger the safety of the structural system or cause any uncomfortable for human activity. On the contrary, for those far distant earthquakes, this type of seismic waves may cause a glitch or, furthermore, breakdown to some important equipments/facilities (such as the high-precision facilities in high-tech Fab) and eventually damage the interests of company if the amplitude becomes significant. The previous study showed that the ground motion features such as time-variant dominant frequencies extracted using moving window singular spectrum analysis (MWSSA) and amplitude characteristics of long-period waves identified from slope change of ground motion Arias Intensity can efficiently indicate the damage severity to the high-precision facilities. However, embedding a large hankel matrix to extract long period seismic waves make the MWSSA become a time-consumed process. In this study, the seismic ground motion data collected from broadband seismometer network located in Taiwan were used (with epicenter distance over 1000 km). To monitor the significant long-period waves, the low frequency components of these seismic ground motion data are extracted using wavelet packet transform (WPT) to obtain wavelet coefficients and the wavelet entropy of coefficients are used to identify the amplitude characteristics of long-period waves. The proposed method is a timesaving process compared to MWSSA and can be easily implemented for real-time detection. Comparison and discussion on this method among these different seismic events and the damage severity to the high-precision facilities in high-tech Fab is made.

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

NASA Astrophysics Data System (ADS)

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

Damage detection and quantification in a structural model under seismic excitation using time-frequency analysis

NASA Astrophysics Data System (ADS)

Chan, Chun-Kai; Loh, Chin-Hsiung; Wu, Tzu-Hsiu

2015-04-01

In civil engineering, health monitoring and damage detection are typically carry out by using a large amount of sensors. Typically, most methods require global measurements to extract the properties of the structure. However, some sensors, like LVDT, cannot be used due to in situ limitation so that the global deformation remains unknown. An experiment is used to demonstrate the proposed algorithms: a one-story 2-bay reinforce concrete frame under weak and strong seismic excitation. In this paper signal processing techniques and nonlinear identification are used and applied to the response measurements of seismic response of reinforced concrete structures subject to different level of earthquake excitations. Both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of RC frame using both input and output response data or output only measurement. From the signal-based damage identification method, which include the enhancement of time-frequency analysis of acceleration responses and the estimation of permanent deformation using directly from acceleration response data. Finally, local deformation measurement from dense optical tractor is also use to quantify the damage of the RC frame structure.

Building Damage Assessment after Earthquake Using Post-Event LiDAR Data

NASA Astrophysics Data System (ADS)

Rastiveis, H.; Eslamizade, F.; Hosseini-Zirdoo, E.

2015-12-01

After an earthquake, damage assessment plays an important role in leading rescue team to help people and decrease the number of mortality. Damage map is a map that demonstrates collapsed buildings with their degree of damage. With this map, finding destructive buildings can be quickly possible. In this paper, we propose an algorithm for automatic damage map generation after an earthquake using post-event LiDAR Data and pre-event vector map. The framework of the proposed approach has four main steps. To find the location of all buildings on LiDAR data, in the first step, LiDAR data and vector map are registered by using a few number of ground control points. Then, building layer, selected from vector map, are mapped on the LiDAR data and all pixels which belong to the buildings are extracted. After that, through a powerful classifier all the extracted pixels are classified into three classes of "debris", "intact building" and "unclassified". Since textural information make better difference between "debris" and "intact building" classes, different textural features are applied during the classification. After that, damage degree for each candidate building is estimated based on the relation between the numbers of pixels labelled as "debris" class to the whole building area. Calculating the damage degree for each candidate building, finally, building damage map is generated. To evaluate the ability proposed method in generating damage map, a data set from Port-au-Prince, Haiti's capital after the 2010 Haiti earthquake was used. In this case, after calculating of all buildings in the test area using the proposed method, the results were compared to the damage degree which estimated through visual interpretation of post-event satellite image. Obtained results were proved the reliability of the proposed method in damage map generation using LiDAR data.

Bearing monitoring

NASA Astrophysics Data System (ADS)

Xu, Roger; Stevenson, Mark W.; Kwan, Chi-Man; Haynes, Leonard S.

2001-07-01

At Ford Motor Company, thrust bearing in drill motors is often damaged by metal chips. Since the vibration frequency is several Hz only, it is very difficult to use accelerometers to pick up the vibration signals. Under the support of Ford and NASA, we propose to use a piezo film as a sensor to pick up the slow vibrations of the bearing. Then a neural net based fault detection algorithm is applied to differentiate normal bearing from bad bearing. The first step involves a Fast Fourier Transform which essentially extracts the significant frequency components in the sensor. Then Principal Component Analysis is used to further reduce the dimension of the frequency components by extracting the principal features inside the frequency components. The features can then be used to indicate the status of bearing. Experimental results are very encouraging.

Evaluation of the damage of cell wall and cell membrane for various extracellular polymeric substance extractions of activated sludge.

PubMed

Guo, Xuesong; Liu, Junxin; Xiao, Benyi

2014-10-20

Extracellular polymeric substances (EPS) are susceptible to contamination by intracellular substances released during the extraction of EPS owing to the damage caused to microbial cell structures. The damage to cell walls and cell membranes in nine EPS extraction processes of activated sludge was evaluated in this study. The extraction of EPS (including proteins, carbohydrates and DNA) was the highest using the NaOH extraction method and the lowest using formaldehyde extraction. All nine EPS extraction methods in this study resulted in cell wall and membrane damage. The damage to cell walls, evaluated by 2-keto-3-deoxyoctonate (KDO) and N-acetylglucosamine content changes in extracted EPS, was the most significant in the NaOH extraction process. Formaldehyde extraction showed a similar extent of damage to cell walls to those detected in the control method (centrifugation), while those in the formaldehyde-NaOH and cation exchange resin extractions were slightly higher than those detected in the control. N-acetylglucosamine was more suitable than KDO for the evaluation of cell wall damage in the EPS extraction of activated sludge. The damage to cell membranes was characterized by two fluorochromes (propidium iodide and FITC Annexin V) with flow cytometry (FCM) measurement. The highest proportion of membrane-damaged cells was detected in NaOH extraction (26.54% of total cells) while membrane-damaged cells comprised 8.19% of total cells in the control. Copyright © 2014 Elsevier B.V. All rights reserved.

Integrated circuit authentication using photon-limited x-ray microscopy.

PubMed

Markman, Adam; Javidi, Bahram

2016-07-15

A counterfeit integrated circuit (IC) may contain subtle changes to its circuit configuration. These changes may be observed when imaged using an x-ray; however, the energy from the x-ray can potentially damage the IC. We have investigated a technique to authenticate ICs under photon-limited x-ray imaging. We modeled an x-ray image with lower energy by generating a photon-limited image from a real x-ray image using a weighted photon-counting method. We performed feature extraction on the image using the speeded-up robust features (SURF) algorithm. We then authenticated the IC by comparing the SURF features to a database of SURF features from authentic and counterfeit ICs. Our experimental results with real and counterfeit ICs using an x-ray microscope demonstrate that we can correctly authenticate an IC image captured using orders of magnitude lower energy x-rays. To the best of our knowledge, this Letter is the first one on using a photon-counting x-ray imaging model and relevant algorithms to authenticate ICs to prevent potential damage.

Applying the LANL Statistical Pattern Recognition Paradigm for Structural Health Monitoring to Data from a Surface-Effect Fast Patrol Boat

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

Hoon Sohn; Charles Farrar; Norman Hunter

2001-01-01

This report summarizes the analysis of fiber-optic strain gauge data obtained from a surface-effect fast patrol boat being studied by the staff at the Norwegian Defense Research Establishment (NDRE) in Norway and the Naval Research Laboratory (NRL) in Washington D.C. Data from two different structural conditions were provided to the staff at Los Alamos National Laboratory. The problem was then approached from a statistical pattern recognition paradigm. This paradigm can be described as a four-part process: (1) operational evaluation, (2) data acquisition & cleansing, (3) feature extraction and data reduction, and (4) statistical model development for feature discrimination. Given thatmore » the first two portions of this paradigm were mostly completed by the NDRE and NRL staff, this study focused on data normalization, feature extraction, and statistical modeling for feature discrimination. The feature extraction process began by looking at relatively simple statistics of the signals and progressed to using the residual errors from auto-regressive (AR) models fit to the measured data as the damage-sensitive features. Data normalization proved to be the most challenging portion of this investigation. A novel approach to data normalization, where the residual errors in the AR model are considered to be an unmeasured input and an auto-regressive model with exogenous inputs (ARX) is then fit to portions of the data exhibiting similar waveforms, was successfully applied to this problem. With this normalization procedure, a clear distinction between the two different structural conditions was obtained. A false-positive study was also run, and the procedure developed herein did not yield any false-positive indications of damage. Finally, the results must be qualified by the fact that this procedure has only been applied to very limited data samples. A more complete analysis of additional data taken under various operational and environmental conditions as well as other structural conditions is necessary before one can definitively state that the procedure is robust enough to be used in practice.« less

A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study.

PubMed

Joshuva, A; Sugumaran, V

2017-03-01

Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

PubMed

Kim, Ju-Won; Park, Seunghee

2018-01-02

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Classifying Physical Morphology of Cocoa Beans Digital Images using Multiclass Ensemble Least-Squares Support Vector Machine

NASA Astrophysics Data System (ADS)

Lawi, Armin; Adhitya, Yudhi

2018-03-01

The objective of this research is to determine the quality of cocoa beans through morphology of their digital images. Samples of cocoa beans were scattered on a bright white paper under a controlled lighting condition. A compact digital camera was used to capture the images. The images were then processed to extract their morphological parameters. Classification process begins with an analysis of cocoa beans image based on morphological feature extraction. Parameters for extraction of morphological or physical feature parameters, i.e., Area, Perimeter, Major Axis Length, Minor Axis Length, Aspect Ratio, Circularity, Roundness, Ferret Diameter. The cocoa beans are classified into 4 groups, i.e.: Normal Beans, Broken Beans, Fractured Beans, and Skin Damaged Beans. The model of classification used in this paper is the Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM), a proposed improvement model of SVM using ensemble method in which the separate hyperplanes are obtained by least square approach and the multiclass procedure uses One-Against- All method. The result of our proposed model showed that the classification with morphological feature input parameters were accurately as 99.705% for the four classes, respectively.

A Fatigue Crack Size Evaluation Method Based on Lamb Wave Simulation and Limited Experimental Data

PubMed Central

He, Jingjing; Ran, Yunmeng; Liu, Bin; Yang, Jinsong; Guan, Xuefei

2017-01-01

This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S0 mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions. PMID:28902148

Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing Fault Diagnosis

PubMed Central

Yasir, Muhammad Naveed; Koh, Bong-Hwan

2018-01-01

This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) fault diagnosis from measured vibration signals. First, the LMD decomposed the vibration data or acceleration measurement into separate product functions that are composed of both amplitude and frequency modulation. MPE then calculated the statistical permutation entropy from the product functions to extract the nonlinear features to assess and classify the condition of the healthy and damaged REB system. The comparative experimental results of the conventional LMD-based multi-scale entropy and MPE were presented to verify the authenticity of the proposed technique. The study found that LMD-MPE’s integrated approach provides reliable, damage-sensitive features when analyzing the bearing condition. The results of REB experimental datasets show that the proposed approach yields more vigorous outcomes than existing methods. PMID:29690526

Structural vibration-based damage classification of delaminated smart composite laminates

NASA Astrophysics Data System (ADS)

Khan, Asif; Kim, Heung Soo; Sohn, Jung Woo

2018-03-01

Separation along the interfaces of layers (delamination) is a principal mode of failure in laminated composites and its detection is of prime importance for structural integrity of composite materials. In this work, structural vibration response is employed to detect and classify delaminations in piezo-bonded laminated composites. Improved layerwise theory and finite element method are adopted to develop the electromechanically coupled governing equation of a smart composite laminate with and without delaminations. Transient responses of the healthy and damaged structures are obtained through a surface bonded piezoelectric sensor by solving the governing equation in the time domain. Wavelet packet transform (WPT) and linear discriminant analysis (LDA) are employed to extract discriminative features from the structural vibration response of the healthy and delaminated structures. Dendrogram-based support vector machine (DSVM) is used to classify the discriminative features. The confusion matrix of the classification algorithm provided physically consistent results.

Standardization of accelerator irradiation procedures for simulation of neutron induced damage in reactor structural materials

NASA Astrophysics Data System (ADS)

Shao, Lin; Gigax, Jonathan; Chen, Di; Kim, Hyosim; Garner, Frank A.; Wang, Jing; Toloczko, Mychailo B.

2017-10-01

Self-ion irradiation is widely used as a method to simulate neutron damage in reactor structural materials. Accelerator-based simulation of void swelling, however, introduces a number of neutron-atypical features which require careful data extraction and, in some cases, introduction of innovative irradiation techniques to alleviate these issues. We briefly summarize three such atypical features: defect imbalance effects, pulsed beam effects, and carbon contamination. The latter issue has just been recently recognized as being relevant to simulation of void swelling and is discussed here in greater detail. It is shown that carbon ions are entrained in the ion beam by Coulomb force drag and accelerated toward the target surface. Beam-contaminant interactions are modeled using molecular dynamics simulation. By applying a multiple beam deflection technique, carbon and other contaminants can be effectively filtered out, as demonstrated in an irradiation of HT-9 alloy by 3.5 MeV Fe ions.

Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing Fault Diagnosis.

PubMed

Yasir, Muhammad Naveed; Koh, Bong-Hwan

2018-04-21

This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) fault diagnosis from measured vibration signals. First, the LMD decomposed the vibration data or acceleration measurement into separate product functions that are composed of both amplitude and frequency modulation. MPE then calculated the statistical permutation entropy from the product functions to extract the nonlinear features to assess and classify the condition of the healthy and damaged REB system. The comparative experimental results of the conventional LMD-based multi-scale entropy and MPE were presented to verify the authenticity of the proposed technique. The study found that LMD-MPE’s integrated approach provides reliable, damage-sensitive features when analyzing the bearing condition. The results of REB experimental datasets show that the proposed approach yields more vigorous outcomes than existing methods.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: II. Experimental validation under varying temperature

NASA Astrophysics Data System (ADS)

Lin, Y. Q.; Ren, W. X.; Fang, S. E.

2011-11-01

Although most vibration-based damage detection methods can acquire satisfactory verification on analytical or numerical structures, most of them may encounter problems when applied to real-world structures under varying environments. The damage detection methods that directly extract damage features from the periodically sampled dynamic time history response measurements are desirable but relevant research and field application verification are still lacking. In this second part of a two-part paper, the robustness and performance of the statistics-based damage index using the forward innovation model by stochastic subspace identification of a vibrating structure proposed in the first part have been investigated against two prestressed reinforced concrete (RC) beams tested in the laboratory and a full-scale RC arch bridge tested in the field under varying environments. Experimental verification is focused on temperature effects. It is demonstrated that the proposed statistics-based damage index is insensitive to temperature variations but sensitive to the structural deterioration or state alteration. This makes it possible to detect the structural damage for the real-scale structures experiencing ambient excitations and varying environmental conditions.

Vibration-based structural health monitoring of the aircraft large component

NASA Astrophysics Data System (ADS)

Pavelko, V.; Kuznetsov, S.; Nevsky, A.; Marinbah, M.

2017-10-01

In the presented paper there are investigated the basic problems of the local system of SHM of large scale aircraft component. Vibration-based damage detection is accepted as a basic condition, and main attention focused to a low-cost solution that would be attractive for practice. The conditions of small damage detection in the full scale structural component at low-frequency excitation were defined in analytical study and modal FEA. In experimental study the dynamic test of the helicopter Mi-8 tail beam was performed at harmonic excitation with frequency close to first natural frequency of the beam. The index of correlation coefficient deviation (CCD) was used for extraction of the features due to embedded pseudo-damage. It is shown that the problem of vibration-based detection of a small damage in the large scale structure at low-frequency excitation can be solved successfully.

Earthquake damage mapping by using remotely sensed data: the Haiti case study

NASA Astrophysics Data System (ADS)

Romaniello, Vito; Piscini, Alessandro; Bignami, Christian; Anniballe, Roberta; Stramondo, Salvatore

2017-01-01

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west-south-west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback-Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

Effects of UV Rays and Thymol/Thymus vulgaris L. Extract in an ex vivo Human Skin Model: Morphological and Genotoxicological Assessment.

PubMed

Cornaghi, Laura; Arnaboldi, Francesca; Calò, Rossella; Landoni, Federica; Baruffaldi Preis, William Franz; Marabini, Laura; Donetti, Elena

2016-01-01

Ultraviolet (UV) radiation is the major environmental factor affecting functions of the skin. Compounds rich in polyphenols, such as Thymus vulgaris leaf extract and thymol, have been proposed for the prevention of UV-induced skin damage. We compared the acute effects induced by UVA and UVB rays on epidermal morphology and proliferation, cytotoxicity, and genotoxicity. Normal human skin explants were obtained from young healthy women (n = 7) after informed consent and cultured at the air-liquid interface overnight. After 24 h, the samples were divided in 2 groups: the former exposed to UVA (16 or 24 J/cm2) and the latter irradiated with UVB (0.24 or 0.72 J/cm2). One hour after the end of irradiation, supernatants were collected for evaluation of the lactate dehydrogenase activity. Twenty-four hours after UVB exposure, biopsies were processed for light and transmission electron microscopy analysis, proliferation, cytotoxicity, and genotoxicity. UVB and UVA rays induced early inhibition of cell proliferation and DNA damage compared to controls. In particular, UVB rays were always more cytotoxic and genotoxic than UVA ones. For this reason, we evaluated the effect of either T. vulgaris L. extract (1.82 µg/ml) or thymol (1 µg/ml) on all samples treated for 1 h before UVB irradiation. While Thymus had a protective action for all of the endpoints evaluated, the action of the extract was less pronounced on epidermal proliferation and morphological features. The results presented in this study could be the basis for investigating the mechanism of thymol and T. vulgaris L. extract against the damage induced by UV radiation. © 2016 S. Karger AG, Basel.

Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

PubMed

Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

2018-06-02

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

The effects of Mucuna pruriens extract on histopathological and biochemical features in the rat model of ischemia.

PubMed

Nayak, Vanishri S; Kumar, Nitesh; D'Souza, Antony S; Nayak, Sunil S; Cheruku, Sri P; Pai, K Sreedhara Ranganath

2017-12-13

Stroke is considered to be one of the most important causes of death worldwide. Global ischemia causes widespread brain injury and infarctions in various regions of the brain. Oxidative stress can be considered an important factor in the development of tissue damage, which is caused because of arterial occlusion with subsequent reperfusion. Kapikacchu or Mucuna pruriens, commonly known as velvet bean, is well known for its aphrodisiac activities. It is also used in the treatment of snakebites, depressive neurosis, and Parkinson's disease. Although this plant has different pharmacological actions, its neuroprotective activity has received minimal attention. Thus, this study was carried out with the aim of evaluating the neuroprotective action of M. pruriens in bilateral carotid artery occlusion-induced global cerebral ischemia in Wistar rats. The carotid arteries of both sides were occluded for 30 min and reperfused to induce global cerebral ischemia. The methanolic plant extract was administered to the study animals for 10 days. The brains of the Wistar rats were isolated by decapitation and observed for histopathological and biochemical changes. Cerebral ischemia resulted in significant neurological damage in the brains of the rats that were not treated by M. pruriens. The group subjected to treatment by the M. pruriens extract showed significant protection against brain damage compared with the negative control group, which indicates the therapeutic potential of this plant in ischemia.

Acoustic emission signal processing for rolling bearing running state assessment using compressive sensing

NASA Astrophysics Data System (ADS)

Liu, Chang; Wu, Xing; Mao, Jianlin; Liu, Xiaoqin

2017-07-01

In the signal processing domain, there has been growing interest in using acoustic emission (AE) signals for the fault diagnosis and condition assessment instead of vibration signals, which has been advocated as an effective technique for identifying fracture, crack or damage. The AE signal has high frequencies up to several MHz which can avoid some signals interference, such as the parts of bearing (i.e. rolling elements, ring and so on) and other rotating parts of machine. However, acoustic emission signal necessitates advanced signal sampling capabilities and requests ability to deal with large amounts of sampling data. In this paper, compressive sensing (CS) is introduced as a processing framework, and then a compressive features extraction method is proposed. We use it for extracting the compressive features from compressively-sensed data directly, and also prove the energy preservation properties. First, we study the AE signals under the CS framework. The sparsity of AE signal of the rolling bearing is checked. The observation and reconstruction of signal is also studied. Second, we present a method of extraction AE compressive feature (AECF) from compressively-sensed data directly. We demonstrate the energy preservation properties and the processing of the extracted AECF feature. We assess the running state of the bearing using the AECF trend. The AECF trend of the running state of rolling bearings is consistent with the trend of traditional features. Thus, the method is an effective way to evaluate the running trend of rolling bearings. The results of the experiments have verified that the signal processing and the condition assessment based on AECF is simpler, the amount of data required is smaller, and the amount of computation is greatly reduced.

Seasonal variations in PM composition from Beijing, China drive liver oxidative stress

NASA Astrophysics Data System (ADS)

Pardo, M.; Rudich, Y.

2017-12-01

Air pollution can cause oxidative stress, inflammation and adverse health effects, but the underlying biological mechanisms are not completely understood. In order to understand how seasonal and chemical variations drive health impacts, we investigated the oxidative stress and inflammation in mice exposed to extracts (water and DCM) from urban PM collected in Beijing (China). Higher levels of pollution components were detected in the heating season (HS, winter) than in the non-heating season (NHS, summer). Higher concentrations of PM were measured in the heating season, mostly from coal and wood burning used for domestic heating. This was accompanied by increased levels of polyaromatic hydrocarbons (PAHs) in the DCM extracts. An increased inflammatory response was detected in the lung and liver with DCM extracts compared to the water extracts, and mostly in the winter aerosol. Reduced antioxidant response was observed in the lung, whereas it was activated in the liver. Gene expression of the Nrf2 transcription factor (A master regulator of stress response that controls the basal oxidative capacity and induces the expression of antioxidant response) and its related genes were induced. In the liver, higher levels of lipid peroxidation adducts were measured, correlated with histologic analysis that revealed morphologic features of damage/proliferation in the liver, indicating oxidative and toxic damage. Altogether, our study suggests that the acute effects of PM can vary by the season with the largest effect observed in winter than summer in Beijing, and that some secondary organs may be susceptible for exposure damage. This suggests that the liver is a potential organ to be influenced from PM especially by PAHs

Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

2018-02-01

This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

Retinoic acid-related orphan receptor-α is induced in the setting of DNA damage and promotes pulmonary emphysema.

PubMed

Shi, Ying; Cao, Jiaofei; Gao, Jane; Zheng, Liang; Goodwin, Andrew; An, Chang Hyoek; Patel, Avignat; Lee, Janet S; Duncan, Steven R; Kaminski, Naftali; Pandit, Kusum V; Rosas, Ivan O; Choi, Augustine M K; Morse, Danielle

2012-09-01

The discovery that retinoic acid-related orphan receptor (Rora)-α is highly expressed in lungs of patients with COPD led us to hypothesize that Rora may contribute to the pathogenesis of emphysema. To determine the role of Rora in smoke-induced emphysema. Cigarette smoke extract in vitro and elastase or cigarette smoke exposure in vivo were used to model smoke-related cell stress and airspace enlargement. Lung tissue from patients undergoing lung transplantation was examined for markers of DNA damage and Rora expression. Rora expression was induced by cigarette smoke in mice and in cell culture. Gene expression profiling of Rora-null mice exposed to cigarette smoke demonstrated enrichment for genes involved in DNA repair. Rora expression increased and Rora translocated to the nucleus after DNA damage. Inhibition of ataxia telangiectasia mutated decreased the induction of Rora. Gene silencing of Rora attenuated apoptotic cell death in response to cigarette smoke extract, whereas overexpression of Rora enhanced apoptosis. Rora-deficient mice were protected from elastase and cigarette smoke induced airspace enlargement. Finally, lungs of patients with COPD showed evidence of increased DNA damage even in the absence of active smoking. Taken together, these findings suggest that DNA damage may contribute to the pathogenesis of emphysema, and that Rora has a previously unrecognized role in cellular responses to genotoxicity. These findings provide a potential link between emphysema and features of premature ageing, including enhanced susceptibility to lung cancer.

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method

PubMed Central

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-01-01

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis. PMID:28029121

Retinal vasculature classification using novel multifractal features

NASA Astrophysics Data System (ADS)

Ding, Y.; Ward, W. O. C.; Duan, Jinming; Auer, D. P.; Gowland, Penny; Bai, L.

2015-11-01

Retinal blood vessels have been implicated in a large number of diseases including diabetic retinopathy and cardiovascular diseases, which cause damages to retinal blood vessels. The availability of retinal vessel imaging provides an excellent opportunity for monitoring and diagnosis of retinal diseases, and automatic analysis of retinal vessels will help with the processes. However, state of the art vascular analysis methods such as counting the number of branches or measuring the curvature and diameter of individual vessels are unsuitable for the microvasculature. There has been published research using fractal analysis to calculate fractal dimensions of retinal blood vessels, but so far there has been no systematic research extracting discriminant features from retinal vessels for classifications. This paper introduces new methods for feature extraction from multifractal spectra of retinal vessels for classification. Two publicly available retinal vascular image databases are used for the experiments, and the proposed methods have produced accuracies of 85.5% and 77% for classification of healthy and diabetic retinal vasculatures. Experiments show that classification with multiple fractal features produces better rates compared with methods using a single fractal dimension value. In addition to this, experiments also show that classification accuracy can be affected by the accuracy of vessel segmentation algorithms.

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method.

PubMed

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-12-24

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.

Seasonal variations in fine particle composition from Beijing prompt oxidative stress response in mouse lung and liver.

PubMed

Pardo, Michal; Xu, Fanfan; Qiu, Xinghua; Zhu, Tong; Rudich, Yinon

2018-06-01

Exposure to air pollution can induce oxidative stress, inflammation and adverse health effects. To understand how seasonal and chemical variations drive health impacts, we investigated indications for oxidative stress and inflammation in mice exposed to water and organic extracts from urban fine particles/PM 2.5 (particles with aerodynamic diameter ≤ 2.5 μm) collected in Beijing, China. Higher levels of pollution components were detected in heating season (HS, winter and part of spring) PM 2.5 than in the non-heating season (NHS, summer and part of spring and autumn) PM 2.5 . HS samples were high in metals for the water extraction and high in polycyclic aromatic hydrocarbons (PAHs) for the organic extraction compared to their controls. An increased inflammatory response was detected in the lung and liver following exposure to the organic extracts compared to the water extracts, and mostly in the HS PM 2.5 . While reduced antioxidant response was observed in the lung, it was activated in the liver, again, more in the HS extracts. Nrf2 transcription factor, a master regulator of stress response that controls the basal oxidative capacity and induces the expression of antioxidant response, and its related genes were induced. In the liver, elevated levels of lipid peroxidation adducts were measured, correlated with histologic analysis that revealed morphologic features of cell damage and proliferation, indicating oxidative and toxic damage. In addition, expression of genes related to detoxification of PAHs was observed. Altogether, the study suggests that the acute effects of PM 2.5 can vary seasonally with stronger health effects in the HS than in the NHS in Beijing, China and that some secondary organs may be susceptible for the exposure damage. Specifically, the liver is a potential organ influenced by exposure to organic components such as PAHs from coal or biomass burning and heating. Copyright © 2018 Elsevier B.V. All rights reserved.

Ventromedial Frontal Cortex Is Critical for Guiding Attention to Reward-Predictive Visual Features in Humans.

PubMed

Vaidya, Avinash R; Fellows, Lesley K

2015-09-16

Adaptively interacting with our environment requires extracting information that will allow us to successfully predict reward. This can be a challenge, particularly when there are many candidate cues, and when rewards are probabilistic. Recent work has demonstrated that visual attention is allocated to stimulus features that have been associated with reward on previous trials. The ventromedial frontal lobe (VMF) has been implicated in learning in dynamic environments of this kind, but the mechanism by which this region influences this process is not clear. Here, we hypothesized that the VMF plays a critical role in guiding attention to reward-predictive stimulus features based on feedback. We tested the effects of VMF damage in human subjects on a visual search task in which subjects were primed to attend to task-irrelevant colors associated with different levels of reward, incidental to the search task. Consistent with previous work, we found that distractors had a greater influence on reaction time when they appeared in colors associated with high reward in the previous trial compared with colors associated with low reward in healthy control subjects and patients with prefrontal damage sparing the VMF. However, this reward modulation of attentional priming was absent in patients with VMF damage. Thus, an intact VMF is necessary for directing attention based on experience with cue-reward associations. We suggest that this region plays a role in selecting reward-predictive cues to facilitate future learning. There has been a swell of interest recently in the ventromedial frontal cortex (VMF), a brain region critical to associative learning. However, the underlying mechanism by which this region guides learning is not well understood. Here, we tested the effects of damage to this region in humans on a task in which rewards were linked incidentally to visual features, resulting in trial-by-trial attentional priming. Controls and subjects with prefrontal damage sparing the VMF showed normal reward priming, but VMF-damaged patients did not. This work sheds light on a potential mechanism through which this region influences behavior. We suggest that the VMF is necessary for directing attention to reward-predictive visual features based on feedback, facilitating future learning and decision-making. Copyright © 2015 the authors 0270-6474/15/3512813-11$15.00/0.

On impact damage detection and quantification for CFRP laminates using structural response data only

NASA Astrophysics Data System (ADS)

Sultan, M. T. H.; Worden, K.; Pierce, S. G.; Hickey, D.; Staszewski, W. J.; Dulieu-Barton, J. M.; Hodzic, A.

2011-11-01

The overall purpose of the research is to detect and attempt to quantify impact damage in structures made from composite materials. A study that uses simplified coupon specimens made from a Carbon Fibre-Reinforced Polymer (CFRP) prepreg with 11, 12 and 13 plies is presented. PZT sensors were placed at three separate locations in each test specimen to record the responses from impact events. To perform damaging impact tests, an instrumented drop-test machine was used and the impact energy was set to cover a range of 0.37-41.72 J. The response signals captured from each sensor were recorded by a data acquisition system for subsequent evaluation. The impacted specimens were examined with an X-ray technique to determine the extent of the damaged areas and it was found that the apparent damaged area grew monotonically with impact energy. A number of simple univariate and multivariate features were extracted from the sensor signals recorded during impact by computing their spectra and calculating frequency centroids. The concept of discordancy from the statistical discipline of outlier analysis is employed in order to separate the responses from non-damaging and damaging impacts. The results show that the potential damage indices introduced here provide a means of identifying damaging impacts from the response data alone.

The application of data mining and cloud computing techniques in data-driven models for structural health monitoring

NASA Astrophysics Data System (ADS)

Khazaeli, S.; Ravandi, A. G.; Banerji, S.; Bagchi, A.

2016-04-01

Recently, data-driven models for Structural Health Monitoring (SHM) have been of great interest among many researchers. In data-driven models, the sensed data are processed to determine the structural performance and evaluate the damages of an instrumented structure without necessitating the mathematical modeling of the structure. A framework of data-driven models for online assessment of the condition of a structure has been developed here. The developed framework is intended for automated evaluation of the monitoring data and structural performance by the Internet technology and resources. The main challenges in developing such framework include: (a) utilizing the sensor measurements to estimate and localize the induced damage in a structure by means of signal processing and data mining techniques, and (b) optimizing the computing and storage resources with the aid of cloud services. The main focus in this paper is to demonstrate the efficiency of the proposed framework for real-time damage detection of a multi-story shear-building structure in two damage scenarios (change in mass and stiffness) in various locations. Several features are extracted from the sensed data by signal processing techniques and statistical methods. Machine learning algorithms are deployed to select damage-sensitive features as well as classifying the data to trace the anomaly in the response of the structure. Here, the cloud computing resources from Amazon Web Services (AWS) have been used to implement the proposed framework.

Standardization of accelerator irradiation procedures for simulation of neutron induced damage in reactor structural materials

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

Shao, Lin; Gigax, Jonathan; Chen, Di

Self-ion irradiation is widely used as a method to simulate neutron damage in reactor structural materials. Accelerator-based simulation of void swelling, however, introduces a number of neutron-atypical features which require careful data extraction and in some cases introduction of innovative irradiation techniques to alleviate these issues. We briefly summarize three such atypical features: defect imbalance effects, pulsed beam effects, and carbon contamination. The latter issue has just been recently recognized as being relevant to simulation of void swelling and is discussed here in greater detail. It is shown that carbon ions are entrained in the ion beam by Coulomb forcemore » drag and accelerated toward the target surface. Beam-contaminant interactions are modeled using molecular dynamics simulation. By applying a multiple beam deflection technique, carbon and other contaminants can be effectively filtered out, as demonstrated in an irradiation of HT-9 alloy by 3.5 MeV Fe ions.« less

Standardization of accelerator irradiation procedures for simulation of neutron induced damage in reactor structural materials

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

Shao, Lin; Gigax, Jonathan; Chen, Di

Self-ion irradiation is widely used as a method to simulate neutron damage in reactor structural materials. Accelerator-based simulation of void swelling, however, introduces a number of neutron-atypical features which require careful data extraction and, in some cases, introduction of innovative irradiation techniques to alleviate these issues. In this paper, we briefly summarize three such atypical features: defect imbalance effects, pulsed beam effects, and carbon contamination. The latter issue has just been recently recognized as being relevant to simulation of void swelling and is discussed here in greater detail. It is shown that carbon ions are entrained in the ion beammore » by Coulomb force drag and accelerated toward the target surface. Beam-contaminant interactions are modeled using molecular dynamics simulation. Finally, by applying a multiple beam deflection technique, carbon and other contaminants can be effectively filtered out, as demonstrated in an irradiation of HT-9 alloy by 3.5 MeV Fe ions.« less

Standardization of accelerator irradiation procedures for simulation of neutron induced damage in reactor structural materials

DOE PAGES

Shao, Lin; Gigax, Jonathan; Chen, Di; ...

2017-06-12

Self-ion irradiation is widely used as a method to simulate neutron damage in reactor structural materials. Accelerator-based simulation of void swelling, however, introduces a number of neutron-atypical features which require careful data extraction and, in some cases, introduction of innovative irradiation techniques to alleviate these issues. In this paper, we briefly summarize three such atypical features: defect imbalance effects, pulsed beam effects, and carbon contamination. The latter issue has just been recently recognized as being relevant to simulation of void swelling and is discussed here in greater detail. It is shown that carbon ions are entrained in the ion beammore » by Coulomb force drag and accelerated toward the target surface. Beam-contaminant interactions are modeled using molecular dynamics simulation. Finally, by applying a multiple beam deflection technique, carbon and other contaminants can be effectively filtered out, as demonstrated in an irradiation of HT-9 alloy by 3.5 MeV Fe ions.« less

Studies of Implicit Prototype Extraction In Patients with Mild Cognitive Impairment and Early Alzheimer’s Disease

PubMed Central

Nosofsky, Robert M.; Denton, Stephen E.; Zaki, Safa R.; Murphy-Knudsen, Anne F.; Unverzagt, Frederick W.

2013-01-01

Studies of incidental category learning support the hypothesis of an implicit prototype-extraction system which is distinct from explicit memory (Smith, 2008). In those studies, patients with explicit-memory impairments due to damage to the medial-temporal lobe performed normally in implicit categorization tasks (Bozoki, Grossman, & Smith, 2006; Knowlton & Squire, 1993). However, alternative interpretations are that: i) even people with impairments to a single memory system have sufficient resources to succeed on the particular categorization tasks that have been tested (Nosofsky & Zaki, 1998; Zaki & Nosofsky, 2001); and ii) working memory can be used at time of test to learn the categories (Palmeri & Flanery, 1999). In the present experiments, patients with amnestic mild cognitive impairment or early Alzheimer’s disease were tested in prototype-extraction tasks to examine these possibilities. In a categorization task involving discrete-feature stimuli, the majority of subjects relied on memories for exceedingly few features, even when the task structure strongly encouraged reliance on broad-based prototypes. In a dot-pattern categorization task, even the memory-impaired patients were able to use working memory at time of test to extract the category structure (at least for the stimulus set used in past work). We argue that the results weaken the past case made in favor of a separate system of implicit-prototype extraction. PMID:22746953

Cardiovascular effects of Phaleria macrocarpa extracts combined with mainstay FAC regimen for breast cancer.

PubMed

Anggadiredja, Kusnandar; Tjandrawinata, Raymond R

2015-01-01

DLBS1425 is a bioactive compound extracted from Phaleria macrocarpa, with anti-proliferative, anti-inflammatory and anti-angiogenic properties against cancer cells. The present study was aimed to assess cardiotoxicity of DLBS1425, compared to the mainstay regimen for breast cancer, 5-fluorouracil:doxorubicin:cyclophosphamide (FAC, given at 500/50/500 mg/m(2)). Treatment with FAC regimen at standard dose resulted in very severe toxicity, so mice had no chance to survive for more than 7 days following initial drug treatment. Furthermore, histological examination on the heart revealed severe muscular damage when mice were given the FAC regimen alone (severe toxicity). FAC as chemotherapeutic regimen exerted high toxicity profile to the cardiovascular cells in this experiment. Meanwhile, treatment with DLBS1425 alone up to a dose equivalent to as high as 300 mg three times daily in human had no hazardous consequences on the heart, hematological feature, as well as general safety. In the cardiovascular cells, DLBS1425 in the presence of FAC regimen (one-eight of the initial dose) gave protection to the cardiac muscle cells as well as other hematological features. Taken together, results of the present study suggest that DLBS1425 is safe when used as adjuvant therapy for breast cancer and may be even protective against cardiac cellular damage produced by chemotherapeutic regimen.

Lightning Pin Injection Test: MOSFETS in "ON" State

NASA Technical Reports Server (NTRS)

Ely, Jay J.; Nguyen, Truong X.; Szatkowski, George N.; Koppen, Sandra V.; Mielnik, John J.; Vaughan, Roger K.; Saha, Sankalita; Wysocki, Philip F.; Celaya, Jose R.

2011-01-01

The test objective was to evaluate MOSFETs for induced fault modes caused by pin-injecting a standard lightning waveform into them while operating. Lightning Pin-Injection testing was performed at NASA LaRC. Subsequent fault-mode and aging studies were performed by NASA ARC researchers using the Aging and Characterization Platform for semiconductor components. This report documents the test process and results, to provide a basis for subsequent lightning tests. The ultimate IVHM goal is to apply prognostic and health management algorithms using the features extracted during aging to allow calculation of expected remaining useful life. A survey of damage assessment techniques based upon inspection is provided, and includes data for optical microscope and X-ray inspection. Preliminary damage assessments based upon electrical parameters are also provided.

3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS

NASA Astrophysics Data System (ADS)

Saeed, R. A.; Galybin, A. N.; Popov, V.

2013-01-01

This paper discusses condition monitoring and fault diagnosis in Francis turbine based on integration of numerical modelling with several different artificial intelligence (AI) techniques. In this study, a numerical approach for fluid-structure (turbine runner) analysis is presented. The results of numerical analysis provide frequency response functions (FRFs) data sets along x-, y- and z-directions under different operating load and different position and size of faults in the structure. To extract features and reduce the dimensionality of the obtained FRF data, the principal component analysis (PCA) has been applied. Subsequently, the extracted features are formulated and fed into multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to identify the size and position of the damage in the runner and estimate the turbine operating conditions. The results demonstrated the effectiveness of this approach and provide satisfactory accuracy even when the input data are corrupted with certain level of noise.

Electronic cigarette aerosols suppress cellular antioxidant defenses and induce significant oxidative DNA damage

PubMed Central

Ganapathy, Vengatesh; Manyanga, Jimmy; Brame, Lacy; McGuire, Dehra; Sadhasivam, Balaji; Floyd, Evan; Rubenstein, David A.; Ramachandran, Ilangovan; Wagener, Theodore

2017-01-01

Background Electronic cigarette (EC) aerosols contain unique compounds in addition to toxicants and carcinogens traditionally found in tobacco smoke. Studies are warranted to understand the public health risks of ECs. Objective The aim of this study was to determine the genotoxicity and the mechanisms induced by EC aerosol extracts on human oral and lung epithelial cells. Methods Cells were exposed to EC aerosol or mainstream smoke extracts and DNA damage was measured using the primer anchored DNA damage detection assay (q-PADDA) and 8-oxo-dG ELISA assay. Cell viability, reactive oxygen species (ROS) and total antioxidant capacity (TAC) were measured using standard methods. mRNA and protein expression were evaluated by RT-PCR and western blot, respectively. Results EC aerosol extracts induced DNA damage in a dose-dependent manner, but independently of nicotine concentration. Overall, EC aerosol extracts induced significantly less DNA damage than mainstream smoke extracts, as measured by q-PADDA. However, the levels of oxidative DNA damage, as indicated by the presence of 8-oxo-dG, a highly mutagenic DNA lesion, were similar or slightly higher after exposure to EC aerosol compared to mainstream smoke extracts. Mechanistically, while exposure to EC extracts significantly increased ROS, it decreased TAC as well as the expression of 8-oxoguanine DNA glycosylase (OGG1), an enzyme essential for the removal of oxidative DNA damage. Conclusions Exposure to EC aerosol extracts suppressed the cellular antioxidant defenses and led to significant DNA damage. These findings emphasize the urgent need to investigate the potential long-term cancer risk of exposure to EC aerosol for vapers and the general public. PMID:28542301

Phase contrast imaging X-ray computed tomography: quantitative characterization of human patellar cartilage matrix with topological and geometrical features

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Coan, Paola; Huber, Markus B.; Diemoz, Paul C.; Wismüller, Axel

2014-03-01

Current assessment of cartilage is primarily based on identification of indirect markers such as joint space narrowing and increased subchondral bone density on x-ray images. In this context, phase contrast CT imaging (PCI-CT) has recently emerged as a novel imaging technique that allows a direct examination of chondrocyte patterns and their correlation to osteoarthritis through visualization of cartilage soft tissue. This study investigates the use of topological and geometrical approaches for characterizing chondrocyte patterns in the radial zone of the knee cartilage matrix in the presence and absence of osteoarthritic damage. For this purpose, topological features derived from Minkowski Functionals and geometric features derived from the Scaling Index Method (SIM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of healthy and osteoarthritic specimens of human patellar cartilage. The extracted features were then used in a machine learning task involving support vector regression to classify ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The best classification performance was observed with high-dimensional geometrical feature vectors derived from SIM (0.95 ± 0.06) which outperformed all Minkowski Functionals (p < 0.001). These results suggest that such quantitative analysis of chondrocyte patterns in human patellar cartilage matrix involving SIM-derived geometrical features can distinguish between healthy and osteoarthritic tissue with high accuracy.

The effect of green, black and white tea on the level of alpha and gamma tocopherols in free radical-induced oxidative damage of human red blood cells.

PubMed

Gawlik, Małgorzata; Czajka, Aneta

2007-01-01

The present study was undertaken to investigate the effect of aqueous tea extracts on lipid peroxidation and alpha and gamma tocopherols concentration in the oxidative damage of human red blood cells (RBC). RBC was taken as the model for study of the oxidative damage was induced by cumene hydroperoxide (cumOOH). The antioxidative property of leaf green tea, leaf and granulate of black tea and white tea at levels 1, 2, 4 g/150 mL of water were evaluated. The correlation was observed between reducing power of tea extract and formation of malondialdehyde--MDA (an indicator of lipid peroxidation) in oxidative damage of RBC. All tea extracts at level of 4 g/150 mL of water significantly decreased concentration of MDA. The extract of green tea in comparison to black and white tea extracts at the same levels seems to be a better protective agent against oxidative stress. The antioxidant synergism between components extracted from leaves of green tea and endogenous alpha tocopherol in the oxidative damage of red blood cells was observed. The consumption of alpha tocopherol in oxidative damage of RBC was the lowest after treatment with the highest dose of green tea extract. All tea extracts did not protect against decrease of gamma tocopherol in human erythrocytes treated with cumOOH.

An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

NASA Astrophysics Data System (ADS)

Su, Zhongqing; Ye, Lin

2004-08-01

The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

Features, Treatment, and Outcomes of Macrophage Activation Syndrome in Childhood-Onset Systemic Lupus Erythematosus.

PubMed

Borgia, R Ezequiel; Gerstein, Maya; Levy, Deborah M; Silverman, Earl D; Hiraki, Linda T

2018-04-01

To describe the features and treatment of macrophage activation syndrome (MAS) in a single-center cohort of patients with childhood-onset systemic lupus erythematosus (SLE), and to compare childhood-onset SLE manifestations and outcomes between those with and those without MAS. We included all patients with childhood-onset SLE followed up at The Hospital for Sick Children from 2002 to 2012, and identified those also diagnosed as having MAS. Demographic, clinical, and laboratory features of MAS and SLE, medication use, hospital and pediatric intensive care unit (PICU) admissions, as well as damage indices and mortality data were extracted from the Lupus database. Student's t-tests and Fisher's exact tests were used to compare continuous and categorical variables, respectively. We calculated incidence rate ratios of hospital and PICU admissions comparing patients with and those without MAS, using Poisson models. Kaplan-Meier survival analysis was used to examine the time to disease damage accrual. Of the 403 patients with childhood-onset SLE, 38 (9%) had MAS. The majority (68%) had concomitant MAS and SLE diagnoses. Fever was the most common MAS clinical feature. The frequency of renal and central nervous system disease, hospital admissions, the average daily dose of steroids, and time to disease damage were similar between those with and those without MAS. We observed a higher mortality rate among those with MAS (5%) than those without MAS (0.2%) (P = 0.02). MAS was most likely to develop concomitantly with childhood-onset SLE diagnosis. The majority of the MAS patients were successfully treated with corticosteroids with no MAS relapses. Although the numbers were small, there was a higher risk of death associated with MAS compared to SLE without MAS. © 2018, American College of Rheumatology.

Developmental toxicity of PAH mixtures in fish early life stages. Part I: adverse effects in rainbow trout.

PubMed

Le Bihanic, Florane; Morin, Bénédicte; Cousin, Xavier; Le Menach, Karyn; Budzinski, Hélène; Cachot, Jérôme

2014-12-01

A new gravel-contact assay using rainbow trout, Oncorhynchus mykiss, embryos was developed to assess the toxicity of polycyclic aromatic hydrocarbons (PAHs) and other hydrophobic compounds. Environmentally realistic exposure conditions were mimicked with a direct exposure of eyed rainbow trout embryos incubated onto chemical-spiked gravels until hatching at 10 °C. Several endpoints were recorded including survival, hatching delay, hatching success, biometry, developmental abnormalities, and DNA damage (comet and micronucleus assays). This bioassay was firstly tested with two model PAHs, fluoranthene and benzo[a]pyrene. Then, the method was applied to compare the toxicity of three PAH complex mixtures characterized by different PAH compositions: a pyrolytic extract from a PAH-contaminated sediment (Seine estuary, France) and two petrogenic extracts from Arabian Light and Erika oils, at two environmental concentrations, 3 and 10 μg g(-1) sum of PAHs. The degree and spectrum of toxicity were different according to the extract considered. Acute effects including embryo mortality and decreased hatching success were observed only for Erika oil extract. Arabian Light and pyrolytic extracts induced mainly sublethal effects including reduced larvae size and hemorrhages. Arabian Light and Erika extracts both induced repairable DNA damage as revealed by the comet assay versus the micronucleus assay. The concentration and proportion of methylphenanthrenes and methylanthracenes appeared to drive the toxicity of the three PAH fractions tested, featuring a toxic gradient as follows: pyrolytic < Arabian Light < Erika. The minimal concentration causing developmental defects was as low as 0.7 μg g(-1) sum of PAHs, indicating the high sensitivity of the assay and validating its use for toxicity assessment of particle-bound pollutants.

A study on ground truth data for impact damaged polymer matrix composites

NASA Astrophysics Data System (ADS)

Wallentine, Sarah M.; Uchic, Michael D.

2018-04-01

This study presents initial results toward correlative characterization of barely-visible impact damage (BVID) in unidirectional carbon fiber reinforced polymer matrix composite laminate plates using nondestructive ultrasonic testing (UT) and destructive serial sectioning microscopy. To produce damage consistent with BVID, plates were impacted using an instrumented drop-weight tower with pneumatic anti-rebound brake. High-resolution, normal-incidence, single-sided, pulse-echo, immersion UT scans were performed to verify and map internal damage after impact testing. UT C-scans were registered to optical images of the specimen via landmark registration and the use of an affine transformation, allowing location of internal damage in reference to the overall plate and enabling specimen preparation for subsequent serial sectioning. The impact-damaged region was extracted from each plate, prepared and mounted for materialographic sectioning. A modified RoboMet.3D version 2 was employed for serial sectioning and optical microscopy characterization of the impact damaged regions. Automated montage capture of sub-micron resolution, bright-field reflection, 12-bit monochrome optical images was performed over the entire specimen cross-section. These optical images were post- processed to produce 3D data sets, including segmentation to improve visualization of damage features. Impact-induced delaminations were analyzed and characterized using both serial sectioning and ultrasonic methods. Those results and conclusions are presented, as well as future direction of the current study.

Damage Detection in Composite Structures with Wavenumber Array Data Processing

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Leckey, Cara; Yu, Lingyu

2013-01-01

Guided ultrasonic waves (GUW) have the potential to be an efficient and cost-effective method for rapid damage detection and quantification of large structures. Attractive features include sensitivity to a variety of damage types and the capability of traveling relatively long distances. They have proven to be an efficient approach for crack detection and localization in isotropic materials. However, techniques must be pushed beyond isotropic materials in order to be valid for composite aircraft components. This paper presents our study on GUW propagation and interaction with delamination damage in composite structures using wavenumber array data processing, together with advanced wave propagation simulations. Parallel elastodynamic finite integration technique (EFIT) is used for the example simulations. Multi-dimensional Fourier transform is used to convert time-space wavefield data into frequency-wavenumber domain. Wave propagation in the wavenumber-frequency domain shows clear distinction among the guided wave modes that are present. This allows for extracting a guided wave mode through filtering and reconstruction techniques. Presence of delamination causes spectral change accordingly. Results from 3D CFRP guided wave simulations with delamination damage in flat-plate specimens are used for wave interaction with structural defect study.

Teeth: Among Nature's Most Durable Biocomposites

NASA Astrophysics Data System (ADS)

Lawn, Brian R.; Lee, James J.-W.; Chai, Herzl

2010-08-01

This paper addresses the durability of natural teeth from a materials perspective. Teeth are depicted as smart biocomposites, highly resistant to cumulative deformation and fracture. Favorable morphological features of teeth at both macroscopic and microscopic levels contribute to an innate damage tolerance. Damage modes are activated readily within the brittle enamel coat but are contained from spreading catastrophically into the vulnerable tooth interior in sustained occlusal loading. Although tooth enamel contains a multitude of microstructural defects that can act as sources of fracture, substantial overloads are required to drive any developing cracks to ultimate failure—nature's strategy is to contain damage rather than avoid it. Tests on model glass-shell systems simulating the basic elements of the tooth enamel/dentin layer structure help to identify important damage modes. Fracture and deformation mechanics provide a basis for analyzing critical conditions for each mode, in terms of characteristic tooth dimensions and materials properties. Comparative tests on extracted human and animal teeth confirm the validity of the model test approach and point to new research directions. Implications in biomechanics, especially as they relate to dentistry and anthropology, are outlined.

Signal decomposition for surrogate modeling of a constrained ultrasonic design space

NASA Astrophysics Data System (ADS)

Homa, Laura; Sparkman, Daniel; Wertz, John; Welter, John; Aldrin, John C.

2018-04-01

The U.S. Air Force seeks to improve the methods and measures by which the lifecycle of composite structures are managed. Nondestructive evaluation of damage - particularly internal damage resulting from impact - represents a significant input to that improvement. Conventional ultrasound can detect this damage; however, full 3D characterization has not been demonstrated. A proposed approach for robust characterization uses model-based inversion through fitting of simulated results to experimental data. One challenge with this approach is the high computational expense of the forward model to simulate the ultrasonic B-scans for each damage scenario. A potential solution is to construct a surrogate model using a subset of simulated ultrasonic scans built using a highly accurate, computationally expensive forward model. However, the dimensionality of these simulated B-scans makes interpolating between them a difficult and potentially infeasible problem. Thus, we propose using the chirplet decomposition to reduce the dimensionality of the data, and allow for interpolation in the chirplet parameter space. By applying the chirplet decomposition, we are able to extract the salient features in the data and construct a surrogate forward model.

Experimental Modal Analysis and Dynaic Strain Fiber Bragg Gratings for Structural Health Monitoring of Composite Aerospace Structures

NASA Astrophysics Data System (ADS)

Panopoulou, A.; Fransen, S.; Gomez Molinero, V.; Kostopoulos, V.

2012-07-01

The objective of this work is to develop a new structural health monitoring system for composite aerospace structures based on dynamic response strain measurements and experimental modal analysis techniques. Fibre Bragg Grating (FBG) optical sensors were used for monitoring the dynamic response of the composite structure. The structural dynamic behaviour has been numerically simulated and experimentally verified by means of vibration testing. The hypothesis of all vibration tests was that actual damage in composites reduces their stiffness and produces the same result as mass increase produces. Thus, damage was simulated by slightly varying locally the mass of the structure at different zones. Experimental modal analysis based on the strain responses was conducted and the extracted strain mode shapes were the input for the damage detection expert system. A feed-forward back propagation neural network was the core of the damage detection system. The features-input to the neural network consisted of the strain mode shapes, extracted from the experimental modal analysis. Dedicated training and validation activities were carried out based on the experimental results. The system showed high reliability, confirmed by the ability of the neural network to recognize the size and the position of damage on the structure. The experiments were performed on a real structure i.e. a lightweight antenna sub- reflector, manufactured and tested at EADS CASA ESPACIO. An integrated FBG sensor network, based on the advantage of multiplexing, was mounted on the structure with optimum topology. Numerical simulation of both structures was used as a support tool at all the steps of the work. Potential applications for the proposed system are during ground qualification extensive tests of space structures and during the mission as modal analysis tool on board, being able via the FBG responses to identify a potential failure.

Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal

PubMed Central

Ahn, Jong-Hyo; Kwak, Dae-Ho; Koh, Bong-Hwan

2014-01-01

This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault. PMID:25196008

FEX: A Knowledge-Based System For Planimetric Feature Extraction

NASA Astrophysics Data System (ADS)

Zelek, John S.

1988-10-01

Topographical planimetric features include natural surfaces (rivers, lakes) and man-made surfaces (roads, railways, bridges). In conventional planimetric feature extraction, a photointerpreter manually interprets and extracts features from imagery on a stereoplotter. Visual planimetric feature extraction is a very labour intensive operation. The advantages of automating feature extraction include: time and labour savings; accuracy improvements; and planimetric data consistency. FEX (Feature EXtraction) combines techniques from image processing, remote sensing and artificial intelligence for automatic feature extraction. The feature extraction process co-ordinates the information and knowledge in a hierarchical data structure. The system simulates the reasoning of a photointerpreter in determining the planimetric features. Present efforts have concentrated on the extraction of road-like features in SPOT imagery. Keywords: Remote Sensing, Artificial Intelligence (AI), SPOT, image understanding, knowledge base, apars.

The development of damage identification methods for buildings with image recognition and machine learning techniques utilizing aerial photographs of the 2016 Kumamoto earthquake

NASA Astrophysics Data System (ADS)

Shohei, N.; Nakamura, H.; Fujiwara, H.; Naoichi, M.; Hiromitsu, T.

2017-12-01

It is important to get schematic information of the damage situation immediately after the earthquake utilizing photographs shot from an airplane in terms of the investigation and the decision-making for authorities. In case of the 2016 Kumamoto earthquake, we have acquired more than 1,800 orthographic projection photographs adjacent to damaged areas. These photos have taken between April 16th and 19th by airplanes, then we have distinguished damages of all buildings with 4 levels, and organized as approximately 296,000 GIS data corresponding to the fundamental Geospatial data published by Geospatial Information Authority of Japan. These data have organized by effort of hundreds of engineers. However, it is not considered practical for more extensive disasters like the Nankai Trough earthquake by only human powers. So, we have been developing the automatic damage identification method utilizing image recognition and machine learning techniques. First, we have extracted training data of more than 10,000 buildings which have equally damage levels divided in 4 grades. With these training data, we have been raster scanning in each scanning ranges of entire images, then clipping patch images which represents damage levels each. By utilizing these patch images, we have been developing discriminant models by two ways. One is a model using the Support Vector Machine (SVM). First, extract a feature quantity of each patch images. Then, with these vector values, calculate the histogram density as a method of Bag of Visual Words (BoVW), then classify borders with each damage grades by SVM. The other one is a model using the multi-layered Neural Network. First, design a multi-layered Neural Network. Second, input patch images and damage levels based on a visual judgement, and then, optimize learning parameters with error backpropagation method. By use of both discriminant models, we are going to discriminate damage levels in each patches, then create the image that shows building damage situations. It would be helpful for more prompt and widespread damage detection than visual judgement. Acknowledgment: This work was supported by CSTI through the Cross-ministerial Strategic Innovation Promotion Program (SIP), titled "Enhancement of societal resiliency against natural disasters"(Funding agency: JST).

A Comparison of Vibration and Oil Debris Gear Damage Detection Methods Applied to Pitting Damage

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.

2000-01-01

Helicopter Health Usage Monitoring Systems (HUMS) must provide reliable, real-time performance monitoring of helicopter operating parameters to prevent damage of flight critical components. Helicopter transmission diagnostics are an important part of a helicopter HUMS. In order to improve the reliability of transmission diagnostics, many researchers propose combining two technologies, vibration and oil monitoring, using data fusion and intelligent systems. Some benefits of combining multiple sensors to make decisions include improved detection capabilities and increased probability the event is detected. However, if the sensors are inaccurate, or the features extracted from the sensors are poor predictors of transmission health, integration of these sensors will decrease the accuracy of damage prediction. For this reason, one must verify the individual integrity of vibration and oil analysis methods prior to integrating the two technologies. This research focuses on comparing the capability of two vibration algorithms, FM4 and NA4, and a commercially available on-line oil debris monitor to detect pitting damage on spur gears in the NASA Glenn Research Center Spur Gear Fatigue Test Rig. Results from this research indicate that the rate of change of debris mass measured by the oil debris monitor is comparable to the vibration algorithms in detecting gear pitting damage.

Lamb wave based damage detection using Matching Pursuit and Support Vector Machine classifier

NASA Astrophysics Data System (ADS)

Agarwal, Sushant; Mitra, Mira

2014-03-01

In this paper, the suitability of using Matching Pursuit (MP) and Support Vector Machine (SVM) for damage detection using Lamb wave response of thin aluminium plate is explored. Lamb wave response of thin aluminium plate with or without damage is simulated using finite element. Simulations are carried out at different frequencies for various kinds of damage. The procedure is divided into two parts - signal processing and machine learning. Firstly, MP is used for denoising and to maintain the sparsity of the dataset. In this study, MP is extended by using a combination of time-frequency functions as the dictionary and is deployed in two stages. Selection of a particular type of atoms lead to extraction of important features while maintaining the sparsity of the waveform. The resultant waveform is then passed as input data for SVM classifier. SVM is used to detect the location of the potential damage from the reduced data. The study demonstrates that SVM is a robust classifier in presence of noise and more efficient as compared to Artificial Neural Network (ANN). Out-of-sample data is used for the validation of the trained and tested classifier. Trained classifiers are found successful in detection of the damage with more than 95% detection rate.

Ginkgo biloba leaf extract induces DNA damage by inhibiting topoisomerase II activity in human hepatic cells.

PubMed

Zhang, Zhuhong; Chen, Si; Mei, Hu; Xuan, Jiekun; Guo, Xiaoqing; Couch, Letha; Dobrovolsky, Vasily N; Guo, Lei; Mei, Nan

2015-09-30

Ginkgo biloba leaf extract has been shown to increase the incidence in liver tumors in mice in a 2-year bioassay conducted by the National Toxicology Program. In this study, the DNA damaging effects of Ginkgo biloba leaf extract and many of its constituents were evaluated in human hepatic HepG2 cells and the underlying mechanism was determined. A molecular docking study revealed that quercetin, a flavonoid constituent of Ginkgo biloba, showed a higher potential to interact with topoisomerase II (Topo II) than did the other Ginkgo biloba constituents; this in silico prediction was confirmed by using a biochemical assay to study Topo II enzyme inhibition. Moreover, as measured by the Comet assay and the induction of γ-H2A.X, quercetin, followed by keampferol and isorhamnetin, appeared to be the most potent DNA damage inducer in HepG2 cells. In Topo II knockdown cells, DNA damage triggered by Ginkgo biloba leaf extract or quercetin was dramatically decreased, indicating that DNA damage is directly associated with Topo II. DNA damage was also observed when cells were treated with commercially available Ginkgo biloba extract product. Our findings suggest that Ginkgo biloba leaf extract- and quercetin-induced in vitro genotoxicity may be the result of Topo II inhibition.

Ginkgo biloba leaf extract induces DNA damage by inhibiting topoisomerase II activity in human hepatic cells

PubMed Central

Zhang, Zhuhong; Chen, Si; Mei, Hu; Xuan, Jiekun; Guo, Xiaoqing; Couch, Letha; Dobrovolsky, Vasily N.; Guo, Lei; Mei, Nan

2015-01-01

Ginkgo biloba leaf extract has been shown to increase the incidence in liver tumors in mice in a 2-year bioassay conducted by the National Toxicology Program. In this study, the DNA damaging effects of Ginkgo biloba leaf extract and many of its constituents were evaluated in human hepatic HepG2 cells and the underlying mechanism was determined. A molecular docking study revealed that quercetin, a flavonoid constituent of Ginkgo biloba, showed a higher potential to interact with topoisomerase II (Topo II) than did the other Ginkgo biloba constituents; this in silico prediction was confirmed by using a biochemical assay to study Topo II enzyme inhibition. Moreover, as measured by the Comet assay and the induction of γ-H2A.X, quercetin, followed by keampferol and isorhamnetin, appeared to be the most potent DNA damage inducer in HepG2 cells. In Topo II knockdown cells, DNA damage triggered by Ginkgo biloba leaf extract or quercetin was dramatically decreased, indicating that DNA damage is directly associated with Topo II. DNA damage was also observed when cells were treated with commercially available Ginkgo biloba extract product. Our findings suggest that Ginkgo biloba leaf extract- and quercetin-induced in vitro genotoxicity may be the result of Topo II inhibition. PMID:26419945

Effect of seven Indian plant extracts on Fenton reaction-mediated damage to DNA constituents.

PubMed

Kar, Indrani; Chattopadhyaya, Rajagopal

2017-11-01

The influences of substoichiometric amounts of seven plant extracts in the Fenton reaction-mediated damage to deoxynucleosides, deoxynucleoside monophosphates, deoxynucleoside triphosphates, and supercoiled plasmid DNA were studied to rationalize anticancer properties reported in some of these extracts. Extracts from Acacia catechu, Emblica officinalis, Spondias dulcis, Terminalia belerica, Terminalia chebula, as well as gallic acid, epicatechin, chebulagic acid and chebulinic acid enhance the extent of damage in Fenton reactions with all monomeric substrates but protect supercoiled plasmid DNA, compared to standard Fenton reactions. The damage to pyrimidine nucleosides/nucleotides is enhanced by these extracts and compounds to a greater extent than for purine ones in a concentration dependent manner. Dolichos biflorus and Hemidesmus indicus extracts generally do not show this enhancement for the monomeric substrates though they protect plasmid DNA. Compared to standard Fenton reactions for deoxynucleosides with ethanol, the presence of these five plant extracts render ethanol scavenging less effective as the radical is generated in the vicinity of the target. Since substoichiometric amounts of these extracts and the four compounds produce this effect, a catalytic mechanism involving the presence of a ternary complex of the nucleoside/nucleotide substrate, a plant compound and the hydroxyl radical is proposed. Such a mechanism cannot operate for plasmid DNA as the planar rings in the extract compounds cannot stack with the duplex DNA bases. These plant extracts, by enhancing Fenton reaction-mediated damage to deoxynucleoside triphosphates, slow down DNA replication in rapidly dividing cancer cells, thus contributing to their anticancer properties.

Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

PubMed

Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

2018-02-01

For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

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

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

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

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

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

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

Habituation of adult sea lamprey repeatedly exposed to damage-released alarm and predator cues

USGS Publications Warehouse

Imre, Istvan; Di Rocco, Richard T.; Brown, Grant E.; Johnson, Nicholas

2016-01-01

Predation is an unforgiving selective pressure affecting the life history, morphology and behaviour of prey organisms. Selection should favour organisms that have the ability to correctly assess the information content of alarm cues. This study investigated whether adult sea lamprey Petromyzon marinus habituate to conspecific damage-released alarm cues (fresh and decayed sea lamprey extract), a heterospecific damage-released alarm cue (white sucker Catostomus commersoniiextract), predator cues (Northern water snake Nerodia sipedon washing, human saliva and 2-phenylethylamine hydrochloride (PEA HCl)) and a conspecific damage-released alarm cue and predator cue combination (fresh sea lamprey extract and human saliva) after they were pre-exposed 4 times or 8 times, respectively, to a given stimulus the previous night. Consistent with our prediction, adult sea lamprey maintained an avoidance response to conspecific damage-released alarm cues (fresh and decayed sea lamprey extract), a predator cue presented at high relative concentration (PEA HCl) and a conspecific damage-released alarm cue and predator cue combination (fresh sea lamprey extract plus human saliva), irrespective of previous exposure level. As expected, adult sea lamprey habituated to a sympatric heterospecific damage-released alarm cue (white sucker extract) and a predator cue presented at lower relative concentration (human saliva). Adult sea lamprey did not show any avoidance of the Northern water snake washing and the Amazon sailfin catfish extract (heterospecific control). This study suggests that conspecific damage-released alarm cues and PEA HCl present the best options as natural repellents in an integrated management program aimed at controlling the abundance of sea lamprey in the Laurentian Great Lakes.

A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images.

PubMed

Acharya, U Rajendra; Bhat, Shreya; Koh, Joel E W; Bhandary, Sulatha V; Adeli, Hojjat

2017-09-01

Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sequential structural damage diagnosis algorithm using a change point detection method

NASA Astrophysics Data System (ADS)

Noh, H.; Rajagopal, R.; Kiremidjian, A. S.

2013-11-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

Cuticular surface damage of Ascaridia galli adult worms treated with Veitchia merrillii betel nuts extract in vitro.

PubMed

Balqis, Ummu; Hambal, Muhammad; Rinidar; Athaillah, Farida; Ismail; Azhar; Vanda, Henni; Darmawi

2017-07-01

The objective of this research was to in vitro evaluate the cuticular surface damage of Ascaridia galli adult worms treated with ethanolic extract of betel nuts Veitchia merrillii . Phytochemical screening was done using FeCl 3 , Wagner and Dragendorff reagents, NaOH, MgHCl, and Liebermann-Burchard reaction test. Amount of 16 worms were segregated into four groups with three replicates. Four worms of each group submerged into phosphate buffered saline, 25 mg/ml, and 75 mg/ml crude ethanolic extract of V. merrillii , and 15 mg/ml albendazole. The effect of these extract was observed 40 h after incubation as soon as worms death. The worms were sectioned transversally and were explored for any cuticular histopathological changes in their body surface under microscope. We found that the ethanolic extract of V. merrillii betel nuts contains tannins, alkaloids, flavonoids, triterpenoids, and saponins. The ethanolic extract of betel nuts V. merrillii induces surface alterations caused cuticular damage of A. galli adult worms. We concluded that ethanolic extract of betel nuts V. merrillii possess anthelmintic activity caused cuticular damage of A. galli adult worms.

Preventing Ultraviolet Light-Induced Damage: The Benefits of Antioxidants

ERIC Educational Resources Information Center

Yip, Cheng-Wai

2007-01-01

Extracts of fruit peels contain antioxidants that protect the bacterium "Escherichia coli" against damage induced by ultraviolet light. Antioxidants neutralise free radicals, thus preventing oxidative damage to cells and deoxyribonucleic acid. A high survival rate of UV-exposed cells was observed when grapefruit or grape peel extract was…

Detection of damaged areas caused by the oil extraction in a steppe region using winter landsat imagery

NASA Astrophysics Data System (ADS)

Mjachina, Ksenya; Hu, Zhiyong; Chibilyev, Alexander

2018-01-01

Oil production in a steppe region disturbs the landscape and damages the steppe ecosystem. The objective of this research was to detect areas damaged by oil production in an oil field within the Russian Volga-Ural steppe region using winter Landsat imagery. We developed a practicable and effective approach using winter snow season multispectral Landsat satellite imagery. To this end, we applied seven algorithms of spectral or texture-based transformation: K-means, maximum likelihood estimation, topsoil grain size index, soil brightness, normalized differential snow index, tasselled cap, and co-occurrence measures. The co-occurrence texture measure variance shows the optimal result of identifying damaged areas. The unique feature of our method is that it can differentiate damaged areas from the bare soil of cropland within a cold steppe region where the area damaged by oil production is mixed with bare (fallow) croplands that have a polygonal shape similar to well pads. Such similarities can lead to confusion in object-based classification. Using the co-occurrence measures, we found that from 1988 to 2015, damaged area is nearly three times as big in the peak period of the oil field development (2001 and 2009) as in 1988. Landscape fragmentation also peaked in 2001 and 2009. Our approach for this project is useful and cost effective regular monitoring of damages from oil production for both the Volga-Ural steppe region and other cold steppe regions.

Assessment of the Antioxidant Activity of Silybum marianum Seed Extract and Its Protective Effect against DNA Oxidation, Protein Damage and Lipid Peroxidation

PubMed Central

Serçe, Aynur; Toptancı, Bircan Çeken; Tanrıkut, Sevil Emen; Altaş, Sevcan; Kızıl, Göksel; Kızıl, Süleyman

2016-01-01

Summary Antioxidant properties of ethanol extract of Silybum marianum (milk thistle) seeds was investigated. We have also investigated the protein damage activated by oxidative Fenton reaction and its prevention by Silybum marianum seed extract. Antioxidant potential of Silybum marianum seed ethanol extract was measured using different in vitro methods, such as lipid peroxidation, 1,1–diphenyl–2–picrylhydrazyl (DPPH) and ferric reducing power assays. The extract significantly decreased DNA damage caused by hydroxyl radicals. Protein damage induced by hydroxyl radicals was also efficiently inhibited, which was confirmed by the presence of protein damage markers, such as protein carbonyl formation and by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE). The present study shows that milk thistle seeds have good DPPH free radical scavenging activity and can prevent lipid peroxidation. Therefore, Silybum marianum can be used as potentially rich source of antioxidants and food preservatives. The results suggest that the seeds may have potential beneficial health effects providing opportunities to develop value-added products. PMID:28115903

Assessment of the Antioxidant Activity of Silybum marianum Seed Extract and Its Protective Effect against DNA Oxidation, Protein Damage and Lipid Peroxidation.

PubMed

Serçe, Aynur; Toptancı, Bircan Çeken; Tanrıkut, Sevil Emen; Altaş, Sevcan; Kızıl, Göksel; Kızıl, Süleyman; Kızıl, Murat

2016-12-01

Antioxidant properties of ethanol extract of Silybum marianum (milk thistle) seeds was investigated. We have also investigated the protein damage activated by oxidative Fenton reaction and its prevention by Silybum marianum seed extract. Antioxidant potential of Silybum marianum seed ethanol extract was measured using different in vitro methods, such as lipid peroxidation, 1,1-diphenyl-2-picrylhydrazyl (DPPH) and ferric reducing power assays. The extract significantly decreased DNA damage caused by hydroxyl radicals. Protein damage induced by hydroxyl radicals was also efficiently inhibited, which was confirmed by the presence of protein damage markers, such as protein carbonyl formation and by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The present study shows that milk thistle seeds have good DPPH free radical scavenging activity and can prevent lipid peroxidation. Therefore, Silybum marianum can be used as potentially rich source of antioxidants and food preservatives. The results suggest that the seeds may have potential beneficial health effects providing opportunities to develop value-added products.

The Researches on Damage Detection Method for Truss Structures

NASA Astrophysics Data System (ADS)

Wang, Meng Hong; Cao, Xiao Nan

2018-06-01

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

PubMed Central

Zhao, Ming; Lin, Jing; Miao, Yonghao; Xu, Xiaoqiang

2016-01-01

Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox. PMID:27827831

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals.

PubMed

Zhao, Ming; Lin, Jing; Miao, Yonghao; Xu, Xiaoqiang

2016-11-02

Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox.

Contrast in Terahertz Images of Archival Documents—Part II: Influence of Topographic Features

NASA Astrophysics Data System (ADS)

Bardon, Tiphaine; May, Robert K.; Taday, Philip F.; Strlič, Matija

2017-04-01

We investigate the potential of terahertz time-domain imaging in reflection mode to reveal archival information in documents in a non-invasive way. In particular, this study explores the parameters and signal processing tools that can be used to produce well-contrasted terahertz images of topographic features commonly found in archival documents, such as indentations left by a writing tool, as well as sieve lines. While the amplitude of the waveforms at a specific time delay can provide the most contrasted and legible images of topographic features on flat paper or parchment sheets, this parameter may not be suitable for documents that have a highly irregular surface, such as water- or fire-damaged documents. For analysis of such documents, cross-correlation of the time-domain signals can instead yield images with good contrast. Analysis of the frequency-domain representation of terahertz waveforms can also provide well-contrasted images of topographic features, with improved spatial resolution when utilising high-frequency content. Finally, we point out some of the limitations of these means of analysis for extracting information relating to topographic features of interest from documents.

Early pest detection in soy plantations from hyperspectral measurements: a case study for caterpillar detection

NASA Astrophysics Data System (ADS)

Tailanián, Matías; Castiglioni, Enrique; Musé, Pablo; Fernández Flores, Germán.; Lema, Gabriel; Mastrángelo, Pedro; Almansa, Mónica; Fernández Liñares, Ignacio; Fernández Liñares, Germán.

2015-10-01

Soybean producers suffer from caterpillar damage in many areas of the world. Estimated average economic losses are annually 500 million USD in Brazil, Argentina, Paraguay and Uruguay. Designing efficient pest control management using selective and targeted pesticide applications is extremely important both from economic and environmental perspectives. With that in mind, we conducted a research program during the 2013-2014 and 2014-2015 planting seasons in a 4,000 ha soybean farm, seeking to achieve early pest detection. Nowadays pest presence is evaluated using manual, labor-intensive counting methods based on sampling strategies which are time consuming and imprecise. The experiment was conducted as follows. Using manual counting methods as ground-truth, a spectrometer capturing reflectance from 400 to 1100 nm was used to measure the reflectance of soy plants. A first conclusion, resulting from measuring the spectral response at leaves level, showed that stress was a property of plants since different leaves with different levels of damage yielded the same spectral response. Then, to assess the applicability of unsupervised classification of plants as healthy, biotic-stressed or abiotic-stressed, feature extraction and selection from leaves spectral signatures, combined with a Supported Vector Machine classifier was designed. Optimization of SVM parameters using grid search with cross-validation, along with classification evaluation by ten-folds cross-validation showed a correct classification rate of 95%, consistently on both seasons. Controlled experiments using cages with different numbers of caterpillars--including caterpillar-free plants--were also conducted to evaluate consistency in trends of the spectral response as well as the extracted features.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Carbon Nanotubes Application in the Extraction Techniques of Pesticides: A Review.

PubMed

Jakubus, Aleksandra; Paszkiewicz, Monika; Stepnowski, Piotr

2017-01-02

Carbon nanotubes (CNTs) are currently one of the most promising groups of materials with some interesting properties, such as lightness, rigidity, high surface area, high mechanical strength in tension, good thermal conductivity or resistance to mechanical damage. These unique properties make CNTs a competitive alternative to conventional sorbents used in analytical chemistry, especially in extraction techniques. The amount of work that discusses the usefulness of CNTs as a sorbent in a variety of extraction techniques has increased significantly in recent years. In this review article, the most important feature and different applications of solid-phase extraction (SPE), including, classical SPE and dispersive SPE using CNTs for pesticides isolation from different matrices, are summarized. Because of high number of articles concerning the applicability of carbon materials to extraction of pesticides, the main aim of proposed publication is to provide updated review of the latest uses of CNTs by covering the period 2006-2015. Moreover, in this review, the recent papers and this one, which are covered in previous reviews, will be addressed and particular attention has been paid on the division of publications in terms of classes of pesticides, in order to systematize the available literature reports.

Effect of defect imbalance on void swelling distributions produced in pure iron irradiated with 3.5 MeV self-ions

DOE PAGES

Shao, Lin; Wei, C. -C.; Gigax, J.; ...

2014-06-10

Ion irradiation has been widely used to simulate radiation damage induced by neutrons. However, there are a number of features of ion-induced damage that differ from neutron-induced damage, and these differences require investigation before behavior arising from neutron bombardment can be confidently predicted from ion data. In this study 3.5 MeV self-ion irradiation of pure iron was used to study the influence on void swelling of the depth-dependent defect imbalance between vacancies and interstitials that arises from various surface effects, forward scattering of displaced atoms, and especially the injected interstitial effect. The depth dependence of void swelling was observed notmore » to follow the behavior anticipated from the depth dependence of the damage rate. Void nucleation and growth develop first in the lower-dose, near-surface region, and then, during continued irradiation, move to progressively deeper and higher-damage depths. This indicates a strong initial suppression of void nucleation in the peak damage region that continued irradiation eventually overcomes. This phenomenon is shown by the Boltzmann transport equation method to be due to depth-dependent defect imbalances created under ion irradiation. These findings thus demonstrate that void swelling does not depend solely on the local dose level and that this sensitivity of swelling to depth must be considered in extracting and interpreting ion-induced swelling data.« less

An alternative medicine, Agaricus blazei, may have induced severe hepatic dysfunction in cancer patients.

PubMed

Mukai, Hirofumi; Watanabe, Toru; Ando, Masashi; Katsumata, Noriyuki

2006-12-01

We report three cases of patients with advanced cancer who showed severe hepatic damage, and two of whom died of fulminant hepatitis. All the patients were taking Agaricus blazei (Himematsutake) extract, one of the most popular complementary and alternative medicines among Japanese cancer patients. In one patient, liver functions recovered gradually after she stopped taking the Agaricus blazei, but she restarted taking it, which resulted in deterioration of the liver function again. The other patients who were admitted for severe liver damage had started taking the Agaricus blazei several days before admission. Although several other factors cannot be completely ruled out as the causes of liver damage, a strong causal relationship between the Agaricus blazei extract and liver damage was suggested and, at least, taking the Agaricus blazei extract made the clinical decision-making process much more complicated. Doctors who are aware of their patients taking the extract may accept it probably because they believe there is no harm in a complementary and alternative medicine. When unexpected liver damage is documented, however, doctors should consider the use of the Agaricus blazei extract as one of its causal factors. It is necessary to evaluate many modes of complementary and alternative medicines, including the Agaricus blazei extract, in rigorous, scientifically designed and peer-reviewed clinical trials.

Cuticular surface damage of Ascaridia galli adult worms treated with Veitchia merrillii betel nuts extract in vitro

PubMed Central

Balqis, Ummu; Hambal, Muhammad; Rinidar; Athaillah, Farida; Ismail; Azhar; Vanda, Henni; Darmawi

2017-01-01

Aim: The objective of this research was to in vitro evaluate the cuticular surface damage of Ascaridia galli adult worms treated with ethanolic extract of betel nuts Veitchia merrillii. Materials and Methods: Phytochemical screening was done using FeCl3, Wagner and Dragendorff reagents, NaOH, MgHCl, and Liebermann–Burchard reaction test. Amount of 16 worms were segregated into four groups with three replicates. Four worms of each group submerged into phosphate buffered saline, 25 mg/ml, and 75 mg/ml crude ethanolic extract of V. merrillii, and 15 mg/ml albendazole. The effect of these extract was observed 40 h after incubation as soon as worms death. The worms were sectioned transversally and were explored for any cuticular histopathological changes in their body surface under microscope. Results: We found that the ethanolic extract of V. merrillii betel nuts contains tannins, alkaloids, flavonoids, triterpenoids, and saponins. The ethanolic extract of betel nuts V. merrillii induces surface alterations caused cuticular damage of A. galli adult worms. Conclusion: We concluded that ethanolic extract of betel nuts V. merrillii possess anthelmintic activity caused cuticular damage of A. galli adult worms. PMID:28831213

DNA Damage Protecting Activity and Antioxidant Potential of Launaea taraxacifolia Leaves Extract.

PubMed

Adinortey, Michael Buenor; Ansah, Charles; Weremfo, Alexander; Adinortey, Cynthia Ayefoumi; Adukpo, Genevieve Etornam; Ameyaw, Elvis Ofori; Nyarko, Alexander Kwadwo

2018-01-01

The leaf extract of Launaea taraxacifolia commonly known as African Lettuce is used locally to treat dyslipidemia and liver diseases, which are associated with oxidative stress. Methanol extract from L. taraxacifolia leaves was tested for its antioxidant activity and its ability to protect DNA from oxidative damage. In vitro antioxidant potential of the leaf extract was evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH), nitric oxide (NO), and hydroxyl (OH) radical scavenging assays. Ferric reducing power, total antioxidant capacity (TAC), metal chelating, and anti-lipid peroxidation ability of the extract were also examined using gallic acid, ascorbic acid, citric acid, and ethylenediaminetetraacetic acid as standards. L. taraxacifolia leaves extract showed antioxidant activity with IC 50 values of 16.18 μg/ml (DPPH), 123.3 μg/ml (NO), 128.2 μg/ml (OH radical), 97.94 μg/ml (metal chelating), 80.28 μg/ml (TAC), and 23 μg/ml (anti-lipid peroxidation activity). L. taraxacifolia leaves extract exhibited a strong capability for DNA damage protection at 20 mg/ml concentration. These findings suggest that the methanolic leaf extract of L. taraxacifolia could be used as a natural antioxidant and also as a preventive therapy against diseases such as arteriosclerosis associated with DNA damage.

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

Harmon, S; Jeraj, R; Galavis, P

Purpose: Sensitivity of PET-derived texture features to reconstruction methods has been reported for features extracted from axial planes; however, studies often utilize three dimensional techniques. This work aims to quantify the impact of multi-plane (3D) vs. single-plane (2D) feature extraction on radiomics-based analysis, including sensitivity to reconstruction parameters and potential loss of spatial information. Methods: Twenty-three patients with solid tumors underwent [{sup 18}F]FDG PET/CT scans under identical protocols. PET data were reconstructed using five sets of reconstruction parameters. Tumors were segmented using an automatic, in-house algorithm robust to reconstruction variations. 50 texture features were extracted using two Methods: 2D patchesmore » along axial planes and 3D patches. For each method, sensitivity of features to reconstruction parameters was calculated as percent difference relative to the average value across reconstructions. Correlations between feature values were compared when using 2D and 3D extraction. Results: 21/50 features showed significantly different sensitivity to reconstruction parameters when extracted in 2D vs 3D (wilcoxon α<0.05), assessed by overall range of variation, Rangevar(%). Eleven showed greater sensitivity to reconstruction in 2D extraction, primarily first-order and co-occurrence features (average Rangevar increase 83%). The remaining ten showed higher variation in 3D extraction (average Range{sub var}increase 27%), mainly co-occurence and greylevel run-length features. Correlation of feature value extracted in 2D and feature value extracted in 3D was poor (R<0.5) in 12/50 features, including eight co-occurrence features. Feature-to-feature correlations in 2D were marginally higher than 3D, ∣R∣>0.8 in 16% and 13% of all feature combinations, respectively. Larger sensitivity to reconstruction parameters were seen for inter-feature correlation in 2D(σ=6%) than 3D (σ<1%) extraction. Conclusion: Sensitivity and correlation of various texture features were shown to significantly differ between 2D and 3D extraction. Additionally, inter-feature correlations were more sensitive to reconstruction variation using single-plane extraction. This work highlights a need for standardized feature extraction/selection techniques in radiomics.« less

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Object-Oriented Analysis of Satellite Images Using Artificial Neural Networks for Post-Earthquake Buildings Change Detection

NASA Astrophysics Data System (ADS)

Khodaverdi zahraee, N.; Rastiveis, H.

2017-09-01

Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged area, the amount and type of damage can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. The purpose of this paper is to propose and implement an automatic approach for mapping destructed buildings after an earthquake using pre- and post-event high resolution satellite images. In the proposed method after preprocessing, segmentation of both images is performed using multi-resolution segmentation technique. Then, the segmentation results are intersected with ArcGIS to obtain equal image objects on both images. After that, appropriate textural features, which make a better difference between changed or unchanged areas, are calculated for all the image objects. Finally, subtracting the extracted textural features from pre- and post-event images, obtained values are applied as an input feature vector in an artificial neural network for classifying the area into two classes of changed and unchanged areas. The proposed method was evaluated using WorldView2 satellite images, acquired before and after the 2010 Haiti earthquake. The reported overall accuracy of 93% proved the ability of the proposed method for post-earthquake buildings change detection.

Multi-damage identification based on joint approximate diagonalisation and robust distance measure

NASA Astrophysics Data System (ADS)

Cao, S.; Ouyang, H.

2017-05-01

Mode shapes or operational deflection shapes are highly sensitive to damage and can be used for multi-damage identification. Nevertheless, one drawback of this kind of methods is that the extracted spatial shape features tend to be compromised by noise, which degrades their damage identification accuracy, especially for incipient damage. To overcome this, joint approximate diagonalisation (JAD) also known as simultaneous diagonalisation is investigated to estimate mode shapes (MS’s) statistically. The major advantage of JAD method is that it efficiently provides the common Eigen-structure of a set of power spectral density matrices. In this paper, a new criterion in terms of coefficient of variation (CV) is utilised to numerically demonstrate the better noise robustness and accuracy of JAD method over traditional frequency domain decomposition method (FDD). Another original contribution is that a new robust damage index (DI) is proposed, which is comprised of local MS distortions of several modes weighted by their associated vibration participation factors. The advantage of doing this is to include fair contributions from changes of all modes concerned. Moreover, the proposed DI provides a measure of damage-induced changes in ‘modal vibration energy’ in terms of the selected mode shapes. Finally, an experimental study is presented to verify the efficiency and noise robustness of JAD method and the proposed DI. The results show that the proposed DI is effective and robust under random vibration situations, which indicates that it has the potential to be applied to practical engineering structures with ambient excitations.

Vertical Feature Mask Feature Classification Flag Extraction

Atmospheric Science Data Center

2013-03-28

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

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging].

PubMed

Liu, De-hua; Zhang, Shu-juan; Wang, Bin; Yu, Ke-qiang; Zhao, Yan-ru; He, Yong

2015-11-01

Hyperspectral imaging technology covered the range of 380-1000 nm was employed to detect defects (bruise and insect damage) of hawthorn fruit. A total of 134 samples were collected, which included damage fruit of 46, pest fruit of 30, injure and pest fruit of 10 and intact fruit of 48. Because calyx · s⁻¹ tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images, which could produce easily confusion between them. Hence, five types of defects including bruise, insect damage, sound, calyx, and stem-end were collected from 230 hawthorn fruits. After acquiring hyperspectral images of hawthorn fruits, the spectral data were extracted from region of interest (ROI). Then, several pretreatment methods of standard normalized variate (SNV), savitzky golay (SG), median filter (MF) and multiplicative scatter correction (MSC) were used and partial least squares method(PLS) model was carried out to obtain the better performance. Accordingly to their results, SNV pretreatment methods assessed by PLS was viewed as best pretreatment method. Lastly, SNV was chosen as the pretreatment method. Spectral features of five different regions were combined with Regression coefficients(RCs) of partial least squares-discriminant analysis (PLS-DA) model was used to identify the important wavelengths and ten wavebands at 483, 563, 645, 671, 686, 722, 777, 819, 837 and 942 nm were selected from all of the wavebands. Using Kennard-Stone algorithm, all kinds of samples were randomly divided into training set (173) and test set (57) according to the proportion of 3:1. And then, least squares-support vector machine (LS-SVM) discriminate model was established by using the selected wavebands. The results showed that the discriminate accuracy of the method was 91.23%. In the other hand, images at ten important wavebands were executed to Principal component analysis (PCA). Using "Sobel" operator and region growing algrorithm "Regiongrow", the edge and defect feature of 86 Hawthorn could be recognized. Lastly, the detect precision of bruised, insect damage and two-defect samples is 95.65%, 86.67% and 100%, respectively. This investigation demonstrated that hyperspectral imaging technology could detect the defects of bruise, insect damage, calyx, and stem-end in hawthorn fruit in qualitative analysis and feature detection which provided a theoretical reference for the defects nondestructive detection of hawthorn fruit.

Iris recognition based on key image feature extraction.

PubMed

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Investigation on the use of artificial neural networks to overcome the effects of environmental and operational changes on guided waves monitoring

NASA Astrophysics Data System (ADS)

El Mountassir, M.; Yaacoubi, S.; Dahmene, F.

2015-07-01

Intelligent feature extraction and advanced signal processing techniques are necessary for a better interpretation of ultrasonic guided waves signals either in structural health monitoring (SHM) or in nondestructive testing (NDT). Such signals are characterized by at least multi-modal and dispersive components. In addition, in SHM, these signals are closely vulnerable to environmental and operational conditions (EOCs), and can be severely affected. In this paper we investigate the use of Artificial Neural Network (ANN) to overcome these effects and to provide a reliable damage detection method with a minimal of false indications. An experimental case of study (full scale pipe) is presented. Damages sizes have been increased and their shapes modified in different steps. Various parameters such as the number of inputs and the number of hidden neurons were studied to find the optimal configuration of the neural network.

Experience improves feature extraction in Drosophila.

PubMed

Peng, Yueqing; Xi, Wang; Zhang, Wei; Zhang, Ke; Guo, Aike

2007-05-09

Previous exposure to a pattern in the visual scene can enhance subsequent recognition of that pattern in many species from honeybees to humans. However, whether previous experience with a visual feature of an object, such as color or shape, can also facilitate later recognition of that particular feature from multiple visual features is largely unknown. Visual feature extraction is the ability to select the key component from multiple visual features. Using a visual flight simulator, we designed a novel protocol for visual feature extraction to investigate the effects of previous experience on visual reinforcement learning in Drosophila. We found that, after conditioning with a visual feature of objects among combinatorial shape-color features, wild-type flies exhibited poor ability to extract the correct visual feature. However, the ability for visual feature extraction was greatly enhanced in flies trained previously with that visual feature alone. Moreover, we demonstrated that flies might possess the ability to extract the abstract category of "shape" but not a particular shape. Finally, this experience-dependent feature extraction is absent in flies with defective MBs, one of the central brain structures in Drosophila. Our results indicate that previous experience can enhance visual feature extraction in Drosophila and that MBs are required for this experience-dependent visual cognition.

Text feature extraction based on deep learning: a review.

PubMed

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

2017-01-01

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

Feature and Statistical Model Development in Structural Health Monitoring

NASA Astrophysics Data System (ADS)

Kim, Inho

All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development. The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally. Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering. The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns. Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.

Vibration-based damage detection in a concrete beam under temperature variations using AR models and state-space approaches

NASA Astrophysics Data System (ADS)

Clément, A.; Laurens, S.

2011-07-01

The Structural Health Monitoring of civil structures subjected to ambient vibrations is very challenging. Indeed, the variations of environmental conditions and the difficulty to characterize the excitation make the damage detection a hard task. Auto-regressive (AR) models coefficients are often used as damage sensitive feature. The presented work proposes a comparison of the AR approach with a state-space feature formed by the Jacobian matrix of the dynamical process. Since the detection of damage can be formulated as a novelty detection problem, Mahalanobis distance is applied to track new points from an undamaged reference collection of feature vectors. Data from a concrete beam subjected to temperature variations and damaged by several static loading are analyzed. It is observed that the damage sensitive features are effectively sensitive to temperature variations. However, the use of the Mahalanobis distance makes possible the detection of cracking with both of them. Early damage (before cracking) is only revealed by the AR coefficients with a good sensibility.

Protective effect of Carica papaya L leaf extract against alcohol induced acute gastric damage and blood oxidative stress in rats.

PubMed

Indran, M; Mahmood, A A; Kuppusamy, U R

2008-09-01

The effects of Carica papaya leaf (CPL) aqueous extract on alcohol induced acute gastric damage and the immediate blood oxidative stress level were studied in rats. The results showed that gastric ulcer index was significantly reduced in rats pretreated with CPL extract as compared with alcohol treated controls. The in vitro studies using 2,2-Diphenyl-1-Picryl-Hydrazyl (DPPH) assay showed strong antioxidant nature of CPL extract. Biochemical analysis indicated that the acute alcohol induced damage is reflected in the alterations of blood oxidative indices and CPL extract offered some protection with reduction in plasma lipid peroxidation level and increased erythrocyte glutathione peroxidase activity. Carica papaya leaf may potentially serve as a good therapeutic agent for protection against gastric ulcer and oxidative stress.

Feature extraction for document text using Latent Dirichlet Allocation

NASA Astrophysics Data System (ADS)

Prihatini, P. M.; Suryawan, I. K.; Mandia, IN

2018-01-01

Feature extraction is one of stages in the information retrieval system that used to extract the unique feature values of a text document. The process of feature extraction can be done by several methods, one of which is Latent Dirichlet Allocation. However, researches related to text feature extraction using Latent Dirichlet Allocation method are rarely found for Indonesian text. Therefore, through this research, a text feature extraction will be implemented for Indonesian text. The research method consists of data acquisition, text pre-processing, initialization, topic sampling and evaluation. The evaluation is done by comparing Precision, Recall and F-Measure value between Latent Dirichlet Allocation and Term Frequency Inverse Document Frequency KMeans which commonly used for feature extraction. The evaluation results show that Precision, Recall and F-Measure value of Latent Dirichlet Allocation method is higher than Term Frequency Inverse Document Frequency KMeans method. This shows that Latent Dirichlet Allocation method is able to extract features and cluster Indonesian text better than Term Frequency Inverse Document Frequency KMeans method.

Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

PubMed

Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

2016-01-01

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

NASA Astrophysics Data System (ADS)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Evaluation of Antioxidant and DNA Damage Protection Activity of the Hydroalcoholic Extract of Desmostachya bipinnata L. Stapf

PubMed Central

Bhimathati, Solomon Sunder Raj

2014-01-01

Desmostachya bipinnata Stapf (Poaceae/Gramineae) is an official drug of ayurvedic pharmacopoeia. Various parts of this plant were used extensively in traditional and folklore medicine to cure various human ailments. The present study was aimed to evaluate the antioxidant and DNA damage protection activity of hydroalcoholic extract of Desmostachya bipinnata both in vitro and in vivo, to provide scientific basis for traditional usage of this plant. The extract showed significant antioxidant activity in a dose-dependent manner with an IC50 value of 264.18 ± 3.47 μg/mL in H2O2 scavenging assay and prevented the oxidative damage to DNA in presence of DNA damaging agent (Fenton's reagent) at a concentration of 50 μg/mL. Also, the presence of extract protected yeast cells in a dose-dependent manner against DNA damaging agent (Hydroxyurea) in spot assay. Moreover, the presence of extract exhibited significant antioxidant activity in vivo by protecting yeast cells against oxidative stressing agent (H2O2). Altogether, the results of current study revealed that Desmostachya bipinnata is a potential source of antioxidants and lends pharmacological credence to the ethnomedical use of this plant in traditional system of medicine, justifying its therapeutic application for free-radical-induced diseases. PMID:24574873

Evaluation of antioxidant and DNA damage protection activity of the hydroalcoholic extract of Desmostachya bipinnata L. Stapf.

PubMed

Golla, Upendarrao; Bhimathati, Solomon Sunder Raj

2014-01-01

Desmostachya bipinnata Stapf (Poaceae/Gramineae) is an official drug of ayurvedic pharmacopoeia. Various parts of this plant were used extensively in traditional and folklore medicine to cure various human ailments. The present study was aimed to evaluate the antioxidant and DNA damage protection activity of hydroalcoholic extract of Desmostachya bipinnata both in vitro and in vivo, to provide scientific basis for traditional usage of this plant. The extract showed significant antioxidant activity in a dose-dependent manner with an IC50 value of 264.18±3.47  μg/mL in H2O2 scavenging assay and prevented the oxidative damage to DNA in presence of DNA damaging agent (Fenton's reagent) at a concentration of 50  μg/mL. Also, the presence of extract protected yeast cells in a dose-dependent manner against DNA damaging agent (Hydroxyurea) in spot assay. Moreover, the presence of extract exhibited significant antioxidant activity in vivo by protecting yeast cells against oxidative stressing agent (H2O2). Altogether, the results of current study revealed that Desmostachya bipinnata is a potential source of antioxidants and lends pharmacological credence to the ethnomedical use of this plant in traditional system of medicine, justifying its therapeutic application for free-radical-induced diseases.

Model-based damage evaluation of layered CFRP structures

NASA Astrophysics Data System (ADS)

Munoz, Rafael; Bochud, Nicolas; Rus, Guillermo; Peralta, Laura; Melchor, Juan; Chiachío, Juan; Chiachío, Manuel; Bond, Leonard J.

2015-03-01

An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission experiment, where a scan has been performed on a damaged specimen. Most ultrasonic techniques in industrial practice consider only a few features of the received signals, namely, time of flight, amplitude, attenuation, frequency contents, and so forth. In this case, once signals are captured, an algorithm is used to reconstruct the complete signal waveform and extract the unknown damage parameters by means of modeling procedures. A linear version of the data processing has been performed, where only Young modulus has been monitored and, in a second nonlinear version, the first order nonlinear coefficient β was incorporated to test the possibility of detection of early damage. The aforementioned physical simulation models are solved by the Transfer Matrix formalism, which has been extended from linear to nonlinear harmonic generation technique. The damage parameter search strategy is based on minimizing the mismatch between the captured and simulated signals in the time domain in an automated way using Genetic Algorithms. Processing all scanned locations, a C-scan of the parameter of each layer can be reconstructed, obtaining the information describing the state of each layer and each interface. Damage can be located and quantified in terms of changes in the selected parameter with a measurable extension. In the case of the nonlinear coefficient of first order, evidence of higher sensitivity to damage than imaging the linearly estimated Young Modulus is provided.

Efficient feature extraction from wide-area motion imagery by MapReduce in Hadoop

NASA Astrophysics Data System (ADS)

Cheng, Erkang; Ma, Liya; Blaisse, Adam; Blasch, Erik; Sheaff, Carolyn; Chen, Genshe; Wu, Jie; Ling, Haibin

2014-06-01

Wide-Area Motion Imagery (WAMI) feature extraction is important for applications such as target tracking, traffic management and accident discovery. With the increasing amount of WAMI collections and feature extraction from the data, a scalable framework is needed to handle the large amount of information. Cloud computing is one of the approaches recently applied in large scale or big data. In this paper, MapReduce in Hadoop is investigated for large scale feature extraction tasks for WAMI. Specifically, a large dataset of WAMI images is divided into several splits. Each split has a small subset of WAMI images. The feature extractions of WAMI images in each split are distributed to slave nodes in the Hadoop system. Feature extraction of each image is performed individually in the assigned slave node. Finally, the feature extraction results are sent to the Hadoop File System (HDFS) to aggregate the feature information over the collected imagery. Experiments of feature extraction with and without MapReduce are conducted to illustrate the effectiveness of our proposed Cloud-Enabled WAMI Exploitation (CAWE) approach.

Antioxidant activity and protective effect of Clitoria ternatea flower extract on testicular damage induced by ketoconazole in rats.

PubMed

Iamsaard, Sitthichai; Burawat, Jaturon; Kanla, Pipatpong; Arun, Supatcharee; Sukhorum, Wannisa; Sripanidkulchai, Bungorn; Uabundit, Nongnut; Wattathorn, Jintanaporn; Hipkaeo, Wiphawi; Fongmoon, Duriya; Kondo, Hisatake

2014-06-01

Ketoconazole (KET), an antifungal drug, has adverse effects on the male reproductive system. Pre-treatments with antioxidant plant against testicular damage induced by KET are required. The flowers of Clitoria ternatea (CT) are proven to have hepatoprotective potential. However, the protective effect on KET-induced testicular damage has not been reported. To investigate the protective effect of CT flower extracts with antioxidant activity on male reproductive parameters including sperm concentration, serum testosterone level, histopathology of the testis, and testicular tyrosine phosphorylation levels in rats induced with KET. The antioxidant activity of CT flower extracts was determined using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) assays. Male rats were treated with CT flower extracts (10, 50, or 100 mg/kg BW) or distilled water via a gastric tube for 28 d (preventive period: Days 1-21) and induced by KET (100 mg/kg BW) via intraperitoneal injection for 7 d (induction period: Days 22-28). After the experiment, all animals were examined for the weights of the testis, epididymis plus vas deferens and seminal vesicle, serum testosterone levels, sperm concentration, histological structures and diameter of testis, and testicular tyrosine phosphorylation levels by immunoblotting. The CT flower extracts had capabilities for DPPH scavenging and high reducing power. At 100 mg/kg BW, the extract had no toxic effects on the male reproductive system. Significantly, in CT+KET groups, CT flower extracts (50 and 100 mg/kg BW) alleviated the reduction of reproductive organ weight parameters, testosterone levels, and sperm concentration. In addition, CT flower extracts gave protection from testicular damage in KET-induced rats. Moreover, in the CT100+KET group, CT flower extracts significantly enhanced the expression of a testicular 50-kDa tyrosine phosphorylated protein compared with that of other groups. C. ternatea flower extracts possessing antioxidant activity are not harmful to the male reproductive system and can protect against testicular damage in KET-induced rats.

The behavioural response of adult Petromyzon marinus to damage-released alarm and predator cues

USGS Publications Warehouse

Imre, István; Di Rocco, Richard; Belanger, Cowan; Brown, Grant; Johnson, Nicholas S.

2014-01-01

Using semi-natural enclosures, this study investigated (1) whether adult sea lamprey Petromyzon marinus show avoidance of damage-released conspecific cues, damage-released heterospecific cues and predator cues and (2) whether this is a general response to injured heterospecific fishes or a specific response to injured P. marinus. Ten replicate groups of 10 adult P. marinus, separated by sex, were exposed to one of the following nine stimuli: deionized water (control), extracts prepared from adult P. marinus, decayed adult P. marinus (conspecific stimuli), sympatric white sucker Catostomus commersonii, Amazon sailfin catfish Pterygoplichthys pardalis (heterospecific stimuli), 2-phenylethylamine (PEA HCl) solution, northern water snake Nerodia sipedon washing, human saliva (predator cues) and an adult P. marinus extract and human saliva combination (a damage-released conspecific cue and a predator cue). Adult P. marinus showed a significant avoidance response to the adult P. marinus extract as well as to C. commersonii, human saliva, PEA and the adult P. marinus extract and human saliva combination. For mobile P. marinus, the N. sipedon washing induced behaviour consistent with predator inspection. Exposure to the P. pardalis extract did not induce a significant avoidance response during the stimulus release period. Mobile adult female P. marinus showed a stronger avoidance behaviour than mobile adult male P. marinus in response to the adult P. marinus extract and the adult P. marinus extract and human saliva combination. The findings support the continued investigation of natural damage-released alarm cue and predator-based repellents for the behavioural manipulation of P. marinus populations in the Laurentian Great Lakes.

Effect of Pelargonium reniforme roots on alcohol-induced liver damage and oxidative stress.

PubMed

Adewusi, Emmanuel Adekanmi; Afolayan, Anthony Jide

2010-09-01

Ethnobotanical surveys conducted on Pelargonium reniforme Curtis (Geraniaceae) have shown that the aqueous root extracts are used to treat alcohol-induced liver damage. We evaluated the antioxidant properties of the extract and its effects on alcohol-induced hepatotoxicity using Wistar rats. Alcohol-induced hepatotoxicity studies were carried out by observing the effect of the aqueous root extract on some liver marker enzymes, bilirubin, and total protein after liver damage. The levels of some phenolic compounds were determined by standard methods. Also, the reducing power of the plant extract and its ability to scavenge 1,1-diphenyl-2-picrylhydrazyl (DPPH*) and 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS*+) radicals were determined to evaluate its antioxidant activity. The results obtained show that the plant extract possessed significant antioxidant activity. It had a significant level of phenolic compounds, scavenged DPPH* and ABTS*+ radicals effectively, and demonstrated good reducing power. This may indicate that the plant contained compounds which can remove toxic metabolites following alcohol abuse. Serum analysis of animals treated with only ethanol showed a significant increase in the levels of liver marker enzymes and total and unconjugated bilirubin, while a significant decrease was observed in the levels of conjugated bilirubin and total proteins. Administration of the plant extract restored the levels of these markers to normal levels, and this indicates the ability of the plant extract to restore normal functioning of a damaged liver. The study shows that P. reniforme is a potential source of antioxidants and compounds which are useful in treating alcoholic liver damage.

Extracts from Hericium erinaceus relieve inflammatory bowel disease by regulating immunity and gut microbiota.

PubMed

Diling, Chen; Xin, Yang; Chaoqun, Zheng; Jian, Yang; Xiaocui, Tang; Jun, Chen; Ou, Shuai; Yizhen, Xie

2017-10-17

Hericium erinaceus (HE), a traditional edible mushroom, is known as a medicine food homology to ameliorate gastrointestinal diseases. To investigate whether HE is clinically effective in alleviating inflammatory bowel disease (IBD), HE extracts (polysaccharide, alcoholic extracts and whole extracts were prepared using solvent extraction methods) were administrated for 2 weeks in rats with IBD induced by trinitro-benzene-sulfonic acid (TNBS) enema (150 mg/kg). Significant clinical and histological changes in IBD rats were identified, including damage activity, common morphous and tissue damage index scores in colonic mucosa and myeloperoxidase (MPO) activity. The damage activity, common morphous and tissue damage index scores in colonic mucosa ( P <0.05) were improved, MPO activities were decreased. Inflammatory factors were also differentially expressed in colonic mucosa in IBD rats, including serum cytokines, Foxp3 and interleukin (IL)-10 were increased while NF-κB p65 and tumor necrosis factor (TNF)-α were decreased ( P <0.05), and T cells were activated ( P <0.05), especially in the alcohol extracts-treated group. We also found that the structure of gut microbiota of the H. erinaceus extracts-treated groups changed significantly by compared with the model group. Further studies revealed that the polysaccharides in HE extracts may play a prebiotic role, whereas the alcoholic extracts show bactericidin-like and immunomodulatory effects. Taken together, we demonstrated that H. erinaceus extracts could promote the growth of beneficial gut bacteria and improve the host immunity in vivo IBD model, which shows clinical potential in relieving IBD by regulating gut microbiota and immune system.

Extracts from Hericium erinaceus relieve inflammatory bowel disease by regulating immunity and gut microbiota

PubMed Central

Diling, Chen; Xin, Yang; Chaoqun, Zheng; Jian, Yang; Xiaocui, Tang; Jun, Chen; Ou, Shuai; Yizhen, Xie

2017-01-01

Hericium erinaceus (HE), a traditional edible mushroom, is known as a medicine food homology to ameliorate gastrointestinal diseases. To investigate whether HE is clinically effective in alleviating inflammatory bowel disease (IBD), HE extracts (polysaccharide, alcoholic extracts and whole extracts were prepared using solvent extraction methods) were administrated for 2 weeks in rats with IBD induced by trinitro-benzene-sulfonic acid (TNBS) enema (150 mg/kg). Significant clinical and histological changes in IBD rats were identified, including damage activity, common morphous and tissue damage index scores in colonic mucosa and myeloperoxidase (MPO) activity. The damage activity, common morphous and tissue damage index scores in colonic mucosa (P <0.05) were improved, MPO activities were decreased. Inflammatory factors were also differentially expressed in colonic mucosa in IBD rats, including serum cytokines, Foxp3 and interleukin (IL)-10 were increased while NF-κB p65 and tumor necrosis factor (TNF)-α were decreased (P <0.05), and T cells were activated (P <0.05), especially in the alcohol extracts-treated group. We also found that the structure of gut microbiota of the H. erinaceus extracts-treated groups changed significantly by compared with the model group. Further studies revealed that the polysaccharides in HE extracts may play a prebiotic role, whereas the alcoholic extracts show bactericidin-like and immunomodulatory effects. Taken together, we demonstrated that H. erinaceus extracts could promote the growth of beneficial gut bacteria and improve the host immunity in vivo IBD model, which shows clinical potential in relieving IBD by regulating gut microbiota and immune system. PMID:29156761

A framework for feature extraction from hospital medical data with applications in risk prediction.

PubMed

Tran, Truyen; Luo, Wei; Phung, Dinh; Gupta, Sunil; Rana, Santu; Kennedy, Richard Lee; Larkins, Ann; Venkatesh, Svetha

2014-12-30

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

Computer-aided diagnosis in phase contrast imaging X-ray computed tomography for quantitative characterization of ex vivo human patellar cartilage.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismuller, Axel

2013-10-01

Visualization of ex vivo human patellar cartilage matrix through the phase contrast imaging X-ray computed tomography (PCI-CT) has been previously demonstrated. Such studies revealed osteoarthritis-induced changes to chondrocyte organization in the radial zone. This study investigates the application of texture analysis to characterizing such chondrocyte patterns in the presence and absence of osteoarthritic damage. Texture features derived from Minkowski functionals (MF) and gray-level co-occurrence matrices (GLCM) were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These texture features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). The best classification performance was observed with the MF features perimeter (AUC: 0.94 ±0.08 ) and "Euler characteristic" (AUC: 0.94 ±0.07 ), and GLCM-derived feature "Correlation" (AUC: 0.93 ±0.07). These results suggest that such texture features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix, enabling classification of cartilage as healthy or osteoarthritic with high accuracy.

Comparative analysis of feature extraction methods in satellite imagery

NASA Astrophysics Data System (ADS)

Karim, Shahid; Zhang, Ye; Asif, Muhammad Rizwan; Ali, Saad

2017-10-01

Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.

Dandelion Extracts Protect Human Skin Fibroblasts from UVB Damage and Cellular Senescence

PubMed Central

Yang, Yafan; Li, Shuangshuang

2015-01-01

Ultraviolet (UV) irradiation causes damage in skin by generating excessive reactive oxygen species (ROS) and induction of matrix metalloproteinases (MMPs), leading to skin photoageing. Dandelion extracts have long been used for traditional Chinese medicine and native American medicine to treat cancers, hepatitis, and digestive diseases; however, less is known on the effects of dandelion extracts in skin photoageing. Here we found that dandelion leaf and flower extracts significantly protect UVB irradiation-inhibited cell viability when added before UVB irradiation or promptly after irradiation. Dandelion leaf and flower extracts inhibited UVB irradiation-stimulated MMP activity and ROS generation. Dandelion root extracts showed less action on protecting HDFs from UVB irradiation-induced MMP activity, ROS generation, and cell death. Furthermore, dandelion leaf and flower but not root extracts stimulated glutathione generation and glutathione reductase mRNA expression in the presence or absence of UVB irradiation. We also found that dandelion leaf and flower extracts help absorb UVB irradiation. In addition, dandelion extracts significantly protected HDFs from H2O2-induced cellular senescence. In conclusion, dandelion extracts especially leaf and flower extracts are potent protective agents against UVB damage and H2O2-induced cellular senescence in HDFs by suppressing ROS generation and MMP activities and helping UVB absorption. PMID:26576225

Zinc Sulphide Overlayer Two-Dimensional Photonic Crystal for Enhanced Extraction of Light from a Micro Cavity Light-Emitting Diode

NASA Astrophysics Data System (ADS)

Mastro, Michael A.; Kim, Chul Soo; Kim, Mijin; Caldwell, Josh; Holm, Ron T.; Vurgaftman, Igor; Kim, Jihyun; Eddy, Charles R., Jr.; Meyer, Jerry R.

2008-10-01

A two-dimensional (2D) ZnS photonic crystal was deposited on the surface of a one-dimensional (1D) III-nitride micro cavity light-emitting diode (LED), to intermix the light extraction features of both structures (1D+2D). The deposition of an ideal micro-cavity optical thickness of ≈λ/2 is impractical for III-nitride LEDs, and in realistic multi-mode devices a large fraction of the light is lost to internal refraction as guided light. Therefore, a 2D photonic crystal on the surface of the LED was used to diffract and thus redirect this guided light out of the semiconductor over several hundred microns. Additionally, the employment of a post-epitaxy ZnS 2D photonic crystal avoided the typical etching into the GaN:Mg contact layer, a procedure which can cause damage to the near surface.

Automatic extraction of planetary image features

NASA Technical Reports Server (NTRS)

LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

2013-01-01

A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

Plantar fascia segmentation and thickness estimation in ultrasound images.

PubMed

Boussouar, Abdelhafid; Meziane, Farid; Crofts, Gillian

2017-03-01

Ultrasound (US) imaging offers significant potential in diagnosis of plantar fascia (PF) injury and monitoring treatment. In particular US imaging has been shown to be reliable in foot and ankle assessment and offers a real-time effective imaging technique that is able to reliably confirm structural changes, such as thickening, and identify changes in the internal echo structure associated with diseased or damaged tissue. Despite the advantages of US imaging, images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. It is therefore a requirement to devise a system that allows better and easier interpretation of PF ultrasound images during diagnosis. This study proposes an automatic segmentation approach which for the first time extracts ultrasound data to estimate size across three sections of the PF (rearfoot, midfoot and forefoot). This segmentation method uses artificial neural network module (ANN) in order to classify small overlapping patches as belonging or not-belonging to the region of interest (ROI) of the PF tissue. Features ranking and selection techniques were performed as a post-processing step for features extraction to reduce the dimension and number of the extracted features. The trained ANN classifies the image overlapping patches into PF and non-PF tissue, and then it is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and area-length calculation algorithms. This new approach is capable of accurately segmenting the PF region, differentiating it from surrounding tissues and estimating its thickness. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Audio Watermark Embedding Technique Applying Auditory Stream Segregation: "G-encoder Mark" Able to Be Extracted by Mobile Phone

NASA Astrophysics Data System (ADS)

Modegi, Toshio

We are developing audio watermarking techniques which enable extraction of embedded data by cell phones. For that we have to embed data onto frequency ranges, where our auditory response is prominent, therefore data embedding will cause much auditory noises. Previously we have proposed applying a two-channel stereo play-back feature, where noises generated by a data embedded left-channel signal will be reduced by the other right-channel signal. However, this proposal has practical problems of restricting extracting terminal location. In this paper, we propose synthesizing the noise reducing right-channel signal with the left-signal and reduces noises completely by generating an auditory stream segregation phenomenon to users. This newly proposed makes the noise reducing right-channel signal unnecessary and supports monaural play-back operations. Moreover, we propose a wide-band embedding method causing dual auditory stream segregation phenomena, which enables data embedding on whole public phone frequency ranges and stable extractions with 3-G mobile phones. From these proposals, extraction precisions become higher than those by the previously proposed method whereas the quality damages of embedded signals become smaller. In this paper we present an abstract of our newly proposed method and experimental results comparing with those by the previously proposed method.

Active sensors for health monitoring of aging aerospace structures

NASA Astrophysics Data System (ADS)

Giurgiutiu, Victor; Redmond, James M.; Roach, Dennis P.; Rackow, Kirk

2000-06-01

A project to develop non-intrusive active sensors that can be applied on existing aging aerospace structures for monitoring the onset and progress of structural damage (fatigue cracks and corrosion) is presented. The state of the art in active sensors structural health monitoring and damage detection is reviewed. Methods based on (a) elastic wave propagation and (b) electro-mechanical (E/M) impedance technique are cited and briefly discussed. The instrumentation of these specimens with piezoelectric active sensors is illustrated. The main detection strategies (E/M impedance for local area detection and wave propagation for wide area interrogation) are discussed. The signal processing and damage interpretation algorithms are tuned to the specific structural interrogation method used. In the high frequency E/M impedance approach, pattern recognition methods are used to compare impedance signatures taken at various time intervals and to identify damage presence and progression from the change in these signatures. In the wave propagation approach, the acousto- ultrasonic methods identifying additional reflection generated from the damage site and changes in transmission velocity and phase are used. Both approaches benefit from the use of artificial intelligence neural networks algorithms that can extract damage features based on a learning process. Design and fabrication of a set of structural specimens representative of aging aerospace structures is presented. Three built-up specimens, (pristine, with cracks, and with corrosion damage) are used. The specimen instrumentation with active sensors fabricated at the University of South Carolina is illustrated. Preliminary results obtained with the E/M impedance method on pristine and cracked specimens are presented.

Effects of ionizing radiation on bio-active plant extracts useful for preventing oxidative damages.

PubMed

Mulinacci, Nadia; Valletta, Alessio; Pasqualetti, Valentina; Innocenti, Marzia; Giuliani, Camilla; Bellumori, Maria; De Angelis, Giulia; Carnevale, Alessia; Locato, Vittoria; Di Venanzio, Cristina; De Gara, Laura; Pasqua, Gabriella

2018-04-02

Humans are exposed to ionizing radiations in medical radiodiagnosis and radiotherapy that cause oxidative damages and degenerative diseases. Airplane pilots, and even more astronauts, are exposed to a variety of potentially harmful factors, including cosmic radiations. Among the phytochemicals, phenols are particularly efficient in countering the oxidative stress. In the present study, different extracts obtained from plant food, plant by-products and dietary supplements, have been compared for their antioxidant properties before and after irradiation of 140 cGy, a dose absorbed during a hypothetical stay of three years in the space. All the dry extracts, characterized in terms of vitamin C and phenolic content, remained chemically unaltered and maintained their antioxidant capability after irradiation. Our results suggest the potential use of these extracts as nutraceuticals to protect humans from oxidative damages, even when these extracts must be stored in an environment exposed to cosmic radiations as in a space station.

Data mining framework for identification of myocardial infarction stages in ultrasound: A hybrid feature extraction paradigm (PART 2).

PubMed

Sudarshan, Vidya K; Acharya, U Rajendra; Ng, E Y K; Tan, Ru San; Chou, Siaw Meng; Ghista, Dhanjoo N

2016-04-01

Early expansion of infarcted zone after Acute Myocardial Infarction (AMI) has serious short and long-term consequences and contributes to increased mortality. Thus, identification of moderate and severe phases of AMI before leading to other catastrophic post-MI medical condition is most important for aggressive treatment and management. Advanced image processing techniques together with robust classifier using two-dimensional (2D) echocardiograms may aid for automated classification of the extent of infarcted myocardium. Therefore, this paper proposes novel algorithms namely Curvelet Transform (CT) and Local Configuration Pattern (LCP) for an automated detection of normal, moderately infarcted and severely infarcted myocardium using 2D echocardiograms. The methodology extracts the LCP features from CT coefficients of echocardiograms. The obtained features are subjected to Marginal Fisher Analysis (MFA) dimensionality reduction technique followed by fuzzy entropy based ranking method. Different classifiers are used to differentiate ranked features into three classes normal, moderate and severely infarcted based on the extent of damage to myocardium. The developed algorithm has achieved an accuracy of 98.99%, sensitivity of 98.48% and specificity of 100% for Support Vector Machine (SVM) classifier using only six features. Furthermore, we have developed an integrated index called Myocardial Infarction Risk Index (MIRI) to detect the normal, moderately and severely infarcted myocardium using a single number. The proposed system may aid the clinicians in faster identification and quantification of the extent of infarcted myocardium using 2D echocardiogram. This system may also aid in identifying the person at risk of developing heart failure based on the extent of infarcted myocardium. Copyright © 2016 Elsevier Ltd. All rights reserved.

The comparison of in vivo antigenotoxic and antioxidative capacity of two propylene glycol extracts of Calendula officinalis (marigold) and vitamin E in young growing pigs.

PubMed

Frankic, T; Salobir, K; Salobir, J

2009-12-01

The objective of the study was to evaluate the protective effect of Calendula officinalis propylene glycol extracts against oxidative DNA damage and lipid peroxidation induced by high polyunsaturated fatty acid (PUFA) intake in young growing pigs. Forty young growing pigs were assigned to five treatment groups: control; oil (linseed oil supplementation); C. officinalis 1 and 2 groups (linseed oil plus 3 ml/day of C. officinalis propylene glycol extracts); and vitamin E group (linseed oil plus 100 mg/kg of vitamin E). Lymphocyte DNA fragmentation and 24-h urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) excretion were measured to determine DNA damage. Lipid peroxidation was studied by analysing plasma and urine malondialdehyde (MDA), and urine isoprostane concentrations (iPF2α-VI), total antioxidant status of plasma and glutathione peroxidase (GPx) assays. C. officinalis 1 (extract from petals) effectively protected DNA from oxidative damage. It indicated a numerical trend towards the reduction of plasma MDA and urinary iPF2α-VI excretion. Its effect was comparable with that of vitamin E. C. officinalis 2 (extract from flower tops) showed less antioxidant potential than the extract from petals. We can conclude that the amount of C. officinalis extracts proposed for internal use by traditional medicine protects the organism against DNA damage induced by high PUFA intake.

Reduction of hydrogen peroxide-induced erythrocyte damage by Carica papaya leaf extract

PubMed Central

Okoko, Tebekeme; Ere, Diepreye

2012-01-01

Objective To investigate the in vitro antioxidant potential of Carica papaya (C. papaya) leaf extract and its effect on hydrogen peroxide-induced erythrocyte damage assessed by haemolysis and lipid peroxidation. Methods Hydroxyl radical scavenging activities, hydrogen ion scavenging activity, metal chelating activity, and the ferrous ion reducing ability were assessed as antioxidant indices. In the other experiment, human erythrocytes were treated with hydrogen peroxide to induce erythrocyte damage. The extract (at various concentrations) was subsequently incubated with the erythrocytes and later analysed for haemolysis and lipid peroxidation as indices for erythrocyte damage. Results Preliminary investigation of the extract showed that the leaf possessed significant antioxidant and free radical scavenging abilities using in vitro models in a concentration dependent manner (P<0.05). The extract also reduced hydrogen peroxide induced erythrocyte haemolysis and lipid peroxidation significantly when compared with ascorbic acid (P<0.05). The IC50 values were 7.33 mg/mL and 1.58 mg/mL for inhibition of haemolysis and lipid peroxidation, respectively. In all cases, ascorbic acid (the reference antioxidant) possessed higher activity than the extract. Conclusions The findings show that C. papaya leaves possess significant bioactive potential which is attributed to the phytochemicals which act in synergy. Thus, the leaves can be exploited for pharmaceutical and nutritional purposes. PMID:23569948

Reduction of hydrogen peroxide-induced erythrocyte damage by Carica papaya leaf extract.

PubMed

Okoko, Tebekeme; Ere, Diepreye

2012-06-01

To investigate the in vitro antioxidant potential of Carica papaya (C. papaya) leaf extract and its effect on hydrogen peroxide-induced erythrocyte damage assessed by haemolysis and lipid peroxidation. Hydroxyl radical scavenging activities, hydrogen ion scavenging activity, metal chelating activity, and the ferrous ion reducing ability were assessed as antioxidant indices. In the other experiment, human erythrocytes were treated with hydrogen peroxide to induce erythrocyte damage. The extract (at various concentrations) was subsequently incubated with the erythrocytes and later analysed for haemolysis and lipid peroxidation as indices for erythrocyte damage. Preliminary investigation of the extract showed that the leaf possessed significant antioxidant and free radical scavenging abilities using in vitro models in a concentration dependent manner (P<0.05). The extract also reduced hydrogen peroxide induced erythrocyte haemolysis and lipid peroxidation significantly when compared with ascorbic acid (P<0.05). The IC50 values were 7.33 mg/mL and 1.58 mg/mL for inhibition of haemolysis and lipid peroxidation, respectively. In all cases, ascorbic acid (the reference antioxidant) possessed higher activity than the extract. The findings show that C. papaya leaves possess significant bioactive potential which is attributed to the phytochemicals which act in synergy. Thus, the leaves can be exploited for pharmaceutical and nutritional purposes.

The Effect of a Grape Seed Extract on Radiation-Induced DNA Damage in Human Lymphocytes

NASA Astrophysics Data System (ADS)

Dicu, Tiberius; Postescu, Ion D.; Foriş, Vasile; Brie, Ioana; Fischer-Fodor, Eva; Cernea, Valentin; Moldovan, Mircea; Cosma, Constantin

2009-05-01

Plant-derived antioxidants due to their phenolic compounds content are reported as potential candidates for reducing the levels of oxidative stress in living organisms. Grape seed extracts are very potent antioxidants and exhibit numerous interesting pharmacologic activities. Hydroethanolic (50/50, v/v) standardized extract was obtained from red grape seed (Vitis vinifera, variety Burgund Mare—BM). The total polyphenols content was evaluated by Folin-Ciocalteu procedure and expressed as μEq Gallic Acid/ml. The aim of this study was to evaluate the potential antioxidant effects of different concentrations of BM extract against 60Co γ-rays induced DNA damage in human lymphocytes. Samples of human lymphocytes were incubated with BM extract (12.5, 25.0 and 37.5 μEq GA/ml, respectively) administered at 30 minutes before in vitro irradiation with γ-rays (2 Gy). The DNA damage and repair in lymphocytes were evaluated using alkaline comet assay. Using the lesion score, the radiation-induced DNA damage was found to be significantly different (p<0.05) from control, both in the absence and presence of BM extract (except the lymphocytes treated with 37.5 μEq GA/ml BM extract). DNA repair analyzed by incubating the irradiated cells at 37° C and 5% CO2 atmosphere for 2 h, indicated a significant difference (p<0.05) in the lymphocytes group treated with 25.0 μEq GA/ml BM extract, immediately and two hours after irradiation. These results suggest radioprotective effects after treatment with BM extract in human lymphocytes.

Structural health management of aerospace hotspots under fatigue loading

NASA Astrophysics Data System (ADS)

Soni, Sunilkumar

Sustainability and life-cycle assessments of aerospace systems, such as aircraft structures and propulsion systems, represent growing challenges in engineering. Hence, there has been an increasing demand in using structural health monitoring (SHM) techniques for continuous monitoring of these systems in an effort to improve safety and reduce maintenance costs. The current research is part of an ongoing multidisciplinary effort to develop a robust SHM framework resulting in improved models for damage-state awareness and life prediction, and enhancing capability of future aircraft systems. Lug joints, a typical structural hotspot, were chosen as the test article for the current study. The thesis focuses on integrated SHM techniques for damage detection and characterization in lug joints. Piezoelectric wafer sensors (PZTs) are used to generate guided Lamb waves as they can be easily used for onboard applications. Sensor placement in certain regions of a structural component is not feasible due to the inaccessibility of the area to be monitored. Therefore, a virtual sensing concept is introduced to acquire sensor data from finite element (FE) models. A full three dimensional FE analysis of lug joints with piezoelectric transducers, accounting for piezoelectrical-mechanical coupling, was performed in Abaqus and the sensor signals were simulated. These modeled sensors are called virtual sensors. A combination of real data from PZTs and virtual sensing data from FE analysis is used to monitor and detect fatigue damage in aluminum lug joints. Experiments were conducted on lug joints under fatigue loads and sensor signals collected were used to validate the simulated sensor response. An optimal sensor placement methodology for lug joints is developed based on a detection theory framework to maximize the detection rate and minimize the false alarm rate. The placement technique is such that the sensor features can be directly correlated to damage. The technique accounts for a number of factors, such as actuation frequency and strength, minimum damage size, damage detection scheme, material damping, signal to noise ratio and sensing radius. Advanced information processing methodologies are discussed for damage diagnosis. A new, instantaneous approach for damage detection, localization and quantification is proposed for applications to practical problems associated with changes in reference states under different environmental and operational conditions. Such an approach improves feature extraction for state awareness, resulting in robust life prediction capabilities.

Fatigue crack detection by nonlinear spectral correlation with a wideband input

NASA Astrophysics Data System (ADS)

Liu, Peipei; Sohn, Hoon

2017-04-01

Due to crack-induced nonlinearity, ultrasonic wave can distort, create accompanying harmonics, multiply waves of different frequencies, and, under resonance conditions, change resonance frequencies as a function of driving amplitude. All these nonlinear ultrasonic features have been widely studied and proved capable of detecting fatigue crack at its very early stage. However, in noisy environment, the nonlinear features might be drown in the noise, therefore it is difficult to extract those features using a conventional spectral density function. In this study, nonlinear spectral correlation is defined as a new nonlinear feature, which considers not only nonlinear modulations in ultrasonic waves but also spectral correlation between the nonlinear modulations. The proposed nonlinear feature is associated with the following two advantages: (1) stationary noise in the ultrasonic waves has little effect on nonlinear spectral correlation; and (2) the contrast of nonlinear spectral correlation between damage and intact conditions can be enhanced simply by using a wideband input. To validate the proposed nonlinear feature, micro fatigue cracks are introduced to aluminum plates by repeated tensile loading, and the experiment is conducted using surface-mounted piezoelectric transducers for ultrasonic wave generation and measurement. The experimental results confirm that the nonlinear spectral correlation can successfully detect fatigue crack with a higher sensitivity than the classical nonlinear coefficient.

ECG Identification System Using Neural Network with Global and Local Features

ERIC Educational Resources Information Center

Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

2016-01-01

This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

The behavioural response of adult Petromyzon marinus to damage-released alarm and predator cues.

PubMed

Imre, I; Di Rocco, R T; Belanger, C F; Brown, G E; Johnson, N S

2014-05-01

Using semi-natural enclosures, this study investigated (1) whether adult sea lamprey Petromyzon marinus show avoidance of damage-released conspecific cues, damage-released heterospecific cues and predator cues and (2) whether this is a general response to injured heterospecific fishes or a specific response to injured P. marinus. Ten replicate groups of 10 adult P. marinus, separated by sex, were exposed to one of the following nine stimuli: deionized water (control), extracts prepared from adult P. marinus, decayed adult P. marinus (conspecific stimuli), sympatric white sucker Catostomus commersonii, Amazon sailfin catfish Pterygoplichthys pardalis (heterospecific stimuli), 2-phenylethylamine (PEA HCl) solution, northern water snake Nerodia sipedon washing, human saliva (predator cues) and an adult P. marinus extract and human saliva combination (a damage-released conspecific cue and a predator cue). Adult P. marinus showed a significant avoidance response to the adult P. marinus extract as well as to C. commersonii, human saliva, PEA and the adult P. marinus extract and human saliva combination. For mobile P. marinus, the N. sipedon washing induced behaviour consistent with predator inspection. Exposure to the P. pardalis extract did not induce a significant avoidance response during the stimulus release period. Mobile adult female P. marinus showed a stronger avoidance behaviour than mobile adult male P. marinus in response to the adult P. marinus extract and the adult P. marinus extract and human saliva combination. The findings support the continued investigation of natural damage-released alarm cue and predator-based repellents for the behavioural manipulation of P. marinus populations in the Laurentian Great Lakes. © 2014 The Fisheries Society of the British Isles.

Effects of a Mangifera indica L. stem bark extract and mangiferin on radiation-induced DNA damage in human lymphocytes and lymphoblastoid cells.

PubMed

Rodeiro, I; Delgado, R; Garrido, G

2014-02-01

Mangifera indica L. (mango) stem bark aqueous extract (MSBE) that has antioxidant, anti-inflammatory and immunomodulatory properties, can be obtained in Cuba. It is rich in polyphenols, where mangiferin is the main component. In this study, we have tested DNA damage and protection effects of MSBE and mangiferin on primary human lymphocytes and lymphoblastoid cells. Cell suspensions were incubated with the products (50-1000 μg/ml) for experiments on damage induction, and evaluation of any potential protective effects (5-100 μg/ml) for 60 min at 37 °C. Irradiation was performed using a γ-ray source, absorbed dose 5 Gy. At the end of exposure, DNA damage, protection and repair processes were evaluated using the comet assay. MSBE (100-1000 μg/ml) induced DNA damage in a concentration dependent manner in both cell types tested, primary cells being more sensitive. Mangiferin (200 μg/ml) only induced light DNA damage at higher concentrations. DNA repair capacity was not affected after MSBE or mangiferin exposure. On the other hand, MSBE (25 and 50 μg/ml) and mangiferin (5-25 ug/ml) protected against gamma radiation-induced DNA damage. These results show MSBE has protector or harmful effects on DNA in vitro depending on the experimental conditions, which suggest that the extract could be acting as an antioxidant or pro-oxidant product. Mangiferin was involved in protective effects of the extract. © 2013 John Wiley & Sons Ltd.

Hepatoprotective activity of sea cucumber Phyllophorus sp. extract in carp (Cyprinus carpio)

NASA Astrophysics Data System (ADS)

Sulmartiwi, Laksmi; Triastuti, Juni; Andriyono, Sapto; Umami, Mardiah Rahma

2017-02-01

Many procedures continuously in aquaculture and scientific research like tagging and vaccinating cause pain, involving damaging tissue and also cause stress responses in fish. Stress responses in fish influence liver because liver have vital role to supply energy and metabolism. Histology alteration in liver is caused by stress response like changes of vacuolation hepatocyte and characteristic colour. Triterpenoid was known had hepatoprotective activity. One of marine organism contained triterpenoid was sea cucumber. Result of research showed that liver tissue in fish with injected acetic acid 5 % (in upper lip) as pain stimulus have histopathology damages such as pyknosis (medium damage level) and oedema (heavy damage level) after 8 hour injection. Injected Lidocaine 1mg/fish as analgesic drug have histopathology damages such as oedema (heavy damages level), necrosis and pyknosis (low damages level). Injected acetic acid 5 % (in upper lip) and ethanolic extract of sea cucumber Phyllophorus sp. dose 5 mg/50 gr body weight shown histopathology damages such as necrosis, edema (medium damage level) and pyknosis (low damage level).

Anti-oxidative protection against iron overload-induced liver damage in mice by Cajanus cajan (L.) Millsp. leaf extract.

PubMed

Sarkar, Rhitajit; Hazra, Bibhabasu; Mandal, Nripendranath

2013-02-01

In view of the contribution of iron deposition in the oxidative pathologic process of liver disease, the potential of 70% methanolic extract of C. cajan leaf (CLME) towards antioxidative protection against iron-overload-induced liver damage in mice has been investigated. DPPH radical scavenging and protection of Fenton reaction induced DNA damage was conducted in vitro. Post oral administration of CLME to iron overloaded mice, the levels of antioxidant and serum enzymes, hepatic iron, serum ferritin, lipid peroxidation, and protein carbonyl and hydroxyproline contents were measured, in comparison to deferasirox treated mice. Oral treatment of the plant extract effectively lowered the elevated levels of liver iron, lipid peroxidation, protein carbonyl and hydroxyproline. There was notable increment in the dropped levels of hepatic antioxidants. The dosage of the plant extract not only made the levels of serum enzymes approach normal value, but also counteracted the overwhelmed serum ferritin level. The in vitro studies indicated potential antioxidant activity of CLME. The histopathological observations also substantiated the ameliorative function of the plant extract. Accordingly, it is suggested that Cajanus cajan leaf can be a useful herbal remedy to suppress oxidative damage caused by iron overload.

Protective effects of grape stem extract against UVB-induced damage in C57BL mice skin.

PubMed

Che, Denis Nchang; Xie, Guang Hua; Cho, Byoung Ok; Shin, Jae Young; Kang, Hyun Ju; Jang, Seon Il

2017-08-01

Humans have become exposed to another form of a trait which is ultraviolet B (UVB) radiation reaching the earth's surface. This has become a major source of oxidative stress that ultimately leads to inflammation, DNA damage, photoaging and pigmentation disorders etc. Although several studies have shown the photo-protective role of different grape parts like the fruits and seeds, little or no data demonstrating the in vivo photo-protective role of grape stem, which is the most discarded part of the grape are available. We evaluated the protective influence of grape stem extract against UVB-induced oxidative damage in C57BL mice characterized by epidermal hyperplasia, pigmentation, collagen degradation and inflammation. Grape stem extract was administered topically 1week before UVB irradiation (120mJ/cm 2 ) and continued until the termination of the experiment. A group of non-irradiated mice and a group of irradiated mice topically administered with propylene were used as a negative and positive control. Epidermal thickness, pigmentation, erythema, mast cell and neutrophil infiltration, collagen degradation and COX-2, Nrf2, and HO-1 expressions were evaluated. Grape stem extract markedly recovered skin damage induced by the UVB radiation through the prevention of epidermal hyperplasia, pigmentation, erythema, mast cell and neutrophil infiltrations, collagen degradation and COX-2, Nrf2, and HO-1 expressions. Our study demonstrated for the first time in C57BL mice that grape stem extract reduces UVB-induced oxidative damage and hence can play a protective role in skin photo-damage. Copyright © 2017. Published by Elsevier B.V.

Repair-Resistant DNA Lesions

PubMed Central

2017-01-01

The eukaryotic global genomic nucleotide excision repair (GG-NER) pathway is the major mechanism that removes most bulky and some nonbulky lesions from cellular DNA. There is growing evidence that certain DNA lesions are repaired slowly or are entirely resistant to repair in cells, tissues, and in cell extract model assay systems. It is well established that the eukaryotic DNA lesion-sensing proteins do not detect the damaged nucleotide, but recognize the distortions/destabilizations in the native DNA structure caused by the damaged nucleotides. In this article, the nature of the structural features of certain bulky DNA lesions that render them resistant to NER, or cause them to be repaired slowly, is compared to that of those that are good-to-excellent NER substrates. Understanding the structural features that distinguish NER-resistant DNA lesions from good NER substrates may be useful for interpreting the biological significance of biomarkers of exposure of human populations to genotoxic environmental chemicals. NER-resistant lesions can survive to replication and cause mutations that can initiate cancer and other diseases. Furthermore, NER diminishes the efficacy of certain chemotherapeutic drugs, and the design of more potent pharmaceuticals that resist repair can be advanced through a better understanding of the structural properties of DNA lesions that engender repair-resistance. PMID:28750166

A Relevance Vector Machine-Based Approach with Application to Oil Sand Pump Prognostics

PubMed Central

Hu, Jinfei; Tse, Peter W.

2013-01-01

Oil sand pumps are widely used in the mining industry for the delivery of mixtures of abrasive solids and liquids. Because they operate under highly adverse conditions, these pumps usually experience significant wear. Consequently, equipment owners are quite often forced to invest substantially in system maintenance to avoid unscheduled downtime. In this study, an approach combining relevance vector machines (RVMs) with a sum of two exponential functions was developed to predict the remaining useful life (RUL) of field pump impellers. To handle field vibration data, a novel feature extracting process was proposed to arrive at a feature varying with the development of damage in the pump impellers. A case study involving two field datasets demonstrated the effectiveness of the developed method. Compared with standalone exponential fitting, the proposed RVM-based model was much better able to predict the remaining useful life of pump impellers. PMID:24051527

A relevance vector machine-based approach with application to oil sand pump prognostics.

PubMed

Hu, Jinfei; Tse, Peter W

2013-09-18

Oil sand pumps are widely used in the mining industry for the delivery of mixtures of abrasive solids and liquids. Because they operate under highly adverse conditions, these pumps usually experience significant wear. Consequently, equipment owners are quite often forced to invest substantially in system maintenance to avoid unscheduled downtime. In this study, an approach combining relevance vector machines (RVMs) with a sum of two exponential functions was developed to predict the remaining useful life (RUL) of field pump impellers. To handle field vibration data, a novel feature extracting process was proposed to arrive at a feature varying with the development of damage in the pump impellers. A case study involving two field datasets demonstrated the effectiveness of the developed method. Compared with standalone exponential fitting, the proposed RVM-based model was much better able to predict the remaining useful life of pump impellers.

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations.

PubMed

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

2016-05-31

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time-frequency domains. The key features are selected based on Pearson's Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL.

Coupling Sensing Hardware with Data Interrogation Software for Structural Health Monitoring

DOE PAGES

Farrar, Charles R.; Allen, David W.; Park, Gyuhae; ...

2006-01-01

The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). The authors' approach is to address the SHM problem in the context of a statistical pattern recognition paradigm. In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition and Cleansing, (3) Feature Extraction and Data Compression, and (4) Statistical Model Development for Feature Discrimination. These processes must be implemented through hardware or software and, in general, some combination of these two approaches will be used. This paper will discussmore » each portion of the SHM process with particular emphasis on the coupling of a general purpose data interrogation software package for structural health monitoring with a modular wireless sensing and processing platform. More specifically, this paper will address the need to take an integrated hardware/software approach to developing SHM solutions.« less

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

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

Wang Xiaojia; Mao Qirong; Zhan Yongzhao

There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions.more » The experiments show that this method can improve the recognition rate and the time of feature extraction.« less

Protective effect of aged garlic extract on the small intestinal damage of rats induced by methotrexate administration.

PubMed

Horie, T; Matsumoto, H; Kasagi, M; Sugiyama, A; Kikuchi, M; Karasawa, C; Awazu, S; Itakura, Y; Fuwa, T

1999-08-01

The methotrexate (MTX) administration to rats causes the damage of small intestine. The small intestinal damage was evaluated by measuring the intestinal permeability of the poorly absorbable compound, fluorescein isothiocyanate (FITC)-labeled dextran (average molecular weight, 4,400) (FD-4) using the in vitro everted intestine technique and by determining the FD-4 that appeared in plasma using the in situ closed loop intestine technique. The MTX administration to rats fed with the standard laboratory diet increased the small intestinal permeability of FD-4 due to the damage of the small intestine. Interestingly, the permeability of FD-4, when MTX was administered to rats fed with the aged garlic extract containing diet, was depressed almost to the level of control rats without the MTX treatment. The present study showed that the aged garlic extract protected the small intestine from the damage induced by the action of MTX on the crypt cells.

Image segmentation-based robust feature extraction for color image watermarking

NASA Astrophysics Data System (ADS)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

NASA Astrophysics Data System (ADS)

Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

2015-07-01

Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

Zingiber zerumbet L. (Smith) extract alleviates the ethanol-induced brain damage via its antioxidant activity.

PubMed

Hamid, Asmah; Ibrahim, Farah Wahida; Ming, Teoh Hooi; Nasrom, Mohd Nazir; Eusoff, Norelina; Husain, Khairana; Abdul Latif, Mazlyzam

2018-03-20

Zingiber zerumbet (L.) Smith belongs to the Zingiberaceae family that is widely distributed throughout the tropics, particularly in Southeast Asia. It is locally known as 'Lempoyang' and traditionally used to treat fever, constipation and to relieve pain. It is also known to possess antioxidant and anti-inflammatory activities. Based on these antioxidant and anti-inflammatory activities, this study was conducted to investigate the effects of ethyl-acetate extract of Z. zerumbet rhizomes against ethanol-induced brain damage in male Wistar rats. Twenty-four male Wistar rats were divided into four groups which consist of normal, 1.8 g/kg ethanol (40% v/v), 200 mg/kg Z. zerumbet extract plus ethanol and 400 mg/kg Z. zerumbet plus ethanol. The extract of Z. zerumbet was given once daily by oral gavage, 30 min prior to ethanol exposure via intraperitoneal route for 14 consecutive days. The rats were then sacrificed. Blood and brain homogenate were subjected to biochemical tests and part of the brain tissue was sectioned for histological analysis. Treatment with ethyl-acetate Z. zerumbet extract at 200 mg/kg and 400 mg/kg significantly reduced the level of malondialdehyde (MDA) and protein carbonyl (p < 0.05) in the brain homogenate. Both doses of extracts also significantly increased the level of serum superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) activities as well as glutathione (GSH) level (p < 0.05). However, administration of ethyl-acetate Z. zerumbet extract at 400 mg/kg showed better protective effects on the ethanol-induced brain damage as shown with higher levels of SOD, CAT, GPx and GSH in the brain homogenate as compared to 200 mg/kg dose. Histological observation of the cerebellum and cerebral cortex showed that the extract prevented the loss of Purkinje cells and retained the number and the shape of the cells. Ethyl-acetate extract of Z. zerumbet has protective effects against ethanol-induced brain damage and this is mediated through its antioxidant properties. Z. zerumbet extract protects against ethanol-induced brain damage via its antioxidant properties.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Quantitative verification of thin-film polyvinylidene fluoride (PVDF) transducer array performance up to 60 °C

NASA Astrophysics Data System (ADS)

Hakoda, Christopher; Ren, Baiyang; Lissenden, Cliff J.; Rose, Joseph L.

2017-02-01

Thin-film PVDF (polyvinylidene fluoride) transducers are appealing as low cost, light weight, durable, and flexible sensors for structural health monitoring applications in aircraft structures. However, due to the relatively low Curie temperature of PVDF, there is a concern that it's performance will drop below acceptable levels during elevated-temperature operating conditions. To verify acceptable performance in these environmental operating conditions, temperature history data were collected between 23-60 °C. The effect of temperature on the thin-film PVDF was investigated and a temperature-independent damage feature was assessed. The temperature dependence of the signal's peak amplitude was investigated in both the time domain and the spectral domain to get two damage features. It was found that the measurement of the incident guided wave by the thin-film PVDF transducer had a temperature dependence that varied with frequency. A third damage feature, the mode ratio, was also calculated in the spectral domain with the goal of defining a damage feature that is temperature independent. A comparison of how well these damage features performed when used to identify a notch in an aluminum plate was made using receiver operating characteristic curves and their respective area under the curve values. This result demonstrated that a temperature-independent damage feature can be calculated, to some degree, by using a mode ratio between two modes of similar temperature dependence.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Ketoconazole-induced testicular damage in rats reduced by Gentiana extract.

PubMed

Amin, Amr

2008-04-01

Ketoconazole (KET) is an antifungal drug with a broad spectrum of activity that also induces reproductive toxicity in humans and animals. The protective effect of Gentiana (GEN) extract (Gentiana lutea) against KET-induced testicular damage was evaluated in male Wistar rats. GEN extract was administered orally (1g/kgbwt/day) for 26 days. Three weeks after extract administration, KET was co-administered intraperitoneally at a dose of 100mg/kg once a day for 5 days. KET-induced reproductive toxicity was associated with clear reductions of the weights of testes and epididymides, sperm indices and serum testosterone levels. KET also induced severe testicular histopathological lesions such as degeneration of the seminiferous tubules and depletion of germ cells. In addition, marked oxidative damage to testicular lipids and alterations of natural antioxidants (catalase (CAT) and superoxide dismutase (SOD)) were reported in association with KET toxicity. Most of the KET-induced effects were greatly decreased with the concomitant application of GEN extract. This study suggests a protective role of GEN extract that could be attributed to its antioxidant properties.

Camera pose refinement by matching uncertain 3D building models with thermal infrared image sequences for high quality texture extraction

NASA Astrophysics Data System (ADS)

Iwaszczuk, Dorota; Stilla, Uwe

2017-10-01

Thermal infrared (TIR) images are often used to picture damaged and weak spots in the insulation of the building hull, which is widely used in thermal inspections of buildings. Such inspection in large-scale areas can be carried out by combining TIR imagery and 3D building models. This combination can be achieved via texture mapping. Automation of texture mapping avoids time consuming imaging and manually analyzing each face independently. It also provides a spatial reference for façade structures extracted in the thermal textures. In order to capture all faces, including the roofs, façades, and façades in the inner courtyard, an oblique looking camera mounted on a flying platform is used. Direct geo-referencing is usually not sufficient for precise texture extraction. In addition, 3D building models have also uncertain geometry. In this paper, therefore, methodology for co-registration of uncertain 3D building models with airborne oblique view images is presented. For this purpose, a line-based model-to-image matching is developed, in which the uncertainties of the 3D building model, as well as of the image features are considered. Matched linear features are used for the refinement of the exterior orientation parameters of the camera in order to ensure optimal co-registration. Moreover, this study investigates whether line tracking through the image sequence supports the matching. The accuracy of the extraction and the quality of the textures are assessed. For this purpose, appropriate quality measures are developed. The tests showed good results on co-registration, particularly in cases where tracking between the neighboring frames had been applied.

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

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

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

Hepatoprotective activity of Psidium guajava Linn. leaf extract.

PubMed

Roy, Chanchal K; Kamath, Jagadish V; Asad, Mohammed

2006-04-01

The study was designed to evaluate the hepatoprotective activity of P. guajava in acute experimental liver injury induced by carbon tetrachloride, paracetamol or thioacetamide and chronic liver damage induced by carbon tetrachloride. The effects observed were compared with a known hepatoprotective agent, silymarin. In the acute liver damage induced by different hepatotoxins, P. guajava leaf extracts (250 and 500mg/kg, po) significantly reduced the elevated serum levels of aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase and bilirubin. The higher dose of the extract (500 mg/kg, po) prevented the increase in liver weight when compared to hepatoxin treated control, while the lower dose was ineffective except in the paracetamol induced liver damage. In the chronic liver injury induced by carbon tetrachloride, the higher dose (500 mg/kg, po) of P. guajava leaf extract was found to be more effective than the lower dose (250 mg/kg, po). Histological examination of the liver tissues supported the hepatoprotection. It is concluded that the aqueous extract of leaves of guava plant possesses good hepatoprotective activity.

Effects of natto extract on endothelial injury in a rat model.

PubMed

Chang, Chin-Hsien; Chen, Kuo-Ti; Lee, Tsong-Hai; Wang, Chao-Hung; Kuo, Yi-Wen; Chiu, Ya-Huang; Hsieh, Ching-Liang; Wu, Chang-Jer; Chang, Yen-Lin

2010-12-01

Vascular endothelial damage has been found to be associated with thrombus formation, which is considered to be a risk factor for cardiovascular disease. A diet of natto leads to a low prevalence of cardiovascular disease. The aim of the present study was to investigate the effects of natto extract on vascular endothelia damage with exposure to laser irradiation. Endothelial damage both in vitro and in vivo was induced by irradiation of rose bengal using a DPSS green laser. Cell viability was determined by MTS assay, and the intimal thickening was verified by a histological approach. The antioxidant content of natto extract was determined for the free radical scavenging activity. Endothelial cells were injured in the presence of rose bengal irradiated in a dose-dependent manner. Natto extract exhibits high levels of antioxidant activity compared with purified natto kinase. Apoptosis of laser-injured endothelial cells was significantly reduced in the presence of natto extract. Both the natto extract and natto kinase suppressed intimal thickening in rats with endothelial injury. The present findings suggest that natto extract suppresses vessel thickening as a synergic effect attributed to its antioxidant and anti-apoptosis properties.

In vitro evaluation of antioxidants of fruit extract of Momordica charantia L. on fibroblasts and keratinocytes.

PubMed

Kumar, Ramadhar; Balaji, S; Sripriya, R; Nithya, N; Uma, T S; Sehgal, P K

2010-02-10

The antioxidant activity of the total aqueous extract (TAE) and total phenolic extract (TPE) of Momordica charantia fruits was assayed by radical-scavenging methods and cytoprotective effects on hydrogen peroxide (H(2)O(2))- and hypoxanthin-xanthin oxidase (HX-XO)-induced damage to rat cardiac fibroblasts (RCFs), NIH 3T3, and keratinocyte (A431). Cell viability was monitored by a 3-[4,5-dimethyltriazol-2-yl]-2,5-diphenyltretrazolium (MTT) assay. For fibroblasts, TPE at 200 and 300 microg/mL showed maximum and consistent cytoprotection against oxidants. The extract at 50 microg/mL also had significant and slightly protective effects on fibroblasts against H(2)O(2)- and HX-XO-induced damage, respectively. RCF was more tolerant toward the damage. For keratinocytes, a dose-dependent relationship of oxidant toxicity was only seen with H(2)O(2) but the protective action of the extract correlated with oxidant dosage. At 200 and 300 microg/mL TPE, cytoprotection was dose-dependent against oxidants. Extracts had no effect on HX-XO toxicity at 50 microg/mL. Pretreatment with both the extracts did not show any cytoprotection.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

Plasma spectrum peak extraction algorithm of laser film damage

NASA Astrophysics Data System (ADS)

Zhao, Dan; Su, Jun-hong; Xu, Jun-qi

2012-10-01

The plasma spectrometry is an emerging method to distinguish the thin-film laser damage. Laser irradiation film surface occurrence of flash, using the spectrometer receives the flash spectrum, extracting the spectral peak, and by means of the spectra of the thin-film materials and the atmosphere has determine the difference, as a standard to determine the film damage. Plasma spectrometry can eliminate the miscarriage of justice which caused by atmospheric flashes, and distinguish high accuracy. Plasma spectra extraction algorithm is the key technology of Plasma spectrometry. Firstly, data de noising and smoothing filter is introduced in this paper, and then during the peak is detecting, the data packet is proposed, and this method can increase the stability and accuracy of the spectral peak recognition. Such algorithm makes simultaneous measurement of Plasma spectrometry to detect thin film laser damage, and greatly improves work efficiency.

Delamination detection by Multi-Level Wavelet Processing of Continuous Scanning Laser Doppler Vibrometry data

NASA Astrophysics Data System (ADS)

Chiariotti, P.; Martarelli, M.; Revel, G. M.

2017-12-01

A novel non-destructive testing procedure for delamination detection based on the exploitation of the simultaneous time and spatial sampling provided by Continuous Scanning Laser Doppler Vibrometry (CSLDV) and the feature extraction capability of Multi-Level wavelet-based processing is presented in this paper. The processing procedure consists in a multi-step approach. Once the optimal mother-wavelet is selected as the one maximizing the Energy to Shannon Entropy Ratio criterion among the mother-wavelet space, a pruning operation aiming at identifying the best combination of nodes inside the full-binary tree given by Wavelet Packet Decomposition (WPD) is performed. The pruning algorithm exploits, in double step way, a measure of the randomness of the point pattern distribution on the damage map space with an analysis of the energy concentration of the wavelet coefficients on those nodes provided by the first pruning operation. A combination of the point pattern distributions provided by each node of the ensemble node set from the pruning algorithm allows for setting a Damage Reliability Index associated to the final damage map. The effectiveness of the whole approach is proven on both simulated and real test cases. A sensitivity analysis related to the influence of noise on the CSLDV signal provided to the algorithm is also discussed, showing that the processing developed is robust enough to measurement noise. The method is promising: damages are well identified on different materials and for different damage-structure varieties.

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

Low complexity feature extraction for classification of harmonic signals

NASA Astrophysics Data System (ADS)

William, Peter E.

In this dissertation, feature extraction algorithms have been developed for extraction of characteristic features from harmonic signals. The common theme for all developed algorithms is the simplicity in generating a significant set of features directly from the time domain harmonic signal. The features are a time domain representation of the composite, yet sparse, harmonic signature in the spectral domain. The algorithms are adequate for low-power unattended sensors which perform sensing, feature extraction, and classification in a standalone scenario. The first algorithm generates the characteristic features using only the duration between successive zero-crossing intervals. The second algorithm estimates the harmonics' amplitudes of the harmonic structure employing a simplified least squares method without the need to estimate the true harmonic parameters of the source signal. The third algorithm, resulting from a collaborative effort with Daniel White at the DSP Lab, University of Nebraska-Lincoln, presents an analog front end approach that utilizes a multichannel analog projection and integration to extract the sparse spectral features from the analog time domain signal. Classification is performed using a multilayer feedforward neural network. Evaluation of the proposed feature extraction algorithms for classification through the processing of several acoustic and vibration data sets (including military vehicles and rotating electric machines) with comparison to spectral features shows that, for harmonic signals, time domain features are simpler to extract and provide equivalent or improved reliability over the spectral features in both the detection probabilities and false alarm rate.

Quantitative 3-D Imaging, Segmentation and Feature Extraction of the Respiratory System in Small Mammals for Computational Biophysics Simulations

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

Trease, Lynn L.; Trease, Harold E.; Fowler, John

2007-03-15

One of the critical steps toward performing computational biology simulations, using mesh based integration methods, is in using topologically faithful geometry derived from experimental digital image data as the basis for generating the computational meshes. Digital image data representations contain both the topology of the geometric features and experimental field data distributions. The geometric features that need to be captured from the digital image data are three-dimensional, therefore the process and tools we have developed work with volumetric image data represented as data-cubes. This allows us to take advantage of 2D curvature information during the segmentation and feature extraction process.more » The process is basically: 1) segmenting to isolate and enhance the contrast of the features that we wish to extract and reconstruct, 2) extracting the geometry of the features in an isosurfacing technique, and 3) building the computational mesh using the extracted feature geometry. “Quantitative” image reconstruction and feature extraction is done for the purpose of generating computational meshes, not just for producing graphics "screen" quality images. For example, the surface geometry that we extract must represent a closed water-tight surface.« less

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

Milk thistle and olive extract: old substances with a new mission against sun-induced skin damage.

PubMed

DI Caprio, Roberta; Monfrecola, Giuseppe; Gasparri, Franco; Micillo, Raffaella; Balato, Anna; Lembo, Serena

2017-11-30

Natural antioxidants represent an effective option in the prevention and/or improvement of ultraviolet radiations (UVR)-induced/aggravated skin conditions. UVR cause DNA damage in keratinocytes, directly, in the form of cyclobutane pyrimidine dimers (CPDs), or indirectly, through oxidative stress production. Failure of the repair system can result in genetic mutations primarily responsible for the initiation of NMSCs. The aim of our study was to evaluate the in vitro protective effect of milk thistle and olive purified extracts on cultured keratinocytes after solar simulator irradiations (SSR). Immortalized keratinocytes were pre-incubated with different concentrations of milk thistle and olive purified extracts, and irradiated with increasing doses of SSR. Thereafter, CPDs and p53 expression were evaluated to assess DNA damage, whereas cellular antioxidants consumption and lipid membranes peroxidation were measured to analyse oxidative stress. The study substances were well tolerated by cells and displayed good cytoprotective and anti-oxidant activities, being milk thistle dry extract more effective in limiting the direct DNA damage, and olive extract particularly able to reduce lipid membrane peroxidation and to increase cellular antioxidants. Both study substances can be defined as safe compounds, showing differential cytoprotective and anti-oxidant activities and might represent interesting options for NMSCs chemoprevention.

Audio feature extraction using probability distribution function

NASA Astrophysics Data System (ADS)

Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.

2015-05-01

Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.

Morphological Feature Extraction for Automatic Registration of Multispectral Images

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2007-01-01

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

A standardized extract of Ginkgo biloba prevents locomotion impairment induced by cassava juice in Wistar rats

PubMed Central

Rivadeneyra-Domínguez, Eduardo; Vázquez-Luna, Alma; Rodríguez-Landa, Juan F.; Díaz-Sobac, Rafael

2014-01-01

The long-term consumption of cassava (Manihot esculenta Crantz) juice produce neurotoxic effects in the rat, characterized by an increased motor activity in the open field test and presence of uncoordinated swim (i.e., lateral swimming), in the swim test; which has been associated with damage in the hippocampus (CA1). On the other hand, flavonoids content in the Ginkgo biloba extract has been reported to produces neuroprotective effects at experimental level; therefore we hypothesized that G. biloba extract may prevents the motor alterations produced by cassava juice and reduce cellular damage in hippocampal neurons of the rat. In present study the effect of vehicle, cassava juice (linamarin, 0.30 mg/kg), G. biloba extract (dry extract, 160 mg/kg), and combination of treatment were evaluated in the open field and swim tests to identify locomotor and hippocampal alterations in adult male Wistar rats. All treatments were administered once per day, every 24 h, for 28 days, by oral rout. The effect was evaluated at 0, 7, 14, 21, and 28 days of treatment. The results show that cassava group from day 14 of treatment increase crossing and rearing in the open field test, as compared with the vehicle group; while in the swim test produces an uncoordinated swim characterized by the lateral swim. In this same group an increase in the number of damage neurons in the hippocampus (CA1) was identified. Interestingly, both behavioral and neuronal alterations produced by cassava juice administration were prevented by treatment with G. biloba extract. The results shown that G. biloba extract exert a protective effect against behavioral and neuronal damage associated with consumption of cassava juice in the rat. These effects are possibly related with flavonoid content in the G. biloba extract. PMID:25309441

Extraction and representation of common feature from uncertain facial expressions with cloud model.

PubMed

Wang, Shuliang; Chi, Hehua; Yuan, Hanning; Geng, Jing

2017-12-01

Human facial expressions are key ingredient to convert an individual's innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

PyEEG: an open source Python module for EEG/MEG feature extraction.

PubMed

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

PubMed Central

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. PMID:21512582

Effect of Allium flavum L. and Allium melanantherum Panč. Extracts on Oxidative DNA Damage and Antioxidative Enzymes Superoxide Dismutase and Catalase.

PubMed

Mitić-Ćulafić, Dragana; Nikolić, Biljana; Simin, Nataša; Jasnić, Nebojša; Četojević-Simin, Dragana; Krstić, Maja; Knežević-Vukčević, Jelena

2016-03-01

Allium flavum L. and Allium melanantherum Panč. are wild growing plants used in traditional diet in Balkan region. While chemical composition and some biological activities of A. flavum have been reported, A. melanantherum, as an endemic in the Balkan Peninsula, has never been comprehensively examined. After chemical characterization of A. melanantherum, we examined the protective effect of methanol extracts of both species against t-butyl hydro-peroxide (t-BOOH)-induced DNA damage and mutagenesis. The bacterial reverse mutation assay was performed on Escherichia coli WP2 oxyR strain. DNA damage was monitored in human fetal lung fibroblasts (MRC-5) with alkaline comet assay. Obtained results indicated that extracts reduced t-BOOH-induced DNA damage up to 70 and 72% for A. flavum and A. melanantherum extract, respectively, and showed no effect on t-BOOH-induced mutagenesis. Since the results indicated modulatory effect on cell-mediated antioxidative defense, the effect of extracts on total protein content, and superoxide dismutase (SOD) and catalase (CAT) amounts and activities were monitored. Both extracts increased total protein content, while the increase of enzyme amount and activity was obtained only with A. melanantherum extract and restricted to CAT. The activity of CuZnSOD family was not affected, while SOD1 and SOD2 amounts were significantly decreased, indicating potential involvement of extracellular CuZnSOD. Obtained results strongly support the traditional use of A. flavum and A. melanantherum in nutrition and recommend them for further study.

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination

NASA Astrophysics Data System (ADS)

Anderson, Dylan; Bapst, Aleksander; Coon, Joshua; Pung, Aaron; Kudenov, Michael

2017-05-01

Hyperspectral imaging provides a highly discriminative and powerful signature for target detection and discrimination. Recent literature has shown that considering additional target characteristics, such as spatial or temporal profiles, simultaneously with spectral content can greatly increase classifier performance. Considering these additional characteristics in a traditional discriminative algorithm requires a feature extraction step be performed first. An example of such a pipeline is computing a filter bank response to extract spatial features followed by a support vector machine (SVM) to discriminate between targets. This decoupling between feature extraction and target discrimination yields features that are suboptimal for discrimination, reducing performance. This performance reduction is especially pronounced when the number of features or available data is limited. In this paper, we propose the use of Supervised Nonnegative Tensor Factorization (SNTF) to jointly perform feature extraction and target discrimination over hyperspectral data products. SNTF learns a tensor factorization and a classification boundary from labeled training data simultaneously. This ensures that the features learned via tensor factorization are optimal for both summarizing the input data and separating the targets of interest. Practical considerations for applying SNTF to hyperspectral data are presented, and results from this framework are compared to decoupled feature extraction/target discrimination pipelines.

Computer-aided diagnosis for phase-contrast X-ray computed tomography: quantitative characterization of human patellar cartilage with high-dimensional geometric features.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Glaser, Christian; Wismüller, Axel

2014-02-01

Phase-contrast computed tomography (PCI-CT) has shown tremendous potential as an imaging modality for visualizing human cartilage with high spatial resolution. Previous studies have demonstrated the ability of PCI-CT to visualize (1) structural details of the human patellar cartilage matrix and (2) changes to chondrocyte organization induced by osteoarthritis. This study investigates the use of high-dimensional geometric features in characterizing such chondrocyte patterns in the presence or absence of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and statistical features derived from gray-level co-occurrence matrices were extracted from 842 regions of interest (ROI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify ROIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic curve (AUC). SIM-derived geometrical features exhibited the best classification performance (AUC, 0.95 ± 0.06) and were most robust to changes in ROI size. These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated and non-subjective manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

PubMed Central

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

PubMed

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations

DTIC Science & Technology

2008-07-01

classification of the targets of interest. The MMI features extracted in this manner have two properties that provide a sound justification for...are generalizations of well- known feature extraction methods such as Principal Components Analysis (PCA) and Independent Component Analysis (ICA...augment (without degrading performance) a large class of generic fusion processes. Ontologies Classifications Feature extraction Feature analysis

Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

NASA Astrophysics Data System (ADS)

Shi, Wenzhong; Deng, Susu; Xu, Wenbing

2018-02-01

For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (< 5 years) landslides and approximately 35% of historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should be filtered using a filtering strategy based on supplementary information provided by expert knowledge or other data sources.

Genotoxic and oxidative effects induced on A549 cells by extract of PM10 collected in an electric steel plant.

PubMed

Cavallo, Delia; Ursini, Cinzia L; Maiello, Raffaele; Apostoli, Pietro; Catalani, Simona; Ciervo, Aureliano; Iavicoli, Sergio

2008-01-01

The present study was aimed at assessing the carcinogenic risk of occupational exposure to PM10 in electric steel plants. PM10 was collected on cellulose filter respectively outside (site 1) and inside (site 2) the furnace area, was measured, extracted and its metal content was analysed by ICP-MS. Cells were exposed for 30 min, 2 and 4 hours to extract of filter from each site diluted at 0.004, 0.008 and 0.02%. The direct/oxidative DNA damage caused by PM10 was evaluated on A549 cells by Fpg-modified comet assay, analysing Tail moment (TM) and comet percentage. Air samples contained 1.08 mg/m3 of PM10 in site 1 and 5.54 mg/m3in site 2 and different amounts of metals with higher levels of Zn, Al, Ni, Pb, Cd, Cr, Ba in site 2 and of Fe, Mn, Sb in site 1. In cells exposed for 2h to PM10 from both sites, an oxidative DNA damage was found concentrations of 0.008% and 0.02%. For site 2, a direct DNA damage at 0.02% was also found. After 4h a direct/oxidative DNA damage was detected at 0.02% for site 2 and an oxidative DNA damage for site 1. The results indicate a moderate DNA damage induction by used diluitions of PM10 extracts with higher extent for more polluted site 2. These findings show the suitability of this experimental model to evaluate early DNA damage induced by complex mixtures containing metals on target organ, suggesting its use to study biological effects of occupational exposure to such substances.

Understanding a reference-free impedance method using collocated piezoelectric transducers

NASA Astrophysics Data System (ADS)

Kim, Eun Jin; Kim, Min Koo; Sohn, Hoon; Park, Hyun Woo

2010-03-01

A new concept of a reference-free impedance method, which does not require direct comparison with a baseline impedance signal, is proposed for damage detection in a plate-like structure. A single pair of piezoelectric (PZT) wafers collocated on both surfaces of a plate are utilized for extracting electro-mechanical signatures (EMS) associated with mode conversion due to damage. A numerical simulation is conducted to investigate the EMS of collocated PZT wafers in the frequency domain at the presence of damage through spectral element analysis. Then, the EMS due to mode conversion induced by damage are extracted using the signal decomposition technique based on the polarization characteristics of the collocated PZT wafers. The effects of the size and the location of damage on the decomposed EMS are investigated as well. Finally, the applicability of the decomposed EMS to the reference-free damage diagnosis is discussed.

Investigation of antioxidant ability of grape seeds extract to prevent oxidatively induced DNA damage by gas chromatography-tandem mass spectrometry.

PubMed

Aybastıer, Önder; Dawbaa, Sam; Demir, Cevdet

2018-01-01

Phenolic compounds have been studied elaborately for their efficacy to improve health and to protect against a wide variety of diseases. Herein this study, different analysis methods were implemented to evaluate the antioxidant properties of catechin and cyanidin using their standard substances and as they found in the grape seeds extracts. Total phenol contents were 107.39±8.94mg GAE/g dw of grape seeds for grape seed extract (GSE) and 218.32±10.66mg GAE/g dw of grape seeds for acid-hydrolyzed grape seed extract (AcGSE). The extracts were analyzed by HPLC-DAD system and the results showed the presence of catechin, gallic acid, chlorogenic acid and ellagic acid in the processed methanolic extract and cyanidin, gallic acid and ellagic acid in the processed acidified methanolic extract. The protective abilities of catechin and cyanidin were tested against the oxidation of DNA. The results showed that cyanidin has better protection of DNA against oxidation than catechin. GSE and AcGSE were revealed to inhibit the oxidatively induced DNA damage. GSE decreased about 57% of damage caused by the Fenton control sample. This study could show new aspects of the antioxidant profiles of cyanidin and catechin. Copyright © 2017 Elsevier B.V. All rights reserved.

The in vivo antioxidant action and the reduction of oxidative stress by boysenberry extract is dependent on base diet constituents in rats.

PubMed

Barnett, Laura E; Broomfield, Anne M; Hendriks, Wouter H; Hunt, Martin B; McGhie, Tony K

2007-06-01

Dietary antioxidants are often defined by in vitro measures of antioxidant activity. Such measures are valid indicators of the antioxidant potential, but provide little evidence of activity as a dietary antioxidant. This study was undertaken to assess the in vivo antioxidant efficacy of a berry fruit extract by measuring biomarkers of oxidative damage to protein (carbonyls), lipids (malondialdehyde), and DNA (8-oxo-2'-deoxyguanosine urinary excretion) and plasma antioxidant status (antioxidant capacity, vitamin E) in rats when fed basal diets containing fish and soybean oils, which are likely to generate different levels of oxidative stress. Boysenberry (Rubus loganbaccus x baileyanus Britt) extract was used as the dietary antioxidant. The basal diets (chow, synthetic/soybean oil, or synthetic/fish oil) had significant effects on the biomarkers of oxidative damage and antioxidant status, with rats fed the synthetic/fish oil diet having the lowest levels of oxidative damage and the highest antioxidant status. When boysenberry extract was added to the diet, there was little change in 8-oxo-2'-deoxyguanosine excretion in urine, oxidative damage to proteins decreased, and plasma malondialdehyde either increased or decreased depending on the basal diet. This study showed that boysenberry extract functioned as an in vivo antioxidant and raised the antioxidant status of plasma while decreasing some biomarkers of oxidative damage, but the effect was highly modified by basal diet. Our results are further evidence of complex interactions among dietary antioxidants, background nutritional status as determined by diet, and the biochemical nature of the compartments in which antioxidants function.

A multiscale optimization approach to detect exudates in the macula.

PubMed

Agurto, Carla; Murray, Victor; Yu, Honggang; Wigdahl, Jeffrey; Pattichis, Marios; Nemeth, Sheila; Barriga, E Simon; Soliz, Peter

2014-07-01

Pathologies that occur on or near the fovea, such as clinically significant macular edema (CSME), represent high risk for vision loss. The presence of exudates, lipid residues of serous leakage from damaged capillaries, has been associated with CSME, in particular if they are located one optic disc-diameter away from the fovea. In this paper, we present an automatic system to detect exudates in the macula. Our approach uses optimal thresholding of instantaneous amplitude (IA) components that are extracted from multiple frequency scales to generate candidate exudate regions. For each candidate region, we extract color, shape, and texture features that are used for classification. Classification is performed using partial least squares (PLS). We tested the performance of the system on two different databases of 652 and 400 images. The system achieved an area under the receiver operator characteristic curve (AUC) of 0.96 for the combination of both databases and an AUC of 0.97 for each of them when they were evaluated independently.

A Chronic Autoimmune Dry Eye Rat Model with Increase in Effector Memory T Cells in Eyeball Tissue.

PubMed

Hou, Aihua; Bose, Tanima; Chandy, K George; Tong, Louis

2017-06-07

Dry eye disease is a very common condition that causes morbidity and healthcare burden and decreases the quality of life. There is a need for a suitable dry eye animal model to test novel therapeutics to treat autoimmune dry eye conditions. This protocol describes a chronic autoimmune dry eye rat model. Lewis rats were immunized with an emulsion containing lacrimal gland extract, ovalbumin, and complete Freund's adjuvant. A second immunization with the same antigens in incomplete Freund's adjuvant was administered two weeks later. These immunizations were administered subcutaneously at the base of the tail. To boost the immune response at the ocular surface and lacrimal glands, lacrimal gland extract and ovalbumin were injected into the forniceal subconjunctiva and lacrimal glands 6 weeks after the first immunization. The rats developed dry eye features, including reduced tear production, decreased tear stability, and increased corneal damage. Immune profiling by flow cytometry showed a preponderance of CD3 + effector memory T cells in the eyeball.

A Chronic Autoimmune Dry Eye Rat Model with Increase in Effector Memory T Cells in Eyeball Tissue

PubMed Central

Hou, Aihua; Bose, Tanima; Chandy, K. George; Tong, Louis

2017-01-01

Dry eye disease is a very common condition that causes morbidity and healthcare burden and decreases the quality of life. There is a need for a suitable dry eye animal model to test novel therapeutics to treat autoimmune dry eye conditions. This protocol describes a chronic autoimmune dry eye rat model. Lewis rats were immunized with an emulsion containing lacrimal gland extract, ovalbumin, and complete Freund's adjuvant. A second immunization with the same antigens in incomplete Freund's adjuvant was administered two weeks later. These immunizations were administered subcutaneously at the base of the tail. To boost the immune response at the ocular surface and lacrimal glands, lacrimal gland extract and ovalbumin were injected into the forniceal subconjunctiva and lacrimal glands 6 weeks after the first immunization. The rats developed dry eye features, including reduced tear production, decreased tear stability, and increased corneal damage. Immune profiling by flow cytometry showed a preponderance of CD3+ effector memory T cells in the eyeball. PMID:28654074

Blueberry (Vaccinium ashei Reade) extract ameliorates ovarian damage induced by subchronic cadmium exposure in mice: Potential δ-ALA-D involvement.

PubMed

Izaguirry, Aryele Pinto; Soares, Melina Bucco; Vargas, Laura Musacchio; Spiazzi, Cristiano Chiapinotto; Dos Santos Brum, Daniela; Noremberg, Simone; Mendez, Andreas Sebastian Loureiro; Santos, Francielli Weber

2017-01-01

Females are born with a finite number of oocyte-containing follicles and ovary damage results in reduced fertility. Cadmium accumulates in the reproductive system, damaging it, and the cigarette smoke is a potential exposure route. Natural therapies are relevant to health benefits and disease prevention. This study verified the effect of cadmium exposure on the ovaries of mice and the blueberry extract as a potential therapy. Blueberry therapy was effective in restoring reactive species levels and δ-aminolevulinate dehydratase activity, and partially improved the viability of cadmium-disrupted follicles. This therapy was not able to restore the 17 β-hydroxysteroid dehydrogenase activity. Extract HPLC evaluation indicated the presence of quercetin, quercitrin, isoquercetin, and ascorbic acid. Ascorbic acid was the major substance and its concentration was 620.24 µg/mL. Thus, cadmium accumulates in the ovaries of mice after subchronic exposure, inducing cellular damage, and the blueberry extract possesses antioxidant properties that could protect, at least in part, the ovarian tissue from cadmium toxicity. © 2015 Wiley Periodicals, Inc. Environ Toxicol 32: 188-196, 2017. © 2015 Wiley Periodicals, Inc.

Xenopus egg extract: A powerful tool to study genome maintenance mechanisms.

PubMed

Hoogenboom, Wouter S; Klein Douwel, Daisy; Knipscheer, Puck

2017-08-15

DNA repair pathways are crucial to maintain the integrity of our genome and prevent genetic diseases such as cancer. There are many different types of DNA damage and specific DNA repair mechanisms have evolved to deal with these lesions. In addition to these repair pathways there is an extensive signaling network that regulates processes important for repair, such as cell cycle control and transcription. Despite extensive research, DNA damage repair and signaling are not fully understood. In vitro systems such as the Xenopus egg extract system, have played, and still play, an important role in deciphering the molecular details of these processes. Xenopus laevis egg extracts contain all factors required to efficiently perform DNA repair outside a cell, using mechanisms conserved in humans. These extracts have been used to study several genome maintenance pathways, including mismatch repair, non-homologous end joining, ICL repair, DNA damage checkpoint activation, and replication fork stability. Here we describe how the Xenopus egg extract system, in combination with specifically designed DNA templates, contributed to our detailed understanding of these pathways. Copyright © 2017. Published by Elsevier Inc.

The fruit extract of Berberis crataegina DC: exerts potent antioxidant activity and protects DNA integrity.

PubMed

Charehsaz, Mohammad; Sipahi, Hande; Celep, Engin; Üstündağ, Aylin; Cemiloğlu Ülker, Özge; Duydu, Yalçın; Aydın, Ahmet; Yesilada, Erdem

2015-04-17

Dried fruits of Berberis crataegina (Berberidaceae) have been frequently consumed as food garniture in Turkish cuisine, while its fruit paste has been used to increase stamina and in particular to prevent from cardiovascular dysfunctions in Northeastern Black Sea region of Turkey. This study investigated this folkloric information in order to explain the claimed healing effects as well as to evaluate possible risks. Total phenolic, flavonoid and proanthocyanidin contents and antioxidant capacity of the methanolic fruit extract were evaluated through several in vitro assays. The cytotoxic and genotoxic effects of B. crataegina fruit extract were also assessed in both cervical cancer cell line (HeLa) and human peripheral blood lymphocytes. The extract showed protective effects against ferric-induced oxidative stress and had a relatively good antioxidant activity. It also ameliorated the H2O2 mediated DNA damage in lymphocytes, suggesting the protective effect against oxidative DNA damage. The methanolic extract of B. crataegina fruits may be a potential antioxidant nutrient and also may exert a protective role against lipid peroxidation as well as oxidative DNA damage.

Leaf extract of Wasabia japonica relieved oxidative stress induced by Helicobacter pylori infection and stress loading in Mongolian gerbils.

PubMed

Sekiguchi, Hirotaka; Takabayashi, Fumiyo; Deguchi, Yuya; Masuda, Hideki; Toyoizumi, Tomoyasu; Masuda, Shuichi; Kinae, Naohide

2010-01-01

Infection with Helicobacter pylori (H. pylori) can induce gastric disorders, and though its presence cannot explain disease pathogenesis and does not have associations with other factors, it is well known that H. pylori infection causes stomach inflammation following oxidative stress. We examined the suppressive effects of a leaf extract of Wasabia japonica on H. pylori infection and on stress loading in Mongolian gerbils. Following oral administration of wasabi extract of 50 and 200 mg/kg B.W./d for 10 d, the animals were exposed to restraint stress for 90 and 270 min. As for the results, the level of 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) in the stomach and oxidative DNA damage in peripheral erythrocytes at 270 min significantly increased. That elevation was significantly suppressed by the addition of the leaf extract. We concluded that the simultaneous loading of H. pylori infection and physical stress loading might induce oxidative DNA damage additively, while a leaf extract attenuated this DNA damage in the stomach as well as the peripheral erythrocytes.

Protective Effect of Hydroalcoholic Extract of Tribulus Terrestris on Cisplatin Induced Renal Tissue Damage in Male Mice

PubMed Central

Raoofi, Amir; Khazaei, Mozafar; Ghanbari, Ali

2015-01-01

Background: According beneficial effects of Tribulus terrestris (TT) extract on tissue damage, the present study investigated the influence of hydroalcoholic extract of TT plant on cisplatin (CIS) (EBEWE Pharma, Unterach, Austria) induced renal tissue damage in male mice. Methods: Thirty mice were divided into five groups (n = 6). The first group (control) was treated with normal saline (0.9% NaCl) and experimental groups with CIS (E1), CIS + 100 mg/kg extract of TT (E2), CIS + 300 mg/kg extract of TT (E3), CIS + 500 mg/kg extract of TT (E4) intraperitoneally. The kidneys were removed after 4 days of injections, and histological evaluations were performed. Results: The data were analyzed using one-way analysis of variance followed by Tukey's post-hoc test, paired-sample t-test, Kruskal–Wallis and Mann–Whitney tests. In the CIS treated group, the whole kidney tissue showed an increased dilatation of Bowman's capsule, medullar congestion, and dilatation of collecting tubules and a decreased in the body weight and kidney weight. These parameters reached to the normal range after administration of fruit extracts of TT for 4 days. Conclusions: The results suggested that the oral administration of TT fruit extract at dose 100, 300 and 500 mg/kg body weight provided protection against the CIS induced toxicity in the mice. PMID:25789143

Photooxidative damage in retinal pigment epithelial cells via GRP78 and the protective role of grape skin polyphenols.

PubMed

Zhao, Zhao; Sun, Tao; Jiang, Yun; Wu, Lijiang; Cai, Xiangzhong; Sun, Xiaodong; Sun, Xiangjun

2014-12-01

Blue light induced oxidative damage and ER stress are related to the pathogenesis of age-related macular degeneration (AMD). However, the mechanism of blue light-induced damage remained obscure. The objective of this work is to assess the photooxidative damage to retinal pigment epithelial cells (RPE) and oxidation-induced changes in expression of ER stress associated apoptotic proteins, and investigate the mechanism underlying the protective effects of grape skin extracts. To mimic lipofuscin-mediated photooxidation in vivo, ARPE-19 cells that accumulated A2E, one of lipofuscin fluorophores, were used as a model system to investigate the mechanism of photooxidative damage and the protective effects of grape skin polyphenols. Exposure of A2E containing ARPE-19 cells to blue light resulted in significant apoptosis and increases in levels of GRP78, CHOP, p-JNK, Bax, cleaved caspase-9, and cleaved caspase-3, indicating that photooxidative damage to RPE cells is mediated by the ER-stress-induced intrinsic apoptotic pathway. Cells in which GRP78 had been knocked down with shRNA were more vulnerable to photooxidative damage. Pre-treatment of blue-light-exposed A2E containing ARPE-19 cells, with grape skin extracts, inhibited apoptosis, in a dose dependent manner. Knockdown GRP78 blocked the protective effect of grape skin extracts.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

Single-trial laser-evoked potentials feature extraction for prediction of pain perception.

PubMed

Huang, Gan; Xiao, Ping; Hu, Li; Hung, Yeung Sam; Zhang, Zhiguo

2013-01-01

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Diagnosis of the three-phase induction motor using thermal imaging

NASA Astrophysics Data System (ADS)

Glowacz, Adam; Glowacz, Zygfryd

2017-03-01

Three-phase induction motors are used in the industry commonly for example woodworking machines, blowers, pumps, conveyors, elevators, compressors, mining industry, automotive industry, chemical industry and railway applications. Diagnosis of faults is essential for proper maintenance. Faults may damage a motor and damaged motors generate economic losses caused by breakdowns in production lines. In this paper the authors develop fault diagnostic techniques of the three-phase induction motor. The described techniques are based on the analysis of thermal images of three-phase induction motor. The authors analyse thermal images of 3 states of the three-phase induction motor: healthy three-phase induction motor, three-phase induction motor with 2 broken bars, three-phase induction motor with faulty ring of squirrel-cage. In this paper the authors develop an original method of the feature extraction of thermal images MoASoID (Method of Areas Selection of Image Differences). This method compares many training sets together and it selects the areas with the biggest changes for the recognition process. Feature vectors are obtained with the use of mentioned MoASoID and image histogram. Next 3 methods of classification are used: NN (the Nearest Neighbour classifier), K-means, BNN (the back-propagation neural network). The described fault diagnostic techniques are useful for protection of three-phase induction motor and other types of rotating electrical motors such as: DC motors, generators, synchronous motors.

Identification and classification of failure modes in laminated composites by using a multivariate statistical analysis of wavelet coefficients

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2017-11-01

Acoustic Emission (AE) is a suitable method to monitor the health of composite structures in real-time. However, AE-based failure mode identification and classification are still complex to apply due to the fact that AE waves are generally released simultaneously from all AE-emitting damage sources. Hence, the use of advanced signal processing techniques in combination with pattern recognition approaches is required. In this paper, AE signals generated from laminated carbon fiber reinforced polymer (CFRP) subjected to indentation test are examined and analyzed. A new pattern recognition approach involving a number of processing steps able to be implemented in real-time is developed. Unlike common classification approaches, here only CWT coefficients are extracted as relevant features. Firstly, Continuous Wavelet Transform (CWT) is applied to the AE signals. Furthermore, dimensionality reduction process using Principal Component Analysis (PCA) is carried out on the coefficient matrices. The PCA-based feature distribution is analyzed using Kernel Density Estimation (KDE) allowing the determination of a specific pattern for each fault-specific AE signal. Moreover, waveform and frequency content of AE signals are in depth examined and compared with fundamental assumptions reported in this field. A correlation between the identified patterns and failure modes is achieved. The introduced method improves the damage classification and can be used as a non-destructive evaluation tool.

Potential role of Saudi red propolis in alleviating lung damage induced by methicillin resistant Staphylococcus aureus virulence in rats.

PubMed

Saddiq, Amna Ali; Mohamed, Azza Mostafa

2016-07-01

The aim of this study was to explore the protective impact of aqueous extract of Saudi red propolis against rat lung damage induced by the pathogenic bacteria namely methicillin resistant Staphylococcus aureus (MRSA) ATCC 6538 strain. Infected rats were received a single intraperitoneal (i.p.) injection of bacterial suspension at a dose of 1 X 10(6) CFU / 100g body weight. Results showed that oral administration of an aqueous extract of propolis (50mg/100g body weight) daily for two weeks to infected rats simultaneously with bacterial infection, effectively ameliorated the alteration of oxidative stress biomarker, malondialdehyde (MDA), as well as the antioxidant markers, glutathione peroxidase (GPx) and superoxide dismutase (SOD), in lungs of infected rats compared with infected untreated ones. Also, the used propolis extract successfully modulated the alterations in proinflammatory mediators, tumor necrosis factor-α (TNF- α) and vascular endothelial growth factor (VEGF) in serum. In addition, the propolis extract successfully modulated the oxidative DNA damage and the apoptosis biomarker, caspase 3, in lungs of S aureus infected rats compared with infected untreated animals. The biochemical results were supported by histo-pathological observation of lung tissues. In conclusion, the beneficial prophylactic role of the aqueous extract of Saudi red propolis against lung damage induced by methicillin resistant S aureus may be related to the antioxidant, anti-inflammatory, immunomodulatory and antiapoptosis of its active constituents.

In vitro and in vivo photoprotective/photochemopreventive potential of Garcinia brasiliensis epicarp extract.

PubMed

Figueiredo, Sônia Aparecida; Vilela, Fernanda Maria Pinto; da Silva, Claudinei Alves; Cunha, Thiago Mattar; Dos Santos, Marcelo Henrique; Fonseca, Maria José Vieira

2014-02-05

The damaging effects of sunlight to the skin has triggered studies that involve the synthesis and extraction of organic compounds from natural sources that can absorb UV radiation, and studies on polyphenolic compounds with antioxidant and anti-inflammatory properties that can be used as photochemopreventive agents for reducing skin damage. We investigated the in vitro and in vivo photoprotective/photochemopreventive potential of Garcinia brasiliensis epicarp extract (GbEE). We evaluated the cell viability of L929 fibroblasts after UVB exposure using a quartz plate containing the extract solution or the GbEE formulation. The in vivo photoprotective effect of the GbEE formulation was evaluated by measuring the UVB damage-induced decrease in endogenous reduced glutathione (GSH), the increase in myeloperoxidase (MPO) activity and secretion of cytokines IL-1β and TNF-α. The in vitro methodology using fibroblasts showed that the photoprotective properties of the GbEE solutions and 10% GbEE formulation were similar to the commercial sunscreen (SPF-15). In vivo results demonstrated of the GbEE formulation in decreasing UVB induced-damage such as GSH depletion, an increased in MPO activity and secretion of cytokines IL-1β and TNF-α. The results showed that the extract has great potential for use as a sunscreen in topical formulations in addition to UV filters. Copyright © 2014 Elsevier B.V. All rights reserved.

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Genetic damage induced by organic extract of coke oven emissions on human bronchial epithelial cells.

PubMed

Zhai, Qingfeng; Duan, Huawei; Wang, Yadong; Huang, Chuanfeng; Niu, Yong; Dai, Yufei; Bin, Ping; Liu, Qingjun; Chen, Wen; Ma, Junxiang; Zheng, Yuxin

2012-08-01

Coke oven emissions are known as human carcinogen, which is a complex mixture of polycyclic aromatic hydrocarbon. In this study, we aimed to clarify the mechanism of action of coke oven emissions induced carcinogenesis and to identify biomarkers of early biological effects in a human bronchial epithelial cell line with CYP1A1 activity (HBE-CYP1A1). Particulate matter was collected in the oven area on glass filter, extracted and analyzed by GC/MS. DNA breaks and oxidative damage were evaluated by alkaline and endonucleases (FPG, hOGG1 and ENDO III)-modified comet assays. Cytotoxicity and chromosomal damage were assessed by the cytokinesis-block micronucleus cytome (CBMN-Cyt) assay. The cells were treated with organic extract of coke oven emissions (OE-COE) representing 5, 10, 20, 40μg/mL extract for 24h. We found that there was a dose-effect relationship between the OE-COE and the direct DNA damage presented by tail length, tail intensity and Olive tail moment in the comet assay. The presence of lesion-specific endonucleases in the assays increased DNA migration after OE-COE treatment when compared to those without enzymes, which indicated that OE-COE produced oxidative damage at the level of pyrimidine and purine bases. The dose-dependent increase of micronuclei, nucleoplasmic bridges and nuclear buds in exposed cells was significant, indicating chromosomal and genomic damage induced by OE-COE. Based on the cytotoxic biomarkers in CBMN-Cyt assay, OE-COE may inhibit nuclear division, interfere with apoptosis, or induce cell necrosis. This study indicates that OE-COE exposure can induce DNA breaks/oxidative damage and genomic instability in HBE-CYP1A1 cells. The FPG-comet assay appears more specific for detecting oxidative DNA damage induced by complex mixtures of genotoxic substances. Copyright © 2012 Elsevier Ltd. All rights reserved.

Finger vein recognition based on the hyperinformation feature

NASA Astrophysics Data System (ADS)

Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Yang, Lu

2014-01-01

The finger vein is a promising biometric pattern for personal identification due to its advantages over other existing biometrics. In finger vein recognition, feature extraction is a critical step, and many feature extraction methods have been proposed to extract the gray, texture, or shape of the finger vein. We treat them as low-level features and present a high-level feature extraction framework. Under this framework, base attribute is first defined to represent the characteristics of a certain subcategory of a subject. Then, for an image, the correlation coefficient is used for constructing the high-level feature, which reflects the correlation between this image and all base attributes. Since the high-level feature can reveal characteristics of more subcategories and contain more discriminative information, we call it hyperinformation feature (HIF). Compared with low-level features, which only represent the characteristics of one subcategory, HIF is more powerful and robust. In order to demonstrate the potential of the proposed framework, we provide a case study to extract HIF. We conduct comprehensive experiments to show the generality of the proposed framework and the efficiency of HIF on our databases, respectively. Experimental results show that HIF significantly outperforms the low-level features.

Decomposition and extraction: a new framework for visual classification.

PubMed

Fang, Yuqiang; Chen, Qiang; Sun, Lin; Dai, Bin; Yan, Shuicheng

2014-08-01

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

Antioxidant and drug detoxification potential of aqueous extract of Annona senegalensis leaves in carbon tetrachloride-induced hepatocellular damage.

PubMed

Ajboye, Taofeek O; Yakubu, Musa T; Salau, Amadu K; Oladiji, Adenike T; Akanji, Musbau A; Okogun, Joseph I

2010-12-01

 Despite the myriad uses of Annona senegalensis Pers. (Annonaceae) leaves in folklore medicine of Nigeria, the basis is yet to be substantiated by scientific investigations.  To investigate the antioxidant (in vitro and in vivo) and drug detoxification potential of aqueous extract of A. senegalensis leaves in CCl₄-induced hepatocellular damage.  In vitro antioxidant activity of the aqueous extract of A. senegalensis leaves was evaluated using 2,2-diphenyl-1-picrylhydrazyl (DPPH), H₂O₂, superoxide ion, 2,2'-azinobis-(3-ethylbenzthiazoline-6-sulfonate) (ABTS) and ferric ion models while in vivo antioxidant and drug detoxification activities of the extract at 100, 200, and 400 mg/kg body weight were done by assaying the levels of enzymic and non-enzymic indices in CCl₄-induced hepatocellular damage.  The extract at 1 mg/mL scavenged DPPH, H₂O₂, superoxide ion, and ABTS radicals, whereas ferric ion was significantly (P <0.05) reduced. The levels of alkaline and acid phosphatases, alanine and aspartate aminotransferases, superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase, glucose-6-phosphate dehydrogenase, reduced glutathione, vitamins C and E, glutathione S-transferase, nicotinamide adenine dinucleotide (reduced):Quinone oxidoreductase, uridyl diphosphoglucuronyl transferase, malondialdehyde, and lipid hydroperoxide that decreased in CCl₄ treated animals were significantly attenuated by the extract in a manner similar to the animals treated with the reference drug.  The ability of the aqueous extract of A. senegalensis leaves to scavenge free radicals in vitro and reversal of CCl₄-induced hepatocellular damage in rats suggest antioxidant and drug detoxification activities. Overall, this study has justified the rationale behind some of the medicinal uses of the plant in folklore medicine of Nigeria.

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

PubMed

Guo, Jian; Qian, Kun; Zhang, Gongxuan; Xu, Huijie; Schuller, Björn

2017-12-01

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

Low-power coprocessor for Haar-like feature extraction with pixel-based pipelined architecture

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Fujita, Yuki; Zhang, Xiangyu; Chen, Lei; Jürgen Mattausch, Hans

2017-04-01

Intelligent analysis of image and video data requires image-feature extraction as an important processing capability for machine-vision realization. A coprocessor with pixel-based pipeline (CFEPP) architecture is developed for real-time Haar-like cell-based feature extraction. Synchronization with the image sensor’s pixel frequency and immediate usage of each input pixel for the feature-construction process avoids the dependence on memory-intensive conventional strategies like integral-image construction or frame buffers. One 180 nm CMOS prototype can extract the 1680-dimensional Haar-like feature vectors, applied in the speeded up robust features (SURF) scheme, using an on-chip memory of only 96 kb (kilobit). Additionally, a low power dissipation of only 43.45 mW at 1.8 V supply voltage is achieved during VGA video procession at 120 MHz frequency with more than 325 fps. The Haar-like feature-extraction coprocessor is further evaluated by the practical application of vehicle recognition, achieving the expected high accuracy which is comparable to previous work.

Acousto-Optic Technology for Topographic Feature Extraction and Image Analysis.

DTIC Science & Technology

1981-03-01

This report contains all findings of the acousto - optic technology study for feature extraction conducted by Deft Laboratories Inc. for the U.S. Army...topographic feature extraction and image analysis using acousto - optic (A-O) technology. A conclusion of this study was that A-O devices are potentially

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using computer-extracted mammographic features to predict occult invasive disease in patients with biopsy proven DCIS. We proposed a computer-vision algorithm based approach to extract mammographic features from magnification views of full field digital mammography (FFDM) for patients with DCIS. After an expert breast radiologist provided a region of interest (ROI) mask for the DCIS lesion, the proposed approach is able to segment individual microcalcifications (MCs), detect the boundary of the MC cluster (MCC), and extract 113 mammographic features from MCs and MCC within the ROI. In this study, we extracted mammographic features from 99 patients with DCIS (74 pure DCIS; 25 DCIS plus invasive disease). The predictive power of the mammographic features was demonstrated through binary classifications between pure DCIS and DCIS with invasive disease using linear discriminant analysis (LDA). Before classification, the minimum redundancy Maximum Relevance (mRMR) feature selection method was first applied to choose subsets of useful features. The generalization performance was assessed using Leave-One-Out Cross-Validation and Receiver Operating Characteristic (ROC) curve analysis. Using the computer-extracted mammographic features, the proposed model was able to distinguish DCIS with invasive disease from pure DCIS, with an average classification performance of AUC = 0.61 +/- 0.05. Overall, the proposed computer-extracted mammographic features are promising for predicting occult invasive disease in DCIS.

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yinggang; Zhang, Libao; Yu, Xianchuan

2015-01-01

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.

PubMed

Awais, Muhammad; Badruddin, Nasreen; Drieberg, Micheal

2017-08-31

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t -tests to select only statistically significant features ( p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

PubMed Central

Badruddin, Nasreen

2017-01-01

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear. PMID:28858220

Baseline-Subtraction-Free (BSF) Damage-Scattered Wave Extraction for Stiffened Isotropic Plates

NASA Technical Reports Server (NTRS)

He, Jiaze; Leser, Patrick E.; Leser, William P.

2017-01-01

Lamb waves enable long distance inspection of structures for health monitoring purposes. However, this capability is diminished when applied to complex structures where damage-scattered waves are often buried by scattering from various structural components or boundaries in the time-space domain. Here, a baseline-subtraction-free (BSF) inspection concept based on the Radon transform (RT) is proposed to identify and separate these scattered waves from those scattered by damage. The received time-space domain signals can be converted into the Radon domain, in which the scattered signals from structural components are suppressed into relatively small regions such that damage-scattered signals can be identified and extracted. In this study, a piezoelectric wafer and a linear scan via laser Doppler vibrometer (LDV) were used to excite and acquire the Lamb-wave signals in an aluminum plate with multiple stiffeners. Linear and inverse linear Radon transform algorithms were applied to the direct measurements. The results demonstrate the effectiveness of the Radon transform as a reliable extraction tool for damage-scattered waves in a stiffened aluminum plate and also suggest the possibility of generalizing this technique for application to a wide variety of complex, large-area structures.

ANALYSIS FOR HOUSE DAMAGE PROPERTY OF 2007 MID-NIIGATA PREFECTURE OFFSHORE EARTHQUAKE

NASA Astrophysics Data System (ADS)

Ochiai, Hirokazu; Yamada, Kento; Ohtsuka, Satoru; Isobe, Koichi

This paper reports the result of correlation analysis for house damage in 2007 Mid-piigata prefecture offshore earthquake by focusing geomorphological land classification and other factors as landform and ground properties with organizing the house damage data of disaster victim certificate conducted by public administrations. In former part of the paper, the features of house damage at 2007 Mid-Niigata prefecture offshore earthquake were analyzed for various influencing factors. The authors discussed the affrecting factors to houses at earthquake. In latter part, the features of house damage at 2007 Mid-Niigata prefecture offshore earthquake was discussed with that at 2004 Mid-Niigata prefecture earthquake. The house damage function of distance from the epicenter was proposed based on the analysis on house damage ratio recorded in two earthquakes.

Quantitative evaluation for small surface damage based on iterative difference and triangulation of 3D point cloud

NASA Astrophysics Data System (ADS)

Zhang, Yuyan; Guo, Quanli; Wang, Zhenchun; Yang, Degong

2018-03-01

This paper proposes a non-contact, non-destructive evaluation method for the surface damage of high-speed sliding electrical contact rails. The proposed method establishes a model of damage identification and calculation. A laser scanning system is built to obtain the 3D point cloud data of the rail surface. In order to extract the damage region of the rail surface, the 3D point cloud data are processed using iterative difference, nearest neighbours search and a data registration algorithm. The curvature of the point cloud data in the damage region is mapped to RGB color information, which can directly reflect the change trend of the curvature of the point cloud data in the damage region. The extracted damage region is divided into three prism elements by a method of triangulation. The volume and mass of a single element are calculated by the method of geometric segmentation. Finally, the total volume and mass of the damage region are obtained by the principle of superposition. The proposed method is applied to several typical injuries and the results are discussed. The experimental results show that the algorithm can identify damage shapes and calculate damage mass with milligram precision, which are useful for evaluating the damage in a further research stage.

Reduced-Order Modeling and Wavelet Analysis of Turbofan Engine Structural Response Due to Foreign Object Damage (FOD) Events

NASA Technical Reports Server (NTRS)

Turso, James; Lawrence, Charles; Litt, Jonathan

2004-01-01

The development of a wavelet-based feature extraction technique specifically targeting FOD-event induced vibration signal changes in gas turbine engines is described. The technique performs wavelet analysis of accelerometer signals from specified locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due to the lack of high-frequency FOD-event test data in the open literature, a reduced-order turbofan structural model (ROM) was synthesized from a finite element model modal analysis to support the investigation. In addition to providing test data for algorithm development, the ROM is used to determine the optimal sensor location for FOD-event detection. In the presence of significant noise, precise location of the FOD event in time was obtained using the developed wavelet-based feature.

Reduced-Order Modeling and Wavelet Analysis of Turbofan Engine Structural Response Due to Foreign Object Damage "FOD" Events

NASA Technical Reports Server (NTRS)

Turso, James A.; Lawrence, Charles; Litt, Jonathan S.

2007-01-01

The development of a wavelet-based feature extraction technique specifically targeting FOD-event induced vibration signal changes in gas turbine engines is described. The technique performs wavelet analysis of accelerometer signals from specified locations on the engine and is shown to be robust in the presence of significant process and sensor noise. It is envisioned that the technique will be combined with Kalman filter thermal/ health parameter estimation for FOD-event detection via information fusion from these (and perhaps other) sources. Due to the lack of high-frequency FOD-event test data in the open literature, a reduced-order turbofan structural model (ROM) was synthesized from a finite-element model modal analysis to support the investigation. In addition to providing test data for algorithm development, the ROM is used to determine the optimal sensor location for FOD-event detection. In the presence of significant noise, precise location of the FOD event in time was obtained using the developed wavelet-based feature.

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

PubMed Central

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

2016-01-01

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time–frequency domains. The key features are selected based on Pearson’s Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL. PMID:27258277

Diabetic peripheral neuropathy assessment through texture based analysis of corneal nerve images

NASA Astrophysics Data System (ADS)

Silva, Susana F.; Gouveia, Sofia; Gomes, Leonor; Negrão, Luís; João Quadrado, Maria; Domingues, José Paulo; Morgado, António Miguel

2015-05-01

Diabetic peripheral neuropathy (DPN) is one common complication of diabetes. Early diagnosis of DPN often fails due to the non-availability of a simple, reliable, non-invasive method. Several published studies show that corneal confocal microscopy (CCM) can identify small nerve fibre damage and quantify the severity of DPN, using nerve morphometric parameters. Here, we used image texture features, extracted from corneal sub-basal nerve plexus images, obtained in vivo by CCM, to identify DPN patients, using classification techniques. A SVM classifier using image texture features was used to identify (DPN vs. No DPN) DPN patients. The accuracies were 80.6%, when excluding diabetic patients without neuropathy, and 73.5%, when including diabetic patients without diabetic neuropathy jointly with healthy controls. The results suggest that texture analysis might be used as a complementing technique for DPN diagnosis, without requiring nerve segmentation in CCM images. The results also suggest that this technique has enough sensitivity to detect early disorders in the corneal nerves of diabetic patients.

Nd:YAG Pulsed Laser based flaw imaging techniques for noncontact NDE of an aluminum plate

NASA Astrophysics Data System (ADS)

Park, Woong-Ki; Lee, Changgil; Park, Seunghee

2012-04-01

Recently, the longitudinal, shear and surface waves have been very widely used as a kind of ultrasonic wave exploration methods to identify internal defects of metallic structures. The ultrasonic wave-based non-destructive testing (NDT) is one of main non-destructive inspection techniques for a health assessment about nuclear power plant, aircraft, ships, and/or automobile manufacturing. In this study, a noncontact pulsed laser-based flaw imaging NDT technique is implemented to detect the damage of a plate-like structure and to identify the location of the damage. To achieve this goal, the Nd:YAG pulsed laser equipment is used to generate a guided wave and scans a specific area to find damage location. The Nd: YAG pulsed laser is used to generate Lamb wave and piezoelectric sensors are installed to measure structural responses. Ann aluminum plate is investigated to verify the effectiveness and the robustness of the proposed NDT approach. A notch is a target to detect, which is inflicted on the surface of an aluminum plate. The damagesensitive features are extracted by comparing the time of flight of the guided wave obtained from an acoustic emission (AE) sensor and make use of the flaw imaging techniques of the aluminum plate.

An optimal baseline selection methodology for data-driven damage detection and temperature compensation in acousto-ultrasonics

NASA Astrophysics Data System (ADS)

Torres-Arredondo, M.-A.; Sierra-Pérez, Julián; Cabanes, Guénaël

2016-05-01

The process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). For the design of a trustworthy health monitoring system, a vast amount of information regarding the inherent physical characteristics of the sources and their propagation and interaction across the structure is crucial. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organising maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and identifiable.

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing.

PubMed

Popescu, Dan; Ichim, Loretta; Stoican, Florin

2017-02-23

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes-fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms.

Nondestructive Evaluation of Metal Fatigue Using Nonlinear Acoustics

NASA Technical Reports Server (NTRS)

Cantrell, John H., Jr.

2008-01-01

Safe-life and damage-tolerant design philosophies of high performance structures have driven the development of various methods to evaluate nondestructively the accumulation of damage in such structures resulting from cyclic loading. Although many techniques have proven useful, none has been able to provide an unambiguous, quantitative assessment of damage accumulation at each stage of fatigue from the virgin state to fracture. A method based on nonlinear acoustics is shown to provide such a means to assess the state of metal fatigue. The salient features of an analytical model are presented of the microelastic-plastic nonlinearities resulting from the interaction of an acoustic wave with fatigue-generated dislocation substructures and cracks that predictably evolve during the metal fatigue process. The interaction is quantified by the material (acoustic) nonlinearity parameter extracted from acoustic harmonic generation measurements. The parameters typically increase monotonically by several hundred percent over the fatigue life of the metal, thus providing a unique measure of the state of fatigue. Application of the model to aluminum alloy 2024-T4, 410Cb stainless steel, and IN100 nickel-base superalloy specimens fatigued using different loading conditions yields good agreement between theory and experiment. Application of the model and measurement technique to the on-site inspection of steam turbine blades is discussed.

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

PubMed Central

Popescu, Dan; Ichim, Loretta; Stoican, Florin

2017-01-01

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms. PMID:28241479

Polyphenol-enriched Vaccinium uliginosum L. fractions reduce retinal damage induced by blue light in A2E-laden ARPE19 cell cultures and mice.

PubMed

Lee, Bom-Lee; Kang, Jung-Hwan; Kim, Hye-Mi; Jeong, Se-Hee; Jang, Dae-Sik; Jang, Young-Pyo; Choung, Se-Young

2016-12-01

Polyphenols exert beneficial effects on vision. We hypothesized that polyphenol components of Vaccinium uliginosum L. (V.U.) extract protect retinal pigment epithelial (RPE) cells against blue light-induced damage. Our aim was to test extracts containing polyphenol components to ascertain effects to reduce damage against blue light in RPEs. We measured the activity in fractions eluted from water, ethanol, and HP20 resin (FH), and found that the FH fraction had the highest beneficial activity. We isolated the individual active compounds from the FH fraction using chromatographic techniques, and found that FH contained flavonoids, anthocyanins, phenyl propanoids, and iridoids. Cell cultures of A2E-laden ARPE-19 exposed to blue light after treatment with V.U. extract fractions and their individual constituents indicated improvement. V uliginosum L extract fractions and constituent compounds significantly reduced A2E photo-oxidation-induced RPE cell death and inhibited intracellular A2E accumulation. Furthermore, Balb/c male mice were exposed to blue light at 10000 lux for 1 h/d for 2 weeks to induce retinal damage. One week after the final blue light exposure, retinal damage evaluated revealed that the outer nuclear layer thickness and nuclei count were improved. Histologic examination of murine photoreceptor cells demonstrated that FH, rich in polyphenols, inhibited the loss of outer nuclear layer thickness and nuclei. Our findings suggest that V.U. extract and eluted fractions are a potential source of bioactive compounds that potentially serve a therapeutic approach for age-related macular degeneration. Copyright © 2016 Elsevier Inc. All rights reserved.

Engagement Assessment Using EEG Signals

NASA Technical Reports Server (NTRS)

Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

2012-01-01

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

The optional selection of micro-motion feature based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Bo; Ren, Hongmei; Xiao, Zhi-he; Sheng, Jing

2017-11-01

Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

PubMed Central

Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

2012-01-01

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.

PubMed

Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui

2017-08-17

It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.

Mimosa (Mimosa caesalpiniifolia) prevents oxidative DNA damage induced by cadmium exposure in Wistar rats.

PubMed

Silva, Marcelo Jose Dias; Vilegas, Wagner; da Silva, Marcelo Aparecido; de Moura, Carolina Foot Gomes; Ribeiro, Flávia Andressa Pidone; da Silva, Victor Hugo Pereira; Ribeiro, Daniel Araki

2014-12-01

The Mimosa (Mimosa caesalpiniifolia) is a plant native from South America; it is used in the traditional medicine systems for treating bacterial, fungal, parasitic and inflammatory conditions. The aim of this study was to evaluate the antigenotoxic and antioxidant activities induced by mimosa (M. caesalpiniifolia) in multiple rodent organs subjected to intoxication with cadmium chloride. A total of 40 Wistar rats (8 weeks old, 250 g) were distributed into eight groups (n = 5), as follows: Control group (non-treated group, CTRL); Cadmium exposed group (Cd); cadmium exposure and treated with extract at 62.5 mg/kg/day; cadmium exposure and treated with extract at 125 mg/kg/day; cadmium exposure and treated with extract at 250 mg/kg/day; cadmium exposure and treated with ethyl acetate fraction at 62.5 mg/kg/day. For evaluating the toxicogenetic potential of mimosa, two groups were included in the study being treated with extract at 250 mg/kg/day and acetate fraction of mimosa at 62 mg/kg/day, only. Extract of mimosa at concentrations of 62.5 and 125 mg decreased DNA damage in animals intoxicated with cadmium when compared to cadmium group. In a similar manner, treatment with ethyl acetate fraction of mimosa at 62.5 mg concentration in animals previously exposed to cadmium reduced genetic damage in peripheral blood cells. In a similar manner, the treatment with ethyl acetate fraction reduced DNA damage in liver cells. Oxidative DNA damage was reduced to animals exposed to cadmium and treated with 125 mg of extract as well as those intoxicated to cadmium and treated with 62.5 of acetate fraction of mimosa. Taken together, our results indicate that mimosa prevents genotoxicity induced by cadmium exposure in liver and peripheral blood cells of rats as a result of antioxidant activity.

Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

NASA Astrophysics Data System (ADS)

Hoell, Simon; Omenzetter, Piotr

2017-07-01

Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.

A judicious multiple hypothesis tracker with interacting feature extraction

NASA Astrophysics Data System (ADS)

McAnanama, James G.; Kirubarajan, T.

2009-05-01

The multiple hypotheses tracker (mht) is recognized as an optimal tracking method due to the enumeration of all possible measurement-to-track associations, which does not involve any approximation in its original formulation. However, its practical implementation is limited by the NP-hard nature of this enumeration. As a result, a number of maintenance techniques such as pruning and merging have been proposed to bound the computational complexity. It is possible to improve the performance of a tracker, mht or not, using feature information (e.g., signal strength, size, type) in addition to kinematic data. However, in most tracking systems, the extraction of features from the raw sensor data is typically independent of the subsequent association and filtering stages. In this paper, a new approach, called the Judicious Multi Hypotheses Tracker (jmht), whereby there is an interaction between feature extraction and the mht, is presented. The measure of the quality of feature extraction is input into measurement-to-track association while the prediction step feeds back the parameters to be used in the next round of feature extraction. The motivation for this forward and backward interaction between feature extraction and tracking is to improve the performance in both steps. This approach allows for a more rational partitioning of the feature space and removes unlikely features from the assignment problem. Simulation results demonstrate the benefits of the proposed approach.

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

PubMed

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection. The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated diagnosis of dry eye using infrared thermography images

NASA Astrophysics Data System (ADS)

Acharya, U. Rajendra; Tan, Jen Hong; Koh, Joel E. W.; Sudarshan, Vidya K.; Yeo, Sharon; Too, Cheah Loon; Chua, Chua Kuang; Ng, E. Y. K.; Tong, Louis

2015-07-01

Dry Eye (DE) is a condition of either decreased tear production or increased tear film evaporation. Prolonged DE damages the cornea causing the corneal scarring, thinning and perforation. There is no single uniform diagnosis test available to date; combinations of diagnostic tests are to be performed to diagnose DE. The current diagnostic methods available are subjective, uncomfortable and invasive. Hence in this paper, we have developed an efficient, fast and non-invasive technique for the automated identification of normal and DE classes using infrared thermography images. The features are extracted from nonlinear method called Higher Order Spectra (HOS). Features are ranked using t-test ranking strategy. These ranked features are fed to various classifiers namely, K-Nearest Neighbor (KNN), Nave Bayesian Classifier (NBC), Decision Tree (DT), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM) to select the best classifier using minimum number of features. Our proposed system is able to identify the DE and normal classes automatically with classification accuracy of 99.8%, sensitivity of 99.8%, and specificity if 99.8% for left eye using PNN and KNN classifiers. And we have reported classification accuracy of 99.8%, sensitivity of 99.9%, and specificity if 99.4% for right eye using SVM classifier with polynomial order 2 kernel.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

PubMed Central

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Traditional child health practices in communities in north-west Ethiopia.

PubMed

Dagnew, M B; Damena, M

1990-01-01

A study in 3 villages in northwest Ethiopia was designed to find the type and extent of health damaging traditional child practices. The result of the survey showed high rates of uvulectomy and milk teeth extraction, and low rates of eyelid incision and female circumcision. More than 84.5% and 98.8% of the mothers surveyed, respectively, reported milk teeth extraction as a useful treatment for diarrhea and eyelid incision in the treatment of eye disease. The damage done by milk teeth extraction includes complications from unhygienic conditions (e.g. ostitis and osteomyelitis). Although many children survive these ill advised procedures and their complications, the considerable damage done to some of the children makes these procedures serious health hazards. Intensive health education with relevant health activities to correct the use of these traditional practices is advised.

Genoprotective effect of Phyllanthus orbicularis extract against UVA, UVB and solar radiation.

PubMed

Vernhes Tamayo, Marioly; Schuch, André Passaglia; Yagura, Teiti; Baly Gil, Luis; Menck, Carlos Frederico Martins; Sánchez-Lamar, Angel

2018-05-16

One approach to protect the human skin against harmful effects of solar ultraviolet (UV) radiation is to use natural products as photoprotectors. In this work, the extract from specie Phyllanthus orbicularis K was evaluated as a protective agent against the photodamage by UVB, UVA artificial lamps and environmental sunlight exposure. The plasmid DNA solutions were exposed to radiations using the DNA-dosimeter system in presence of plant extract. The DNA repair enzymes, E. coli Formamidopyrimidine-DNA glycosylase (Fpg) and T4 bacteriophage endonuclease V (T4-endo V), were employed to discriminate oxidized DNA damage and cyclobutane pyrimidine dimers (CPD) respectively. The supercoiled and relaxed forms of DNA were separated through electrophoretic migration in agarose gels. These DNA forms were quantified to determine strands break, representing the types of lesion levels. The results showed that, in presence of P. orbicularis extract, the CPD and oxidative damage were reduced in irradiated DNA samples. The photoprotective effect of extract was more evident for UVB and sunlight radiation than for UVA. This work documents the UV absorbing properties of P. orbicularis aqueous extract and opens up new vistas in its characterization as protective agent against DNA damage induced by environmental sunlight radiation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Extract of Ginkgo biloba promotes neuronal regeneration in the hippocampus after exposure to acrylamide.

PubMed

Huang, Wei-Ling; Ma, Yu-Xin; Fan, Yu-Bao; Lai, Sheng-Min; Liu, Hong-Qing; Liu, Jing; Luo, Li; Li, Guo-Ying; Tian, Su-Min

2017-08-01

Previous studies have demonstrated a neuroprotective effect of extract of Ginkgo biloba against neuronal damage, but have mainly focused on antioxidation of extract of Ginkgo biloba . To date, limited studies have determined whether extrasct of Ginkgo biloba has a protective effect on neuronal damage. In the present study, acrylamide and 30, 60, and 120 mg/kg extract of Ginkgo biloba were administered for 4 weeks by gavage to establish mouse models. Our results showed that 30, 60, and 120 mg/kg extract of Ginkgo biloba effectively alleviated the abnormal gait of poisoned mice, and up-regulated protein expression levels of doublecortin (DCX), brain-derived neurotrophic factor, and growth associated protein-43 (GAP-43) in the hippocampus. Simultaneously, DCX- and GAP-43-immunoreactive cells increased. These findings suggest that extract of Ginkgo biloba can mitigate neurotoxicity induced by acrylamide, and thereby promote neuronal regeneration in the hippocampus of acrylamide-treated mice.

User-oriented summary extraction for soccer video based on multimodal analysis

NASA Astrophysics Data System (ADS)

Liu, Huayong; Jiang, Shanshan; He, Tingting

2011-11-01

An advanced user-oriented summary extraction method for soccer video is proposed in this work. Firstly, an algorithm of user-oriented summary extraction for soccer video is introduced. A novel approach that integrates multimodal analysis, such as extraction and analysis of the stadium features, moving object features, audio features and text features is introduced. By these features the semantic of the soccer video and the highlight mode are obtained. Then we can find the highlight position and put them together by highlight degrees to obtain the video summary. The experimental results for sports video of world cup soccer games indicate that multimodal analysis is effective for soccer video browsing and retrieval.

Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

NASA Astrophysics Data System (ADS)

Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

2017-01-01

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods

USGS Publications Warehouse

Germaine, Stephen S.; O'Donnell, Michael S.; Aldridge, Cameron L.; Baer, Lori; Fancher, Tammy; McBeth, Jamie; McDougal, Robert R.; Waltermire, Robert; Bowen, Zachary H.; Diffendorfer, James; Garman, Steven; Hanson, Leanne

2012-01-01

We evaluated how well three leading information-extraction software programs (eCognition, Feature Analyst, Feature Extraction) and manual hand digitization interpreted information from remotely sensed imagery of a visually complex gas field in Wyoming. Specifically, we compared how each mapped the area of and classified the disturbance features present on each of three remotely sensed images, including 30-meter-resolution Landsat, 10-meter-resolution SPOT (Satellite Pour l'Observation de la Terre), and 0.6-meter resolution pan-sharpened QuickBird scenes. Feature Extraction mapped the spatial area of disturbance features most accurately on the Landsat and QuickBird imagery, while hand digitization was most accurate on the SPOT imagery. Footprint non-overlap error was smallest on the Feature Analyst map of the Landsat imagery, the hand digitization map of the SPOT imagery, and the Feature Extraction map of the QuickBird imagery. When evaluating feature classification success against a set of ground-truthed control points, Feature Analyst, Feature Extraction, and hand digitization classified features with similar success on the QuickBird and SPOT imagery, while eCognition classified features poorly relative to the other methods. All maps derived from Landsat imagery classified disturbance features poorly. Using the hand digitized QuickBird data as a reference and making pixel-by-pixel comparisons, Feature Extraction classified features best overall on the QuickBird imagery, and Feature Analyst classified features best overall on the SPOT and Landsat imagery. Based on the entire suite of tasks we evaluated, Feature Extraction performed best overall on the Landsat and QuickBird imagery, while hand digitization performed best overall on the SPOT imagery, and eCognition performed worst overall on all three images. Error rates for both area measurements and feature classification were prohibitively high on Landsat imagery, while QuickBird was time and cost prohibitive for mapping large spatial extents. The SPOT imagery produced map products that were far more accurate than Landsat and did so at a far lower cost than QuickBird imagery. Consideration of degree of map accuracy required, costs associated with image acquisition, software, operator and computation time, and tradeoffs in the form of spatial extent versus resolution should all be considered when evaluating which combination of imagery and information-extraction method might best serve any given land use mapping project. When resources permit, attaining imagery that supports the highest classification and measurement accuracy possible is recommended.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

iFeature: a python package and web server for features extraction and selection from protein and peptide sequences.

PubMed

Chen, Zhen; Zhao, Pei; Li, Fuyi; Leier, André; Marquez-Lago, Tatiana T; Wang, Yanan; Webb, Geoffrey I; Smith, A Ian; Daly, Roger J; Chou, Kuo-Chen; Song, Jiangning

2018-03-08

Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection, and dimensionality reduction algorithms, greatly facilitating training, analysis, and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. jiangning.song@monash.edu; kcchou@gordonlifescience.org; roger.daly@monash.edu. Supplementary data are available at Bioinformatics online.

Effect of Proximity of Features on the Damage Threshold During Submicron Additive Manufacturing Via Two-Photon Polymerization

DOE PAGES

Saha, Sourabh K.; Divin, Chuck; Cuadra, Jefferson A.; ...

2017-05-12

Two-photon polymerization (TPP) is a laser writing process that enables fabrication of millimeter scale three-dimensional (3D) structures with submicron features. In TPP, writing is achieved via nonlinear two-photon absorption that occurs at high laser intensities. Thus, it is essential to carefully select the incident power to prevent laser damage during polymerization. Currently, the feasible range of laser power is identified by writing small test patterns at varying power levels. Here in this paper, we demonstrate that the results of these tests cannot be generalized, because the damage threshold power depends on the proximity of features and reduces by as muchmore » as 47% for overlapping features. We have identified that this reduction occurs primarily due to an increase in the single-photon absorptivity of the resin after curing. We have captured the damage from proximity effects via X-ray 3D computed tomography (CT) images of a non-homogenous part that has varying feature density. Part damage manifests as internal spherical voids that arise due to boiling of the resist. We have empirically quantified this proximity effect by identifying the damage threshold power at different writing speeds and feature overlap spacings. In addition, we present a first-order analytical model that captures the scaling of this proximity effect. Based on this model and the experiments, we have identified that the proximity effect is more significant at high writing speeds; therefore, it adversely affects the scalability of manufacturing. The scaling laws and the empirical data generated here can be used to select the appropriate TPP writing parameters.« less

Effect of Proximity of Features on the Damage Threshold During Submicron Additive Manufacturing Via Two-Photon Polymerization

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

Saha, Sourabh K.; Divin, Chuck; Cuadra, Jefferson A.

Two-photon polymerization (TPP) is a laser writing process that enables fabrication of millimeter scale three-dimensional (3D) structures with submicron features. In TPP, writing is achieved via nonlinear two-photon absorption that occurs at high laser intensities. Thus, it is essential to carefully select the incident power to prevent laser damage during polymerization. Currently, the feasible range of laser power is identified by writing small test patterns at varying power levels. Here in this paper, we demonstrate that the results of these tests cannot be generalized, because the damage threshold power depends on the proximity of features and reduces by as muchmore » as 47% for overlapping features. We have identified that this reduction occurs primarily due to an increase in the single-photon absorptivity of the resin after curing. We have captured the damage from proximity effects via X-ray 3D computed tomography (CT) images of a non-homogenous part that has varying feature density. Part damage manifests as internal spherical voids that arise due to boiling of the resist. We have empirically quantified this proximity effect by identifying the damage threshold power at different writing speeds and feature overlap spacings. In addition, we present a first-order analytical model that captures the scaling of this proximity effect. Based on this model and the experiments, we have identified that the proximity effect is more significant at high writing speeds; therefore, it adversely affects the scalability of manufacturing. The scaling laws and the empirical data generated here can be used to select the appropriate TPP writing parameters.« less

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

PubMed

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

In vivo evaluation of the genetic toxicity of Rubus niveus Thunb. (Rosaceae) extract and initial screening of its potential chemoprevention against doxorubicin-induced DNA damage.

PubMed

Tolentino, Flora; Araújo, Priscila Alves de; Marques, Eduardo de Souza; Petreanu, Marcel; Andrade, Sérgio Faloni de; Niero, Rivaldo; Perazzo, Fábio F; Rosa, Paulo César Pires; Maistro, Edson Luis

2015-04-22

Rubus niveus Thunb. plant belongs to Rosaceae family and have been used traditionally to treat wounds, burns, inflammation, dysentery, diarrhea and for curing excessive bleeding during menstrual cycle. The present study was undertaken to investigate the in vivo genotoxicity of Rubus niveus aerial parts extract and its possible chemoprotection on doxorubicin (DXR)-induced DNA damage. In parallel, the main phytochemicals constituents in the extract were determined. The animals were exposed to the extract for 24 and 48 h, and the doses selected were 500, 1000 and 2000 mg/kg b.w. administered by gavage alone or prior to DXR (30 mg/kg b.w.) administered by intraperitoneal injection. The endpoints analyzed were DNA damage in bone marrow and peripheral blood cells assessed by the alkaline alkaline (pH>13) comet assay and bone marrow micronucleus test. The results of chemical analysis of the extract showed the presence of tormentic acid, stigmasterol, quercitinglucoronide (miquelianin) and niga-ichigoside F1 as main compounds. Both cytogenetic endpoints analyzed showed that there were no statistically significant differences (p>0.05) between the negative control and the treated groups with the two higher doses of Rubus niveus extract alone, demonstrating absence of genotoxic and mutagenic effects. Aneugenic/clastogenic effect was observed only at 2000 mg/kg dose. On the other hand, in the both assays and all tested doses were observed a significant reduction of DNA damage and chromosomal aberrations in all groups co-treated with DXR and extract compared to those which received only DXR. These results indicate that Rubus niveus aerial parts extract did not revealed any genotoxic effect, but presented some aneugenic/clastogenic effect at higher dose; and suggest that it could be a potential adjuvant against development of second malignant neoplasms caused by the cancer chemotherapic DXR. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Strawberry Polyphenols Attenuate Ethanol-Induced Gastric Lesions in Rats by Activation of Antioxidant Enzymes and Attenuation of MDA Increase

PubMed Central

Alvarez-Suarez, José M.; Dekanski, Dragana; Ristić, Slavica; Radonjić, Nevena V.; Petronijević, Nataša D.; Giampieri, Francesca; Astolfi, Paola; González-Paramás, Ana M.; Santos-Buelga, Celestino; Tulipani, Sara; Quiles, José L.; Mezzetti, Bruno; Battino, Maurizio

2011-01-01

Background and Aim Free radicals are implicated in the aetiology of gastrointestinal disorders such as gastric ulcer, colorectal cancer and inflammatory bowel disease. Strawberries are common and important fruit due to their high content of essential nutrient and beneficial phytochemicals which seem to have relevant biological activity on human health. In the present study we investigated the antioxidant and protective effects of three strawberry extracts against ethanol-induced gastric mucosa damage in an experimental in vivo model and to test whether strawberry extracts affect antioxidant enzyme activities in gastric mucosa. Methods/Principal Findings Strawberry extracts were obtained from Adria, Sveva and Alba cultivars. Total antioxidant capacity and radical scavenging capacity were performed by TEAC, ORAC and electron paramagnetic resonance assays. Identification and quantification of anthocyanins was carried out by HPLC-DAD-MS analyses. Different groups of animals received 40 mg/day/kg body weight of strawberry crude extracts for 10 days. Gastric damage was induced by ethanol. The ulcer index was calculated together with the determination of catalase and SOD activities and MDA contents. Strawberry extracts are rich in anthocyanins and present important antioxidant capacity. Ethanol caused severe gastric damage and strawberry consumption protected against its deleterious role. Antioxidant enzyme activities increased significantly after strawberry extract intake and a concomitantly decrease in gastric lipid peroxidation was found. A significant correlation between total anthocyanin content and percent of inhibition of ulcer index was also found. Conclusions Strawberry extracts prevented exogenous ethanol-induced damage to rats' gastric mucosa. These effects seem to be associated with the antioxidant activity and phenolic content in the extract as well as with the capacity of promoting the action of antioxidant enzymes. A diet rich in strawberries might exert a beneficial effect in the prevention of gastric diseases related to generation of reactive oxygen species. PMID:22016781

Protective Effect of Baccharis trimera Extract on Acute Hepatic Injury in a Model of Inflammation Induced by Acetaminophen

PubMed Central

Pádua, Bruno da Cruz; Rossoni Júnior, Joamyr Victor; de Brito Magalhães, Cíntia Lopes; Chaves, Míriam Martins; Silva, Marcelo Eustáquio; Pedrosa, Maria Lucia; de Souza, Gustavo Henrique Bianco; Brandão, Geraldo Célio; Rodrigues, Ivanildes Vasconcelos; Lima, Wanderson Geraldo; Costa, Daniela Caldeira

2014-01-01

Background. Acetaminophen (APAP) is a commonly used analgesic and antipyretic. When administered in high doses, APAP is a clinical problem in the US and Europe, often resulting in severe liver injury and potentially acute liver failure. Studies have demonstrated that antioxidants and anti-inflammatory agents effectively protect against the acute hepatotoxicity induced by APAP overdose. Methods. The present study attempted to investigate the protective effect of B. trimera against APAP-induced hepatic damage in rats. The liver-function markers ALT and AST, biomarkers of oxidative stress, antioxidant parameters, and histopathological changes were examined. Results. The pretreatment with B. trimera attenuated serum activities of ALT and AST that were enhanced by administration of APAP. Furthermore, pretreatment with the extract decreases the activity of the enzyme SOD and increases the activity of catalase and the concentration of total glutathione. Histopathological analysis confirmed the alleviation of liver damage and reduced lesions caused by APAP. Conclusions. The hepatoprotective action of B. trimera extract may rely on its effect on reducing the oxidative stress caused by APAP-induced hepatic damage in a rat model. General Significance. These results make the extract of B. trimera a potential candidate drug capable of protecting the liver against damage caused by APAP overdose. PMID:25435714

DNA and BSA damage inhibitory activities, and anti-acetylcholinesterase, anti-porcine α-amylase and antioxidant properties of Dolichos lablab beans.

PubMed

Habib, Hosam M; Theuri, Serah W; Kheadr, Ehab; Mohamed, Fedah E

2017-02-22

The underutilized Kenyan variety of Dolichos lablab bean seeds serve as a good source of natural antioxidants, which can probably be effective in reducing the risk of occurrence of several diseases. This study was undertaken for the first time to address the limited knowledge regarding the antioxidant activities of lablab beans. Moreover, their DNA damage inhibitory activity, bovine serum albumin (BSA) damage inhibitory activity, and the inhibition of acetylcholinesterase and porcine α-amylase were also investigated. The antioxidant capacity of Dolichos lablab bean seeds extracted with methanol, water or methanol/water combination was evaluated by the ferric-reducing antioxidant power (FRAP) assay, free radical-scavenging activity, 1,1-diphenyl-2-picrylhydrazyl (DPPH), nitric oxide (NO) radical-scavenging assay, and 2,20-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS). Results reported in the present study indicate that water, methanol and water/methanol extracts of lablab bean flour exhibited good antioxidant activity by effectively scavenging various free radicals, such as DPPH, NO, and ABTS radicals. The extracts also exhibited protective effects against DNA and BSA damage and inhibitory effects on porcine α-amylase. Findings of this study suggest that extracts from the lablab bean flour would have potential application in food supplements, and pharmaceutical and cosmetic industries.

Robust digital image watermarking using distortion-compensated dither modulation

NASA Astrophysics Data System (ADS)

Li, Mianjie; Yuan, Xiaochen

2018-04-01

In this paper, we propose a robust feature extraction based digital image watermarking method using Distortion- Compensated Dither Modulation (DC-DM). Our proposed local watermarking method provides stronger robustness and better flexibility than traditional global watermarking methods. We improve robustness by introducing feature extraction and DC-DM method. To extract the robust feature points, we propose a DAISY-based Robust Feature Extraction (DRFE) method by employing the DAISY descriptor and applying the entropy calculation based filtering. The experimental results show that the proposed method achieves satisfactory robustness under the premise of ensuring watermark imperceptibility quality compared to other existing methods.

Coronectomy of third molar: a reduced risk technique for inferior alveolar nerve damage.

PubMed

Ahmed, Chkoura; Wafae, El Wady; Bouchra, Taleb

2011-05-01

Causing damage to the inferior alveolar nerve (IAN) when extracting lower third molars is due to the intimate relationship between the nerve and the roots of the teeth. When the proximity radiologic markers between the IAN and the root of the third molars are present, the technique of coronectomy can be proposed as an alternative to extraction to minimize the risk of nerve injury, with minimal complications. Nerve injury after the extraction of the mandibular third molar is a serious complication. The technique of coronectomy can be proposed to minimize the risk.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

DNA excision repair in cell extracts from human cell lines exhibiting hypersensitivity to DNA-damaging agents

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

Hansson, J.; Keyse, S.M.; Lindahl, T.

Whole cell extracts from human lymphoid cell lines can perform in vitro DNA repair synthesis in plasmids damaged by agents including UV or cis-diamminedichloroplatinum(II) (cis-DDP). Extracts from xeroderma pigmentosum (XP) cells are defective in repair synthesis. We have now studied in vitro DNA repair synthesis using extracts from lymphoblastoid cell lines representing four human hereditary syndromes with increased sensitivity to DNA-damaging agents. Extracts of cell lines from individuals with the sunlight-sensitive disorders dysplastic nevus syndrome or Cockayne's syndrome (complementation groups A and B) showed normal DNA repair synthesis in plasmids with UV photoproducts. This is consistent with in vivo measurementsmore » of the overall DNA repair capacity in such cell lines. A number of extracts were prepared from two cell lines representing the variant form of XP (XP-V). Half of the extracts prepared showed normal levels of in vitro DNA repair synthesis in plasmids containing UV lesions, but the remainder of the extracts from the same cell lines showed deficient repair synthesis, suggesting the possibility of an unusually labile excision repair protein in XP-V. Fanconi's anemia (FA) cells show cellular hypersensitivity to cross-linking agents including cis-DDP. Extracts from cell lines belonging to two different complementation groups of FA showed normal DNA repair synthesis in plasmids containing cis-DDP or UV adducts. Thus, there does not appear to be an overall excision repair defect in FA, but the data do not exclude a defect in the repair of interstrand DNA cross-links.« less

Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

NASA Astrophysics Data System (ADS)

Li, Quanbao; Wei, Fajie; Zhou, Shenghan

2017-05-01

The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

Protective Effect of Onion Extract on Bleomycin-Induced Cytotoxicity and Genotoxicity in Human Lymphocytes

PubMed Central

Cho, Yoon Hee; Lee, Joong Won; Woo, Hae Dong; Lee, Sunyeong; Kim, Yang Jee; Lee, Younghyun; Shin, Sangah; Joung, Hyojee; Chung, Hai Won

2016-01-01

Following one of the world’s largest nuclear accidents, occured at Fukushima, Japan in 2011, a significant scientific effort has focused on minimizing the potential adverse health effects due to radiation exposure. The use of natural dietary antioxidants to reduce the risk of radiation-induced oxidative DNA damage is a simple strategy for minimizing radiation-related cancer rates and improving overall health. The onion is among the richest sources of dietary flavonoids and is an important food for increasing their overall intake. Therefore, we examined the effect of an onion extract on cyto- and geno-toxicity in human lymphocytes treated with bleomycin (BLM), a radiomimetic agent. In addition, we measured the frequency of micronuclei (MN) and DNA damage following treatment with BLM using a cytokinesis-blocked micronucleus assay and a single cell gel electrophoresis assay. We observed a significant increase in cell viability in lymphocytes treated with onion extract then exposed to BLM compared to cells treated with BLM alone. The frequency of BLM induced MN and DNA damage increased in a dose-dependent manner; however, when lymphocytes were pretreated with onion extract (10 and 20 μL/mL), the frequency of BLM-induced MN was decreased at all doses of BLM and DNA damage was decreased at 3 μg/mL of BLM. These results suggest that onion extract may have protective effects against BLM-induced cyto- and genotoxicity in human lymphocytes. PMID:26907305

Protective Effect of Onion Extract on Bleomycin-Induced Cytotoxicity and Genotoxicity in Human Lymphocytes.

PubMed

Cho, Yoon Hee; Lee, Joong Won; Woo, Hae Dong; Lee, Sunyeong; Kim, Yang Jee; Lee, Younghyun; Shin, Sangah; Joung, Hyojee; Chung, Hai Won

2016-02-19

Following one of the world's largest nuclear accidents, occured at Fukushima, Japan in 2011, a significant scientific effort has focused on minimizing the potential adverse health effects due to radiation exposure. The use of natural dietary antioxidants to reduce the risk of radiation-induced oxidative DNA damage is a simple strategy for minimizing radiation-related cancer rates and improving overall health. The onion is among the richest sources of dietary flavonoids and is an important food for increasing their overall intake. Therefore, we examined the effect of an onion extract on cyto- and geno-toxicity in human lymphocytes treated with bleomycin (BLM), a radiomimetic agent. In addition, we measured the frequency of micronuclei (MN) and DNA damage following treatment with BLM using a cytokinesis-blocked micronucleus assay and a single cell gel electrophoresis assay. We observed a significant increase in cell viability in lymphocytes treated with onion extract then exposed to BLM compared to cells treated with BLM alone. The frequency of BLM induced MN and DNA damage increased in a dose-dependent manner; however, when lymphocytes were pretreated with onion extract (10 and 20 μL/mL), the frequency of BLM-induced MN was decreased at all doses of BLM and DNA damage was decreased at 3 μg/mL of BLM. These results suggest that onion extract may have protective effects against BLM-induced cyto- and genotoxicity in human lymphocytes.

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

PubMed Central

Wen, Tingxi; Zhang, Zhongnan

2017-01-01

Abstract In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy. PMID:28489789

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification.

PubMed

Wen, Tingxi; Zhang, Zhongnan

2017-05-01

In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

Diabetic Rethinopathy Screening by Bright Lesions Extraction from Fundus Images

NASA Astrophysics Data System (ADS)

Hanđsková, Veronika; Pavlovičova, Jarmila; Oravec, Miloš; Blaško, Radoslav

2013-09-01

Retinal images are nowadays widely used to diagnose many diseases, for example diabetic retinopathy. In our work, we propose the algorithm for the screening application, which identifies the patients with such severe diabetic complication as diabetic retinopathy is, in early phase. In the application we use the patient's fundus photography without any additional examination by an ophtalmologist. After this screening identification, other examination methods should be considered and the patient's follow-up by a doctor is necessary. Our application is composed of three principal modules including fundus image preprocessing, feature extraction and feature classification. Image preprocessing module has the role of luminance normalization, contrast enhancement and optical disk masking. Feature extraction module includes two stages: bright lesions candidates localization and candidates feature extraction. We selected 16 statistical and structural features. For feature classification, we use multilayer perceptron (MLP) with one hidden layer. We classify images into two classes. Feature classification efficiency is about 93 percent.

Ginger Treatment Ameliorates Alcohol-induced Myocardial Damage by Suppression of Hyperlipidemia and Cardiac Biomarkers in Rats.

PubMed

Subbaiah, Ganjikunta Venkata; Mallikarjuna, Korivi; Shanmugam, Bhasha; Ravi, Sahukari; Taj, Patan Usnan; Reddy, Kesireddy Sathyavelu

2017-01-01

Alcohol-induced hyperlipidemia is positively correlated with cardiovascular diseases. Several herbal extracts have been reported to protect the cardiac injury and suppress the hyperlipidemia. However, the effect of ginger extracts on alcohol-induced hyperlipidemia and associated myocardial damage remains unclear. This study investigated the cardio-protective properties of ginger ethanolic extract (Gt) against alcohol-induced myocardial damage, and further distinguished the association between hyperlipidemia and occurrence of myocardial damage in rats. Twenty four Wistar male albino rats (250 ± 20 g) were divided into four groups including, Normal control (NC) (0.9% NaCl), Ginger treated (Gt) (200 mg/Kg b.w.), Alcohol treated (At) (20% of 6g/kg b.w. alcohol), and Alcohol along with Ginger treatment (At+Gt). In this study, lipid profiles such as fatty acids, triglycerides, total cholesterol, phospholipids, low density lipoprotein and high density lipoproteins, and cardiac biomarkers, including LDH, AST, CK-MB, cTn-T and cTn-I were examined in rats. Furthermore, histopathological studies were also conducted. We found that alcohol-induced myocardial damage was associated with increased lipid profile except high density lipoprotein in alcohol treated (20%, 6g/kg b.w.) rats compared with control. Ginger treatment significantly reduced the alcohol-induced lipid profiles except high density lipoproteins. Furthermore, elevated cardiac biomarkers activity with alcohol intoxication was substantially suppressed by ginger treatment. In addition, ginger treatment for 7-weeks significantly minimized the alcohol-induced myocardial damage. Our results concluded that ginger could protect alcohol-induced myocardial damage by suppression of hyperlipidemia and cardiac biomarkers. Ginger extract could alleviate the myocardial injury partially due to the suppression of circulating FFAs and TG levels.Increased circulating cholesterol, LDL and phospholipids with alcohol intake were substantially suppressed by ginger treatmentAlcohol, induced an increase in cardiac damage biomarkers, CK-MB, cTn-T and cTn-I were remarkably suppressed by ginger treatmentPerformed histopathological studies by transmission electron microscopy and light microscopy shows additional convincing evidence on ginger cardio-protective effects. The drastic changes were rehabilitated in cardiac tissue by ginger treatment may be it acts as a good antioxidant and possessing hypolipidemic activity.Collectively, our findings confirm hypothesis that ginger has cardio protective potential through suppression of hyperlipidemia, preserving the tissue damage bio markers, cardiac biomarkers in plasma and preservation of histoarchitecture of myocytes. Abbreviations used: Gt: Ginger Ethanolic Extract; NC: Normal Control; At: Alcohol treated; MI: Myocardial Infarction.

In vivo antioxidant effect of aqueous root bark, stem bark and leaves extracts of Vitex doniana in CCl4 induced liver damage rats.

PubMed

Adetoro, Kadejo Olubukola; Bolanle, James Dorcas; Abdullahi, Sallau Balarebe; Ahmed, Ozigi Abdulrahaman

2013-05-01

The antioxidant effects of aqueous root bark, stem bark and leaves of Vitex doniana (V. doniana) were evaluated in carbon tetrachloride (CCl4) induced liver damage and non induced liver damage albino rats. A total of 60 albino rats (36 induced liver damage and 24 non induced liver damage) were assigned into liver damage and non liver damage groups of 6 rats in a group. The animals in the CCl4 induced liver damage groups, were induced by intraperitoneal injection with a single dose of CCl4 (148 mg·ml(-1)·kg(-1) body weight) as a 1:1 (v/v) solution in olive oil and were fasted for 36 h before the subsequent treatment with aqueous root bark, stem bark and leaves extracts of V. doniana and vitamin E as standard drug (100 mg/kg body weighy per day) for 21 d, while the animals in the non induced groups were only treated with the daily oral administration of these extracts at the same dose. The administration of CCl4 was done once a week for a period of three weeks. The liver of CCl4 induced not treated group showed that the induction with CCl4, significantly (P<0.05) increased thiobarbituric acid reactive substance (TBARS) and significantly (P<0.05) decreased superoxide dismutase (SOD) and catalase (CAT). However there was no significant (P>0.05) difference between TBARS, SOD and CAT in the liver of the induced treated groups and normal control group. In the kidney, TBARS showed no significant (P>0.05) difference between the normal and the induced groups, SOD was significantly (P<0.05) reduced in the CCl4 group compared to standard drug and normal control groups, CAT was significantly (P<0.05) increased in root and vitamin E groups when compared to induced not treated group. The studies also showed that when the extracts were administered to normal animals, there was no significant (P>0.05) change in the liver and kidney level of TBARS, SOD and CAT compared with the normal control except in the kidney of animals treated with stem extract where TBARS was significantly (P<0.05) lowered compared to control group. The result of the present study suggests that application of V. doniana plant would play an important role in increasing the antioxidant effect and reducing the oxidative damage that formed both in liver and in kidney tissues. However stem bark has potential to improve renal function in normal rats.

Antimutagenic and free radical scavenger effects of leaf extracts from Accacia salicina

PubMed Central

2011-01-01

Background Three extracts were prepared from the leaves of Accacia salicina; ethyl acetate (EA), chloroform (Chl) and petroleum ether (PE) extracts and was designed to examine antimutagenic, antioxidant potenty and oxidative DNA damage protecting activity. Methods Antioxidant activity of A. salicina extracts was determined by the ability of each extract to protect against plasmid DNA strand scission induced by hydroxyl radicals. An assay for the ability of these extracts to prevent mutations induced by various oxidants in Salmonella typhimurium TA102 and TA 104 strains was conducted. In addition, nonenzymatic methods were employed to evaluate anti-oxidative effects of tested extracts. Results These extracts from leaf parts of A. salicina showed no mutagenicity either with or without the metabolic enzyme preparation (S9). The highest protections against methylmethanesulfonate induced mutagenicity were observed with all extracts and especially chloroform extract. This extract exhibited the highest inhibitiory level of the Ames response induced by the indirect mutagen 2- aminoanthracene. All extracts exhibited the highest ability to protect plasmid DNA against hydroxyl radicals induced DNA damages. The ethyl acetate (EA) and chloroform (Chl) extracts showed with high TEAC values radical of 0.95 and 0.81 mM respectively, against the ABTS.+. Conclusion The present study revealed the antimutagenic and antioxidant potenty of plant extract from Accacia salicina leaves. PMID:22132863

Detection of Delamination in Concrete Bridge Decks Using Mfcc of Acoustic Impact Signals

NASA Astrophysics Data System (ADS)

Zhang, G.; Harichandran, R. S.; Ramuhalli, P.

2010-02-01

Delamination of the concrete cover is a commonly observed damage in concrete bridge decks. The delamination is typically initiated by corrosion of the upper reinforcing bars and promoted by freeze-thaw cycling and traffic loading. The detection of delamination is important for bridge maintenance and acoustic non-destructive evaluation (NDE) is widely used due to its low cost, speed, and easy implementation. In traditional acoustic approaches, the inspector sounds the surface of the deck by impacting it with a hammer or bar, or by dragging a chain, and assesses delamination by the "hollowness" of the sound. The detection of the delamination is subjective and requires extensive training. To improve performance, this paper proposes an objective method for delamination detection. In this method, mel-frequency cepstral coefficients (MFCC) of the signal are extracted. Some MFCC are then selected as features for detection purposes using a mutual information criterion. Finally, the selected features are used to train a classifier which is subsequently used for detection. In this work, a simple quadratic Bayesian classifier is used. Different numbers of features are used to compare the performance of the detection method. The results show that the performance first increases with the number of features, but then decreases after an optimal value. The optimal number of features based on the recorded signals is four, and the mean error rate is only 3.3% when four features are used. Therefore, the proposed algorithm has sufficient accuracy to be used in field detection.

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

Visual feature integration with an attention deficit.

PubMed

Arguin, M; Cavanagh, P; Joanette, Y

1994-01-01

Treisman's feature integration theory proposes that the perception of illusory conjunctions of correctly encoded visual features is due to the failure of an attentional process. This hypothesis was examined by studying brain-damaged subjects who had previously been shown to have difficulty in attending to contralesional stimulation. These subjects exhibited a massive feature integration deficit for contralesional stimulation relative to ipsilesional displays. In contrast, both normal age-matched controls and brain-damaged subjects who did not exhibit any evidence of an attention deficit showed comparable feature integration performance with left- and right-hemifield stimulation. These observations indicate the crucial function of attention for visual feature integration in normal perception.

Extraction of ECG signal with adaptive filter for hearth abnormalities detection

NASA Astrophysics Data System (ADS)

Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti

2018-04-01

This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

Recursive Feature Extraction in Graphs

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

2014-08-14

ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

Reliability based impact localization in composite panels using Bayesian updating and the Kalman filter

NASA Astrophysics Data System (ADS)

Morse, Llewellyn; Sharif Khodaei, Zahra; Aliabadi, M. H.

2018-01-01

In this work, a reliability based impact detection strategy for a sensorized composite structure is proposed. Impacts are localized using Artificial Neural Networks (ANNs) with recorded guided waves due to impacts used as inputs. To account for variability in the recorded data under operational conditions, Bayesian updating and Kalman filter techniques are applied to improve the reliability of the detection algorithm. The possibility of having one or more faulty sensors is considered, and a decision fusion algorithm based on sub-networks of sensors is proposed to improve the application of the methodology to real structures. A strategy for reliably categorizing impacts into high energy impacts, which are probable to cause damage in the structure (true impacts), and low energy non-damaging impacts (false impacts), has also been proposed to reduce the false alarm rate. The proposed strategy involves employing classification ANNs with different features extracted from captured signals used as inputs. The proposed methodologies are validated by experimental results on a quasi-isotropic composite coupon impacted with a range of impact energies.

Pomegranate extract protects against cerebral ischemia/reperfusion injury and preserves brain DNA integrity in rats.

PubMed

Ahmed, Maha A E; El Morsy, Engy M; Ahmed, Amany A E

2014-08-21

Interruption to blood flow causes ischemia and infarction of brain tissues with consequent neuronal damage and brain dysfunction. Pomegranate extract is well tolerated, and safely consumed all over the world. Interestingly, pomegranate extract has shown remarkable antioxidant and anti-inflammatory effects in experimental models. Many investigators consider natural extracts as novel therapies for neurodegenerative disorders. Therefore, this study was carried out to investigate the protective effects of standardized pomegranate extract against cerebral ischemia/reperfusion-induced brain injury in rats. Adult male albino rats were randomly divided into sham-operated control group, ischemia/reperfusion (I/R) group, and two other groups that received standardized pomegranate extract at two dose levels (250, 500 mg/kg) for 15 days prior to ischemia/reperfusion (PMG250+I/R, and PMG500+I/R groups). After I/R or sham operation, all rats were sacrificed and brains were harvested for subsequent biochemical analysis. Results showed reduction in brain contents of MDA (malondialdehyde), and NO (nitric oxide), in addition to enhancement of SOD (superoxide dismutase), GPX (glutathione peroxidase), and GRD (glutathione reductase) activities in rats treated with pomegranate extract prior to cerebral I/R. Moreover, pomegranate extract decreased brain levels of NF-κB p65 (nuclear factor kappa B p65), TNF-α (tumor necrosis factor-alpha), caspase-3 and increased brain levels of IL-10 (interleukin-10), and cerebral ATP (adenosine triphosphate) production. Comet assay showed less brain DNA (deoxyribonucleic acid) damage in rats protected with pomegranate extract. The present study showed, for the first time, that pre-administration of pomegranate extract to rats, can offer a significant dose-dependent neuroprotective activity against cerebral I/R brain injury and DNA damage via antioxidant, anti-inflammatory, anti-apoptotic and ATP-replenishing effects. Copyright © 2014 Elsevier Inc. All rights reserved.

Robust image features: concentric contrasting circles and their image extraction

NASA Astrophysics Data System (ADS)

Gatrell, Lance B.; Hoff, William A.; Sklair, Cheryl W.

1992-03-01

Many computer vision tasks can be simplified if special image features are placed on the objects to be recognized. A review of special image features that have been used in the past is given and then a new image feature, the concentric contrasting circle, is presented. The concentric contrasting circle image feature has the advantages of being easily manufactured, easily extracted from the image, robust extraction (true targets are found, while few false targets are found), it is a passive feature, and its centroid is completely invariant to the three translational and one rotational degrees of freedom and nearly invariant to the remaining two rotational degrees of freedom. There are several examples of existing parallel implementations which perform most of the extraction work. Extraction robustness was measured by recording the probability of correct detection and the false alarm rate in a set of images of scenes containing mockups of satellites, fluid couplings, and electrical components. A typical application of concentric contrasting circle features is to place them on modeled objects for monocular pose estimation or object identification. This feature is demonstrated on a visually challenging background of a specular but wrinkled surface similar to a multilayered insulation spacecraft thermal blanket.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

A method for real-time implementation of HOG feature extraction

NASA Astrophysics Data System (ADS)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.

PubMed

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-09-13

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

Aged garlic extract protects against methotrexate-induced apoptotic cell injury of IEC-6 cells.

PubMed

Horie, Toshiharu; Li, Tiesong; Ito, Kousei; Sumi, Shin-ichiro; Fuwa, Toru

2006-03-01

Gastrointestinal toxicity is one of the most serious side effects of methotrexate (MTX) treatment. The side effects often disrupt the cancer chemotherapy. We previously reported that aged garlic extract (AGE) protects the small intestine of rats from MTX-induced damage. In this study, the protection of AGE against MTX-induced damage of IEC-6 cells originating from the rat jejunum crypt was investigated. MTX decreased the viability of IEC-6 cells, but this effect was prevented by AGE (0.5%). The MTX-induced apoptosis of IEC-6 cells was depressed by AGE. These results indicated that AGE protects IEC-6 cells from the MTX-induced damage. AGE may be useful in cancer chemotherapy with MTX because it reduces MTX-induced intestinal damage.

Using input feature information to improve ultraviolet retrieval in neural networks

NASA Astrophysics Data System (ADS)

Sun, Zhibin; Chang, Ni-Bin; Gao, Wei; Chen, Maosi; Zempila, Melina

2017-09-01

In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.

Wire bonding quality monitoring via refining process of electrical signal from ultrasonic generator

NASA Astrophysics Data System (ADS)

Feng, Wuwei; Meng, Qingfeng; Xie, Youbo; Fan, Hong

2011-04-01

In this paper, a technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis (PCA) method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

A Hybrid Neural Network and Feature Extraction Technique for Target Recognition.

DTIC Science & Technology

target features are extracted, the extracted data being evaluated in an artificial neural network to identify a target at a location within the image scene from which the different viewing angles extend.

Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

NASA Astrophysics Data System (ADS)

Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

2017-09-01

Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

A harmonic linear dynamical system for prominent ECG feature extraction.

PubMed

Thi, Ngoc Anh Nguyen; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc

2014-01-01

Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

Myricitrin and bioactive extract of Albizia amara leaves: DNA protection and modulation of fertility and antioxidant-related genes expression.

PubMed

Kassem, Mona El Said; Ibrahim, Lamya Fawzy; Hussein, Sameh Reda; El-Sharawy, Reham; El-Ansari, Mohamed Amin; Hassanane, Mahrousa Mohamed; Booles, Hoda Fahime

2016-11-01

Albizia species are reported to exhibit many biological activities including antiovulatory properties in female rats and antispermatogenic and antiandrogenic activities in male rats. The present study investigates the flavonoids of Albizia amara (Roxb.) B. Boivin (Fabaceae) leaves and evaluates their activity on gene expression of fertility and antioxidant glutathione-S-transferase-related genes of treated female mice in addition to their effect on DNA damage. Plant materials were extracted by using 70% methanol for 48 h, the extract was chromatographed on a polyamide 6S column, each isolated compound was purified by using Sephadex LH-20 column; its structure was elucidated by chemical and spectral methods. Both the leaves extract and myricitrin (200, 30 mg/kg bw/d, respectively) were assayed for their effect on DNA damage in female mice after four weeks treatment using Comet assay. Their modulatory activity on gene expression of fertility (aromatase CYP19 and luteinizing hormone LH) and antioxidant glutathione-S-transferase (GST)-related genes of treated female mice were investigated by real-time PCR (qPCR). Quercetin-3-O-gentiobioside, myricitrin, quercetin-3-O-α-rhamnopyranoside, myricetin, quercetin and kaempferol were isolated and identified from the studied taxa. Myricitrin and the extract induced low rate of DNA damage (4.8% and 5%, respectively), compared with the untreated control (4.2%) and significantly down-regulated the expression of CYP19 and LH genes and up-regulated GST gene. Our results highlight the potential effect of the leaves extract of Albizia amara and myricitrin as fertility-regulating phytoconstituents with ability to protect DNA from damage and cells from oxidative stress.

The effects of strawberry tree water leaf extract, arbutin and hydroquinone on haematological parameters and levels of primary DNA damage in white blood cells of rats.

PubMed

Jurica, Karlo; Brčić Karačonji, Irena; Kopjar, Nevenka; Shek-Vugrovečki, Ana; Cikač, Tihana; Benković, Vesna

2018-04-06

Strawberry tree (Arbutus unedo L., Ericaceae) leaves represent a potent source of biologically active compounds and have been used for a long to relieve symptoms of various health impairments and diseases. Two major compounds related to their beneficial activities in animals and humans are arbutin and hydroquinone. To establish potential benefit/risk ratio associated with daily oral administration of strawberry tree water leaf extract, arbutin and hydroquinone in doses expected to be non-toxic. We performed a 14-day and a 28-day study on male and female Lewis rats and evaluated main haematological parameters and the effects of treatments on the levels of primary DNA damage in white blood cells (WBC) using the alkaline comet assay. Our findings suggest no significant changes in the haematological parameters following prolonged exposure to strawberry tree water leaf extract, arbutin, and hydroquinone. However, hydroquinone causes increased, and extract as well as arbutin decreased WBC count in male rats compared to control after 14 days of treatment. DNA damage measured in WBC of rats treated with all compounds was below 10% of the DNA in the comet tail, which indicates low genotoxicity. The genotoxic potential of strawberry water leaf extract was within acceptable limits and reflected effects of a complex chemical composition upon DNA. We also observed slight gender- and exposure time- related differences in primary DNA damage in the leucocytes of control and treated rats. Future studies should investigate which doses of strawberry tree water leaf extract would be most promising for the potential use as a substitute for bearberry leaves for treatment of urinary infection. Copyright © 2017 Elsevier B.V. All rights reserved.

Earthquake Building Damage Mapping Based on Feature Analyzing Method from Synthetic Aperture Radar Data

NASA Astrophysics Data System (ADS)

An, L.; Zhang, J.; Gong, L.

2018-04-01

Playing an important role in gathering information of social infrastructure damage, Synthetic Aperture Radar (SAR) remote sensing is a useful tool for monitoring earthquake disasters. With the wide application of this technique, a standard method, comparing post-seismic to pre-seismic data, become common. However, multi-temporal SAR processes, are not always achievable. To develop a post-seismic data only method for building damage detection, is of great importance. In this paper, the authors are now initiating experimental investigation to establish an object-based feature analysing classification method for building damage recognition.

Shock induced damage in copper: A before and after, three-dimensional study

NASA Astrophysics Data System (ADS)

Menasche, David B.; Lind, Jonathan; Li, Shiu Fai; Kenesei, Peter; Bingert, John F.; Lienert, Ulrich; Suter, Robert M.

2016-04-01

We report on the microstructural features associated with the formation of incipient spall and damage in a fully recrystallized, high purity copper sample. Before and after ballistic shock loading, approximately 0.8 mm3 of the sample's crystal lattice orientation field is mapped using non-destructive near-field High Energy Diffraction Microscopy. Absorption contrast tomography is used to image voids after loading. This non-destructive interrogation of damage initiation allows for novel characterization of spall points vis-a-vis microstructural features and a fully 3D examination of microstructural topology and its influence on incipient damage. The spalled region is registered with and mapped back onto the pre-shock orientation field. As expected, the great majority of voids occur at grain boundaries and higher order microstructural features; however, we find no statistical preference for particular grain boundary types. The damaged region contains a large volume of Σ-3 (60 °<111 >) connected domains with a large area fraction of incoherent Σ-3 boundaries.

Neuroprotection of Chrysanthemum indicum Linne against cerebral ischemia/reperfusion injury by anti-inflammatory effect in gerbils

PubMed Central

Yoo, Ki-Yeon; Kim, In Hye; Cho, Jeong-Hwi; Ahn, Ji Hyeon; Park, Joon Ha; Lee, Jae-Chul; Tae, Hyun-Jin; Kim, Dae Won; Kim, Jong-Dai; Hong, Seongkweon; Won, Moo-Ho; Kang, Il Jun

2016-01-01

In this study, we tried to verify the neuroprotective effect of Chrysanthemum indicum Linne (CIL) extract, which has been used as a botanical drug in East Asia, against ischemic damage and to explore the underlying mechanism involving the anti-inflammatory approach. A gerbil was given CIL extract for 7 consecutive days followed by bilateral carotid artery occlusion to make a cerebral ischemia/reperfusion model. Then, we found that CIL extracts protected pyramidal neurons in the hippocampal CA1 region (CA1) from ischemic damage using neuronal nucleus immunohistochemistry and Fluoro-Jade B histofluorescence. Accordingly, interleukin-13 immunoreactivities in the CA1 pyramidal neurons of CIL-pretreated animals were maintained or increased after cerebral ischemia/reperfusion. These findings indicate that the pre-treatment of CIL can attenuate neuronal damage/death in the brain after cerebral ischemia/reperfusion via an anti-inflammatory approach. PMID:27073380

Seabuckthron (Hippophae rhamnoides L.) leaf extract ameliorates the gamma radiation mediated DNA damage and hepatic alterations.

PubMed

Khan, Amitava; Manna, Krishnendu; Chinchubose; Das, Dipesh Kr; Sinha, Mahuya; Kesh, Swaraj Bandhu; Das, Ujjal; Dey, Rakhi Sharma; Banerji, Asoke; Dey, Sanjit

2014-10-01

In vitro assessment showed that H. rhamnoides (HrLE) extract possessed free radical scavenging activities and can protect gamma (gamma) radiation induced supercoiled DNA damage. For in vivo study, Swiss albino mice were administered with HrLE (30 mg/kg body weight) for 15 consecutive days before exposing them to a single dose of 5 Gy of beta radiation. HrLE significantly prevented the radiation induced genomic DNA damage indicated as a significant reduction in the comet parameters. The lipid peroxidation, liver function enzymes, expression of phosphorylated NFkappaB (p65) and IkappaBalpha increased whereas the endogenous antioxidants diminished upon radiation exposure compared to control. Pretreatment of HrLE extract ameliorated these changes. Based on the present results it can be concluded that H. rhamnoides possess a potential preventive element in planned and accidental nuclear exposures.

Self-diagnosis of damage in fibrous composites using electrical resistance measurements

NASA Astrophysics Data System (ADS)

Kang, Ji Ho; Paty, Spandana; Kim, Ran Y.; Tandon, G. P.

2006-03-01

The objective of this research was to develop a practical integrated approach using extracted features from electrical resistance measurements and coupled electromechanical models of damage, for in situ damage detection and sensing in carbon fiber reinforced plastic (CFRP) composite structures. To achieve this objective, we introduced specific known damage (in terms of type, size, and location) into CFRP laminates and established quantitative relationships with the electrical resistance measurements. For processing of numerous measurement data, an autonomous data acquisition system was devised. We also established a specimen preparation procedure and a method for electrode setup. Coupon and panel CFRP laminate specimens with several known damage were tested and post-processed with the measurement data. Coupon specimens with various sizes of artificial delaminations obtained by inserting Teflon film were manufactured and the resistance was measured. The measurement results showed that increase of delamination size led to increase of resistance implying that it is possible to sense the existence and size of delamination. Encouraged by the results of coupon specimens, we implemented the measurement system on panel specimens. Three different quasi-isotropic panels were designed and manufactured: a panel with artificial delamination by inserting Teflon film at the midplane, a panel with artificial delamination by inserting Teflon film between the second and third plies from the surface, and an undamaged panel. The first two panels were designed to determine the feasibility of detecting delamination using the developed measurement system. The third panel had no damage at first, and then three different sizes of holes were drilled at a chosen location. Panels were prepared using the established procedures with six electrode connections on each side making a total of twenty-four electrode connections for a panel. All possible pairs of electrodes were scanned and the resistance was measured for each pair. The measurement results showed the possibility of the established measurement system for an in-situ damage detection method for CFRP composite structures.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images.

PubMed

Gumaei, Abdu; Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-05-15

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang's method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

A multiple maximum scatter difference discriminant criterion for facial feature extraction.

PubMed

Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei

2007-12-01

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.

Phase space interrogation of the empirical response modes for seismically excited structures

NASA Astrophysics Data System (ADS)

Paul, Bibhas; George, Riya C.; Mishra, Sudib K.

2017-07-01

Conventional Phase Space Interrogation (PSI) for structural damage assessment relies on exciting the structure with low dimensional chaotic waveform, thereby, significantly limiting their applicability to large structures. The PSI technique is presently extended for structure subjected to seismic excitations. The high dimensionality of the phase space for seismic response(s) are overcome by the Empirical Mode Decomposition (EMD), decomposing the responses to a number of intrinsic low dimensional oscillatory modes, referred as Intrinsic Mode Functions (IMFs). Along with their low dimensionality, a few IMFs, retain sufficient information of the system dynamics to reflect the damage induced changes. The mutually conflicting nature of low-dimensionality and the sufficiency of dynamic information are taken care by the optimal choice of the IMF(s), which is shown to be the third/fourth IMFs. The optimal IMF(s) are employed for the reconstruction of the Phase space attractor following Taken's embedding theorem. The widely referred Changes in Phase Space Topology (CPST) feature is then employed on these Phase portrait(s) to derive the damage sensitive feature, referred as the CPST of the IMFs (CPST-IMF). The legitimacy of the CPST-IMF is established as a damage sensitive feature by assessing its variation with a number of damage scenarios benchmarked in the IASC-ASCE building. The damage localization capability, remarkable tolerance to noise contamination and the robustness under different seismic excitations of the feature are demonstrated.

Camptosorus sibiricus rupr aqueous extract prevents lung tumorigenesis via dual effects against ROS and DNA damage.

PubMed

He, Shugui; Ou, Rilan; Wang, Wensheng; Ji, Liyan; Gao, Hui; Zhu, Yuanfeng; Liu, Xiaomin; Zheng, Hongming; Liu, Zhongqiu; Wu, Peng; Lu, Linlin

2018-06-28

Camptosorus sibiricus Rupr (CSR) is a widely used herbal medicine with antivasculitis, antitrauma, and antitumor effects. However, the effect of CSR aqueous extract on B[a]P-initiated tumorigenesis and the underlying mechanism remain unclear. Moreover, the compounds in CSR aqueous extract need to be identified and structurally characterized. We aim to investigate the chemopreventive effect of CSR and the underlying molecular mechanism. A B[a]P-stimulated normal cell model (BEAS.2B) and lung adenocarcinoma animal model were established on A/J mice. In B[a]P-treated BEAS.2B cells, the protective effects of CSR aqueous extract on B[a]P-induced DNA damage and ROS production were evaluated through flow cytometry, Western blot, real-time quantitative PCR, single-cell gel electrophoresis, and immunofluorescence. Moreover, a model of B[a]P-initiated lung adenocarcinoma was established on A/J mice to determine the chemopreventive effect of CSR in vivo. The underlying mechanism was analyzed via immunohistochemistry and microscopy. Furthermore, the new compounds in CSR aqueous extract were isolated and structurally characterized using IR, HR-ESI-MS, and 1D and 2D NMR spectroscopy. CSR effectively suppressed ROS production by re-activating Nrf2-mediated reductases HO-1 and NQO-1. Simultaneously, CSR attenuated the DNA damage of BEAS.2B cells in the presence of B[a]P. Moreover, CSR at 1.5 and 3 g/kg significantly suppressed tumorigenesis with tumor inhibition ratios of 36.65% and 65.80%, respectively. The tumor volume, tumor size, and multiplicity of B[a]P-induced lung adenocarcinoma were effectively decreased by CSR in vivo. After extracting and identifying the compounds in CSR aqueous extract, three new triterpene saponins were isolated and characterized structurally. CSR aqueous extract prevents lung tumorigenesis by exerting dual effects against ROS and DNA damage, suggesting that CSR is a novel and effective agent for B[a]P-induced carcinogenesis. Moreover, by isolating and structurally characterizing three new triterpene saponins, our study further standardized the quality of CSR aqueous extract, which could widen CSR clinical applications. Copyright © 2017 Elsevier B.V. All rights reserved.

Photoprotective Effects of a Multifunctional Hair Care Formulation Containing Botanical Extracts, Vitamins, and UV Filters.

PubMed

Leite, Marcella Gabarra Almeida; Maia Campos, Patricia M B G

2018-05-04

The aim of this study was to develop and evaluate the efficacy of a multifunctional hair care formulation-Hair BB Cream-containing botanical extracts of Camellia sinensis, Vitis vinifera, and Euterpe orleacea, vitamins, amino acids, UV filters, and silicones for hair treatment and prevention of UV damages. The in vitro antioxidant activity of the botanical extracts was evaluated using the DPPH and chemiluminescence methods. A tensile test, combability, shine, and image analysis were performed to evaluate the efficacy of the formulation. To evaluate protection against UV damage, the hair strands were submitted to UV radiation without and with the application of the Hair BB Cream. The results showed that the application of the Hair BB Cream promoted a reduction in combability values and an increase in break stress and gloss values. After exposure to UV radiation, the hair treated with the BB Cream formulation showed no difference in the mechanical properties test, indicating protection against UV damage. In conclusion, the multifunctional formulation showed several benefits of single product acting in the prevention of UV damage and the treatment of hair damage. Thus, the Hair BB Cream proposed can be suggested as an effective multifunctional hair care product. © 2018 The American Society of Photobiology.

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Wild chrysanthemum extract prevents UVB radiation-induced acute cell death and photoaging.

PubMed

Sun, Sujiao; Jiang, Ping; Su, Weiting; Xiang, Yang; Li, Jian; Zeng, Lin; Yang, Shuangjuan

2016-03-01

Wild chrysanthemum (Chrysanthemum indicum L.) is traditionally used in folk medicine as an anti-inflammatory agent. It is also used in the southwest plateau region of China to prevent ultraviolet-induced skin damage. However, the role and mechanism by which wild chrysanthemum prevents UV-induced skin damage and photoaging have never been investigated in vitro. In the present study, we found that aqueous extracts from wild chrysanthemum strongly reduced high-dose UVB-induced acute cell death of human immortalized keratinocytic HaCat cells. Wild chrysanthemum extract was also demonstrated to reduce low-dose UVB-induced expression of the photoaging-related matrix metalloproteinases MMP-2 and MMP-9. The ROS level elevated by UVB irradiation was strongly attenuated by wild chrysanthemum extract. Further study revealed that wild chrysanthemum extract reduced UVB-triggered ERK1/2 and p38 MAPK phosphorylation and their protective role, which is partially dependent on inhibiting p38 activation. These results suggest that wild chrysanthemum extract can protect the skin from UVB-induced acute skin damage and photoaging by reducing the intracellular reactive oxygen species (ROS) level and inhibiting p38 MAPK phosphorylation. The present study confirmed the protective role of wild chrysanthemum against UV-induced skin disorders in vitro and indicated the possible mechanism. Further study to identify the active components in wild chrysanthemum extract would be useful for developing new drugs for preventing and treating skin diseases, including skin cancer and photoaging, induced by UV irradiation.

Methodology for classification of geographical features with remote sensing images: Application to tidal flats

NASA Astrophysics Data System (ADS)

Revollo Sarmiento, G. N.; Cipolletti, M. P.; Perillo, M. M.; Delrieux, C. A.; Perillo, Gerardo M. E.

2016-03-01

Tidal flats generally exhibit ponds of diverse size, shape, orientation and origin. Studying the genesis, evolution, stability and erosive mechanisms of these geographic features is critical to understand the dynamics of coastal wetlands. However, monitoring these locations through direct access is hard and expensive, not always feasible, and environmentally damaging. Processing remote sensing images is a natural alternative for the extraction of qualitative and quantitative data due to their non-invasive nature. In this work, a robust methodology for automatic classification of ponds and tidal creeks in tidal flats using Google Earth images is proposed. The applicability of our method is tested in nine zones with different morphological settings. Each zone is processed by a segmentation stage, where ponds and tidal creeks are identified. Next, each geographical feature is measured and a set of shape descriptors is calculated. This dataset, together with a-priori classification of each geographical feature, is used to define a regression model, which allows an extensive automatic classification of large volumes of data discriminating ponds and tidal creeks against other various geographical features. In all cases, we identified and automatically classified different geographic features with an average accuracy over 90% (89.7% in the worst case, and 99.4% in the best case). These results show the feasibility of using freely available Google Earth imagery for the automatic identification and classification of complex geographical features. Also, the presented methodology may be easily applied in other wetlands of the world and perhaps employing other remote sensing imagery.

Hepatoprotective and Antioxidant Effect of Mangifera Indica Leaf Extracts against Mercuric Chloride-induced Liver Toxicity in Mice.

PubMed

Karuppanan, Muthupillai; Krishnan, Manigandan; Padarthi, Pavankumar; Namasivayam, Elangovan

2014-01-01

To explore the antioxidant and hepatoprotective effect of ethanolic Mangifera indica (EMI) and methanolic Mangifera indica (MMI) leaf extracts in mercuric chloride (HgCl 2 ) induced toxicity in Swiss albino mice. Toxicity in mice was induced with HgCl 2 (5.0 mg/kg, i.p.), followed by oral intervention with EMI and MMI extracts (25 mg and 50 mg/kg. body wt.) for 30 days. The extent of liver damage was assessed from the extents of histopathological, morphological, antioxidant and liver enzymes. Mercuric chloride-induced mice showed an increased cellular damage whereas leaf extracts of EMI and MMI-treated mice showed recovery of damaged hepatocytes. Mercuric chloride intoxicated mice exhibited a significant (p < 0.05) elevation in the liver enzymes (Aspartate amino transferase and Alanine amino transferase) and gradual decline in the cellular radical scavenging enzyme levels (Catalase, Glutathione-s-transferase and Glutathione peroxidase. The combined treatment with EMI and MMI leaf extracts significantly (p < 0.05) reversed these parameters. However, the effects of MMI leaf extract (50 mg/kg) were superior to those of EMI- treated mice possibly due to its potent radical scavenging property. These results suggest that oral supplementation of Mangifera indica extract remarkably reduces hepatotoxicity in mice possibly through its antioxidant potentials. How to cite this article: Karuppanan M, Krishnan M, Padarthi P, Namasivayam E. Hepatoprotec-tive and Antioxidant Effect of Mangifera Indica Leaf Extracts against Mercuric Chloride-induced Liver Toxicity in Mice. Euroasian J Hepato-Gastroenterol 2014;4(1):18-24.

Hepatoprotective and Antioxidant Effect of Mangifera Indica Leaf Extracts against Mercuric Chloride-induced Liver Toxicity in Mice

PubMed Central

Karuppanan, Muthupillai; Krishnan, Manigandan; Padarthi, Pavankumar

2014-01-01

ABSTRACT Background To explore the antioxidant and hepatoprotective effect of ethanolic Mangifera indica (EMI) and methanolic Mangifera indica (MMI) leaf extracts in mercuric chloride (HgCl2) induced toxicity in Swiss albino mice. Materials and methods Toxicity in mice was induced with HgCl2 (5.0 mg/kg, i.p.), followed by oral intervention with EMI and MMI extracts (25 mg and 50 mg/kg. body wt.) for 30 days. Results and discussion The extent of liver damage was assessed from the extents of histopathological, morphological, antioxidant and liver enzymes. Mercuric chloride-induced mice showed an increased cellular damage whereas leaf extracts of EMI and MMI-treated mice showed recovery of damaged hepatocytes. Mercuric chloride intoxicated mice exhibited a significant (p < 0.05) elevation in the liver enzymes (Aspartate amino transferase and Alanine amino transferase) and gradual decline in the cellular radical scavenging enzyme levels (Catalase, Glutathione-s-transferase and Glutathione peroxidase. The combined treatment with EMI and MMI leaf extracts significantly (p < 0.05) reversed these parameters. However, the effects of MMI leaf extract (50 mg/kg) were superior to those of EMI- treated mice possibly due to its potent radical scavenging property. These results suggest that oral supplementation of Mangifera indica extract remarkably reduces hepatotoxicity in mice possibly through its antioxidant potentials. How to cite this article: Karuppanan M, Krishnan M, Padarthi P, Namasivayam E. Hepatoprotec-tive and Antioxidant Effect of Mangifera Indica Leaf Extracts against Mercuric Chloride-induced Liver Toxicity in Mice. Euroasian J Hepato-Gastroenterol 2014;4(1):18-24. PMID:29264314

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

NASA Astrophysics Data System (ADS)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Novel Features for Brain-Computer Interfaces

PubMed Central

Woon, W. L.; Cichocki, A.

2007-01-01

While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques. PMID:18364991

Assessment of the protective potential of Premna tomentosa (L. Verbenaceae) extract on lipid profile and lipid-metabolizing enzymes in acetaminophen-intoxicated rats.

PubMed

Devi, Kasi Pandima; Sreepriya, Meenakshi; Balakrishna, Kedike; Veluchamy, Gopalasamy; Devaki, Thiruvegadam

2004-06-01

The liver is often damaged by environmental toxins, poor eating habits, alcohol and over-the-counter drug use that damage and weaken the liver, leading to important public health problems such as hepatitis, cirrhosis, and alcoholic liver diseases. It is cardinal to treat liver disorders, because it affects the biochemistry of the cell directly. Damage to the liver can be prevented by including a balanced diet that includes nutrients and herbs that support a healthy liver. Premna tomentosa (PT) is one such herbal drug used widely in India for the treatment of liver disorders, and we have already reported the hepatoprotective potential and antioxidant property of methanolic extract of PT leaves. Because injury to the liver can promote a variety of reactions with consequent effect on lipids, the present study was designed to elucidate the hypolipidemic effect of PT extract in acetaminophen (AA)-induced hepatotoxicity in rats. Animals were pretreated with PT extract (750 mg/kg, orally) for 15 days and then induced with hepatotoxicity by AA (640 mg/kg, intraperitoneally). PT extract pretreatment significantly inhibited induced alterations in the levels of cholesterol, triglycerides, free fatty acids, phospholipids, serum lipoproteins, and lipid-metabolizing enzymes. The results indicate that PT extract improves lipid metabolism and has the potential for use in hepatic disorders. Copyright Mary Ann Liebert, Inc.

Extracts of Crataegus oxyacantha and Rosmarinus officinalis Attenuate Ischemic Myocardial Damage by Decreasing Oxidative Stress and Regulating the Production of Cardiac Vasoactive Agents.

PubMed

Cuevas-Durán, Raúl Enrique; Medrano-Rodríguez, Juan Carlos; Sánchez-Aguilar, María; Soria-Castro, Elizabeth; Rubio-Ruíz, María Esther; Del Valle-Mondragón, Leonardo; Sánchez-Mendoza, Alicia; Torres-Narvaéz, Juan Carlos; Pastelín-Hernández, Gustavo; Ibarra-Lara, Luz

2017-11-14

Numerous studies have supported a role for oxidative stress in the development of ischemic damage and endothelial dysfunction. Crataegus oxyacantha ( Co ) and Rosmarinus officinalis ( Ro ) extracts are polyphenolic-rich compounds that have proven to be efficient in the treatment of cardiovascular diseases. We studied the effect of extracts from Co and Ro on the myocardial damage associated with the oxidative status and to the production of different vasoactive agents. Rats were assigned to the following groups: (a) sham; (b) vehicle-treated myocardial infarction (MI) (MI-V); (c) Ro extract-treated myocardial infarction (MI- Ro ); (d) Co extract-treated myocardial infarction (MI- Co ); or (e) Ro+Co -treated myocardial infarction (MI- Ro+Co ). Ro and Co treatments increased total antioxidant capacity, the expression of superoxide dismutase (SOD)-Cu 2+ /Zn 2+ , SOD-Mn 2+ , and catalase, with the subsequent decline of malondialdehyde and 8-hydroxy-2'-deoxyguanosine levels. The extracts diminished vasoconstrictor peptide levels (angiotensin II and endothelin-1), increased vasodilators agents (angiotensin 1-7 and bradikinin) and improved nitric oxide metabolism. Polyphenol treatment restored the left intraventricular pressure and cardiac mechanical work. We conclude that Ro and Co treatment attenuate morphological and functional ischemic-related changes by both an oxidant load reduction and improvement of the balance between vasoconstrictors and vasodilators.

Extracts of Crataegus oxyacantha and Rosmarinus officinalis Attenuate Ischemic Myocardial Damage by Decreasing Oxidative Stress and Regulating the Production of Cardiac Vasoactive Agents

PubMed Central

Cuevas-Durán, Raúl Enrique; Medrano-Rodríguez, Juan Carlos; Sánchez-Aguilar, María; Soria-Castro, Elizabeth; Del Valle-Mondragón, Leonardo; Sánchez-Mendoza, Alicia; Torres-Narvaéz, Juan Carlos; Pastelín-Hernández, Gustavo; Ibarra-Lara, Luz

2017-01-01

Numerous studies have supported a role for oxidative stress in the development of ischemic damage and endothelial dysfunction. Crataegus oxyacantha (Co) and Rosmarinus officinalis (Ro) extracts are polyphenolic-rich compounds that have proven to be efficient in the treatment of cardiovascular diseases. We studied the effect of extracts from Co and Ro on the myocardial damage associated with the oxidative status and to the production of different vasoactive agents. Rats were assigned to the following groups: (a) sham; (b) vehicle-treated myocardial infarction (MI) (MI-V); (c) Ro extract-treated myocardial infarction (MI-Ro); (d) Co extract-treated myocardial infarction (MI-Co); or (e) Ro+Co-treated myocardial infarction (MI-Ro+Co). Ro and Co treatments increased total antioxidant capacity, the expression of superoxide dismutase (SOD)-Cu2+/Zn2+, SOD-Mn2+, and catalase, with the subsequent decline of malondialdehyde and 8-hydroxy-2′-deoxyguanosine levels. The extracts diminished vasoconstrictor peptide levels (angiotensin II and endothelin-1), increased vasodilators agents (angiotensin 1–7 and bradikinin) and improved nitric oxide metabolism. Polyphenol treatment restored the left intraventricular pressure and cardiac mechanical work. We conclude that Ro and Co treatment attenuate morphological and functional ischemic-related changes by both an oxidant load reduction and improvement of the balance between vasoconstrictors and vasodilators. PMID:29135932

Progression of cartilage damage and meniscal pathology over 30 months is associated with an increase in radiographic tibiofemoral joint space narrowing in persons with knee OA--the MOST study.

PubMed

Crema, M D; Nevitt, M C; Guermazi, A; Felson, D T; Wang, K; Lynch, J A; Marra, M D; Torner, J; Lewis, C E; Roemer, F W

2014-10-01

To determine the association of MRI-assessed worsening of tibiofemoral cartilage damage, meniscal damage, meniscal extrusion, separately and together, with progression of radiographic joint space narrowing (JSN). The Multicenter Osteoarthitis Study (MOST) Study is a cohort study of subjects with or at risk for knee osteoarthritis (OA). Knees with radiographic OA Kellgren-Lawrence grade 2 at baseline and with baseline and 30-month 1.0 T MRIs were selected for reading using the WORMS system for cartilage damage, meniscal damage, and meniscal extrusion. The association of worsening of cartilage damage, meniscal damage, and/or meniscal extrusion with increases in the JSN was performed using logistic regression. A total of 276 knees (one per subject) were included (women 68.5%, mean age 62.9 ± 7.8, mean body mass index (BMI) 30.2 ± 5.0). Worsening of each MRI feature was associated with any increase in JSN (P < 0.01). Worsening of cartilage damage was more frequently observed than worsening of meniscal damage and extrusion, and was significantly associated with both slow and fast progression of JSN. An increasing risk of JSN worsening was associated with increasing number of worsening MRI features (P for trend < 0.0001). Worsening of tibiofemoral cartilage damage, meniscal damage, and meniscal extrusion are independent predictors of JSN progression in the same compartment. Worsening of cartilage damage is more frequently observed in JSN when compared to meniscal worsening. A strong cumulative effect on JSN progression is observed for worsening of more than one MRI feature. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Corchorus olitorius (jute) extract induced cytotoxicity and genotoxicity on human multiple myeloma cells (ARH-77).

PubMed

İşeri, Özlem Darcansoy; Yurtcu, Erkan; Sahin, Feride Iffet; Haberal, Mehmet

2013-06-01

Corchorus olitorius L. (Malvaceae) has industrial importance in world jute production and is a widely cultivated and consumed crop in Cyprus and in some Arabic countries. The present study investigated cytotoxic and genotoxic effects of leaf extracts (LE) and seed extracts (SE) of the C. olitorius on the multiple myeloma-derived ARH-77 cells. The extracts were also evaluated for their total phenol content (TPC) and free radical scavenging activity (FRSA). C. olitorius was collected from Nicosia, Cyprus. TPC and FRSA were measured by Folin-Ciocalteu and DPPH free radical methods, respectively. Cytotoxicity was evaluated by the MTT assay (4-2048 µg/mL range), and DNA damage (at IC50 and ½IC50) was measured by the comet assay. The LE had significantly higher total phenol (78 mg GAE/g extract) than the SE (2 mg GAE/g extract) with significantly higher FRSA (IC50 LE: 23 µg/mL and IC50 SE: 10 401 µg/mL). Both LE and SE exerted cytotoxic effects on cells after 48 h. The IC50 of SE (17 µg/mL) was lower than LE (151 µg/mL), which demonstrates its higher cytotoxicity on cells. The extracts were applied at 150 and 75 µg/mL for LE and at 17 and 8.5 µg/mL for SE, and the results of the comet assay revealed that the extracts induced genotoxic damage on ARH-77 cells. In both 48 h leaf and seed extract treatments, genotoxic damage significantly increased with increasing concentrations at relevant cytotoxic concentrations. To our knowledge, this is the first report demonstrating the high cytotoxic potential of C. olitorius SE and the genotoxic potential of LE and SE.

SU-F-R-45: The Prognostic Value of Radiotherapy Based On the Changes of Texture Features Between Pre-Treatment and Post-Treatment FDG PET Image for NSCLC Patients

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

Ma, C; Yin, Y

Purpose: The purpose of this research is investigating which texture features extracted from FDG-PET images by gray-level co-occurrence matrix(GLCM) have a higher prognostic value than the other texture features. Methods: 21 non-small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F-FDG PET/CT scans with both pre-treatment and post-treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB.The differences can be calculatedmore » by using post-treatment features to subtract pre-treatment features. Finally, the SPSS software was used to get the Pearson correlation coefficients and Spearman rank correlation coefficients between the change ratios of texture features and change ratios of clinical features. Results: The Pearson and Spearman rank correlation coefficient between contrast and SUV maximum is 0.785 and 0.709. The P and S value between inverse difference moment and tumor volume is 0.953 and 0.942. Conclusion: This preliminary study showed that the relationships between different texture features and the same clinical feature are different. Finding the prognostic value of contrast and inverse difference moment were higher than the other three textures extracted by GLCM.« less

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

PubMed

Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

2015-06-01

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

Research of facial feature extraction based on MMC

NASA Astrophysics Data System (ADS)

Xue, Donglin; Zhao, Jiufen; Tang, Qinhong; Shi, Shaokun

2017-07-01

Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.

Preeclampsia is associated with ACE I/D polymorphism, obesity and oxidative damage in Mexican women.

PubMed

González-Garrido, José A; García-Sánchez, José R; Tovar-Rodríguez, José M; Olivares-Corichi, Ivonne M

2017-10-01

This study sought to determine whether the angiotensin-converting enzyme insertion/deletion (ACE I/D) polymorphism, obesity and oxidative damage are risk factors for the development of preeclampsia in Mexican women. A total of 66 women with preeclampsia (PE) and 37 women with normal pregnancies (NP) were included in the study. DNA was extracted from whole blood, and the ACE I/D polymorphism was evaluated by polymerase chain reaction. ACE activity and oxidative damage were assessed in plasma. The intergroup comparisons were analyzed by an analysis of variance (ANOVA) with post hoc tests. Hardy-Weinberg equilibrium (HWE) was tested by x 2 analysis, odds ratios (OR) were calculated as a measure of the degree of relative risk of preeclampsia, and for correlations, we used Spearman's correlation coefficient. The frequency of the DD genotype was higher in PE (34.84%) than NP (10.82%). The OR of the DD genotype and D allele were associated with a 4.4-fold (CI=95% 2.24-14) and 3-fold (CI=95% 1.69-5.62) increased risk of developing PE, respectively. Major ACE activity in the DD genotype and obesity were features of the PE group; oxidative damage to proteins and a reduction in the activity of the antioxidant system showed a correlation with BMI (p<0.01). Our results suggest that ACE I/D polymorphism, high ACE activity, body mass index and oxidative damage may play key roles in the pathogenesis of PE in the Mexican population. Furthermore, these findings could be used as predictive factors of PE. Copyright © 2017 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Capability of geometric features to classify ships in SAR imagery

NASA Astrophysics Data System (ADS)

Lang, Haitao; Wu, Siwen; Lai, Quan; Ma, Li

2016-10-01

Ship classification in synthetic aperture radar (SAR) imagery has become a new hotspot in remote sensing community for its valuable potential in many maritime applications. Several kinds of ship features, such as geometric features, polarimetric features, and scattering features have been widely applied on ship classification tasks. Compared with polarimetric features and scattering features, which are subject to SAR parameters (e.g., sensor type, incidence angle, polarization, etc.) and environment factors (e.g., sea state, wind, wave, current, etc.), geometric features are relatively independent of SAR and environment factors, and easy to be extracted stably from SAR imagery. In this paper, the capability of geometric features to classify ships in SAR imagery with various resolution has been investigated. Firstly, the relationship between the geometric feature extraction accuracy and the SAR imagery resolution is analyzed. It shows that the minimum bounding rectangle (MBR) of ship can be extracted exactly in terms of absolute precision by the proposed automatic ship-sea segmentation method. Next, six simple but effective geometric features are extracted to build a ship representation for the subsequent classification task. These six geometric features are composed of length (f1), width (f2), area (f3), perimeter (f4), elongatedness (f5) and compactness (f6). Among them, two basic features, length (f1) and width (f2), are directly extracted based on the MBR of ship, the other four are derived from those two basic features. The capability of the utilized geometric features to classify ships are validated on two data set with different image resolutions. The results show that the performance of ship classification solely by geometric features is close to that obtained by the state-of-the-art methods, which obtained by a combination of multiple kinds of features, including scattering features and geometric features after a complex feature selection process.

Question analysis for Indonesian comparative question

NASA Astrophysics Data System (ADS)

Saelan, A.; Purwarianti, A.; Widyantoro, D. H.

2017-01-01

Information seeking is one of human needs today. Comparing things using search engine surely take more times than search only one thing. In this paper, we analyzed comparative questions for comparative question answering system. Comparative question is a question that comparing two or more entities. We grouped comparative questions into 5 types: selection between mentioned entities, selection between unmentioned entities, selection between any entity, comparison, and yes or no question. Then we extracted 4 types of information from comparative questions: entity, aspect, comparison, and constraint. We built classifiers for classification task and information extraction task. Features used for classification task are bag of words, whether for information extraction, we used lexical, 2 previous and following words lexical, and previous label as features. We tried 2 scenarios: classification first and extraction first. For classification first, we used classification result as a feature for extraction. Otherwise, for extraction first, we used extraction result as features for classification. We found that the result would be better if we do extraction first before classification. For the extraction task, classification using SMO gave the best result (88.78%), while for classification, it is better to use naïve bayes (82.35%).

Neuroprotection and enhanced neurogenesis by extract from the tropical plant Knema laurina after inflammatory damage in living brain tissue.

PubMed

Häke, Ines; Schönenberger, Silvia; Neumann, Jens; Franke, Katrin; Paulsen-Merker, Katrin; Reymann, Klaus; Ismail, Ghazally; Bin Din, Laily; Said, Ikram M; Latiff, A; Wessjohann, Ludger; Zipp, Frauke; Ullrich, Oliver

2009-01-03

Inflammatory reactions in the CNS, resulting from a loss of control and involving a network of non-neuronal and neuronal cells, are major contributors to the onset and progress of several major neurodegenerative diseases. Therapeutic strategies should therefore keep or restore the well-controlled and finely-tuned balance of immune reactions, and protect neurons from inflammatory damage. In our study, we selected plants of the Malaysian rain forest by an ethnobotanic survey, and investigated them in cell-based-assay-systems and in living brain tissue cultures in order to identify anti-inflammatory and neuroprotective effects. We found that alcoholic extracts from the tropical plant Knema laurina (Black wild nutmeg) exhibited highly anti-inflammatory and neuroprotective effects in cell culture experiments, reduced NO- and IL-6-release from activated microglia cells dose-dependently, and protected living brain tissue from microglia-mediated inflammatory damage at a concentration of 30 microg/ml. On the intracellular level, the extract inhibited ERK-1/2-phosphorylation, IkB-phosphorylation and subsequently NF-kB-translocation in microglia cells. K. laurina belongs to the family of Myristicaceae, which have been used for centuries for treatment of digestive and inflammatory diseases and is also a major food plant of the Giant Hornbill. Moreover, extract from K. laurina promotes also neurogenesis in living brain tissue after oxygen-glucose deprivation. In conclusion, extract from K. laurina not only controls and limits inflammatory reaction after primary neuronal damage, it promotes moreover neurogenesis if given hours until days after stroke-like injury.

Nonlinear features for classification and pose estimation of machined parts from single views

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-10-01

A new nonlinear feature extraction method is presented for classification and pose estimation of objects from single views. The feature extraction method is called the maximum representation and discrimination feature (MRDF) method. The nonlinear MRDF transformations to use are obtained in closed form, and offer significant advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We consider MRDFs on image data, provide a new 2-stage nonlinear MRDF solution, and show it specializes to well-known linear and nonlinear image processing transforms under certain conditions. We show the use of MRDF in estimating the class and pose of images of rendered solid CAD models of machine parts from single views using a feature-space trajectory neural network classifier. We show new results with better classification and pose estimation accuracy than are achieved by standard principal component analysis and Fukunaga-Koontz feature extraction methods.

Information based universal feature extraction

NASA Astrophysics Data System (ADS)

Amiri, Mohammad; Brause, Rüdiger

2015-02-01

In many real world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, they mostly remain task-specific, although humans who perform such a task always use the same image features, trained in early childhood. It seems that universal feature sets exist, but they are not yet systematically found. In our contribution, we tried to find those universal image feature sets that are valuable for most image related tasks. In our approach, we trained a neural network by natural and non-natural images of objects and background, using a Shannon information-based algorithm and learning constraints. The goal was to extract those features that give the most valuable information for classification of visual objects hand-written digits. This will give a good start and performance increase for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract features which are valid in all three kinds of tasks.

Sensor-based auto-focusing system using multi-scale feature extraction and phase correlation matching.

PubMed

Jang, Jinbeum; Yoo, Yoonjong; Kim, Jongheon; Paik, Joonki

2015-03-10

This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF) algorithm consists of four steps: (i) acquisition of left and right images using AF points in the region-of-interest; (ii) feature extraction in the left image under low illumination and out-of-focus blur; (iii) the generation of two feature images using the phase difference between the left and right images; and (iv) estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

PubMed Central

Jang, Jinbeum; Yoo, Yoonjong; Kim, Jongheon; Paik, Joonki

2015-01-01

This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF) algorithm consists of four steps: (i) acquisition of left and right images using AF points in the region-of-interest; (ii) feature extraction in the left image under low illumination and out-of-focus blur; (iii) the generation of two feature images using the phase difference between the left and right images; and (iv) estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems. PMID:25763645

A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery

NASA Astrophysics Data System (ADS)

Wang, Ke; Guo, Ping; Luo, A.-Li

2017-03-01

Spectral feature extraction is a crucial procedure in automated spectral analysis. This procedure starts from the spectral data and produces informative and non-redundant features, facilitating the subsequent automated processing and analysis with machine-learning and data-mining techniques. In this paper, we present a new automated feature extraction method for astronomical spectra, with application in spectral classification and defective spectra recovery. The basic idea of our approach is to train a deep neural network to extract features of spectra with different levels of abstraction in different layers. The deep neural network is trained with a fast layer-wise learning algorithm in an analytical way without any iterative optimization procedure. We evaluate the performance of the proposed scheme on real-world spectral data. The results demonstrate that our method is superior regarding its comprehensive performance, and the computational cost is significantly lower than that for other methods. The proposed method can be regarded as a new valid alternative general-purpose feature extraction method for various tasks in spectral data analysis.

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

PubMed Central

Zhang, Xin; Cui, Jintian; Wang, Weisheng; Lin, Chao

2017-01-01

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. PMID:28640181

An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal.

PubMed

Houshyarifar, Vahid; Chehel Amirani, Mehdi

2016-08-12

In this paper we present a method to predict Sudden Cardiac Arrest (SCA) with higher order spectral (HOS) and linear (Time) features extracted from heart rate variability (HRV) signal. Predicting the occurrence of SCA is important in order to avoid the probability of Sudden Cardiac Death (SCD). This work is a challenge to predict five minutes before SCA onset. The method consists of four steps: pre-processing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In second step, bispectrum features of HRV signal and time-domain features are obtained. Six features are extracted from bispectrum and two features from time-domain. In the next step, these features are reduced to one feature by the linear discriminant analysis (LDA) technique. Finally, KNN and support vector machine-based classifiers are used to classify the HRV signals. We used two database named, MIT/BIH Sudden Cardiac Death (SCD) Database and Physiobank Normal Sinus Rhythm (NSR). In this work we achieved prediction of SCD occurrence for six minutes before the SCA with the accuracy over 91%.

Automated Image Registration Using Morphological Region of Interest Feature Extraction

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2005-01-01

With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

NASA Astrophysics Data System (ADS)

de Lautour, Oliver R.; Omenzetter, Piotr

2010-07-01

Developed for studying long sequences of regularly sampled data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures: a 3-storey bookshelf structure and the ASCE Phase II Experimental SHM Benchmark Structure, in undamaged and limited number of damaged states. The coefficients of the AR models were considered to be damage-sensitive features and used as input into an Artificial Neural Network (ANN). The ANN was trained to classify damage cases or estimate remaining structural stiffness. The results showed that the combination of AR models and ANNs are efficient tools for damage classification and estimation, and perform well using small number of damage-sensitive features and limited sensors.

Rapid Extraction of Landslide and Spatial Distribution Analysis after Jiuzhaigou Ms7.0 Earthquake Based on Uav Images

NASA Astrophysics Data System (ADS)

Jiao, Q. S.; Luo, Y.; Shen, W. H.; Li, Q.; Wang, X.

2018-04-01

Jiuzhaigou earthquake led to the collapse of the mountains and formed lots of landslides in Jiuzhaigou scenic spot and surrounding roads which caused road blockage and serious ecological damage. Due to the urgency of the rescue, the authors carried unmanned aerial vehicle (UAV) and entered the disaster area as early as August 9 to obtain the aerial images near the epicenter. On the basis of summarizing the earthquake landslides characteristics in aerial images, by using the object-oriented analysis method, landslides image objects were obtained by multi-scale segmentation, and the feature rule set of each level was automatically built by SEaTH (Separability and Thresholds) algorithm to realize the rapid landslide extraction. Compared with visual interpretation, object-oriented automatic landslides extraction method achieved an accuracy of 94.3 %. The spatial distribution of the earthquake landslide had a significant positive correlation with slope and relief and had a negative correlation with the roughness, but no obvious correlation with the aspect. The relationship between the landslide and the aspect was not found and the probable reason may be that the distance between the study area and the seismogenic fault was too far away. This work provided technical support for the earthquake field emergency, earthquake landslide prediction and disaster loss assessment.

Effect of lemon verbena supplementation on muscular damage markers, proinflammatory cytokines release and neutrophils' oxidative stress in chronic exercise.

PubMed

Funes, Lorena; Carrera-Quintanar, Lucrecia; Cerdán-Calero, Manuela; Ferrer, Miguel D; Drobnic, Franchek; Pons, Antoni; Roche, Enrique; Micol, Vicente

2011-04-01

Intense exercise is directly related to muscular damage and oxidative stress due to excessive reactive oxygen species (ROS) in both, plasma and white blood cells. Nevertheless, exercise-derived ROS are essential to regulate cellular adaptation to exercise. Studies on antioxidant supplements have provided controversial results. The purpose of this study was to determine the effect of moderate antioxidant supplementation (lemon verbena extract) in healthy male volunteers that followed a 90-min running eccentric exercise protocol for 21 days. Antioxidant enzymes activities and oxidative stress markers were measured in neutrophils. Besides, inflammatory cytokines and muscular damage were determined in whole blood and serum samples, respectively. Intense running exercise for 21 days induced antioxidant response in neutrophils of trained male through the increase of the antioxidant enzymes catalase, glutathione peroxidase and glutathione reductase. Supplementation with moderate levels of an antioxidant lemon verbena extract did not block this cellular adaptive response and also reduced exercise-induced oxidative damage of proteins and lipids in neutrophils and decreased myeloperoxidase activity. Moreover, lemon verbena supplementation maintained or decreased the level of serum transaminases activity indicating a protection of muscular tissue. Exercise induced a decrease of interleukin-6 and interleukin-1β levels after 21 days measured in basal conditions, which was not inhibited by antioxidant supplementation. Therefore, moderate antioxidant supplementation with lemon verbena extract protects neutrophils against oxidative damage, decreases the signs of muscular damage in chronic running exercise without blocking the cellular adaptation to exercise.

Distinctive Feature Extraction for Indian Sign Language (ISL) Gesture using Scale Invariant Feature Transform (SIFT)

NASA Astrophysics Data System (ADS)

Patil, Sandeep Baburao; Sinha, G. R.

2017-02-01

India, having less awareness towards the deaf and dumb peoples leads to increase the communication gap between deaf and hard hearing community. Sign language is commonly developed for deaf and hard hearing peoples to convey their message by generating the different sign pattern. The scale invariant feature transform was introduced by David Lowe to perform reliable matching between different images of the same object. This paper implements the various phases of scale invariant feature transform to extract the distinctive features from Indian sign language gestures. The experimental result shows the time constraint for each phase and the number of features extracted for 26 ISL gestures.

Evaluation of in vivo solid phase microextraction for minimally invasive analysis of nonvolatile phytochemicals in Amazonian plants.

PubMed

Musteata, Florin Marcel; Sandoval, Manuel; Ruiz-Macedo, Juan C; Harrison, Kathleen; McKenna, Dennis; Millington, William

2016-08-24

Although solid phase microextraction (SPME) has been used extensively for fingerprinting volatile compounds emitted by plants, there are very few such reports for direct insertion SPME. In this research, direct contact of SPME probes with the interstitial fluid of plants was investigated as a method for phytochemical analysis. Medicinal plants from the Amazon have been the source of numerous drugs used in western medicine. However, a large number of species used in traditional medicine have not been characterized chemically, partly due to the difficulty of field work. In this project, the phytochemical composition of plants from several genera was fingerprinted by combining convenient field sampling by solid phase microextraction (SPME) with laboratory analysis by LC-MS. The new method was compared with classical sampling followed by liquid extraction (LE). SPME probes were prepared by coating stainless steel wires with a mixture of polyacrylonitrile and either RP-amide or HS-F5 silica particles. Sampling was performed by inserting the microextraction probes into various tissues of living plants in their natural environment. After in vivo extraction, the probes were sealed under vacuum and refrigerated until analyzed. The probes were desorbed in mobile phase and analyzed on a Waters Acquity UPLC with triple quadrupole mass spectrometer in positive ion mode. Twenty Amazonian plant species were sampled and unique metabolomic fingerprints were obtained. In addition, quantitative analysis was performed for previously identified compounds in three species. Comparison of the fingerprints obtained by in vivo SPME with those obtained by LE showed that 27% of the chromatographic features were unique to SPME, 57% were unique to LE, and 16% were common to both methods. In vivo SPME caused minimal damage to the plants, was much faster than traditional liquid extraction, and provided unique fingerprints for all investigated plants. SPME revealed unique chromatographic features, undetected by traditional extraction, although it produced only half as many peaks as ethanol extraction. Copyright © 2016 Elsevier B.V. All rights reserved.

Computer-aided screening system for cervical precancerous cells based on field emission scanning electron microscopy and energy dispersive x-ray images and spectra

NASA Astrophysics Data System (ADS)

Jusman, Yessi; Ng, Siew-Cheok; Hasikin, Khairunnisa; Kurnia, Rahmadi; Osman, Noor Azuan Bin Abu; Teoh, Kean Hooi

2016-10-01

The capability of field emission scanning electron microscopy and energy dispersive x-ray spectroscopy (FE-SEM/EDX) to scan material structures at the microlevel and characterize the material with its elemental properties has inspired this research, which has developed an FE-SEM/EDX-based cervical cancer screening system. The developed computer-aided screening system consisted of two parts, which were the automatic features of extraction and classification. For the automatic features extraction algorithm, the image and spectra of cervical cells features extraction algorithm for extracting the discriminant features of FE-SEM/EDX data was introduced. The system automatically extracted two types of features based on FE-SEM/EDX images and FE-SEM/EDX spectra. Textural features were extracted from the FE-SEM/EDX image using a gray level co-occurrence matrix technique, while the FE-SEM/EDX spectra features were calculated based on peak heights and corrected area under the peaks using an algorithm. A discriminant analysis technique was employed to predict the cervical precancerous stage into three classes: normal, low-grade intraepithelial squamous lesion (LSIL), and high-grade intraepithelial squamous lesion (HSIL). The capability of the developed screening system was tested using 700 FE-SEM/EDX spectra (300 normal, 200 LSIL, and 200 HSIL cases). The accuracy, sensitivity, and specificity performances were 98.2%, 99.0%, and 98.0%, respectively.

Antimutagenic and free radical scavenger effects of leaf extracts from Accacia salicina.

PubMed

Boubaker, Jihed; Mansour, Hedi Ben; Ghedira, Kamel; Chekir-Ghedira, Leila

2011-12-01

Three extracts were prepared from the leaves of Accacia salicina; ethyl acetate (EA), chloroform (Chl) and petroleum ether (PE) extracts and was designed to examine antimutagenic, antioxidant potenty and oxidative DNA damage protecting activity. Antioxidant activity of A. salicina extracts was determined by the ability of each extract to protect against plasmid DNA strand scission induced by hydroxyl radicals. An assay for the ability of these extracts to prevent mutations induced by various oxidants in Salmonella typhimurium TA102 and TA 104 strains was conducted. In addition, nonenzymatic methods were employed to evaluate anti-oxidative effects of tested extracts. These extracts from leaf parts of A. salicina showed no mutagenicity either with or without the metabolic enzyme preparation (S9). The highest protections against methylmethanesulfonate induced mutagenicity were observed with all extracts and especially chloroform extract. This extract exhibited the highest inhibitiory level of the Ames response induced by the indirect mutagen 2- aminoanthracene. All extracts exhibited the highest ability to protect plasmid DNA against hydroxyl radicals induced DNA damages. The ethyl acetate (EA) and chloroform (Chl) extracts showed with high TEAC values radical of 0.95 and 0.81 mM respectively, against the ABTS(.+). The present study revealed the antimutagenic and antioxidant potenty of plant extract from Accacia salicina leaves. © 2011 Boubaker et al; licensee BioMed Central Ltd.

Automatic Extraction of Planetary Image Features

NASA Technical Reports Server (NTRS)

Troglio, G.; LeMoigne, J.; Moser, G.; Serpico, S. B.; Benediktsson, J. A.

2009-01-01

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration.

Waveform fitting and geometry analysis for full-waveform lidar feature extraction

NASA Astrophysics Data System (ADS)

Tsai, Fuan; Lai, Jhe-Syuan; Cheng, Yi-Hsiu

2016-10-01

This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.

PubMed

Hussain, Lal; Ahmed, Adeel; Saeed, Sharjil; Rathore, Saima; Awan, Imtiaz Ahmed; Shah, Saeed Arif; Majid, Abdul; Idris, Adnan; Awan, Anees Ahmed

2018-02-06

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

Composition of herba pogostemonis water extract and protection of infected mice against salmonella typhimurium-induced liver damage and mortality by stimulation of innate immune cells

USDA-ARS?s Scientific Manuscript database

We investigated the composition and antibacterial effects of a lyophilized hot water extract of the medicinal herb Herba Pogostemonis (HP) against murine salmonellosis. The extract contained 31 compounds characterized by GC/MS. Although the extract showed significant inhibitory activity on Salmonell...

Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals

PubMed Central

Van Dijck, Gert; Van Hulle, Marc M.

2011-01-01

The damage caused by corrosion in chemical process installations can lead to unexpected plant shutdowns and the leakage of potentially toxic chemicals into the environment. When subjected to corrosion, structural changes in the material occur, leading to energy releases as acoustic waves. This acoustic activity can in turn be used for corrosion monitoring, and even for predicting the type of corrosion. Here we apply wavelet packet decomposition to extract features from acoustic emission signals. We then use the extracted wavelet packet coefficients for distinguishing between the most important types of corrosion processes in the chemical process industry: uniform corrosion, pitting and stress corrosion cracking. The local discriminant basis selection algorithm can be considered as a standard for the selection of the most discriminative wavelet coefficients. However, it does not take the statistical dependencies between wavelet coefficients into account. We show that, when these dependencies are ignored, a lower accuracy is obtained in predicting the corrosion type. We compare several mutual information filters to take these dependencies into account in order to arrive at a more accurate prediction. PMID:22163921

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

A method for automatic feature points extraction of human vertebrae three-dimensional model

NASA Astrophysics Data System (ADS)

Wu, Zhen; Wu, Junsheng

2017-05-01

A method for automatic extraction of the feature points of the human vertebrae three-dimensional model is presented. Firstly, the statistical model of vertebrae feature points is established based on the results of manual vertebrae feature points extraction. Then anatomical axial analysis of the vertebrae model is performed according to the physiological and morphological characteristics of the vertebrae. Using the axial information obtained from the analysis, a projection relationship between the statistical model and the vertebrae model to be extracted is established. According to the projection relationship, the statistical model is matched with the vertebrae model to get the estimated position of the feature point. Finally, by analyzing the curvature in the spherical neighborhood with the estimated position of feature points, the final position of the feature points is obtained. According to the benchmark result on multiple test models, the mean relative errors of feature point positions are less than 5.98%. At more than half of the positions, the error rate is less than 3% and the minimum mean relative error is 0.19%, which verifies the effectiveness of the method.

Extraction of linear features on SAR imagery

NASA Astrophysics Data System (ADS)

Liu, Junyi; Li, Deren; Mei, Xin

2006-10-01

Linear features are usually extracted from SAR imagery by a few edge detectors derived from the contrast ratio edge detector with a constant probability of false alarm. On the other hand, the Hough Transform is an elegant way of extracting global features like curve segments from binary edge images. Randomized Hough Transform can reduce the computation time and memory usage of the HT drastically. While Randomized Hough Transform will bring about a great deal of cells invalid during the randomized sample. In this paper, we propose a new approach to extract linear features on SAR imagery, which is an almost automatic algorithm based on edge detection and Randomized Hough Transform. The presented improved method makes full use of the directional information of each edge candidate points so as to solve invalid cumulate problems. Applied result is in good agreement with the theoretical study, and the main linear features on SAR imagery have been extracted automatically. The method saves storage space and computational time, which shows its effectiveness and applicability.

Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Jiang, Li; Xuan, Jianping; Shi, Tielin

2013-12-01

Generally, the vibration signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery fault diagnosis to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to fault diagnosis is put forward and applied to fault recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional vibration signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the fault recognition performance and outperforms the other four feature extraction methods.

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

PubMed Central

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-01-01

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features. PMID:27649171

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

A Blend of Extracts from Houttuynia cordata, Nelumbo nucifera, and Camellia sinensis Protects Against Ethanol-Induced Liver Damage in C57BL/6 Mice.

PubMed

You, Yanghee; Lee, Hyunmi; Yoon, Ho-Geun; Park, Jeongjin; Kim, Ok-Kyung; Kim, Kyungmi; Lee, Min-Jae; Lee, Yoo-Hyun; Lee, Jeongmin; Jun, Woojin

2018-02-01

The protective activity of a mixture of aqueous and ethanolic extracts from Houttuynia cordata Thunb, Nelumbo nucifera G. leaves, and Camellia sinensis seed (HNC) was evaluated in C57BL/6 mice. Pretreatment with HNC prevented the elevation of serum aspartate aminotransferase and alanine aminotransferase caused by ethanol-induced hepatic damage. The HNC-treated mice showed significantly lower triglyceride levels, reduced CYP2E1 activity, and increased antioxidant enzyme activities and lipogenic mRNA levels. These results suggest that HNC might be a candidate agent for liver protection against ethanol-induced oxidative damage, through enhancement of antioxidant and antilipogenic activity.

Effects of Korean Red Ginseng extract on busulfan-induced dysfunction of the male reproductive system.

PubMed

Jung, Seok-Won; Kim, Hyeon-Joong; Lee, Byung-Hwan; Choi, Sun-Hye; Kim, Hyun-Sook; Choi, Yang-Kyu; Kim, Joon Yong; Kim, Eun-Soo; Hwang, Sung-Hee; Lim, Kwang Yong; Kim, Hyoung-Chun; Jang, Minhee; Park, Seong Kyu; Cho, Ik-Hyun; Nah, Seung-Yeol

2015-07-01

Anticancer agents induce a variety of adverse effects when administered to cancer patients. Busulfan is a known antileukemia agent. When administered for treatment of leukemia in young patients, busulfan could cause damage to the male reproductive system as one of its adverse effects, resulting in sterility. We investigated the effects of Korean Red Ginseng extract (KRGE) on busulfan-induced damage and/or dysfunction of the male reproductive system. We found that administration of busulfan to mice: decreased testis weight; caused testicular histological damage; reduced the total number of sperm, sperm motility, serum testosterone concentration; and eventually, litter size. Preadministration of KRGE partially attenuated various busulfan-induced damages to the male reproductive system. These results indicate that KRGE has a protective effect against busulfan-induced damage to the male reproduction system. The present study shows a possibility that KRGE could be applied as a useful agent to prevent or protect the male reproductive system from the adverse side effects induced by administration of anticancer agents such as busulfan.

What’s in a URL? Genre Classification from URLs

DTIC Science & Technology

2012-01-01

webpages with access to the content of a document and feature extraction from URLs alone. Feature Extraction from Webpages Stylistic and structural...2010). Character n-grams (sequence of n characters) are attractive because of their simplicity and because they encapsulate both lexical and stylistic ...report might be stylistic . Feature Extraction from URLs The syntactic characteristics of URLs have been fairly sta- ble over the years. URL terms are

Detection of goal events in soccer videos

NASA Astrophysics Data System (ADS)

Kim, Hyoung-Gook; Roeber, Steffen; Samour, Amjad; Sikora, Thomas

2005-01-01

In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

PubMed Central

Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-01-01

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used. PMID:29762519

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Volumetric quantitative characterization of human patellar cartilage with topological and geometrical features on phase-contrast X-ray computed tomography.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Wismüller, Axel

2015-11-01

Phase-contrast X-ray computed tomography (PCI-CT) has attracted significant interest in recent years for its ability to provide significantly improved image contrast in low absorbing materials such as soft biological tissue. In the research context of cartilage imaging, previous studies have demonstrated the ability of PCI-CT to visualize structural details of human patellar cartilage matrix and capture changes to chondrocyte organization induced by osteoarthritis. This study evaluates the use of geometrical and topological features for volumetric characterization of such chondrocyte patterns in the presence (or absence) of osteoarthritic damage. Geometrical features derived from the scaling index method (SIM) and topological features derived from Minkowski Functionals were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. These features were subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver operating characteristic curve (AUC). Our results show that the classification performance of SIM-derived geometrical features (AUC: 0.90 ± 0.09) is significantly better than Minkowski Functionals volume (AUC: 0.54 ± 0.02), surface (AUC: 0.72 ± 0.06), mean breadth (AUC: 0.74 ± 0.06) and Euler characteristic (AUC: 0.78 ± 0.04) (p < 10(-4)). These results suggest that such geometrical features can provide a detailed characterization of the chondrocyte organization in the cartilage matrix in an automated manner, while also enabling classification of cartilage as healthy or osteoarthritic with high accuracy. Such features could potentially serve as diagnostic imaging markers for evaluating osteoarthritis progression and its response to different therapeutic intervention strategies.

Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d GIS Model

NASA Astrophysics Data System (ADS)

Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Zhina

2016-06-01

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

Automated feature extraction and classification from image sources

USGS Publications Warehouse

,

1995-01-01

The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

Prominent feature extraction for review analysis: an empirical study

NASA Astrophysics Data System (ADS)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Tseng, Tzu-Liang B.; Zheng, Bin; Zhang, Jianying; Qian, Wei

2015-03-01

A novel breast cancer risk analysis approach is proposed for enhancing performance of computerized breast cancer risk analysis using bilateral mammograms. Based on the intensity of breast area, five different sub-regions were acquired from one mammogram, and bilateral features were extracted from every sub-region. Our dataset includes 180 bilateral mammograms from 180 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including sub-region segmentation, bilateral feature extraction, feature selection, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under the curve (AUC) is 0.763 ± 0.021 when applying the multiple sub-region features to our testing dataset. The positive predictive value and the negative predictive value were 0.60 and 0.73, respectively. The study demonstrates that (1) features extracted from multiple sub-regions can improve the performance of our scheme compared to using features from whole breast area only; (2) a classifier using asymmetry bilateral features can effectively predict breast cancer risk; (3) incorporating texture and morphological features with density features can boost the classification accuracy.

Line fitting based feature extraction for object recognition

NASA Astrophysics Data System (ADS)

Li, Bing

2014-06-01

Image feature extraction plays a significant role in image based pattern applications. In this paper, we propose a new approach to generate hierarchical features. This new approach applies line fitting to adaptively divide regions based upon the amount of information and creates line fitting features for each subsequent region. It overcomes the feature wasting drawback of the wavelet based approach and demonstrates high performance in real applications. For gray scale images, we propose a diffusion equation approach to map information-rich pixels (pixels near edges and ridge pixels) into high values, and pixels in homogeneous regions into small values near zero that form energy map images. After the energy map images are generated, we propose a line fitting approach to divide regions recursively and create features for each region simultaneously. This new feature extraction approach is similar to wavelet based hierarchical feature extraction in which high layer features represent global characteristics and low layer features represent local characteristics. However, the new approach uses line fitting to adaptively focus on information-rich regions so that we avoid the feature waste problems of the wavelet approach in homogeneous regions. Finally, the experiments for handwriting word recognition show that the new method provides higher performance than the regular handwriting word recognition approach.

Artificially intelligent recognition of Arabic speaker using voice print-based local features

NASA Astrophysics Data System (ADS)

Mahmood, Awais; Alsulaiman, Mansour; Muhammad, Ghulam; Akram, Sheeraz

2016-11-01

Local features for any pattern recognition system are based on the information extracted locally. In this paper, a local feature extraction technique was developed. This feature was extracted in the time-frequency plain by taking the moving average on the diagonal directions of the time-frequency plane. This feature captured the time-frequency events producing a unique pattern for each speaker that can be viewed as a voice print of the speaker. Hence, we referred to this technique as voice print-based local feature. The proposed feature was compared to other features including mel-frequency cepstral coefficient (MFCC) for speaker recognition using two different databases. One of the databases used in the comparison is a subset of an LDC database that consisted of two short sentences uttered by 182 speakers. The proposed feature attained 98.35% recognition rate compared to 96.7% for MFCC using the LDC subset.

Investigation of an expert health monitoring system for aeronautical structures based on pattern recognition and acousto-ultrasonics

NASA Astrophysics Data System (ADS)

Tibaduiza-Burgos, Diego Alexander; Torres-Arredondo, Miguel Angel

2015-08-01

Aeronautical structures are subjected to damage during their service raising the necessity for periodic inspection and maintenance of their components so that structural integrity and safe operation can be guaranteed. Cost reduction related to minimizing the out-of-service time of the aircraft, together with the advantages offered by real-time and safe-life service monitoring, have led to a boom in the design of inexpensive and structurally integrated transducer networks comprising actuators, sensors, signal processing units and controllers. These kinds of automated systems are normally referred to as smart structures and offer a multitude of new solutions to engineering problems and multi-functional capabilities. It is thus expected that structural health monitoring (SHM) systems will become one of the leading technologies for assessing and assuring the structural integrity of future aircraft. This study is devoted to the development and experimental investigation of an SHM methodology for the detection of damage in real scale complex aeronautical structures. The work focuses on each aspect of the SHM system and highlights the potentialities of the health monitoring technique based on acousto-ultrasonics and data-driven modelling within the concepts of sensor data fusion, feature extraction and pattern recognition. The methodology is experimentally demonstrated on an aircraft skin panel and fuselage panel for which several damage scenarios are analysed. The detection performance in both structures is quantified and presented.

Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

NASA Astrophysics Data System (ADS)

Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

2017-09-01

Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

An ethanol extract derived from Bonnemaisonia hamifera scavenges ultraviolet B (UVB) radiation-induced reactive oxygen species and attenuates UVB-induced cell damage in human keratinocytes.

PubMed

Piao, Mei Jing; Hyun, Yu Jae; Cho, Suk Ju; Kang, Hee Kyoung; Yoo, Eun Sook; Koh, Young Sang; Lee, Nam Ho; Ko, Mi Hee; Hyun, Jin Won

2012-12-14

The present study investigated the photoprotective properties of an ethanol extract derived from the red alga Bonnemaisonia hamifera against ultraviolet B (UVB)-induced cell damage in human HaCaT keratinocytes. The Bonnemaisonia hamifera ethanol extract (BHE) scavenged the superoxide anion generated by the xanthine/xanthine oxidase system and the hydroxyl radical generated by the Fenton reaction (FeSO₄ + H₂O₂), both of which were detected by using electron spin resonance spectrometry. In addition, BHE exhibited scavenging activity against the 1,1-diphenyl-2-picrylhydrazyl radical and intracellular reactive oxygen species (ROS) that were induced by either hydrogen peroxide or UVB radiation. BHE reduced UVB-induced apoptosis, as shown by decreased apoptotic body formation and DNA fragmentation. BHE also attenuated DNA damage and the elevated levels of 8-isoprostane and protein carbonyls resulting from UVB-mediated oxidative stress. Furthermore, BHE absorbed electromagnetic radiation in the UVB range (280-320 nm). These results suggest that BHE protects human HaCaT keratinocytes against UVB-induced oxidative damage by scavenging ROS and absorbing UVB photons, thereby reducing injury to cellular components.

A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming

NASA Astrophysics Data System (ADS)

Paino, A.; Keller, J.; Popescu, M.; Stone, K.

2014-06-01

In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image "chips" taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

Antigenotoxic Effect of Trametes spp. Extracts against DNA Damage on Human Peripheral White Blood Cells

PubMed Central

Živković, Lada; Stajić, Mirjana; Vukojević, Jelena; Milovanović, Ivan; Spremo-Potparević, Biljana

2015-01-01

Trametes species have been used for thousands of years in traditional and conventional medicine for the treatment of various types of diseases. The goal was to evaluate possible antigenotoxic effects of mycelium and basidiocarp extracts of selected Trametes species and to assess dependence on their antioxidant potential. Trametes versicolor, T. hirsuta, and T. gibbosa were the species studied. Antigenotoxic potentials of extracts were assessed on human peripheral white blood cells with basidiocarp and mycelium extracts of the species. The alkaline comet test was used for detection of DNA strand breaks and alkali-labile sites, as well as the extent of DNA migration. DPPH assay was used to estimate antioxidative properties of extracts. Fruiting body extracts of T. versicolor and T. gibbosa as well as T. hirsuta extracts, except that at 20.0 mg/mL, were not genotoxic agents. T. versicolor extract had at 5.0 mg/mL the greatest antigenotoxic effect in both pre- and posttreatment of leukocytes. The mycelium extracts of the three species had no genotoxic activity and significant antigenotoxic effect against H2O2-induced DNA damage, both in pre- and posttreatment. The results suggest that extracts of these three species could be considered as strong antigenotoxic agents able to stimulate genoprotective response of cells. PMID:26258163

Protective effect of red orange extract supplementation against UV-induced skin damages: photoaging and solar lentigines.

PubMed

Puglia, Carmelo; Offerta, Alessia; Saija, Antonella; Trombetta, Domenico; Venera, Cardile

2014-06-01

Exposure of the skin to solar ultraviolet (UV) radiations causes important oxidative damages that result in clinical and hystopathological changes, contributing to premature skin aging. Hyperpigmented lesions, also known as age spots, are one of the most visible alterations in skin photoaging. Skin is naturally equipped with antioxidant systems against UV-induced ROS generation; however, these antioxidant defenses are not completely efficient during exposure to sunlight. Oral antioxidants are able to counteract the harmful effects of UV radiation and to strengthen the physiological skin antioxidant defenses. The present study was performed to evaluate the in vivo skin photo-protecting and anti-aging effects of a red orange (Citrus sinensis varieties Moro, Tarocco and Sanguinello) extract supplementation. Previous studies showed that red orange extracts possess strong in vitro free radical scavenging/antioxidant activity and photo-protective effects on human skin. The photo-protective effects of red orange extract intake against UV-induced skin erythema and melanin production in solar lentigo was evaluated on healthy volunteers by an objective instrumental method (reflectance spectrophotometry). Data obtained from in vivo studies showed that supplementation of red orange extract (100 mg/daily) for 15 days brought a significant reduction in the UV-induced skin erythema degree. Moreover, skin age spots pigmentation (melanin content) decreased from 27% to 7% when subjects were exposed to solar lamp during red orange extract supplementation. Red orange extract intake can strengthen physiological antioxidant skin defenses, protecting skin from the damaging processes involved in photo-aging and leading to an improvement in skin appearance and pigmentation. © 2014 Wiley Periodicals, Inc.

Zingiber officinale Mitigates Brain Damage and Improves Memory Impairment in Focal Cerebral Ischemic Rat

PubMed Central

Wattanathorn, Jintanaporn; Jittiwat, Jinatta; Tongun, Terdthai; Muchimapura, Supaporn; Ingkaninan, Kornkanok

2011-01-01

Cerebral ischemia is known to produce brain damage and related behavioral deficits including memory. Recently, accumulating lines of evidence showed that dietary enrichment with nutritional antioxidants could reduce brain damage and improve cognitive function. In this study, possible protective effect of Zingiber officinale, a medicinal plant reputed for neuroprotective effect against oxidative stress-related brain damage, on brain damage and memory deficit induced by focal cerebral ischemia was elucidated. Male adult Wistar rats were administrated an alcoholic extract of ginger rhizome orally 14 days before and 21 days after the permanent occlusion of right middle cerebral artery (MCAO). Cognitive function assessment was performed at 7, 14, and 21 days after MCAO using the Morris water maze test. The brain infarct volume and density of neurons in hippocampus were also determined. Furthermore, the level of malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) in cerebral cortex, striatum, and hippocampus was also quantified at the end of experiment. The results showed that cognitive function and neurons density in hippocampus of rats receiving ginger rhizome extract were improved while the brain infarct volume was decreased. The cognitive enhancing effect and neuroprotective effect occurred partly via the antioxidant activity of the extract. In conclusion, our study demonstrated the beneficial effect of ginger rhizome to protect against focal cerebral ischemia. PMID:21197427

A grain boundary damage model for delamination

NASA Astrophysics Data System (ADS)

Messner, M. C.; Beaudoin, A. J.; Dodds, R. H.

2015-07-01

Intergranular failure in metallic materials represents a multiscale damage mechanism: some feature of the material microstructure triggers the separation of grain boundaries on the microscale, but the intergranular fractures develop into long cracks on the macroscale. This work develops a multiscale model of grain boundary damage for modeling intergranular delamination—a failure of one particular family of grain boundaries sharing a common normal direction. The key feature of the model is a physically-consistent and mesh independent, multiscale scheme that homogenizes damage at many grain boundaries on the microscale into a single damage parameter on the macroscale to characterize material failure across a plane. The specific application of the damage framework developed here considers delamination failure in modern Al-Li alloys. However, the framework may be readily applied to other metals or composites and to other non-delamination interface geometries—for example, multiple populations of material interfaces with different geometric characteristics.

Insect detection and nitrogen management for irrigated potatoes using remote sensing from small unmanned aircraft systems

NASA Astrophysics Data System (ADS)

Hunt, E. Raymond; Rondon, Silvia I.; Hamm, Philip B.; Turner, Robert W.; Bruce, Alan E.; Brungardt, Josh J.

2016-05-01

Remote sensing with small unmanned aircraft systems (sUAS) has potential applications in agriculture because low flight altitudes allow image acquisition at very high spatial resolution. We set up experiments at the Oregon State University Hermiston Agricultural Research and Extension Center with different platforms and sensors to assess advantages and disadvantages of sUAS for precision farming. In 2013, we conducted an experiment with 4 levels of N fertilizer, and followed the changes in the normalized difference vegetation index (NDVI) over time. In late June, there were no differences in chlorophyll content or leaf area index (LAI) among the 3 higher application rates. Consistent with the field data, only plots with the lowest rate of applied N were distinguished by low NDVI. In early August, N deficiency was determined by NDVI, but it was too late to mitigate losses in potato yield and quality. Populations of the Colorado potato beetle (CPB) may rapidly increase, devouring the shoots, thus early detection and treatment could prevent yield losses. In 2014, we conducted an experiment with 4 levels of CPB infestation. Over one day, damage from CPB in some plots increased from 0 to 19%. A visual ranking of damage was not correlated with the total number of CPB or treatment. Plot-scale vegetation indices were not correlated with damage, although the damaged area determined by object-based feature extraction was highly correlated. Methods based on object-based image analysis of sUAS data have potential for early detection and reduced cost.

Feature extraction using convolutional neural network for classifying breast density in mammographic images

NASA Astrophysics Data System (ADS)

Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

2017-03-01

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is still required for evaluating the results.

Persistent activation of DNA damage signaling in response to complex mixtures of PAHs in air particulate matter

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

Jarvis, Ian W.H., E-mail: Ian.Jarvis@ki.se; Bergvall, Christoffer, E-mail: Christoffer.Bergvall@anchem.su.se; Bottai, Matteo, E-mail: Matteo.Bottai@ki.se

2013-02-01

Complex mixtures of polycyclic aromatic hydrocarbons (PAHs) are present in air particulate matter (PM) and have been associated with many adverse human health effects including cancer and respiratory disease. However, due to their complexity, the risk of exposure to mixtures is difficult to estimate. In the present study the effects of binary mixtures of benzo[a]pyrene (BP) and dibenzo[a,l]pyrene (DBP) and complex mixtures of PAHs in urban air PM extracts on DNA damage signaling was investigated. Applying a statistical model to the data we observed a more than additive response for binary mixtures of BP and DBP on activation of DNAmore » damage signaling. Persistent activation of checkpoint kinase 1 (Chk1) was observed at significantly lower BP equivalent concentrations in air PM extracts than BP alone. Activation of DNA damage signaling was also more persistent in air PM fractions containing PAHs with more than four aromatic rings suggesting larger PAHs contribute a greater risk to human health. Altogether our data suggests that human health risk assessment based on additivity such as toxicity equivalency factor scales may significantly underestimate the risk of exposure to complex mixtures of PAHs. The data confirms our previous findings with PAH-contaminated soil (Niziolek-Kierecka et al., 2012) and suggests a possible role for Chk1 Ser317 phosphorylation as a biological marker for future analyses of complex mixtures of PAHs. -- Highlights: ► Benzo[a]pyrene (BP), dibenzo[a,l]pyrene (DBP) and air PM PAH extracts were compared. ► Binary mixture of BP and DBP induced a more than additive DNA damage response. ► Air PM PAH extracts were more potent than toxicity equivalency factor estimates. ► Larger PAHs (> 4 rings) contribute more to the genotoxicity of PAHs in air PM. ► Chk1 is a sensitive marker for persistent activation of DNA damage signaling from PAH mixtures.« less

Feature extraction applied to agricultural crops as seen by LANDSAT

NASA Technical Reports Server (NTRS)

Kauth, R. J.; Lambeck, P. F.; Richardson, W.; Thomas, G. S.; Pentland, A. P. (Principal Investigator)

1979-01-01

The physical interpretation of the spectral-temporal structure of LANDSAT data can be conveniently described in terms of a graphic descriptive model called the Tassled Cap. This model has been a source of development not only in crop-related feature extraction, but also for data screening and for haze effects correction. Following its qualitative description and an indication of its applications, the model is used to analyze several feature extraction algorithms.

Optical character recognition with feature extraction and associative memory matrix

NASA Astrophysics Data System (ADS)

Sasaki, Osami; Shibahara, Akihito; Suzuki, Takamasa

1998-06-01

A method is proposed in which handwritten characters are recognized using feature extraction and an associative memory matrix. In feature extraction, simple processes such as shifting and superimposing patterns are executed. A memory matrix is generated with singular value decomposition and by modifying small singular values. The method is optically implemented with two liquid crystal displays. Experimental results for the recognition of 25 handwritten alphabet characters clearly shows the effectiveness of the method.

Spectral Analysis of Breast Cancer on Tissue Microarrays: Seeing Beyond Morphology

DTIC Science & Technology

2005-04-01

Harvey N., Szymanski J.J., Bloch J.J., Mitchell M. investigation of image feature extraction by a genetic algorithm. Proc. SPIE 1999;3812:24-31. 11...automated feature extraction using multiple data sources. Proc. SPIE 2003;5099:190-200. 15 4 Spectral-Spatial Analysis of Urine Cytology Angeletti et al...Appendix Contents: 1. Harvey, N.R., Levenson, R.M., Rimm, D.L. (2003) Investigation of Automated Feature Extraction Techniques for Applications in

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

PubMed Central

Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh J. E.

2015-01-01

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$T^{2}$ \\end{document} statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%. PMID:27170898

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

PubMed Central

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction. PMID:26397832

Automation of lidar-based hydrologic feature extraction workflows using GIS

NASA Astrophysics Data System (ADS)

Borlongan, Noel Jerome B.; de la Cruz, Roel M.; Olfindo, Nestor T.; Perez, Anjillyn Mae C.

2016-10-01

With the advent of LiDAR technology, higher resolution datasets become available for use in different remote sensing and GIS applications. One significant application of LiDAR datasets in the Philippines is in resource features extraction. Feature extraction using LiDAR datasets require complex and repetitive workflows which can take a lot of time for researchers through manual execution and supervision. The Development of the Philippine Hydrologic Dataset for Watersheds from LiDAR Surveys (PHD), a project under the Nationwide Detailed Resources Assessment Using LiDAR (Phil-LiDAR 2) program, created a set of scripts, the PHD Toolkit, to automate its processes and workflows necessary for hydrologic features extraction specifically Streams and Drainages, Irrigation Network, and Inland Wetlands, using LiDAR Datasets. These scripts are created in Python and can be added in the ArcGIS® environment as a toolbox. The toolkit is currently being used as an aid for the researchers in hydrologic feature extraction by simplifying the workflows, eliminating human errors when providing the inputs, and providing quick and easy-to-use tools for repetitive tasks. This paper discusses the actual implementation of different workflows developed by Phil-LiDAR 2 Project 4 in Streams, Irrigation Network and Inland Wetlands extraction.

Effect of heat treatment on the antioxidative and antigenotoxic activity of extracts from persimmon (Diospyros kaki L.) peel.

PubMed

Kim, So-Young; Jeong, Seok-Moon; Kim, Sun-Jung; Jeon, Kyung-Im; Park, Eunju; Park, Hae-Ryong; Lee, Seung-Cheol

2006-04-01

Heat treatment of persimmon peel (PP) increased the antioxidative activity of the 70% ethanolic extract (EE) and water extract (WE) from PP. EE and WE both prevented H2O2-induced DNA damage to human peripheral lymphocytes. The antioxidative and antigenotoxic activities of the PP extracts were significantly affected by heating.

Hepatoprotective Effect of Houttuynia cordata Thunb Extract against Carbon Tetrachloride-induced Hepatic Damage in Mice

PubMed Central

Kang, H.; Koppula, S.

2014-01-01

Houttuynia cordata Thunb (Saururaceae) is a traditional medicinal herb used to treat several disease symptoms. The present study was focused on the hepatoprotective effects of H. cordata ethyl acetate extract in experimental mice. Further the antioxidant potential of the extract was also evaluated to substantiate its hepatoprotective properties. Carbon tetrachloride-induced hepatic damage in mice was used to measure the serum biochemical parameters. Morphological changes in hepatocyte architecture were studied by haematoxylin and eosin staining. In vitro alkyl and hydroxyl free radical scavenging assays were performed to evaluate the antioxidant effect. Administration of H. cordata extract significantly reduced the elevated serum levels and regulated the altered levels of serum cholesterol in carbon tetrachloride-treated mice (P<0.05). The morphological changes in hepatocyte architecture were also reversed by H. cordata treatment. Further, the extract showed significant antioxidant actions by scavenging the alkyl and hydroxyl free radicals. The concentration of the extract necessary for 50% scavenging of alkyl and hydroxyl radicals was 15.5 and 410 μg/ml, respectively. H. cordata extract exhibited significant hepatoprotective property in carbon tetrachloride-induced hepatotoxicity in mice. The strong antioxidant activities possessed by the extract might be responsible for such actions. PMID:25284923

Hepatoprotective Effect of Houttuynia cordata Thunb Extract against Carbon Tetrachloride-induced Hepatic Damage in Mice.

PubMed

Kang, H; Koppula, S

2014-07-01

Houttuynia cordata Thunb (Saururaceae) is a traditional medicinal herb used to treat several disease symptoms. The present study was focused on the hepatoprotective effects of H. cordata ethyl acetate extract in experimental mice. Further the antioxidant potential of the extract was also evaluated to substantiate its hepatoprotective properties. Carbon tetrachloride-induced hepatic damage in mice was used to measure the serum biochemical parameters. Morphological changes in hepatocyte architecture were studied by haematoxylin and eosin staining. In vitro alkyl and hydroxyl free radical scavenging assays were performed to evaluate the antioxidant effect. Administration of H. cordata extract significantly reduced the elevated serum levels and regulated the altered levels of serum cholesterol in carbon tetrachloride-treated mice (P<0.05). The morphological changes in hepatocyte architecture were also reversed by H. cordata treatment. Further, the extract showed significant antioxidant actions by scavenging the alkyl and hydroxyl free radicals. The concentration of the extract necessary for 50% scavenging of alkyl and hydroxyl radicals was 15.5 and 410 μg/ml, respectively. H. cordata extract exhibited significant hepatoprotective property in carbon tetrachloride-induced hepatotoxicity in mice. The strong antioxidant activities possessed by the extract might be responsible for such actions.

Antimicrobial efficacy of Cinnamomum javanicum plant extract against Listeria monocytogenes and its application potential with smoked salmon.

PubMed

Yuan, Wenqian; Lee, Hui Wen; Yuk, Hyun-Gyun

2017-11-02

Extracts from medicinal plants have been reported to possess good antimicrobial properties, but a majority of them remain unexplored. This study aimed at identifying a novel plant extract with antimicrobial activity, to validate its efficacy in food model, and to elucidate its composition and antimicrobial mechanism. A total of 125 plant extracts were screened, and Cinnamomum javanicum leaf and stem extract showed potential antimicrobial activity against Listeria monocytogenes (MIC=0.13mg/mL). Total phenolic content of the extract was 78.3mg GAE/g extract and its antioxidant activity was 57.2-326.5mg TE/g extract. When applied on cold smoked salmon, strong strain-dependent antimicrobial effectiveness was observed, with L. monocytogenes LM2 (serotype 4b) and LM8 (serotype 3a) being more resistant compared to SSA81 (serotype 1/2a). High extract concentration (16mg/mL) was needed to inhibit or reduce the growth of L. monocytogenes on smoked salmon, which resulted in surface color change. GC-MS revealed that eucalyptol (25.54 area%) was the most abundant compound in the crude extract. Both crude extract and eucalyptol induced significant membrane damages in treated L. monocytogenes. These results suggest anti-L. monocytogenes activity of C. javanicum plant extract, identified its major volatile components, and elucidated its membrane-damaging antimicrobial mechanisms. Copyright © 2017 Elsevier B.V. All rights reserved.

Feature Extraction and Selection Strategies for Automated Target Recognition

NASA Technical Reports Server (NTRS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Feature extraction and selection strategies for automated target recognition

NASA Astrophysics Data System (ADS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-04-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Neuroprotective effects of tanshinone I from Danshen extract in a mouse model of hypoxia-ischemia

PubMed Central

Lee, Jae-Chul; Park, Joon Ha; Park, Ok Kyu; Kim, In Hye; Yan, Bing Chun; Ahn, Ji Hyeon; Kwon, Seung-Hae; Choi, Jung Hoon

2013-01-01

Hypoxia-ischemia leads to serious neuronal damage in some brain regions and is a strong risk factor for stroke. The aim of this study was to investigate the neuroprotective effect of tanshinone I (TsI) derived from Danshen (Radix Salvia miltiorrhiza root extract) against neuronal damage using a mouse model of cerebral hypoxia-ischemia. Brain infarction and neuronal damage were examined using 2,3,5-triphenyltetrazolium chloride (TTC) staining, hematoxylin and eosin histochemistry, and Fluoro-Jade B histofluorescence. Pre-treatment with TsI (10 mg/kg) was associated with a significant reduction in infarct volume 1 day after hypoxia-ischemia was induced. In addition, TsI protected against hypoxia-ischemia-induced neuronal death in the ipsilateral region. Our present findings suggest that TsI has strong potential for neuroprotection against hypoxic-ischemic damage. These results may be used in research into new anti-stroke medications. PMID:24179693

Investigation of the Hepatoprotective Effect of Prunus mume Sieb. et Zucc Extract in a Mouse Model of Alcoholic Liver Injury Through High-Resolution Metabolomics.

PubMed

Khan, Adnan; Pan, Jeong Hoon; Cho, Seongha; Lee, Sojung; Kim, Young Jun; Park, Youngja H

2017-08-01

This study aimed to identify the changes in the metabolomics profile of liver damage caused by alcohol consumption and verify the beneficial effect of Prunus mume Sieb. et Zucc extract (PME) in protection of alcohol-induced injury by attenuating the level of identified metabolites. Mice were treated with PME and saline or untreated once daily for 5 days, followed by alcohol injection. The plasma samples were analyzed using liquid chromatography-mass spectrometry-based high-resolution metabolomics followed by a multivariate statistical analysis using MetaboAnalyst 3.0 to obtain significantly expressed metabolites, using a false discovery rate threshold of q = 0.05. Metabolites were annotated using Metlin database and mapped through Kyoto Encyclopedia of Genes and Genomes (KEGG). Among 4999 total features, 101 features were significant among alcohol- and PME-treated mice groups. All the samples cluster showed a clear separation in the heat map, and the scores plot of orthogonal partial least squares-discriminant analysis (OPLS-DA) model discriminated the three groups. Phosphatidylcholine, Saikosaponin BK1, Ganoderiol I, and N-2-[4-(3,3-dimethylallyloxy) phenyl] ethylcinnamide were among the significant compounds with a low intensity in alcohol group compared to PME group, suggesting that these compounds have a relation in the development of PME's protective effect. The study confirms the hepatoprotective, antioxidant, and anti-inflammatory effects of PME against alcohol-induced liver steatosis, inflammation, and apoptosis.

Protection of cadmium chloride induced DNA damage by Lamiaceae plants

PubMed Central

Thirugnanasampandan, Ramaraj; Jayakumar, Rajarajeswaran

2011-01-01

Objective To analyze the total phenolic content, DNA protecting and radical scavenging activity of ethanolic leaf extracts of three Lamiaceae plants, i.e. Anisomelos malabarica (A. malabarica), Leucas aspera (L. aspera) and Ocimum basilicum (O. basilicum). Methods The total polyphenols and flavonoids were analyzed in the ethanolic leaf extracts of the lamiaceae plants. To determine the DNA protecting activity, various concentrations of the plant extracts were prepared and treated on cultured HepG2 human lung cancer cells. The pretreated cells were exposed to H2O2 to induce DNA damage through oxidative stress. Comet assay was done and the tail length of individual comets was measured. Nitric oxide and superoxide anion scavenging activities of lamiaceae plants were analyzed. Results Among the three plant extracts, the highest amount of total phenolic content was found in O. basilicum (189.33 mg/g), whereas A. malabarica showed high levels of flavonoids (10.66 mg/g). O. basilicum also showed high levels of DNA protecting (85%) and radical scavenging activity. Conclusions The results of this study shows that bioactive phenols present in lamiaceae plants may prevent carcinogenesis through scavenging free radicals and inhibiting DNA damage. PMID:23569799

SCHISTOSOMICIDAL ACTIVITY OF THE CRUDE EXTRACT OF ARTOCARPUS LAKOOCHA.

PubMed

Preyavichyapugdee, Narin; Sangfuang, Manaw; Chaiyapum, Saowapark; Sriburin, Suwatcharaporn; Pootaeng-on, Yupa; Chusongsang, Phiraphol; Jiraungkoorskul, Wannee; Preyavichyapugdee, Mananya; Sobhon, Prasert

2016-01-01

Puag-Haad is a traditional anthelmintic drug used to treat taeniasis in Thailand and Lao PDR. It is derived from the aqueous extract of the plant Artocarpus lakoocha. We investigated the in vitro anthelmintic properties of Puag-Haad against Schistosoma mansoni. Adult worms were incubated in M-199 medium containing 250, 500 and 750 μg/ml of Puag-Haad or praziquantel (PZQ) at a concentration of 175 μg/ml for 3, 6, 12 and 24 hours. The relative motility (RM value), survival index (SI) and tegument alterations seen under scanning electron microscope were assessed at each incubation time. The results showed the crude extract of A. lakoocha at a concentration of 250 μg/ml was more effective in causing damage than PZQ at a concentration of 175 μg/ml using RM and SI values. The major target organ affected by Puag-Haad was the tegument. The damage was greater at higher concentrations of the crude extract. It is likely tetrahydroxystilbene (THS), the main compound in Puag-Haad, caused the damage. THS could be a future candidate as a schistosomal drug. Further studies are needed to explore its mechanism, efficiency and safety in vivo.

UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning

NASA Astrophysics Data System (ADS)

Fernandez Galarreta, J.; Kerle, N.; Gerke, M.

2015-06-01

Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.

Ensemble methods with simple features for document zone classification

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Xie, Bingqing

2012-01-01

Document layout analysis is of fundamental importance for document image understanding and information retrieval. It requires the identification of blocks extracted from a document image via features extraction and block classification. In this paper, we focus on the classification of the extracted blocks into five classes: text (machine printed), handwriting, graphics, images, and noise. We propose a new set of features for efficient classifications of these blocks. We present a comparative evaluation of three ensemble based classification algorithms (boosting, bagging, and combined model trees) in addition to other known learning algorithms. Experimental results are demonstrated for a set of 36503 zones extracted from 416 document images which were randomly selected from the tobacco legacy document collection. The results obtained verify the robustness and effectiveness of the proposed set of features in comparison to the commonly used Ocropus recognition features. When used in conjunction with the Ocropus feature set, we further improve the performance of the block classification system to obtain a classification accuracy of 99.21%.

A method of vehicle license plate recognition based on PCANet and compressive sensing

NASA Astrophysics Data System (ADS)

Ye, Xianyi; Min, Feng

2018-03-01

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.

Covariance of dynamic strain responses for structural damage detection

NASA Astrophysics Data System (ADS)

Li, X. Y.; Wang, L. X.; Law, S. S.; Nie, Z. H.

2017-10-01

A new approach to address the practical problems with condition evaluation/damage detection of structures is proposed based on the distinct features of a new damage index. The covariance of strain response function (CoS) is a function of modal parameters of the structure. A local stiffness reduction in structure would cause monotonous increase in the CoS. Its sensitivity matrix with respect to local damages of structure is negative and narrow-banded. The damage extent can be estimated with an approximation to the sensitivity matrix to decouple the identification equations. The CoS sensitivity can be calibrated in practice from two previous states of measurements to estimate approximately the damage extent of a structure. A seven-storey plane frame structure is numerically studied to illustrate the features of the CoS index and the proposed method. A steel circular arch in the laboratory is tested. Natural frequencies changed due to damage in the arch and the damage occurrence can be judged. However, the proposed CoS method can identify not only damage happening but also location, even damage extent without need of an analytical model. It is promising for structural condition evaluation of selected components.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Chinese character recognition based on Gabor feature extraction and CNN

NASA Astrophysics Data System (ADS)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Human listening studies reveal insights into object features extracted by echolocating dolphins

NASA Astrophysics Data System (ADS)

Delong, Caroline M.; Au, Whitlow W. L.; Roitblat, Herbert L.

2004-05-01

Echolocating dolphins extract object feature information from the acoustic parameters of object echoes. However, little is known about which object features are salient to dolphins or how they extract those features. To gain insight into how dolphins might be extracting feature information, human listeners were presented with echoes from objects used in a dolphin echoic-visual cross-modal matching task. Human participants performed a task similar to the one the dolphin had performed; however, echoic samples consisting of 23-echo trains were presented via headphones. The participants listened to the echoic sample and then visually selected the correct object from among three alternatives. The participants performed as well as or better than the dolphin (M=88.0% correct), and reported using a combination of acoustic cues to extract object features (e.g., loudness, pitch, timbre). Participants frequently reported using the pattern of aural changes in the echoes across the echo train to identify the shape and structure of the objects (e.g., peaks in loudness or pitch). It is likely that dolphins also attend to the pattern of changes across echoes as objects are echolocated from different angles.

Phenolic Content of Hypodaphnis Zenkeri and Its Antioxidant Effects against Fenton Reactions’ Mediated Oxidative Injuries on Liver Homogenate

PubMed Central

Moukette Moukette, Bruno; Pieme, Constant Anatole; Nya Biapa, Prosper Cabral; Njimou, Jacques Romain; Ama Moor, Vicky Jocelyne; Stoller, Marco; Bravi, Marco; Ngogang, Jeanne Yonkeu

2014-01-01

Under oxidative stress conditions, endogenous antioxidant defenses are unable to completely inactivate the free radicals generated by an excessive production of reactive oxygen species (ROS). This state causes serious cell damage leading to a variety of human diseases. Natural antioxidants can protect cells against oxidative stress. Hypaodaphnis zenkeri (H. zenkiri) is a plant consumed as a spice in the Cameroonian diet, and its bark has been used in traditional medicine for the treatment of several diseases. The present study aims at investigating the antioxidant activity, which includes free radical scavenging and protective properties of an extract from H. Zenkiri against oxidative damage on a liver homogenate. The free radical assays determined the scavenging activities of 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl (OH), nitrite oxide (NO) and 2,2-azinobis(3-ethylbenzthiazoline)-6-sulfonic acid (ABTS) radicals and the enzymes, whose protection was to be considered in the liver homogenate, including superoxide dismutase, catalase, and peroxidase. The antioxidative activities were studied using the ferric reducing antioxidant power (FRAP), reductive activity, and phosphomolybdenum antioxidant power (PAP) methods. In addition, the phenolic contents of the extracts were examined. The results showed that these extracts demonstrated significant scavenging properties and antioxidant activities, with the hydro-ethanolic extract of the bark of H. zenkeri (EEH) being the most potent. This extract had the highest total polyphenol (21.77 ± 0.05 mg caffeic acid (CAE)/g dried extract (DE)) and flavonoids (3.34 ± 0.13 mg quercetin (QE)/g dried extract) content. The same extract had significantly greater protective effects on enzyme activities compared to other extracts. The high performance liquied chromatography (HPLC) profile showed higher levels of caffeic acid, OH-tyrosol acid, and rutin in the leaves compared to the bark of H. zenkeri. In conclusion, the ethanolic and hydro-ethanolic extracts of the bark and leaves from H. zenkeri showed an antioxidant and protective potential against oxidative damage. PMID:26785245

The Extract of D. dasycarpus Ameliorates Oxazolone-Induced Skin Damage in Mice by Anti-Inflammatory and Antioxidant Mechanisms.

PubMed

Chang, Tsong-Min; Yang, Ting-Ya; Niu, Yu-Lin; Huang, Huey-Chun

2018-06-15

Dictamni dasycarpus is a type of Chinese medicine made from the root bark of D. dasycarpus . It has been reported to show a wide spectrum of biological and pharmacological effects, for example, it has been used widely for the treatment of rheumatism, nettle rash, itching, jaundice, chronic hepatitis and skin diseases. In the current study, D. dasycarpus extract was investigated for its antioxidant and anti-inflammatory effects, as well as its capability to alleviate oxazolone-induced skin damage in mice. The possible anti-inflammatory mechanism of D. dasycarpus extract against oxidative challenge was elucidated by measuring the levels of reactive oxygen species (ROS) production, interleukin-6, Tumor necrosis factor-α, NLRP3 (NACHT, LRR and PYD domains-containing protein 3 (NALP3)) inflammasome and interleukin-1β in HaCaT cells. D. dasycarpus extract did not affect cell viability in basal conditions. The extract significantly reduced oxazolone-induced epidermal swelling compared to untreated animal in the hairless albino mice (ICR mice) model. At the molecular level, Western blot assays indicated that the D. dasycarpus extract attenuated oxazolone-induced activation of apoptosis-associated speck-like protein containing CARD (ASC), procaspase-1, NF-κB and mitogen-activated protein kinase (MAPKs) such as c-Jun N-terminal protein kinase (JNK) and p38. This study demonstrates that D. dasycarpus extract could protect skin cells against oxidative and inflammatory insult by modulating the intracellular levels of ROS, TNF-α, interleukin-1, interleukin-6, NLR family pyrin domain containing 3 (NLRP3) inflammasome generation, antioxidant enzyme activity and cell signaling pathways. D. dasycarpus extract also attenuated the expression of NF-κB in HaCaT keratinocytes and thereby effectively downregulated inflammatory responses in the skin. Furthermore, D. dasycarpus extract alleviated oxazolone-induced damage in mice. Our results suggest the potential application of D. dasycarpus extract in preventing inflammatory processes in dermatitis.

TU-CD-BRB-05: Radiation Damage Signature of White Matter Fiber Bundles Using Diffusion Tensor Imaging (DTI)

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

Zhu, T; Chapman, C; Lawrence, T

2015-06-15

Purpose: To develop an automated and scalable approach and identify temporal, spatial and dosimetric patterns of radiation damage of white matter (WM) fibers following partial brain irradiation. Methods: An automated and scalable approach was developed to extract DTI features of 22 major WM fibers from 33 patients with low-grade/benign tumors treated by radiation therapy (RT). DTI scans of the patients were performed pre-RT, 3- and 6-week during RT, and 1, 6 and 18 months after RT. The automated tractography analysis was applied to 198 datasets as: (1) intra-subject registration of longitudinal DTI, (2) spatial normalization of individual-patient DTI to themore » Johns Hopkins WM Atlas, (3) automatic fiber tracking regulated by the WM Atlas, and (4) segmentation of WM into 22 major tract profiles. Longitudinal percentage changes in fractional anisotropy (FA), and mean, axial and radial diffusivity (MD/AD/RD) of each tract from pre-RT were quantified and correlated to 95%, 90% and 80% percentiles of doses and mean doses received by the tract. Heatmaps were used to identify clusters of significant correlation and reveal temporal, spatial and dosimetric signatures of WM damage. A multivariate linear regression was further carried out to determine influence of clinical factors. Results: Of 22 tracts, AD/MD changes in 12 tracts had significant correlation with doses, especially at 6 and 18 months post-RT, indicating progressive radiation damage after RT. Most interestingly, the DTI-index changes in the elongated tracts were associated with received maximum doses, suggesting a serial-structure behavior; while short association fibers were affected by mean doses, indicating a parallel-structure response. Conclusion: Using an automated DTI-tractography analysis of whole brain WM fibers, we reveal complex radiation damage patterns of WM fibers. Damage in WM fibers that play an important role in the neural network could be associated with late neurocognitive function declines after brain irradiation. NIH NS064973.« less

Extracting the normal lung dose-response curve from clinical DVH data: a possible role for low dose hyper-radiosensitivity, increased radioresistance

NASA Astrophysics Data System (ADS)

Gordon, J. J.; Snyder, K.; Zhong, H.; Barton, K.; Sun, Z.; Chetty, I. J.; Matuszak, M.; Ten Haken, R. K.

2015-09-01

In conventionally fractionated radiation therapy for lung cancer, radiation pneumonitis’ (RP) dependence on the normal lung dose-volume histogram (DVH) is not well understood. Complication models alternatively make RP a function of a summary statistic, such as mean lung dose (MLD). This work searches over damage profiles, which quantify sub-volume damage as a function of dose. Profiles that achieve best RP predictive accuracy on a clinical dataset are hypothesized to approximate DVH dependence. Step function damage rate profiles R(D) are generated, having discrete steps at several dose points. A range of profiles is sampled by varying the step heights and dose point locations. Normal lung damage is the integral of R(D) with the cumulative DVH. Each profile is used in conjunction with a damage cutoff to predict grade 2 plus (G2+) RP for DVHs from a University of Michigan clinical trial dataset consisting of 89 CFRT patients, of which 17 were diagnosed with G2+ RP. Optimal profiles achieve a modest increase in predictive accuracy—erroneous RP predictions are reduced from 11 (using MLD) to 8. A novel result is that optimal profiles have a similar distinctive shape: enhanced damage contribution from low doses (<20 Gy), a flat contribution from doses in the range ~20-40 Gy, then a further enhanced contribution from doses above 40 Gy. These features resemble the hyper-radiosensitivity / increased radioresistance (HRS/IRR) observed in some cell survival curves, which can be modeled using Joiner’s induced repair model. A novel search strategy is employed, which has the potential to estimate RP dependence on the normal lung DVH. When applied to a clinical dataset, identified profiles share a characteristic shape, which resembles HRS/IRR. This suggests that normal lung may have enhanced sensitivity to low doses, and that this sensitivity can affect RP risk.

Feature extraction with deep neural networks by a generalized discriminant analysis.

PubMed

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

An approach for automatic classification of grouper vocalizations with passive acoustic monitoring.

PubMed

Ibrahim, Ali K; Chérubin, Laurent M; Zhuang, Hanqi; Schärer Umpierre, Michelle T; Dalgleish, Fraser; Erdol, Nurgun; Ouyang, B; Dalgleish, A

2018-02-01

Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50-350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classification of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the species that produced these sounds. Experimental results showed that the overall percentage of identification using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been implemented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.

Evaluation of a web based informatics system with data mining tools for predicting outcomes with quantitative imaging features in stroke rehabilitation clinical trials

NASA Astrophysics Data System (ADS)

Wang, Ximing; Kim, Bokkyu; Park, Ji Hoon; Wang, Erik; Forsyth, Sydney; Lim, Cody; Ravi, Ragini; Karibyan, Sarkis; Sanchez, Alexander; Liu, Brent

2017-03-01

Quantitative imaging biomarkers are used widely in clinical trials for tracking and evaluation of medical interventions. Previously, we have presented a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials. The system integrates imaging features extraction tools and a web-based statistical analysis tool. The tools include a generalized linear mixed model(GLMM) that can investigate potential significance and correlation based on features extracted from clinical data and quantitative biomarkers. The imaging features extraction tools allow the user to collect imaging features and the GLMM module allows the user to select clinical data and imaging features such as stroke lesion characteristics from the database as regressors and regressands. This paper discusses the application scenario and evaluation results of the system in a stroke rehabilitation clinical trial. The system was utilized to manage clinical data and extract imaging biomarkers including stroke lesion volume, location and ventricle/brain ratio. The GLMM module was validated and the efficiency of data analysis was also evaluated.

Feature extraction from multiple data sources using genetic programming

NASA Astrophysics Data System (ADS)

Szymanski, John J.; Brumby, Steven P.; Pope, Paul A.; Eads, Damian R.; Esch-Mosher, Diana M.; Galassi, Mark C.; Harvey, Neal R.; McCulloch, Hersey D.; Perkins, Simon J.; Porter, Reid B.; Theiler, James P.; Young, Aaron C.; Bloch, Jeffrey J.; David, Nancy A.

2002-08-01

Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (GENIE) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Effects of lemon verbena extract (Recoverben®) supplementation on muscle strength and recovery after exhaustive exercise: a randomized, placebo-controlled trial.

PubMed

Buchwald-Werner, Sybille; Naka, Ioanna; Wilhelm, Manfred; Schütz, Elivra; Schoen, Christiane; Reule, Claudia

2018-01-01

Exhaustive exercise causes muscle damage accompanied by oxidative stress and inflammation leading to muscle fatigue and muscle soreness. Lemon verbena leaves, commonly used as tea and refreshing beverage, demonstrated antioxidant and anti-inflammatory properties. The aim of this study was to investigate the effects of a proprietary lemon verbena extract (Recoverben®) on muscle strength and recovery after exhaustive exercise in comparison to a placebo product. The study was performed as a randomized, placebo-controlled, double-blind study with parallel design. Forty-four healthy males and females, which were 22-50 years old and active in sports, were randomized to 400 mg lemon verbena extract once daily or placebo. The 15 days intervention was divided into 10 days supplementation prior to the exhaustive exercise day (intensive jump-protocol), one day during the test and four days after. Muscle strength (MVC), muscle damage (CK), oxidative stress (GPx), inflammation (IL6) and volunteer-reported muscle soreness intensity were assessed pre and post exercise. Participants in the lemon verbena group benefited from less muscle damage as well as faster and full recovery. Compared to placebo, lemon verbena extract receiving participants had significantly less exercise-related loss of muscle strength ( p  = 0.0311) over all timepoints, improved glutathione peroxidase activity by trend ( p  = 0.0681) and less movement induced pain ( p  = 0.0788) by trend. Creatine kinase and IL-6 didn't show significant discrimmination between groups. Lemon verbena extract (Recoverben®) has been shown to be a safe and well-tolerated natural sports ingredient, by reducing muscle damage after exhaustive exercise. The trial was registered in the clinical trials registry (clinical trial.gov NCT02923102). Registered 28 September 2016.

DNA protective effect of ginseng and the antagonistic effect of Chinese turnip: A supplementation study.

PubMed

Szeto, Yim Tong; Wong, Kam Shing; Han, Andrea; Pak, Sok Cheon; Kalle, Wouter

2016-01-01

The aim of this clinical study is to provide scientific evidence for supporting traditional Chinese application and usage to the patients. For this purpose, we tested the ability if Panax ginseng extract to lower oxidative damage to nuclear DNA in human lymphocytes by comparing the effect of cooked Chinese turnip on this effect. Seven healthy subjects (4 males and 3 females from 37 to 60 years) participated two occasions which were at least 2 weeks apart. About 2 mL of fasting blood sample for baseline measurement was taken on arrival. They were requested to ingest the content of 5 ginseng capsules in 200 mL water. The subject remained fasting for 2 h until the second blood sample taken. In the other occasion, the experiment was repeated except a piece of cooked turnip (10 g) was taken with the ginseng extract. The two occasions could be interchanged. Comet assay was performed on two specimens on the same day for the evaluation of lymphocytic DNA damage with or without oxidative stress. For the group with ginseng supplementation, there was a significant decrease in comet score for hydrogen peroxide (H 2 O 2 ) treatment over the 2-h period while no change in DNA damage for unstressed sample. For the group with ginseng together with turnip supplementation, there was no significant difference in comet score for both H 2 O 2 treatment and phosphate-buffered saline treatment. Ginseng extract could reduce DNA damage mediated by H 2 O 2 effectively, but this protection effect was antagonized by the ingestion of cooked turnip at the same time. In the current study, commercial ginseng extract was used for supplementing volunteers. Ginseng extract could protect DNA from oxidative stress in vivo while turnip diminished the protection.

DNA damaging potential of Ganoderma lucidum extracts.

PubMed

Gurovic, María Soledad Vela; Viceconte, Fátima R; Pereyra, Marcelo T; Bidegain, Maximiliano A; Cubitto, María Amelia

2018-05-10

Ganoderma lucidum (Lingzhi or Reishi) is a medicinal mushroom historically used in Asian countries to treat a wide variety of diseases and prolong life. In the last years, G. lucidum has been internationally recognized as an effective adjuvant in cancer treatment. Among active components, the most recent research indicates that polysaccharides modulate the immune response favoring the recovery from toxicity of chemo and radiotherapy while triterpenes are cytotoxic to tumoral cells mainly by altering gene expression. Beyond this body of evidence on the efficacy of G. lucidum in cancer treatment, it is not yet understood whether these extracts exert the same mechanisms of action than current antitumoral drugs. In this study, we tested the DNA damaging potential of G. lucidum extracts by the β-galactosidase biochemical prophage induction assay (BIA) using doxorubicin, a DNA intercalating agent, as a positive control. This assay was traditionally used to screen microbial metabolites towards antitumoral agents. Here, we used this bacterial assay for the first time to assess DNA damage of herbal drugs. After a bioguided assay, only a purified fraction of G. lucidum containing a mixture of C16 and C18:1 fatty acids exerted weak activity which could not be attributed to direct interaction with DNA. At the same concentrations, the induction observed for doxorubicin was clearly contrasting. The micro BIA assay could be successfully used to demonstrate differences in cellular effects between G. lucidum extracts and doxorubicin. These results showed that G. lucidum extracts display weak DNA damaging potential. Since DNA injury promotes aging and cancer, our results substantiate the traditional use of this mushroom to prolong life. Copyright © 2018 Elsevier B.V. All rights reserved.

Damage detection of building structures under ambient excitation through the analysis of the relationship between the modal participation ratio and story stiffness

NASA Astrophysics Data System (ADS)

Park, Hyo Seon; Oh, Byung Kwan

2018-03-01

This paper presents a new approach for the damage detection of building structures under ambient excitation based on the inherent modal characteristics. In this study, without the extraction of modal parameters widely utilized in the previous studies on damage detection, a new index called the modal participation ratio (MPR), which is a representative value of the modal response extracted from dynamic responses measured in ambient vibration tests, is proposed to evaluate the change of the system of a structure according to the reduction of the story stiffness. The relationship between the MPR, representing a modal contribution for a specific mode and degree of freedom in buildings, and the story stiffness damage factor (SSDF), representing the extent of reduction in the story stiffness, is analyzed in various damage scenarios. From the analyses with three examples, several rules for the damage localization of building structures are found based on the characteristics of the MPR variation for the first mode subject to change in the SSDF. In addition, a damage severity function, derived from the relationship between the MPR for the first mode in the lowest story and the SSDF, is constructed to identify the severity of story stiffness reduction. Furthermore, the locations and severities of multiple damages are identified via the superposition of the presented damage severity functions. The presented method was applied to detect damage in a three-dimensional reinforced concrete (RC) structure.

Supporting the Growing Needs of the GIS Industry

NASA Technical Reports Server (NTRS)

2003-01-01

Visual Learning Systems, Inc. (VLS), of Missoula, Montana, has developed a commercial software application called Feature Analyst. Feature Analyst was conceived under a Small Business Innovation Research (SBIR) contract with NASA's Stennis Space Center, and through the Montana State University TechLink Center, an organization funded by NASA and the U.S. Department of Defense to link regional companies with Federal laboratories for joint research and technology transfer. The software provides a paradigm shift to automated feature extraction, as it utilizes spectral, spatial, temporal, and ancillary information to model the feature extraction process; presents the ability to remove clutter; incorporates advanced machine learning techniques to supply unparalleled levels of accuracy; and includes an exceedingly simple interface for feature extraction.

Oxidant-induced damage to equine erythrocytes from exposure to Pistacia atlantica, Pistacia terebinthus, and Pistacia chinensis.

PubMed

Walter, Kyla M; Moore, Caroline E; Bozorgmanesh, Rana; Magdesian, K Gary; Woods, Leslie W; Puschner, Birgit

2014-11-01

Two horses were referred for methemoglobinemia and hemolytic anemia following 5 acute deaths in their herd from an unidentified toxin source. Horses have a greater risk than other mammalian species of developing methemoglobinemia and hemolytic anemia following ingestion of oxidizing toxins, due to deficiencies in the mechanisms that protect against oxidative damage in erythrocytes. Their susceptibility to oxidative erythrocyte damage is evident in the numerous cases of red maple (Acer rubrum) toxicosis. The suspected toxins causing A. rubrum toxicosis are tannic acid, gallic acid, and a metabolite of gallic acid, pyrogallol. These compounds can be found in a variety of plants, posing a risk to equine health. In order to quickly identify toxin sources, 2 rapid in vitro assays were developed to screen plant extracts for the ability to induce methemoglobin formation or cause hemolysis in healthy equine donor erythrocytes. The plant extract screening focused on 3 species of the genus Pistacia: P. atlantica, P. terebinthus, and P. chinensis, which were located in the horse pasture. Extracts of the seeds and leaves of each species induced methemoglobin formation and resulted in hemolysis, with seed extracts having greater potency. The in vitro assays used in the current study provide a useful diagnostic method for the rapid identification of oxidizing agents from unidentified sources. There is no effective treatment for oxidative erythrocyte damage in horses, making rapid identification and removal of the source essential for the prevention of poisoning. © 2014 The Author(s).

Oxidant-induced damage to equine erythrocytes from exposure to Pistacia atlantica, Pistacia terebinthus, and Pistacia chinensis

PubMed Central

Walter, Kyla M.; Moore, Caroline E.; Bozorgmanesh, Rana; Magdesian, K. Gary; Woods, Leslie W.; Puschner, Birgit

2017-01-01

Two horses were referred for methemoglobinemia and hemolytic anemia following 5 acute deaths in their herd from an unidentified toxin source. Horses have a greater risk than other mammalian species of developing methemoglobinemia and hemolytic anemia following ingestion of oxidizing toxins, due to deficiencies in the mechanisms that protect against oxidative damage in erythrocytes. Their susceptibility to oxidative erythrocyte damage is evident in the numerous cases of red maple (Acer rubrum) toxicosis. The suspected toxins causing A. rubrum toxicosis are tannic acid, gallic acid, and a metabolite of gallic acid, pyrogallol. These compounds can be found in a variety of plants, posing a risk to equine health. In order to quickly identify toxin sources, 2 rapid in vitro assays were developed to screen plant extracts for the ability to induce methemoglobin formation or cause hemolysis in healthy equine donor erythrocytes. The plant extract screening focused on 3 species of the genus Pistacia: P. atlantica, P. terebinthus, and P. chinensis, which were located in the horse pasture. Extracts of the seeds and leaves of each species induced methemoglobin formation and resulted in hemolysis, with seed extracts having greater potency. The in vitro assays used in the current study provide a useful diagnostic method for the rapid identification of oxidizing agents from unidentified sources. There is no effective treatment for oxidative erythrocyte damage in horses, making rapid identification and removal of the source essential for the prevention of poisoning. PMID:25227420

Protective effect of dry olive leaf extract in adrenaline induced DNA damage evaluated using in vitro comet assay with human peripheral leukocytes.

PubMed

Cabarkapa, Andrea; Zivković, Lada; Zukovec, Dijana; Djelić, Ninoslav; Bajić, Vladan; Dekanski, Dragana; Spremo-Potparević, Biljana

2014-04-01

Excessive release of stress hormone adrenaline is accompanied by generation of reactive oxygen species which may cause disruption of DNA integrity leading to cancer and age-related disorders. Phenolic-rich plant product dry olive leaf extract (DOLE) is known to modulate effects of various oxidants in human cells. The aim was to evaluate the effect of commercial DOLE against adrenaline induced DNA damage in human leukocytes by using comet assay. Peripheral blood leukocytes from 6 healthy subjects were treated in vitro with three final concentrations of DOLE (0.125, 0.5, and 1mg/mL) for 30 min at 37°C under two different protocols, pretreatment and post-treatment. Protective effect of DOLE was assessed from its ability to attenuate formation of DNA lesions induced by adrenaline. Compared to cells exposed only to adrenaline, DOLE displayed significant reduction (P<0.001) of DNA damage at all three concentrations and under both experimental protocols. Pearson correlation analysis revealed a significant positive association between DOLE concentration and leukocytes DNA damage (P<0.05). Antigenotoxic effect of the extract was more pronounced at smaller concentrations. Post-treatment with 0.125 mg/mL DOLE was the most effective against adrenaline genotoxicity. Results indicate genoprotective and antioxidant properties in dry olive leaf extract, strongly supporting further explorations of its underlying mechanisms of action. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

Evaluation of antimutagenic and protective effects of Parkinsonia aculeata L. leaves against H2O2 induced damage in pBR322 DNA.

PubMed

Sharma, Sonia; Sharma, Sushant; Vig, Adarsh Pal

2016-01-01

The in vitro antimutagenic and DNA protecting potential of organic (methanol, hexane, n-butanol) and aqueous extract/fractions of Parkinsonia aculeata L. (Fabaceae) was investigated by employing Ames assay and DNA nicking assay. DNA damage by hydroxyl radicals was effectively inhibited by all the extract/fractions. A marked antimutagenic effect was observed against 4-Nitro-o-phenylenediamine and sodium azide (direct acting mutagens) and 2-Aminofluorene (indirect acting mutagen) in TA98 and TA100 strains of Salmonella typhimurium. In Ames assay, two different modes of experiments i.e. pre-incubation and co-incubation were performed and it was observed that all the extract/fractions showed better results in the pre-incubation as compared to co- incubation mode. Out of all the extract/fractions tested, n-butanol fraction was found to be the most effective in preventing DNA damage and inhibiting mutagenesis. UHPLC analysis of extract/fractions revealed presence of polyphenols such as gallic acid, catechin, chlorogenic acid, caffeic acid, umbelliferone, coumaric acid, rutin, and ellagic acid etc. DNA protecting and antimutagenic activity of this plant could be attributed to presence of these polyphenols. The results of this study indicate the presence of potent antioxidant factors in Parkinsonia aculeata L, which are being explored further for their mechanism of action.

Feature extraction inspired by V1 in visual cortex

NASA Astrophysics Data System (ADS)

Lv, Chao; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Xin, Peng; Zhu, Mingning; Ma, Hongqiang

2018-04-01

Target feature extraction plays an important role in pattern recognition. It is the most complicated activity in the brain mechanism of biological vision. Inspired by high properties of primary visual cortex (V1) in extracting dynamic and static features, a visual perception model was raised. Firstly, 28 spatial-temporal filters with different orientations, half-squaring operation and divisive normalization were adopted to obtain the responses of V1 simple cells; then, an adjustable parameter was added to the output weight so that the response of complex cells was got. Experimental results indicate that the proposed V1 model can perceive motion information well. Besides, it has a good edge detection capability. The model inspired by V1 has good performance in feature extraction and effectively combines brain-inspired intelligence with computer vision.

Variogram-based feature extraction for neural network recognition of logos

NASA Astrophysics Data System (ADS)

Pham, Tuan D.

2003-03-01

This paper presents a new approach for extracting spatial features of images based on the theory of regionalized variables. These features can be effectively used for automatic recognition of logo images using neural networks. Experimental results on a public-domain logo database show the effectiveness of the proposed approach.

Control of key pecan insect pests using biorational pesticides.

PubMed

Shapiro-Ilan, David I; Cottrell, Ted E; Jackson, Mark A; Wood, Bruce W

2013-02-01

Key pecan insect pests include the black pecan aphid, Melanocallis caryaefoliae (Davis), pecan weevil, Curculio caryae (Horn), and stink bugs (Hemiptera: Pentatomidae). Alternative control tactics are needed for management of these pests in organic and conventional systems. Our objective was to evaluate the potential utility of several alternative insecticides including three plant extract formulations, eucalyptus extract, citrus extract-8.92%, and citrus extract-19.4%, and two microbial insecticides, Chromobacterium subtsugae (Martin et al.) and Isaria fumosorosea (Wize). In the laboratory, eucalyptus extract, citrus extract-8.92%, citrus extract-19.4%, and C. subtsugae caused M. caryaefoliae mortality (mortality was reached approximately 78, 83, and 96%, respectively). In field tests, combined applications of I. fumosorosea with eucalyptus extract were synergistic and caused up to 82% mortality in M. caryaefoliae. In laboratory assays focusing on C. caryae suppression, C. subtsugae reduced feeding and oviposition damage, eucalyptus extract and citrus extract-19.4% were ineffective, and antagonism was observed when citrus extract-19.4% was combined with the entomopathogenic nematode, Steinernema carpocapsae (Weiser). In field tests, C. subtsugae reduced C. caryae damage by 55% within the first 3d, and caused 74.5% corrected mortality within 7 d posttreatment. In the laboratory, C. subtsugae and eucalyptus extract did not cause mortality in the brown stink bug, Euschistus servus (Say). Applications of C. subtsugae for suppression of C. caryae, and eucalyptus extract plus I. fumosorosea for control of M. caryaefoliae show promise as alternative insecticides and should be evaluated further.

Antioxidant and genoprotective effects of spent coffee extracts in human cells.

PubMed

Bravo, Jimena; Arbillaga, Leire; de Peña, M Paz; Cid, Concepcion

2013-10-01

Spent coffee has been shown as a good source of hydrophilic antioxidant compounds. The ability of two spent coffee extracts rich in caffeoylquinic acids, mainly dicaffeoylquinic acids, and caffeine (Arabica filter and Robusta espresso) to protect against oxidation and DNA damage in human cells (HeLa) was evaluated at short (2 h) and long (24 h) exposure times. Cell viability (MTT) was not affected by spent coffee extracts (>80%) up to 1000 μg/mL after 2 h. Both spent coffee extracts significantly reduced the increase of ROS level and DNA strand breaks (29-73% protection by comet assay) induced by H₂O₂. Pretreatment of cells with robusta spent coffee extract also decreased Ro photosensitizer-induced oxidative DNA damage after 24 h exposure. The higher effectiveness of Robusta spent coffee extract, with less caffeoylquinic acids and melanoidins, might be due to other antioxidant compounds, such as caffeine and other Maillard reaction products. This work evidences the potential antioxidant and genoprotective properties of spent coffee in human cells. Copyright © 2013 Elsevier Ltd. All rights reserved.

Final Results of Shuttle MMOD Impact Database

NASA Technical Reports Server (NTRS)

Hyde, J. L.; Christiansen, E. L.; Lear, D. M.

2015-01-01

The Shuttle Hypervelocity Impact Database documents damage features on each Orbiter thought to be from micrometeoroids (MM) or orbital debris (OD). Data is divided into tables for crew module windows, payload bay door radiators and thermal protection systems along with other miscellaneous regions. The combined number of records in the database is nearly 3000. Each database record provides impact feature dimensions, location on the vehicle and relevant mission information. Additional detail on the type and size of particle that produced the damage site is provided when sampling data and definitive spectroscopic analysis results are available. Guidelines are described which were used in determining whether impact damage is from micrometeoroid or orbital debris impact based on the findings from scanning electron microscopy chemical analysis. Relationships assumed when converting from observed feature sizes in different shuttle materials to particle sizes will be presented. A small number of significant impacts on the windows, radiators and wing leading edge will be highlighted and discussed in detail, including the hypervelocity impact testing performed to estimate particle sizes that produced the damage.

Face-iris multimodal biometric scheme based on feature level fusion

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

PubMed

Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

2017-01-01

The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease.

PubMed

Segovia, F; Górriz, J M; Ramírez, J; Phillips, C

2016-01-01

Neuroimaging data as (18)F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

DARHT Multi-intelligence Seismic and Acoustic Data Analysis

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

Stevens, Garrison Nicole; Van Buren, Kendra Lu; Hemez, Francois M.

The purpose of this report is to document the analysis of seismic and acoustic data collected at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory for robust, multi-intelligence decision making. The data utilized herein is obtained from two tri-axial seismic sensors and three acoustic sensors, resulting in a total of nine data channels. The goal of this analysis is to develop a generalized, automated framework to determine internal operations at DARHT using informative features extracted from measurements collected external of the facility. Our framework involves four components: (1) feature extraction, (2) data fusion, (3) classification, andmore » finally (4) robustness analysis. Two approaches are taken for extracting features from the data. The first of these, generic feature extraction, involves extraction of statistical features from the nine data channels. The second approach, event detection, identifies specific events relevant to traffic entering and leaving the facility as well as explosive activities at DARHT and nearby explosive testing sites. Event detection is completed using a two stage method, first utilizing signatures in the frequency domain to identify outliers and second extracting short duration events of interest among these outliers by evaluating residuals of an autoregressive exogenous time series model. Features extracted from each data set are then fused to perform analysis with a multi-intelligence paradigm, where information from multiple data sets are combined to generate more information than available through analysis of each independently. The fused feature set is used to train a statistical classifier and predict the state of operations to inform a decision maker. We demonstrate this classification using both generic statistical features and event detection and provide a comparison of the two methods. Finally, the concept of decision robustness is presented through a preliminary analysis where uncertainty is added to the system through noise in the measurements.« less

A neural joint model for entity and relation extraction from biomedical text.

PubMed

Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong

2017-03-31

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

Imaging of Scleral Collagen Deformation Using Combined Confocal Raman Microspectroscopy and Polarized Light Microscopy Techniques.

PubMed

Chakraborty, Nilay; Wang, Mian; Solocinski, Jason; Kim, Wonsuk; Argento, Alan

2016-01-01

This work presents an optospectroscopic characterization technique for soft tissue microstructure using site-matched confocal Raman microspectroscopy and polarized light microscopy. Using the technique, the microstructure of soft tissue samples is directly observed by polarized light microscopy during loading while spatially correlated spectroscopic information is extracted from the same plane, verifying the orientation and arrangement of the collagen fibers. Results show the response and orientation of the collagen fiber arrangement in its native state as well as during tensile and compressive loadings in a porcine sclera model. An example is also given showing how the data can be used with a finite element program to estimate the strain in individual collagen fibers. The measurements demonstrate features that indicate microstructural reorganization and damage of the sclera's collagen fiber arrangement under loading. The site-matched confocal Raman microspectroscopic characterization of the tissue provides a qualitative measure to relate the change in fibrillar arrangement with possible chemical damage to the collagen microstructure. Tests and analyses presented here can potentially be used to determine the stress-strain behavior, and fiber reorganization of the collagen microstructure in soft tissue during viscoelastic response.

The major human AP endonuclease (Ape1) is involved in the nucleotide incision repair pathway

PubMed Central

Gros, Laurent; Ishchenko, Alexander A.; Ide, Hiroshi; Elder, Rhoderick H.; Saparbaev, Murat K.

2004-01-01

In nucleotide incision repair (NIR), an endonuclease nicks oxidatively damaged DNA in a DNA glycosylase-independent manner, providing the correct ends for DNA synthesis coupled to the repair of the remaining 5′-dangling modified nucleotide. This mechanistic feature is distinct from DNA glycosylase-mediated base excision repair. Here we report that Ape1, the major apurinic/apyrimidinic endonuclease in human cells, is the damage- specific endonuclease involved in NIR. We show that Ape1 incises DNA containing 5,6-dihydro-2′-deoxyuridine, 5,6-dihydrothymidine, 5-hydroxy-2′-deoxyuridine, alpha-2′-deoxyadenosine and alpha-thymidine adducts, generating 3′-hydroxyl and 5′-phosphate termini. The kinetic constants indicate that Ape1-catalysed NIR activity is highly efficient. The substrate specificity and protein conformation of Ape1 is modulated by MgCl2 concentrations, thus providing conditions under which NIR becomes a major activity in cell-free extracts. While the N-terminal region of Ape1 is not required for AP endonuclease function, we show that it regulates the NIR activity. The physiological relevance of the mammalian NIR pathway is discussed. PMID:14704345

Mid-Infrared Spectroscopy of Carbon Stars in the Small Magellanic Cloud

DTIC Science & Technology

2006-07-10

nod. Before extracting spectra from fit a variety of spectral feature shapes using MgS considerably the images, we used the imclean software package...mined from neighboring pixels. In addition to the dust features , the IRS wavelength range also To extract spectra from the cleaned and differenced...Example of the extraction of the molecular bands and the SiC dust 24 jIm, and they avoid any potential problems at the joint be- feature from the spectrum

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

NASA Astrophysics Data System (ADS)

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

2012-10-01

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

Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data.

PubMed

Ozery-Flato, Michal; Yanover, Chen; Gottlieb, Assaf; Weissbrod, Omer; Parush Shear-Yashuv, Naama; Goldschmidt, Yaara

2017-01-01

We present a framework for feature engineering, tailored for longitudinal structured data, such as electronic health records (EHRs). To fast-track feature engineering and extraction, the framework combines general-use plug-in extractors, a multi-cohort management mechanism, and modular memoization. Using this framework, we rapidly extracted thousands of features from diverse and large healthcare data sources in multiple projects.

Feature generation using genetic programming with application to fault classification.

PubMed

Guo, Hong; Jack, Lindsay B; Nandi, Asoke K

2005-02-01

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.

Data-Science Analysis of the Macro-scale Features Governing the Corrosion to Crack Transition in AA7050-T7451

NASA Astrophysics Data System (ADS)

Co, Noelle Easter C.; Brown, Donald E.; Burns, James T.

2018-05-01

This study applies data science approaches (random forest and logistic regression) to determine the extent to which macro-scale corrosion damage features govern the crack formation behavior in AA7050-T7451. Each corrosion morphology has a set of corresponding predictor variables (pit depth, volume, area, diameter, pit density, total fissure length, surface roughness metrics, etc.) describing the shape of the corrosion damage. The values of the predictor variables are obtained from white light interferometry, x-ray tomography, and scanning electron microscope imaging of the corrosion damage. A permutation test is employed to assess the significance of the logistic and random forest model predictions. Results indicate minimal relationship between the macro-scale corrosion feature predictor variables and fatigue crack initiation. These findings suggest that the macro-scale corrosion features and their interactions do not solely govern the crack formation behavior. While these results do not imply that the macro-features have no impact, they do suggest that additional parameters must be considered to rigorously inform the crack formation location.

An ensemble method for extracting adverse drug events from social media.

PubMed

Liu, Jing; Zhao, Songzheng; Zhang, Xiaodi

2016-06-01

Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study is to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media. We develop a feature-based approach that utilizes various lexical, syntactic, and semantic features. Information-gain-based feature selection is performed to address high-dimensional features. Then, we evaluate the effectiveness of four well-known kernel-based approaches (i.e., subset tree kernel, tree kernel, shortest dependency path kernel, and all-paths graph kernel) and several ensembles that are generated by adopting different combination methods (i.e., majority voting, weighted averaging, and stacked generalization). All of the approaches are tested using three data sets: two health-related discussion forums and one general social networking site (i.e., Twitter). When investigating the contribution of each feature subset, the feature-based approach attains the best area under the receiver operating characteristics curve (AUC) values, which are 78.6%, 72.2%, and 79.2% on the three data sets. When individual methods are used, we attain the best AUC values of 82.1%, 73.2%, and 77.0% using the subset tree kernel, shortest dependency path kernel, and feature-based approach on the three data sets, respectively. When using classifier ensembles, we achieve the best AUC values of 84.5%, 77.3%, and 84.5% on the three data sets, outperforming the baselines. Our experimental results indicate that ADE extraction from social media can benefit from feature selection. With respect to the effectiveness of different feature subsets, lexical features and semantic features can enhance the ADE extraction capability. Kernel-based approaches, which can stay away from the feature sparsity issue, are qualified to address the ADE extraction problem. Combining different individual classifiers using suitable combination methods can further enhance the ADE extraction effectiveness. Copyright © 2016 Elsevier B.V. All rights reserved.

Palmprint verification using Lagrangian decomposition and invariant interest points

NASA Astrophysics Data System (ADS)

Gupta, P.; Rattani, A.; Kisku, D. R.; Hwang, C. J.; Sing, J. K.

2011-06-01

This paper presents a palmprint based verification system using SIFT features and Lagrangian network graph technique. We employ SIFT for feature extraction from palmprint images whereas the region of interest (ROI) which has been extracted from wide palm texture at the preprocessing stage, is considered for invariant points extraction. Finally, identity is established by finding permutation matrix for a pair of reference and probe palm graphs drawn on extracted SIFT features. Permutation matrix is used to minimize the distance between two graphs. The propsed system has been tested on CASIA and IITK palmprint databases and experimental results reveal the effectiveness and robustness of the system.

Chromosome-damaging effect of betel leaf.

PubMed

Sadasivan, G; Rani, G; Kumari, C K

1978-05-01

The chewing of betel leaf with other ingredients is a widespread addiction in India. The chromosome damaging effect was studied in human leukocyte cultures. There was an increase in the frequency of chromatid aberrations when the leaf extract was added to cultures.

ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

NASA Astrophysics Data System (ADS)

Kora, Padmavathi; Sri Rama Krishna, K.

2016-12-01

Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.

The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

PubMed

Ma, Teng; Li, Hui; Yang, Hao; Lv, Xulin; Li, Peiyang; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2017-01-01

Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300. Copyright © 2016 Elsevier B.V. All rights reserved.

Antibacterial and antioxidant activities of Vaccinium corymbosum L. leaf extract

PubMed Central

Pervin, Mehnaz; Hasnat, Md Abul; Lim, Beong Ou

2013-01-01

Objective To investigate antibacterial and antioxidant activity of the leaf extract of tropical medicinal herb and food plant Vaccinium corymbosum L. (V. corymbosum). Methods Free radical scavenging activity on DPPH, ABTS, and nitrites were used to analyse phenoic and flavonoid contents of leaf extract. Other focuses included the determination of antioxidant enzymatic activity (SOD, CAT and GPx), metal chelating activity, reduction power, lipid peroxidation inhibition and the prevention of oxidative DNA damage. Antibacterial activity was determined by using disc diffusion for seven strains of bacteria. Results Results found that V. corymbosum leaf extract had significant antibacterial activity. The tested extract displayed the highest activity (about 23.18 mm inhibition zone) against Salmonella typhymurium and the lowest antibacterial activity was observed against Enterococcus faecalis (about 14.08 mm inhibition zone) at 10 mg/ disc. The IC50 values for DPPH, ABTS and radical scavenging activity were 0.120, 0.049 and 1.160 mg/mL, respectively. V. corymbosum leaf extract also showed dose dependent reduction power, lipid peroxidation, DNA damage prevention and significant antioxidant enzymatic activity. Conclusions These findings demonstrate that leaf extract of V. corymbosum could be used as an alternative therapy for antibiotic-resistant bacteria and help prevent various free radical related diseases.

Lipophilic components of the brown seaweed, Ascophyllum nodosum, enhance freezing tolerance in Arabidopsis thaliana.

PubMed

Rayirath, Prasanth; Benkel, Bernhard; Mark Hodges, D; Allan-Wojtas, Paula; Mackinnon, Shawna; Critchley, Alan T; Prithiviraj, Balakrishnan

2009-06-01

Extracts of the brown seaweed Ascophyllum nodosum enhance plant tolerance against environmental stresses such as drought, salinity, and frost. However, the molecular mechanisms underlying this improved stress tolerance and the nature of the bioactive compounds present in the seaweed extracts that elicits stress tolerance remain largely unknown. We investigated the effect of A. nodosum extracts and its organic sub-fractions on freezing tolerance of Arabidopsis thaliana. Ascophyllum nodosum extracts and its lipophilic fraction significantly increased tolerance to freezing temperatures in in vitro and in vivo assays. Untreated plants exhibited severe chlorosis, tissue damage, and failed to recover from freezing treatments while the extract-treated plants recovered from freezing temperature of -7.5 degrees C in in vitro and -5.5 degrees C in in vivo assays. Electrolyte leakage measurements revealed that the LT(50) value was lowered by 3 degrees C while cell viability staining demonstrated a 30-40% reduction in area of damaged tissue in extract treated plants as compared to water controls. Moreover, histological observations of leaf sections revealed that extracts have a significant effect on maintaining membrane integrity during freezing stress. Treated plants exhibited 70% less chlorophyll damage during freezing recovery as compared to the controls, and this correlated with reduced expression of the chlorphyllase genes AtCHL1 and AtCHL2. Further, the A. nodosum extract treatment modulated the expression of the cold response genes, COR15A, RD29A, and CBF3, resulting in enhanced tolerance to freezing temperatures. More than 2.6-fold increase in expression of RD29A, 1.8-fold increase of CBF3 and two-fold increase in the transcript level of COR15A was observed in plants treated with lipophilic fraction of A. nodosum at -2 degrees C. Taken together, the results suggest that chemical components in A. nodosum extracts protect membrane integrity and affect the expression of stress response genes leading to freezing stress tolerance in A. thaliana.

UVA-UVB photoprotective activity of topical formulations containing Morinda citrifolia extract.

PubMed

Serafini, Mairim Russo; Detoni, Cassia Britto; Menezes, Paula dos Passos; Pereira Filho, Rose Nely; Fortes, Vanessa Silveira; Vieira, Maria José Fonseca; Guterres, Sílvia Stanisçuaski; Cavalcanti de Albuquerque Junior, Ricardo Luiz; Araújo, Adriano Antunes de Souza

2014-01-01

Exposure to solar radiation, particularly its ultraviolet (UV) component, has a variety of harmful effects on human health. Some of these effects include sunburn cell formations, basal and squamous cell cancers, melanoma, cataracts, photoaging of the skin, and immune suppression. The beneficial photoprotective effects of topical formulations with the extract, Morinda citrifolia, have not been investigated. This present study aims to investigate the potential benefits of M. citrifolia topical application on the dorsal skin of mice, exposed to UVA-UVB light. Using 7 days of treatment, [before (baseline values) and 20 h after UV exposure], the thickness, skin barrier damage (TEWL), erythema, and histological alterations were evaluated. The results showed that the formulations containing the extract protected the skin against UV-induced damage.

Finite element model updating and damage detection for bridges using vibration measurement.

DOT National Transportation Integrated Search

2013-12-01

In this report, the results of a study on developing a damage detection methodology based on Statistical Pattern Recognition are : presented. This methodology uses a new damage sensitive feature developed in this study that relies entirely on modal :...

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

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

PubMed

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

2017-08-18

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

Senna occidentalis leaf extract possesses antitrypanosomal activity and ameliorates the trypanosome-induced anemia and organ damage.

PubMed

Ibrahim, M A; Aliyu, A B; Sallau, A B; Bashir, M; Yunusa, I; Umar, T S

2010-05-01

The in vitro and in vivo antitrypanosomal effects of the ethanol extract of Senna occidentalis leaf were investigated. The crude extract exhibited an in vitro activity against Trypanosoma brucei brucei as it completely eliminated parasites' motility within 10 minutes postincubation with 6.66 mg/ml of effective extract concentration. The extract was further used to treat experimentally T. brucei brucei infected rats at concentrations of 100 and 200 mg/kg body weight, beginning on day 5 post infections (p.i.). At the termination of the experiment on Day 11 p.i., the extract significantly (P < 0.05) kept the parasitemia lower than was recorded in the infected untreated rats. All the infected animals developed anemia, the severity of which was significantly (P < 0.05) ameliorated by the extract treatment. The infection caused significant (P < 0.05) increases in serum alanine and aspartate aminotransferases as well as serum urea and creatinine levels. However, treatment of infected animals with the extract significantly (P < 0.05) prevented the trypanosome-induced increase in these biochemical indices. Furthermore, the T. brucei infection caused hepatomegaly and splenomegaly that were significantly (P < 0.05) ameliorated by the extract administration. It was concluded that orally administered ethanol extract of S. occidentalis leaf possessed anti-T. brucei brucei activity and could ameliorate the disease-induced anemia and organ damage.

Wild Raspberry Subjected to Simulated Gastrointestinal Digestion Improves the Protective Capacity against Ethyl Carbamate-Induced Oxidative Damage in Caco-2 Cells

PubMed Central

Chen, Wei; Xu, Yang; Zhang, Lingxia; Li, Ya; Zheng, Xiaodong

2016-01-01

Ethyl carbamate (EC), a probable human carcinogen, occurs widely in many fermented foods. Previous studies indicated that EC-induced cytotoxicity was associated with oxidative stress. Wild raspberries are rich in polyphenolic compounds, which possess potent antioxidant activity. This study was conducted to investigate the protective effect of wild raspberry extracts produced before (RE) and after in vitro simulated gastrointestinal digestion (RD) on EC-induced oxidative damage in Caco-2 cells. Our primary data showed that ethyl carbamate could result in cytotoxicity and genotoxicity in Caco-2 cells and raspberry extract after digestion (RD) may be more effective than that before digestion (RE) in attenuating toxicity caused by ethyl carbamate. Further investigation by fluorescence microscope revealed that RD may significantly ameliorate EC-induced oxidative damage by scavenging the overproduction of intracellular reactive oxygen species (ROS), maintaining mitochondrial function and preventing glutathione (GSH) depletion. In addition, HPLC-ESI-MS results showed that the contents of identified polyphenolic compounds (esculin, kaempferol O-hexoside, and pelargonidin O-hexoside) were remarkably increased after digestion, which might be related to the better protective effect of RD. Overall, our results demonstrated that raspberry extract undergoing simulated gastrointestinal digestion may improve the protective effect against EC-induced oxidative damage in Caco-2 cells. PMID:26788245

Antimicrobial efficacy of Syzygium antisepticum plant extract against Staphylococcus aureus and methicillin-resistant S. aureus and its application potential with cooked chicken.

PubMed

Yuan, Wenqian; Yuk, Hyun-Gyun

2018-06-01

For the past decades, there has been a growing demand for natural antimicrobials in the food industry. Plant extracts have attracted strong research interests due to their wide-spectrum antimicrobial activities, but only a limited number have been investigated thoroughly. The present study aimed at identifying a novel anti-staphylococcal plant extract, to validate its activity in a food model, and to investigate on its composition and antimicrobial mechanism. Four plant extracts were evaluated against Staphylococcus aureus and methicillin-resistant S. aureus (MRSA) in vitro, with Syzygium antisepticum leaf extract showing the strongest antimicrobial activity (MIC = 0.125 mg/mL). Relatively high total phenolic content (276.3 mg GAE/g extract) and antioxidant activities (90.2-138.0 mg TE/g extract) were measured in S. antisepticum extract. Food validation study revealed that higher extract concentration (32 mg/mL) was able to inhibit or reduce staphylococcal growth in cooked chicken, but caused color change on meat surface. By GC-MS, β-caryophyllene (12.76 area%) was identified as the dominant volatile compound in extract. Both crude extract and pure β-caryophyllene induced membrane damages in S. aureus. These results suggested good anti-staphylococcal properties of S. antisepticum plant extract, identified its major volatile composition and its membrane-damaging antimicrobial mechanism. Copyright © 2017 Elsevier Ltd. All rights reserved.

New Finger Biometric Method Using Near Infrared Imaging

PubMed Central

Lee, Eui Chul; Jung, Hyunwoo; Kim, Daeyeoul

2011-01-01

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%. PMID:22163741

Shape Adaptive, Robust Iris Feature Extraction from Noisy Iris Images

PubMed Central

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-01-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate. PMID:24696801

Shape adaptive, robust iris feature extraction from noisy iris images.

PubMed

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-10-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.

Anthocyanin-rich blackberry extract suppresses the DNA-damaging properties of topoisomerase I and II poisons in colon carcinoma cells.

PubMed

Esselen, Melanie; Boettler, Ute; Teller, Nicole; Bachler, Simone; Hutter, Melanie; Rufer, Corinna E; Skrbek, Susanne; Marko, Doris

2011-07-13

In the present study, we addressed the question whether cyanidin-3-glucoside (C3G) or complex C3G-rich blackberry extracts affect human topoisomerases with special emphasis on the contribution of the potential degradation products phloroglucinol aldehyde (PGA) and protocatechuic acid (PCA). In HT29 colon carcinoma cells a C3G-rich blackberry extract suppressed camptothecin- (CPT-) or doxorubicin- (DOX-) induced stabilization of the covalent DNA-topoisomerase intermediate, thus antagonizing the effects of these classical topoisomerase poisons on DNA integrity. As a single compound, C3G (100 μM) decreased the DNA-damaging effects of CPT as well, but did not significantly affect those induced by DOX. At the highest applied concentration (100 μM), cyanidin protected DNA from CPT- and DOX-induced damage. Earlier reports on DNA-damaging properties of cyanidin were found to result most likely from the formation of hydrogen peroxide as an artifact in the cell culture medium when the incubation was performed in the absence of catalase. The suppression of hydrogen peroxide accumulation, achieved by the addition of catalase, demonstrated that cyanidin does not exhibit DNA-damaging properties in HT29 cells (up to 100 μM). The observed effects on topoisomerase interference and DNA protection against CPT or DOX were clearly limited to the parent compound and were not observed for the potential cyanidin degradation products PGA and PCA.

Feature extraction from high resolution satellite imagery as an input to the development and rapid update of a METRANS geographic information system (GIS).

DOT National Transportation Integrated Search

2011-06-01

This report describes an accuracy assessment of extracted features derived from three : subsets of Quickbird pan-sharpened high resolution satellite image for the area of the : Port of Los Angeles, CA. Visual Learning Systems Feature Analyst and D...

Structural Integrity and Aging-Related Issues of Helicopters

DTIC Science & Technology

2000-10-01

inherently damage lolerant , any damage- inspection in critical locations where tests have indicated tolerant features in airframe design only enhances...required, so European Rotorcraft Forum. Marseilles, France, 15- that helicopters are equipped with such features as fly- 17 September 1998 . by-wire and...fatigue Evaluation of structural integrity issues of aging helicopters. The Structure," 29 April, 1998 . extended safe-life approach encompasses the best

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.